[
{
"path": ".gitignore",
"content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# UV\n# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n#uv.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/latest/usage/project/#working-with-version-control\n.pdm.toml\n.pdm-python\n.pdm-build/\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n# Abstra\n# Abstra is an AI-powered process automation framework.\n# Ignore directories containing user credentials, local state, and settings.\n# Learn more at https://abstra.io/docs\n.abstra/\n\n# Visual Studio Code\n# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore \n# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore\n# and can be added to the global gitignore or merged into this file. However, if you prefer, \n# you could uncomment the following to ignore the enitre vscode folder\n# .vscode/\n\n# Ruff stuff:\n.ruff_cache/\n\n# PyPI configuration file\n.pypirc\n\n# Cursor\n# Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to\n# exclude from AI features like autocomplete and code analysis. Recommended for sensitive data\n# refer to https://docs.cursor.com/context/ignore-files\n.cursorignore\n.cursorindexingignore"
},
{
"path": "LICENSE",
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},
{
"path": "README.md",
"content": "# qgss-2025\nQiskit Global Summer School: The Past, Present and Future of Quantum Computing\n\n# Launch on qBraid\n\nqBraid is an official Jupyter Lab cloud platform host for the Qiskit Global Summer School this year. Follow our quick tutorial [here](https://docs.qbraid.com/lab/user-guide/qgss-2025) to get started with no set-up, installations, or hassle.\n\nTo open the QGSS files, labs, and resources directly on qBraid, click this button:\n\n[](https://account.qbraid.com/?gitHubUrl=https://github.com/qiskit-community/qgss-2025.git&api=v2)\n\n\n"
},
{
"path": "community-labs/README.md",
"content": "# QGSS-2025-community-labs\n\n\nAs part of the future leaders in quantum hackathon: https://aiforgood.itu.int/the-future-leaders-in-quantum-hackathon/ the winners in the education category got the chance to provide their created educational labs to participants on the Qiskit Global Summer School as community labs. The labs linked here are optional for the Qiskit Global Summer School and provide additional challenges in case you still want to do more and we highly recommend them if you are interested in learning more about the subjects.\n\nWe were really happy with what the participants of the FLIQ hackathon created and present here the winners:\n\n\n1. Quantum Error Correction with the Surface-17 Code by Dev Verma\n2. Quantum Simulation of GUT Baryogenesis by Mukul Kumar & Vivek Pal\n3. Grover's Algorithm - Quantum Searching with Qiskit by Felix Arkle & Sebastian Gotto\n4. CHSH Game: Testing the Limits of Reality with Quantum Mechanics by Lakshmi Yendapalli\n\n\n# 1. Quantum Error Correction with the Surface-17 Code\n\n## Author Dev Verma\n\nI recently graduated from NTU as a scholar in Applied Physics. I am passionate about quantum computing and cryptography and will be working towards growing my understanding and expertise in these fields. \n\n## About the Challenge: \n\nMy Jupyter notebook is a tutorial on quantum error correction, focusing on the surface-17 code, which is one of the most promising approaches to quantum error correction. The tutorial assumes no background in error correction; it builds the reader's understanding from simple classical repetition codes to the stabiliser formalism, and ultimately the surface-17 code. It also discusses the heavy-hex lattice architecture, which is more realistic for the current dominant quantum hardware configuration - IBM's superconducting circuits.\n\n## Link:\n\nThe challenge can be found on the github of Dev Verma: https://github.com/TheSonOfKrypton/FLIQ-Hackathon-2025/blob/main/QEC%20-%20Surface-17%20Code.ipynb\n\n\n\n# 2. Quantum Simulation of GUT Baryogenesis\n\n## Authors Mukul Kumar & Vivek Pal\n\nMukul Kumar is an 18-year-old dropout student researching nature simulations on quantum computers and he created this challenge together with Vivek Pal.\n\n## About the Challenge: \n\nOur challenge is basically a fun experiment that helps you learn about the beginning of the universe and some unsolved mysteries, all of which we are going to study with quantum circuits.\n\n## Link:\n\nThe challenge can be found on the github of Mukul Kumar: https://github.com/mk0dz/qgss25/blob/main/lab.ipynb\n\n\n\n# 3. Grover's Algorithm - Quantum Searching with Qiskit\n\n## Authors Felix Arkle & Sebastian Gotto\n\nFelix Arkle: Currently an undergraduate student at Newcastle University studying computer science. Sebastian Gotto: A recent graduate of UCL having studied Statistics, Economics and Finance.\n\n## About the Challenge: \n\nThis lab focuses on building and demonstrating Grover's algorithm, a quantum search algorithm. You'll construct the necessary quantum circuits, including the oracle and diffusion operators. This lab is suitable for beginners.\n\n## Link:\n\nThe challenge can be found on the github of Felix Arkle: https://github.com/Bumsparkle/Grovers-Algorithm-Tutorial/blob/main/grovers_algorithm_tutorial.ipynb\n\n\n\n# 4. CHSH Game: Testing the Limits of Reality with Quantum Mechanics\n\n## Author Lakshmi Yendapalli\n\nLakshmi Yendapalli is a former strategy consultant turned computer scientist, currently pursuing her master’s in Computer Science at Georgia Tech. She’s especially fascinated by what mischief qubits are up to when no one’s looking.\n\n## About the Challenge: \n\nEver wanted to recreate some of the experiments that earned the 2022 Nobel Prize in Physics? How about proving Einstein wrong while we’re at it? In this hands-on lab on the CHSH game, we’ll explore the surprising foundations of quantum mechanics. Starting from the EPR paradox and Einstein-Bohr debates, we’ll journey through the development of Bell's theorem, culminating in the CHSH inequality. Then we’ll jump into Qiskit to build and run quantum circuits that experimentally demonstrate the CHSH inequality.\n\n## Link:\n\nThe challenge can be found on the github of Lakshmi Yendapalli: https://github.com/lakshmi-yendapalli/CHSH_game_tutorial/tree/main\n\n"
},
{
"path": "functions-labs/README.md",
"content": "## Exclusive Access to Qiskit Functions\n\nAs part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\n\nIf you encounter the error `QiskitServerlessException: Credentials couldn't be verified`, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\n\n**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**"
},
{
"path": "functions-labs/algorithmiq/TEM_lab_validation.py",
"content": "from __future__ import annotations\n\nimport os\nfrom functools import cached_property\nfrom typing import TYPE_CHECKING, Self\n\nimport numpy as np\nfrom numpy.random import default_rng\nfrom pydantic import (\n BaseModel,\n ConfigDict,\n computed_field,\n field_validator,\n model_validator,\n)\nimport base64\nimport io\nimport zlib\nimport hashlib\n\nfrom qiskit.quantum_info import Statevector\nfrom qiskit.primitives.containers.estimator_pub import EstimatorPub\nfrom qiskit_ibm_runtime.options import NoiseLearnerOptions, TwirlingOptions\n\nUNSUPPORTED_GATES = [\n \"reset\",\n \"if_else\",\n \"for_loop\",\n \"switch_case\",\n \"measure\",\n]\n\nclass TEMOptions(BaseModel):\n tem_max_bond_dimension: int = (500,)\n compute_shadows_bias_from_observable: bool = False\n shadows_bias: np.ndarray = np.array([1 / 3, 1 / 3, 1 / 3])\n max_execution_time: int | None = None\n num_randomizations: int = 32\n max_layers_to_learn: int = 4\n default_precision: float = 0.02\n shots_per_randomization: int = 128\n layer_pair_depths: list[int] = [0, 1, 2, 4, 16, 32]\n\n model_config = ConfigDict(extra=\"forbid\", arbitrary_types_allowed=True)\n\n @field_validator(\"tem_max_bond_dimension\")\n @classmethod\n def bd_must_be_less_than_equal_max_bond_dimension(cls, v: int) -> int:\n if v > 1000:\n raise ValueError(\n f\"Unsupported option: Max bond dimension too large. tem_max_bond_dimension must be <= {1000:d}.\"\n )\n return v\n\n @field_validator(\"max_execution_time\")\n @classmethod\n def max_execution_time_must_be_greater_than_60_seconds(\n cls, v: int | None\n ) -> int | None:\n if v is not None and v < 60:\n raise ValueError(\n f\"Unsupported option: max_execution_time must be greater or equal than {60:d} seconds.\"\n )\n return v\n\n @field_validator(\"shadows_bias\")\n @classmethod\n def validate_shadows_bias(cls, v: list | tuple | np.ndarray) -> np.ndarray:\n v = np.array(v)\n shape = v.shape\n if len(shape) > 2 and (shape[-1] != 3):\n raise ValueError(\n \"Unsupported option: Mitigation_bias must be a 1d array of size 3 or a 2d one of shape (num_qubits, 3).\"\n )\n if not np.allclose(v.sum(axis=-1), 1, atol=1e-5):\n raise ValueError(\n \"Unsupported option: Not a valid probability distribution: the sum of the bias values must be 1.\"\n )\n if np.any(v < 0.1):\n raise ValueError(\n \"Unsupported option: All the bias values must be >= 0.1. for QPU real implementations.\"\n )\n return v\n\n @model_validator(mode=\"after\")\n def validate_shadows_bias_from_observable(self) -> Self:\n if self.compute_shadows_bias_from_observable and not np.allclose(\n self.shadows_bias, np.array([1 / 3, 1 / 3, 1 / 3]), atol=1e-8\n ):\n raise ValueError(\n \"Unsupported option: When compute_shadows_bias_from_observable is True, \"\n \"you cannot also pass a custom-bias\"\n )\n return self\n\n @computed_field(return_type=NoiseLearnerOptions)\n @property\n def noise_learner_options(self) -> NoiseLearnerOptions:\n return NoiseLearnerOptions(\n num_randomizations=self.num_randomizations,\n max_layers_to_learn=self.max_layers_to_learn,\n shots_per_randomization=self.shots_per_randomization,\n layer_pair_depths=self.layer_pair_depths,\n twirling_strategy=\"active-accum\",\n environment={\"job_tags\": [32, \"noise_learning\"]},\n )\n\n @computed_field(return_type=TwirlingOptions)\n @property\n def twirling_options(self) -> TwirlingOptions:\n return TwirlingOptions(\n enable_gates=True,\n enable_measure=False,\n num_randomizations=1,\n shots_per_randomization=\"auto\",\n strategy=\"active-accum\",\n )\n\n\nclass TEMInputs:\n def __init__(\n self,\n arguments: dict,\n ) -> None:\n pubs: list[EstimatorPub] = arguments[\"pubs\"]\n self.ibm_instance: str | None = arguments.get(\"instance\")\n self.options: TEMOptions = TEMOptions(**arguments.get(\"options\", {}))\n if not isinstance(pubs, list):\n pubs = [pubs]\n self.pubs = [EstimatorPub.coerce(pub) for pub in pubs]\n self.rng = default_rng(32)\n for pub in self.pubs:\n self.validate(pub)\n\n def validate(self, pub: EstimatorPub) -> None:\n if pub.parameter_values.num_parameters > 0:\n raise ValueError(\n \"Unsupported circuit: parametrized circuits are not supported.\"\n )\n gate_types = pub.circuit.count_ops()\n for gate in UNSUPPORTED_GATES:\n if gate in gate_types:\n raise ValueError(f\"Unsupported circuit: gate {gate} is not supported.\")\n\ndef validation(\n arguments: dict,\n) -> None:\n try:\n _t = TEMInputs(arguments)\n print(\"TEM input checks out\")\n except Exception as e:\n print(\"TEM input validation failed:\", e)\n raise e\n\n\ndef check_circuit(circuit):\n with io.BytesIO() as buff:\n np.save(buff, np.round(Statevector(circuit).probabilities(),7), allow_pickle=False)\n buff.seek(0)\n serialized_data = buff.read()\n serialized_data = zlib.compress(serialized_data)\n series = base64.standard_b64encode(serialized_data).decode(\"utf-8\")\n hashed_circ = hashlib.sha256(series.encode(\"utf-8\")).hexdigest()\n try:\n assert hashed_circ == \"67b19a21308681a6d3d76e91eb4086773e0d861150f14a186ea30bd12c3aacff\"\n print(\"Circuit verification successful! The quantum state matches the expected reference.\")\n except AssertionError:\n print(f\"Circuit verification failed! Got hash: {hashed_circ}\")\n raise\n"
},
{
"path": "functions-labs/algorithmiq/algorithmiq_tensor-network_error_mitigation (TEM).ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Running error mitigated many-body physics experiments with TEM function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This collection of exercised is based on the following reference - [ArXiv: Kicked Ising](https://arxiv.org/abs/2411.00765). This reference discusses a real simulation quantum hardware of up to 91 qubits. In this lab, you are going to recreate a similar simulation on a smaller circuit size.\\n\",\n \"\\n\",\n \"The kicked Ising model corresponds to the usual Ising model: $$ \\\\hat{H}_{\\\\text{I}} = J \\\\sum_{n=0}^{N-2} \\\\hat{Z}_n \\\\hat{Z}_{n+1} + h \\\\sum_{n=0}^{N-1} \\\\hat{Z}_n $$\\n\",\n \" to which is applied a transverse kick: $$ \\\\hat{H}_{K} = b \\\\sum_{n=0}^{N-1} \\\\hat{X}_n $$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"* [Part 1: Run on local simulation](#part-1-run-on-local-simulation)\\n\",\n \"* [Part 2: Run on real devices](#part-2-run-on-real-devices)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Setup\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Install dependencies\\n\",\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"%pip install \\\"qiskit[visualization]\\\" qiskit-aer qiskit-ibm-catalog\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You should have Grader `>=0.22.11`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.quantum_info import SparsePauliOp\\n\",\n \"from qiskit.primitives import StatevectorEstimator\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"from qiskit_ibm_catalog import QiskitFunctionsCatalog\\n\",\n \"from qiskit_ibm_runtime import EstimatorV2, QiskitRuntimeService\\n\",\n \"\\n\",\n \"from TEM_lab_validation import check_circuit, validation # These are the grading functions to test inputs\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_algorithmiq_function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**Exclusive Access to Qiskit Functions**\\n\",\n \"\\n\",\n \"As part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\\n\",\n \"\\n\",\n \"If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`. in the cell below, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\\n\",\n \"\\n\",\n \"**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Qiskit Functions Catalog\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"catalog = QiskitFunctionsCatalog(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"# You should see a list of Qiskit Functions available to you\\n\",\n \"# If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`,\\n\",\n \"# it means your access is not yet active\\n\",\n \"catalog.list()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" Load Qiskit Function\\n\",\n \"\\n\",\n \"Find the correct function name from the list above, or refer to the [Qiskit Functions Catalog](https://quantum.cloud.ibm.com/functions) to locate the appropriate function name string. The name should follow the format: `\\\"[provider]/[title]\\\"`.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load Algorithmiq TEM function\\n\",\n \"\\n\",\n \"function_name = \\\"\\\" # TODO\\n\",\n \"tem = catalog.load(function_name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_algorithmiq_function(tem)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 1: Run on local simulation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The goal of the notebook is to simulate the dynamic of a state under the transverse kicked Ising Hamiltonian, whose time evolution can be implemented by a Floquet unitary $\\\\hat{U}_{\\\\text{KI}} = e^{-i \\\\hat{H}_K} e^{-i \\\\hat{H}_I} $. \\n\",\n \"\\n\",\n \"The build of this unitary is implemented in the next cell. The circuit starts with a state preparation phase, in which the first qubit of the line is in the state $|+\\\\rangle$, while the other states are in the Bell state $(|00\\\\rangle + |11\\\\rangle)/\\\\sqrt{2}$. This is followed by a brickwork structure of 2-qubit blocks which are decomposed in a few single qubit gates (depending on $h$ and $b$ and a repeated $ZZ$ interaction with coupling $J$).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Task 1: \\n\",\n \"Write a function that returns the desired circuit for a given number of qubits `n_qubits` and a given number of timesteps `t_steps` (with parameters `h`, `b` and `J`).\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def kicked_ising_circuit(n_qubits, t_steps, J, b, h):\\n\",\n \" ki_circuit = QuantumCircuit(n_qubits)\\n\",\n \"\\n\",\n \" # State preparation\\n\",\n \" # Hadamard gates on first qubit\\n\",\n \" ki_circuit.h(0)\\n\",\n \"\\n\",\n \" # Further Hadamard gates on odd qubits and\\n\",\n \" # Cnot gates between odd(control) and even(target) qubits, leaving out the 0th\\n\",\n \" for i in range(1, n_qubits - 1, 2):\\n\",\n \" ki_circuit.h(i)\\n\",\n \" ki_circuit.cx(i, i + 1)\\n\",\n \"\\n\",\n \" ki_circuit.barrier()\\n\",\n \" ### Write your code below here ###\\n\",\n \"\\n\",\n \" # Time steps (t)\\n\",\n \" # Apply the kicked Ising model gates\\n\",\n \" # if t%2=0:\\n\",\n \" # Rz(h) on odd qubits\\n\",\n \" # Rzz(2J) between even(i) and odd(i+1) qubits\\n\",\n \" # Rx(2b) on all qubits\\n\",\n \" # Rzz(2J) between even(i) and odd(i+1) qubits\\n\",\n \" # Rz(h) on odd qubits\\n\",\n \" # barrier on all qubits\\n\",\n \" # if t%2=1:\\n\",\n \" # Rz(h) on even qubits (skipping the 0th)\\n\",\n \" # Rzz(2J) between odd(i) and even(i+1) qubits\\n\",\n \" # Rx(2b) on all qubits (skipping the 0th)\\n\",\n \" # Rzz(2J) between odd(i) and even(i+1) qubits\\n\",\n \" # Rz(h) on even qubits (skipping the 0th)\\n\",\n \" # barrier on all qubits\\n\",\n \" # Note: The first qubit (0th) is not involved when t is odd.\\n\",\n \"\\n\",\n \" \\n\",\n \" return ki_circuit\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's visualize the expected output:\\n\",\n \"`kicked_ising_circuit(11, 5, J=np.pi / 4, b=np.pi / 4, h=0.0).draw(\\\"mpl\\\")`\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And now try to replicate the same plot:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"kicked_ising_circuit(11, 5, J=np.pi / 4, b=np.pi / 4, h=0.0).draw(\\\"mpl\\\") # Expected result type: QuantumCircuit\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Task 2: \\n\",\n \"\\n\",\n \"The quantity we want to observe is the correlation function. The [reference paper](https://arxiv.org/abs/2411.00765) discusses how this quantity can be rewritten as just an $X$ Pauli operator on the $n-th$ qubit. Furthermore, at a given time-step T, the $\\\\langle\\\\psi|X|\\\\psi\\\\rangle$ is equal to one only at the T-th qubit, and zero otherwise.\\n\",\n \"\\n\",\n \"In Qiskit one can define observables using the `SparsePauliOp` class. Write a function that returns the right observable to measure for `n_qubit`s if we want to check the correlation function on qubit `n`. \\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Warnings: \\n\",\n \"Qiskit uses a reversed ordering of the qubits, i.e. qubit 0 is the rightmost in a string\\n\",\n \"
\\n\",\n \" \\n\",\n \" Task 3: \\n\",\n \"We have seen how to check the correctness of the observable via statevector simulation, it's now time to estimate the correlation function.\\n\",\n \"\\n\",\n \"In qiskit, the expectation value of observables on a given circuit can be estimated using the [EstimatorV2 primitive](https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.EstimatorV2). To run the estimation locally you can check [this guide](https://docs.quantum.ibm.com/guides/local-testing-mode#aersimulator-examples)\\n\",\n \"\\n\",\n \"Let's start with a Clifford circuit, (we set $J = b = \\\\pi/4$, $h=0$). One should be able to observe that $C_n(T) = \\\\delta_{n,T}$, i.e. the correlation function after $T$ time steps, is exactly $1$ for qubit $T$ and zero otherwise.\\n\",\n \"\\n\",\n \"Check the documentation and try to estimate the correlation function for different qubits at a fixed time step. Let's say `n_qubits = 10` and `T = 3` for example.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"The goal of this task is to replicate the previous image (left) for a smaller number of qubits, say 10. For this, you should measure X on each qubit $i$ and check its expectation value to be in line with $\\\\delta_{i,T}$. Try simulating it first with a statevector simulator using `StatevectorEstimator` and then with the `AerSimulator` using `EstimatorV2`.\\n\",\n \"
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"num_qubits = 10\\n\",\n \"time_steps = 3\\n\",\n \"circ = kicked_ising_circuit(num_qubits, time_steps, np.pi / 4, np.pi / 4, 0.0)\\n\",\n \"sv_estimator = StatevectorEstimator()\\n\",\n \"aer_sim = AerSimulator()\\n\",\n \"estimator = EstimatorV2(mode=aer_sim)\\n\",\n \"\\n\",\n \"### Write your code below here ###\\n\",\n \"\\n\",\n \"observables = [ \\n\",\n \"\\n\",\n \"] # TODO: Build an array of \\\"X_i\\\" operators for each qubit\\n\",\n \"\\n\",\n \"# TODO: Make use of sv_estimator\\n\",\n \"sv_job = \\n\",\n \"sv_evs = \\n\",\n \"\\n\",\n \"# TODO: Now use the aer_sim and estimator\\n\",\n \"aer_result = \\n\",\n \"aer_evs = \\n\",\n \"\\n\",\n \"### Don't change any code past this line ###\\n\",\n \"\\n\",\n \"print(\\\"Statevector expectation values:\\\", sv_evs)\\n\",\n \"print(\\\"Expectation value:\\\", aer_evs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's check that the circuit follows the expected structure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Don't change any code in this cell ###\\n\",\n \"check_circuit(circ)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Task 4: Execute different timesteps \\n\",\n \"\\n\",\n \"You can now try and reproduce the image reported above. Since this would require considerable QPU time, we can reproduce the image just by running a simulation. The next part of the exercise will show how to use TEM function on part of the observables that go into this image, thus an extension will be easier to write if needed.\\n\",\n \"\\n\",\n \"Using Task 3 as reference, you will have to implement a total of `num_qubits` layers with an increasing number of timesteps (`0, 1, ... , num_qubits-1`). For each layer, you should define a kicked Ising circuit with the chosen and fixed `num_qubits` and the corresponding incremental number of timesteps (you can use the utility from Task 1). For each layer, you should also provide a list of operators that implement the X observable for each qubit separately at each timestep (you can use the observables variable defined in Task 3). The output for each layer should be a pub (`(circuit, observables)` tuple) that is appended to `multiple_pubs`. This pub will then be used to call `estimator.run` and get a series of expectation values. Plotting the results should reveal a diagonal.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"multiple_pubs = []\\n\",\n \"\\n\",\n \"### Write your code below here ###\\n\",\n \"\\n\",\n \"for n in range(num_qubits):\\n\",\n \"# TODO: create num_qubits different circuits with incremental layers,\\n\",\n \"# all measuring the same observables and place them together in a PUB,\\n\",\n \"# append pub to multiple_pubs\\n\",\n \"\\n\",\n \"# TODO: run EstimatorV2\\n\",\n \"aer_sim = \\n\",\n \"estimator =\\n\",\n \"job = \\n\",\n \"\\n\",\n \"### Don't change any code past this line ###\\n\",\n \"\\n\",\n \"aer_result = job.result()\\n\",\n \"aer_evs = np.transpose(np.array([np.array(i.data.evs) for i in aer_result]))\\n\",\n \"print(\\\"Expectation value:\\\", aer_evs)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Don't change any code in this cell ###\\n\",\n \"plt.imshow(aer_evs, aspect=\\\"auto\\\", cmap=\\\"viridis\\\")\\n\",\n \"plt.colorbar(label=\\\"Expectation Value\\\")\\n\",\n \"plt.ylabel(\\\"Index\\\")\\n\",\n \"plt.xlabel(\\\"Time Steps\\\")\\n\",\n \"plt.title(\\\"Expectation Values Over Time Steps\\\")\\n\",\n \"plt.yticks(ticks=np.arange(num_qubits), labels=[f\\\"Q{i}\\\" for i in range(num_qubits)])\\n\",\n \"plt.xticks(\\n\",\n \" ticks=np.arange(len(aer_evs)), labels=[f\\\"T{i + 1}\\\" for i in range(len(aer_evs))]\\n\",\n \")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 2: Run on real devices\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once you have verified that everything works on local simulation, you are ready to run on a real backend. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Task 5: Select backend and transpile \\n\",\n \"\\n\",\n \"\\n\",\n \"The first step is *transpiling* the circuit so that it is decomposed in the right set of gates (the IBM Quantum documentation refers to it as ISA).\\n\",\n \"\\n\",\n \"This can be achieved quite simply with `generate_preset_pass_manager`. [Documentation](https://docs.quantum.ibm.com/api/qiskit/0.42/qiskit.transpiler.preset_passmanagers.generate_preset_pass_manager).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first step is to select an backend from the available devices. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Warnings: \\n\",\n \"Please keep in mind that the TEM algorithm currently supports only Heron QPUs, **do not select Eagle QPUs**.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn)\\n\",\n \"backend_name = \\\"ibm_kingston\\\" # TODO: Replace with your desired backend name\\n\",\n \"backend = service.backend(backend_name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For a 10-qubit, 3-step circuit, mantaining the parameters to obtain the clifford circuit that have been used so far, the TEM function should use about **5 minutes of QPU time**.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Transpile for the backend\\n\",\n \"\\n\",\n \"During transpilatio,n it helps to ask to the transpiler to take into account *readout* error of the qubits. This can be obtained by adding a dummy measurement round to the ideal circuit, which can then be removed after transpilation. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Tips: \\n\",\n \"Use the `.apply_layout` method.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"transpiler = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend, seed_transpiler=42\\n\",\n \")\\n\",\n \"circ.measure_all() # dummy measurement\\n\",\n \"circuit_isa = transpiler.run(circ)\\n\",\n \"circ.remove_final_measurements() # removal of dummy measurement\\n\",\n \"circuit_isa.remove_final_measurements() # removal of dummy measurement\\n\",\n \"\\n\",\n \"### Write your code below here ###\\n\",\n \"\\n\",\n \"observable_isa = [\\n\",\n \"\\n\",\n \"] # TODO: Observable now must be defined on the whole QPU\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lets inspect the layers resulting from the transpilation, as these are determinant for the TEM algorithm.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Don't change any code in this cell ###\\n\",\n \"# Draw the ISA circuit\\n\",\n \"circuit_isa.draw(output='mpl')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can see from the circuit that there are only two possible learnable layers involving different CZ gates at the same time-step, recognizable as the first pair of CZ and the second pair, respectively before and after the first barrier. It is good practice to know how many layers of noise will have to be learnt by the `NoiseLearner` as it is a parameter of the TEM function. In this case there are only two and the default maximum is 4 so we don't need to modify it.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Task 6: Run TEM \\n\",\n \"Let's try the Algorithmiq TEM Qiskit Function. The function uses a tensor-network mitigated method described in [Arxiv:TEM algorithm](https://arxiv.org/abs/2307.11740). The `options` dictionary contains various settings and is explained in the [TEM Qiskit Function Documentation](https://docs.quantum.ibm.com/guides/algorithmiq-tem).\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 3 minutes 30 seconds (based on tests on `ibm_fez`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Write your code below here ###\\n\",\n \"\\n\",\n \"pubs = # TODO Fill this line of Pubs\\n\",\n \"\\n\",\n \"### Don't change any code past this line ###\\n\",\n \"\\n\",\n \"options = {\\n\",\n \" # \\\"max_layers_to_learn\\\": we don't need this since we only have 2 learnable layers and the default is 4\\n\",\n \" \\\"compute_shadows_bias_from_observable\\\": True, # It helps optimizing the measurement stage since \\n\",\n \" # the observable is strongly biased toward the X operator for all the qubits\\n\",\n \"}\\n\",\n \"\\n\",\n \"tem_validation_arguments = {\\n\",\n \" \\\"pubs\\\": pubs,\\n\",\n \" \\\"backend_name\\\": backend_name,\\n\",\n \" \\\"options\\\": options,\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let us validate the our input data to check for error before going to real hardware\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Don't change any code in this cell ###\\n\",\n \"validation(tem_validation_arguments)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now run TEM\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Don't change any code in this cell ###\\n\",\n \"tem_job = tem.run(**tem_validation_arguments)\\n\",\n \"job_id = tem_job.job_id\\n\",\n \"print(f\\\"Job ID: {job_id}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can check the status of your job again at [quantum.ibm.com](quantum.ibm.com) or with:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Don't change any code in this cell ###\\n\",\n \"print(tem_job.status())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To reuse the results later on, you can just simply use the following code (the id needs to be printed and saved beforehand):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Don't change any code in this cell ###\\n\",\n \"catalog = QiskitFunctionsCatalog(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"job = catalog.job(job_id)\\n\",\n \"results = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Task 7: Get the results and compare \\n\",\n \"\\n\",\n \"We can now check the raw results and the mitigated ones. Follow the docs to extract expectation values and standard errors from the function result.\\n\",\n \"We can also check how much quantum runtime was used for each call at [quantum.ibm.com](quantum.ibm.com). (Or calling `job.metrics()` from the Python code)\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Don't change any code in this cell ###\\n\",\n \"tem_results = tem_job.result()[0]\\n\",\n \"tem_evs = tem_results.data.evs\\n\",\n \"unmitigated_evs = tem_results.metadata['evs_non_mitigated']\\n\",\n \"print(\\\"TEM Results:\\\", tem_evs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Align the obtained results to compare the statevector simulation, unmitigated hardware run and the result of the TEM Qiskit Function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sv_evs = np.array(sv_evs)\\n\",\n \"unmitigated_evs = np.array(unmitigated_evs)\\n\",\n \"tem_evs = np.array(tem_evs)\\n\",\n \"\\n\",\n \"### Write your code below here ###\\n\",\n \"# TODO create a bar plot comparing the different expectation values\\n\",\n \"\\n\",\n \"### Don't change any code past this line ###\\n\",\n \"plt.xlabel(\\\"Index\\\")\\n\",\n \"plt.ylabel(\\\"Expectation Value\\\")\\n\",\n \"plt.title(\\\"Comparison of Expectation Values\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You should see that the TEM output closed the gap between the ideal simulated result and the unmitigated result.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feedback Survey\\n\",\n \"\\n\",\n \"We’d love to hear about your experience using the Qiskit Function! Your feedback is valuable and will help Qiskit Function providers enhance their tools and services. Please take a moment to share your thoughts by completing our short 2 min [feedback survey](https://airtable.com/app6VujlNUHZuOnAF/pagpw6TgP9UEt4TAT/form).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Davide Materia\\n\",\n \"\\n\",\n \"**Advised by:** Elena Peña Tapia\\n\",\n \"\\n\",\n \"**Version:** 1.2.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit packages versions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_runtime\\n\",\n \"import qiskit_ibm_catalog\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit IBM Runtime: {qiskit_ibm_runtime.__version__}')\\n\",\n \"print(f'Qiskit IBM Catalog: {qiskit_ibm_catalog.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "functions-labs/colibri-td/colibritd_quick-pde.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b6d1e3ec\",\n \"metadata\": {},\n \"source\": [\n \"# Model a flowing non-viscous fluid using QUICK-PDE\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"a6f69b77\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Note that the execution time of this function is generally greater than 20 minutes,\\n\",\n \"so you might want to split this lab in two sections: the first one, in which\\n\",\n \"you read through it and launch the jobs, and the second one a few hours later\\n\",\n \"(giving ample time for the jobs to complete), to work with the results of the jobs.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5d80bb5d\",\n \"metadata\": {},\n \"source\": [\n \"## Table of Contents\\n\",\n \"\\n\",\n \"- [Background](#background)\\n\",\n \"- [Setup](#setup)\\n\",\n \"- [Step 1: Set properties of the problem to solve](#step-1)\\n\",\n \"- [Step 2 (if needed): Optimize problem for quantum hardware execution](#step-2)\\n\",\n \"- [Step 3: Use the result](#step-3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8bf80006\",\n \"metadata\": {},\n \"source\": [\n \"## Background\\n\",\n \"\\n\",\n \"This lab seeks to teach on an introductory level how to use the QUICK-PDE\\n\",\n \"function to solve complex multi-physics problems on 156Q Heron R2 QPUs by using\\n\",\n \"ColibriTD's H-DES (Hybrid Differential Equation Solver). \\n\",\n \"The underlying algorithm is described in [the H-DES paper.](https://arxiv.org/abs/2410.01130).\\n\",\n \"Note that this solver can also solve non-linear equation. \\n\",\n \"\\n\",\n \"Multi-physics problems - including fluids dynamics, heat diffusion, and material\\n\",\n \"deformation, to name a few - can be ubiquitously described by Partial\\n\",\n \"Differential Equations (PDEs).\\n\",\n \"\\n\",\n \"Such problems are highly relevant for various industries and constitute an\\n\",\n \"important branch of applied mathematics. However, solving non-linear\\n\",\n \"multivariate coupled PDEs with classical tools remains challenging due to the\\n\",\n \"requirement of an exponentially large amount of resources.\\n\",\n \"\\n\",\n \"This function is appropriate for equations with increasing complexity and\\n\",\n \"variables, and is the first step to unlock possibilities that were once\\n\",\n \"considered intractable. To fully describe a problem modeled by PDEs, it is\\n\",\n \"necessary to know the initial and boundary conditions. These can strongly\\n\",\n \"change the solution of the PDE and the path to find their solution.\\n\",\n \"\\n\",\n \"In this lab, you will learn how to:\\n\",\n \"\\n\",\n \"1. Define the parameters of the initial condition function.\\n\",\n \"2. Adjust the qubit number (used to encode the function of the differential equation), depth, and shot number. \\n\",\n \"3. Run QUICK-PDE to solve the underlying differential equation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5486cc33\",\n \"metadata\": {},\n \"source\": [\n \"## Setup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3b37c2d4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Install dependencies\\n\",\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"%pip install \\\"qiskit[visualization]\\\" qiskit_ibm_catalog\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"10265f99\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5e1e3cda\",\n \"metadata\": {},\n \"source\": [\n \"You should have Grader `>=0.22.11`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b91c7113\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"from qiskit_ibm_catalog import QiskitFunctionsCatalog\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from grader import grade_ex1, grade_ex2, grade_ex3\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_colibritd_function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"293bcaf9\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**Exclusive Access to Qiskit Functions**\\n\",\n \"\\n\",\n \"As part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\\n\",\n \"\\n\",\n \"If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`. in the cell below, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\\n\",\n \"\\n\",\n \"**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2eb51f62\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Qiskit Functions Catalog\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"catalog = QiskitFunctionsCatalog(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"# You should see a list of Qiskit Functions available to you\\n\",\n \"# If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`,\\n\",\n \"# it means your access is not yet active\\n\",\n \"catalog.list()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d3559cba\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" Load Qiskit Function\\n\",\n \"\\n\",\n \"Find the correct function name from the list above, or refer to the [Qiskit Functions Catalog](https://quantum.cloud.ibm.com/functions) to locate the appropriate function name string. The name should follow the format: `\\\"[provider]/[title]\\\"`.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"61d78897\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load ColibriTD QUICK PDE function\\n\",\n \"\\n\",\n \"function_name = \\\"\\\" # TODO\\n\",\n \"quick = catalog.load(function_name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1228de26\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_colibritd_function(quick)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"642a4fb3\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## Step 1: Set properties of the problem to solve\\n\",\n \"\\n\",\n \"This lab covers the user experience from two perspectives: the\\n\",\n \"physical problem determined by the initial conditions, and the algorithmic\\n\",\n \"component in solving a fluid dynamics example on a quantum computer. \\n\",\n \"\\n\",\n \"Computational Fluid Dynamics (CFD) has a broad range of applications, thus it is\\n\",\n \"important to study and solve the underlying PDEs. An important family of PDEs\\n\",\n \"are the Navier-Stokes equations, which are a system of nonlinear partial\\n\",\n \"differential equations describing the motion of fluids. They are highly relevant\\n\",\n \"for scientific problems and engineering applications.\\n\",\n \"\\n\",\n \"Under certain conditions the Navier-Stokes equations reduce to Burger's\\n\",\n \"equation, a convection–diffusion equation describing phenomena occurring in\\n\",\n \"fluid dynamics, gas dynamics, and nonlinear acoustic etc. Basically, it models\\n\",\n \"dissipative systems.\\n\",\n \"\\n\",\n \"The one-dimensional version of the equation depends on two variables:\\n\",\n \"$t \\\\in \\\\mathbb{R}_{\\\\geq 0}$ modeling the temporal dimension, $x \\\\in \\\\mathbb{R}$\\n\",\n \"representing the spatial dimension. The general form of the equation called\\n\",\n \"viscous Burger's equation and reads:\\n\",\n \"\\n\",\n \"$$\\\\frac{\\\\partial u}{\\\\partial t} + u \\\\frac{\\\\partial u}{\\\\partial x} = \\\\nu \\\\frac{\\\\partial^2 u}{\\\\partial^2 x},$$\\n\",\n \"\\n\",\n \"$u(x,t)$ is the fluid speed field at a given position $x$ and time $t$ and $\\\\nu$\\n\",\n \"is the viscosity of the fluid. Viscosity is an important property of a fluid\\n\",\n \"measuring its rate-dependent resistance to motion or deformation and thus it\\n\",\n \"plays a crucial role in the determination of the dynamics of a fluid. When the\\n\",\n \"viscosity of the fluid is null ($\\\\nu$ = 0), the equation becomes a conservation\\n\",\n \"equations that can develop discontinuities (shock waves), due to the lack of its\\n\",\n \"internal resistance. In this case, the equation is called inviscid Burgers\\n\",\n \"equation and is a special case of nonlinear wave equation.\\n\",\n \"\\n\",\n \"Strictly speaking, inviscid flows do not occur in nature, but when modeling\\n\",\n \"aerodynamic flow, due to the infinitesimal effect of transport, using an\\n\",\n \"inviscid description of the problem can be very useful. Surprisingly, more than\\n\",\n \"70% of aerodynamic theory deal with inviscid flows.\\n\",\n \"\\n\",\n \"In this lab we offer the inviscid Burger equation as CFD example to solve\\n\",\n \"on IBM devices using QUICK-PDE, given by the equation: \\n\",\n \"$$\\\\frac{\\\\partial u}{\\\\partial t} + u\\\\frac{\\\\partial u}{\\\\partial x} = 0, $$ \\n\",\n \"\\n\",\n \"The initial condition for this problem is set to a linear function: \\n\",\n \"$$u(t=0,x) = ax + b,\\\\text{ with }a,b\\\\in\\\\mathbb{R}$$ \\n\",\n \"where $a$ and $b$, are arbitrary constants influencing the shape of the\\n\",\n \"solution. The user can play with $a$ and $b$ and see how they influence the\\n\",\n \"solving process and the solution.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"24927a98\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" Exercise 1: Select the physics problem and set the boundary condition \\n\",\n \"\\n\",\n \"Please choose `cfd` as the use case and set the initial conditions with the physical parameters `a=1.0` and `b=1.0`\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"eaef9465\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Write your code below here ###\\n\",\n \"# Select the use case and set the initial condition values. \\n\",\n \"use_case = \\\"\\\"\\n\",\n \"physical_parameters = {}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c8a0d5b2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_ex1(use_case,physical_parameters)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9c959d93\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\\\\\n\",\n \"If you do not want to run the job, we provide an example output and you can proceed to the second part of the assignment.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 25 minutes (based on tests on `ibm_aachen`)\\n\",\n \"\\n\",\n \"
\\n\",\n \" Exercise 2: Adjust execution parameters \\n\",\n \"\\n\",\n \"This exercise should guide the user through the available options in running QUICK-PDE.\\n\",\n \"\\n\",\n \"- Please choose `cfd` as the use case and set the initial conditions with the physical parameters `a=0.5` and `b=0.25`.\\n\",\n \"- For these parameters we will not start from the default values, but follow the explanations above and adjust the input parameters.\\n\",\n \"- Attribute 2 qubits for the variable $t$ and one qubit for variable $x$. Please read [the documentation](https://quantum.cloud.ibm.com/docs/en/guides/colibritd-pde) if you want to know more details.\\n\",\n \"- The depth should be set to three.\\n\",\n \"- The number of shots can influence the optimization process of the algorithm as well the precision of the solution function.\\n\",\n \"- For an efficient, yet precise execution we apply a sequential optimization scheme with four different values of shots: `500`, `2500`, `5000`, `10000`. Please insert them in a list as input. \\n\",\n \"- The algorithm should start from random circuit parameters, please put the flag accordingly.\\n\",\n \"- To be more efficient, select `session` mode and run on a Heron backend of your choice. \\n\",\n \"
\\n\",\n \" Exercise 2.2: Adjust execution parameters \\n\",\n \"\\n\",\n \"This exercise should guide the user through the available options in running QUICK-PDE.\\n\",\n \"\\n\",\n \"- Please use the input above, but add one additional input. \\n\",\n \"- After analysing the loss evolution above, we set maximum number of iterations to lower values, to save time: `100`, `10`, `2`, `1` . Please insert them in a list as input. \\n\",\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 28 minutes 3 seconds (based on tests on `ibm_aachen`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a6be6c17\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Run the quantum job with correct input below here ###\\n\",\n \"job_2 = quick.run(\\n\",\n \" use_case=use_case,\\n\",\n \" physical_parameters=physical_parameters,\\n\",\n \" nb_qubits=nb_qubits,\\n\",\n \" depth=depth,\\n\",\n \" shots=nb_shots,\\n\",\n \" max_iters= max_iters,\\n\",\n \" sigma = [0.5, 0.25, 0.1, 0.05],\\n\",\n \" initialization=initialization,\\n\",\n \" backend_name=backend_name,\\n\",\n \" mode=mode,\\n\",\n \")\\n\",\n \"job_2_id = job_2.job_id\\n\",\n \"print(f\\\"Job ID: {job_2_id}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e40802e1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job_2.status() # Check the status is DONE before getting the result\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"57593f59\",\n \"metadata\": {},\n \"source\": [\n \"Analyze now the converge of the loss and see that for these physical parameter, i.e. `a=0.5` and `b=0.25` it converges easier than for the physical parameters `a=1.0` and `b=1.0`. Note that by setting maximum iterations limits (`max_iters`) the calculation is faster. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b20812f8\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: Refresh Cell many times**\\n\",\n \"\\n\",\n \"Refresh this cell several times to print new loss values and see how the VQA cycle evolves until the final loss is printed\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"31d58841\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#check the loss function and compare between the two jobs \\n\",\n \"print(job_2.logs())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"808793e8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Check output ###\\n\",\n \"print(job_2.result())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"50b94af2\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## Step 3: Use the result\\n\",\n \"\\n\",\n \"With your solution, you can now choose what to do with it. The following demonstrates how to plot the result.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"888bcee0\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" Exercise 3: Process the final output and visualize \\n\",\n \"\\n\",\n \"This step is for retrieving the final solution and to visualize it. The solution is the result from the job. With this you can obtain the sample points for $t$ and for $x$ by extracting them from the solution dictionary. \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fe0a7d02\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Write your code below here ###\\n\",\n \"#Retrieve the solution and use the solution object to retrieve the data used to plot\\n\",\n \"solution = solution_job\\n\",\n \"t_samples = \\n\",\n \"x_samples = \\n\",\n \"u_functions = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8cbd8fb7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot the solution of the first simulation job\\n\",\n \"_ = plt.figure()\\n\",\n \"ax = plt.axes(projection=\\\"3d\\\")\\n\",\n \"\\n\",\n \"#Construct the meshgrid for 3D plotting. \\n\",\n \"t, x = np.meshgrid(t_samples, x_samples)\\n\",\n \"\\n\",\n \"# plot the solution using the 3d plotting capabilities of pyplot\\n\",\n \"ax.plot_surface(\\n\",\n \" t,\\n\",\n \" x,\\n\",\n \" u_functions,\\n\",\n \" edgecolor=\\\"royalblue\\\",\\n\",\n \" lw=0.25,\\n\",\n \" rstride=26,\\n\",\n \" cstride=26,\\n\",\n \" alpha=0.3,\\n\",\n \")\\n\",\n \"ax.scatter(t, x, solution, marker=\\\".\\\")\\n\",\n \"ax.set(xlabel=\\\"t\\\", ylabel=\\\"x\\\", zlabel=\\\"u(t,x)\\\")\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"81b003ba\",\n \"metadata\": {},\n \"source\": [\n \"Do the same for the second job\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6680ada4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Write your code below here ###\\n\",\n \"#Retrieve the solution and use the solution object to retrieve the data used to plot\\n\",\n \"solution_2 = \\n\",\n \"t_samples_2 = \\n\",\n \"x_samples_2 = \\n\",\n \"u_functions_2 = \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"058ab2d8\",\n \"metadata\": {},\n \"source\": [\n \"Notice the difference of initial condition for the second run and its effect on\\n\",\n \"the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6dab21c9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Write your code below here ###\\n\",\n \"# Plot the solution of the second simulation job\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eef5f2f2\",\n \"metadata\": {},\n \"source\": [\n \"Note that for:\\n\",\n \"\\n\",\n \"$a=1.0$ and $b=1.0$\\n\",\n \"\\n\",\n \"we used (3+1+1)*10=50 qubits and 10000 shots per function evaluation to compute the loss function at each iteration steps were used.\\n\",\n \"\\n\",\n \"While for\\n\",\n \"\\n\",\n \"$a=0.5$ and $b=0.25$\\n\",\n \" \\n\",\n \"we used (2+1+1)*10=40 qubits and a sequence of 500, 2000, 5000, 10000 was used with faster convergence. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f59e392c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compare the solution for different initial condition, i.e\\n\",\n \"# a=1.0, b=1.0 vs a = 0.5, b=0.25\\n\",\n \"_ = plt.figure()\\n\",\n \"ax = plt.axes(projection=\\\"3d\\\")\\n\",\n \"\\n\",\n \"#Construct the meshgrid for 3D plotting. \\n\",\n \"t, x = np.meshgrid(t_samples, x_samples)\\n\",\n \"\\n\",\n \"# plot the solution using the 3d plotting capabilities of pyplot\\n\",\n \"ax.plot_surface(\\n\",\n \" t,\\n\",\n \" x,\\n\",\n \" u_functions,\\n\",\n \" edgecolor=\\\"royalblue\\\",\\n\",\n \" lw=0.25,\\n\",\n \" rstride=26,\\n\",\n \" cstride=26,\\n\",\n \" alpha=0.3,\\n\",\n \")\\n\",\n \"ax.plot_surface(\\n\",\n \" t,\\n\",\n \" x,\\n\",\n \" u_functions_2,\\n\",\n \" edgecolor=\\\"orange\\\",\\n\",\n \" lw=0.25,\\n\",\n \" rstride=26,\\n\",\n \" cstride=26,\\n\",\n \" alpha=0.3,\\n\",\n \")\\n\",\n \"ax.scatter(t, x, u_functions, marker=\\\".\\\")\\n\",\n \"ax.scatter(t, x, u_functions_2, marker=\\\"*\\\")\\n\",\n \"ax.set(xlabel=\\\"t\\\", ylabel=\\\"x\\\")# zlabel=\\\"u(t,x)\\\")\\n\",\n \"ax.set(title=\\\"fluid velocity u(t,x) for different initial conditions\\\")\\n\",\n \"#ax.view_init(elev=15, azim=90)\\n\",\n \"#ax.set_zlim(0,2.0)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ba1b56cd\",\n \"metadata\": {},\n \"source\": [\n \"## Thank You for Participating!\\n\",\n \"\\n\",\n \"Congratulations on completing **QUICK-PDE lab**! Today, you've delved into a multiphysics simulation Qiskit functions and learned how to solve a PDE on a quantum computer. You explored how these powerful tools can simplify quantum computing workflows and enhance your research and development projects.\\n\",\n \"\\n\",\n \"As we continue through this summer school, remember that experimentation and iteration are at the heart of quantum development. Use the knowledge you gained today as a foundation for deeper explorations into Qiskit and quantum applications. We encourage you to keep testing, exploring, and sharing your findings with the Qiskit community.\\n\",\n \"\\n\",\n \"Feel free to refer back to this notebook, check out the other track, revisit exercises, or try some of the optional sections to refine your skills. We hope this journey has inspired you to push the boundaries of quantum computing!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c6eeae94\",\n \"metadata\": {},\n \"source\": [\n \"# Feedback Survey\\n\",\n \"\\n\",\n \"We’d love to hear about your experience using the Qiskit Function! Your feedback is valuable and will help Qiskit Function providers enhance their tools and services. Please take a moment to share your thoughts by completing our short 2 min [feedback survey](https://airtable.com/app6VujlNUHZuOnAF/pagpw6TgP9UEt4TAT/form).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6ef6ae82\",\n \"metadata\": {},\n \"source\": [\n \"# Additional Information\\n\",\n \"\\n\",\n \"**Created by**: Karla Baumann\\n\",\n \"\\n\",\n \"**Advised by**: Junye Huang\\n\",\n \"\\n\",\n \"**Version**: 1.1.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1b94ca08\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskit packages versions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"30beee8b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_catalog\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit IBM Catalog: {qiskit_ibm_catalog.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"description\": \"Use the QUICK-PDE function to model a flowing non-viscous fluid.\",\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.9\"\n },\n \"title\": \"Model a flowing non-viscous fluid using QUICK-PDE\"\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "functions-labs/colibri-td/grader.py",
"content": "def grade_ex1(use_case, physical_parameters):\n if use_case != 'cfd':\n return \"Please choose the correct use case\"\n\n if physical_parameters != {\"a\": 1.0, \"b\": 1.0}:\n return \"Please choose the correct physical parameters\"\n \n return 'Congratulations 🎉! Your answer is correct.'\n \n\ndef grade_ex2(use_case, physical_parameters, nb_qubits, depth, nb_shots, initialization, mode):\n if use_case != 'cfd':\n return \"Please choose the correct use case\"\n\n if physical_parameters != {\"a\": 0.5, \"b\": 0.25}:\n return \"Please choose the correct physical parameters\" \n \n if nb_qubits !={\"u\": {\"t\": 2, \"x\":1}}:\n return \"Please choose the correct qubit number\" \n \n if depth !={\"u\": 3}:\n return \"Please choose the correct depth\" \n \n if nb_shots !=[500, 2500, 5000, 10000]:\n return \"Please provide the correct list of shots\"\n\n if initialization != \"RANDOM\":\n return \"Please choose the correct initialization\"\n\n if mode !=\"session\":\n return \"Please choose session mode to submit your quantum task\"\n\n return 'Congratulations 🎉! Your answer is correct.'\n\n\ndef grade_ex3(use_case, physical_parameters, nb_qubits, depth, nb_shots, max_iters, initialization, mode):\n if use_case != \"cfd\":\n return \"Please choose the correct use case\"\n\n if physical_parameters != {\"a\": 0.5, \"b\": 0.25}:\n return \"Please choose the correct physical parameters\"\n\n if nb_qubits !={\"u\": {\"t\": 2, \"x\":1}}:\n return \"Please choose the correct qubit number\"\n\n if depth !={\"u\": 3}:\n return \"Please choose the correct depth\"\n\n if nb_shots !=[500, 2500, 5000, 10000]:\n return \"Please provide the correct list of shots\"\n\n if max_iters != [100, 20, 2, 1]:\n \"Please provide the correct list of max_iters\"\n\n if initialization != \"RANDOM\":\n return \"Please choose the correct initialization\"\n\n if mode !=\"session\":\n return \"Please choose session mode to submit your quantum task\"\n"
},
{
"path": "functions-labs/global-data-quantum/gdq_quantum_portfolio_optimizer.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Optimize a portfolio with the Quantum Portfolio Optimizer Qiskit function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This exercise demonstrates how to use Qiskit’s quantum portfolio optimizer function to solve financial portfolio optimization problems. We show how to formulate and solve dynamic portfolio optimization problems in a simple and accessible way, making it suitable for execution on a quantum computer or simulator with no quantum computing expertise required. The objective is to illustrate how quantum algorithms apply to real-world financial problems using intuitive tools and workflows provided by Qiskit.\\n\",\n \"\\n\",\n \"In this exercise, we also show how to fine-tune the quantum portfolio optimizer settings. Although this fine-tuning is not necessary for basic usage, these advanced options provide insights into how experienced users can leverage quantum computing to improve efficiency and accuracy. For further details, consult the [documentation](https://docs.quantum.ibm.com/guides/global-data-quantum-optimizer) of the global data quantum portfolio optimizer.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Table of Contents\\n\",\n \"\\n\",\n \"- [Function Description](#function-description)\\n\",\n \"- [DPO Job Execution Example](#dpo-job-execution-example)\\n\",\n \"- [Exercise 1: DPO Job Execution](#exercise-1-dpo-job-execution)\\n\",\n \"- [Exercise 2: Resuming Job Execution](#exercise-2-resuming-job-execution)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Setup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Install dependencies\\n\",\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"%pip install \\\"qiskit[visualization]\\\"~=2.1.0 qiskit-serverless~=0.24.0 qiskit-ibm-catalog~=0.8.0 yfinance==0.2.60 pandas==2.1.4\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You should have Grader `>=0.22.11`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import yfinance as yf\\n\",\n \"import pandas as pd\\n\",\n \"from qiskit_ibm_catalog import QiskitFunctionsCatalog\\n\",\n \"from grader import grade_ex1a, grade_ex1b, grade_ex2\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_gdq_function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**Exclusive Access to Qiskit Functions**\\n\",\n \"\\n\",\n \"As part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\\n\",\n \"\\n\",\n \"If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`. in the cell below, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\\n\",\n \"\\n\",\n \"**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Qiskit Functions Catalog\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"catalog = QiskitFunctionsCatalog(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"# You should see a list of Qiskit Functions available to you\\n\",\n \"# If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`,\\n\",\n \"# it means your access is not yet active\\n\",\n \"catalog.list()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" Load Qiskit Function\\n\",\n \"\\n\",\n \"Find the correct function name from the list above, or refer to the [Qiskit Functions Catalog](https://quantum.cloud.ibm.com/functions) to locate the appropriate function name string. The name should follow the format: `\\\"[provider]/[title]\\\"`.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load Global Data Quantum Quantum Portfolio Optimizer function\\n\",\n \"\\n\",\n \"function_name = \\\"\\\" # TODO\\n\",\n \"dpo_solver = catalog.load(function_name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_gdq_function(dpo_solver)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Function Description\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The dynamic portfolio optimization problem involves determining the optimal investment strategy over multiple time periods in order to maximize the expected return of the portfolio and minimize risks, often under certain constraints such as transaction costs, or risk aversion. Unlike standard portfolio optimization, which considers a single time to rebalance the portfolio, the dynamic version accounts for the evolving nature of assets and rebalance the investment based on changes in asset performance over time.\\n\",\n \"\\n\",\n \"To solve the dynamic portfolio optimization problem, we formulate it as a QUBO (Quadratic Unconstrained Binary Optimization) problem. In this approach, the variables are discretized based on the number of assets in the portfolio, the number of time steps considered, and the number of resolution bits used to define the investment strategy.\\n\",\n \"\\n\",\n \"Following the formulation described in our [manuscript](https://arxiv.org/pdf/2412.19150), the QUBO problem is framed as a multi-objective optimization task, aiming to maximize the expected return, minimize risks, and reduce transaction costs (expenses associated with changing positions over time). Additionally, we introduce a penalty term to enforce the maximum investment per asset.\\n\",\n \"\\n\",\n \"The final goal is to obtain a binary string as a solution, indicating how much to invest in each asset at each point in time. To illustrate this, consider a simplified case with 3 assets and 3 time steps. \\n\",\n \"\\n\",\n \"| Date | META (%) | AAPL (%) | GOOGL (%) |\\n\",\n \"|------------|----------|----------|------------|\\n\",\n \"| 2024-07-01 | 16.67 | 50.00 | 33.33 |\\n\",\n \"| 2024-08-01 | 50.00 | 50.00 | 0.00 |\\n\",\n \"| 2024-09-01 | 42.86 | 42.86 | 14.29 |\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## DPO Job Execution Example\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the cells below, we show how to solve a dynamic portfolio optimization problem using the quantum portfolio optimizer Qiskit Function. Specifically, we model and solve a three-period portfolio allocation problem involving three financial assets. The optimization can be performed using a binary encoding with a resolution of two bits, providing a simple yet insightful framework to understand how to leverage the capabilities of a quantum investment portfolio optimizer. This exercise is designed to introduce key concepts in quantum finance while leveraging Qiskit’s functions for implementing quantum algorithms in a practical financial context.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, we have to load the historical data of the assets. For this example, we build our portfolio using three major technology companies: Meta Platforms Inc. (ticker: META), Apple Inc. (ticker: AAPL), and Alphabet Inc. (ticker: GOOGL). These assets serve as the basis for constructing and optimizing our portfolio over three time periods.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To work with the data effectively, it must be structured as a JSON object that maps each asset's ticker symbol to a dictionary of closing prices by date. Each date should follow the YYYY-MM-DD format, and prices can be either normalized or raw. All assets must share the same set of dates to ensure consistency; if any asset is missing data for a given date, the missing value should be filled—typically using forward fill with the last known price.\\n\",\n \"\\n\",\n \"To simplify the process, we use the provided function, which only requires the date range and the list of asset tickers. It automatically downloads the data, aligns the dates, fills missing values, and returns the data in the correct JSON format.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Example: Follow the example to learn how to use the tool. It is not necessary to execute it, but doing so can help confirm that everything is correctly configured.\\n\",\n \"\\n\",\n \"
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def load_asset_data(symbols, start_date, end_date):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Downloads and prepares historical close price data for the given list of asset symbols.\\n\",\n \" Also includes weekends by forward-filling the last known value.\\n\",\n \"\\n\",\n \" Parameters:\\n\",\n \" - symbols (list of str): Ticker symbols (e.g., ['META', 'AAPL', 'GOOGL'])\\n\",\n \" - start_date (str): Start date in 'YYYY-MM-DD' format\\n\",\n \" - end_date (str): End date in 'YYYY-MM-DD' format\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" - assets (dict): Dictionary representation of the DataFrame with prices per date and symbol\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" series_list = []\\n\",\n \" symbol_names = [symbol.replace(\\\".\\\", \\\"_\\\") for symbol in symbols]\\n\",\n \"\\n\",\n \" # Create a full date index including weekends\\n\",\n \" full_index = pd.date_range(start=start_date, end=end_date, freq='D')\\n\",\n \"\\n\",\n \" for symbol, name in zip(symbols, symbol_names):\\n\",\n \" print(f\\\"Downloading data for {symbol}...\\\")\\n\",\n \" data = yf.download(symbol, start=start_date, end=end_date)[\\\"Close\\\"]\\n\",\n \" data.name = name\\n\",\n \"\\n\",\n \" # Reindex to include weekends\\n\",\n \" data = data.reindex(full_index)\\n\",\n \"\\n\",\n \" # Fill missing values (e.g., weekends or holidays) by forward/backward fill\\n\",\n \" data.ffill(inplace=True)\\n\",\n \" data.bfill(inplace=True)\\n\",\n \"\\n\",\n \" series_list.append(data)\\n\",\n \"\\n\",\n \" # Combine all series into a single DataFrame\\n\",\n \" df = pd.concat(series_list, axis=1)\\n\",\n \"\\n\",\n \" # Convert index to string for consistency\\n\",\n \" df.index = df.index.astype(str)\\n\",\n \"\\n\",\n \" # Convert DataFrame to dictionary\\n\",\n \" assets = df.to_dict()\\n\",\n \" return assets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"Tip: \\n\",\n \"\\n\",\n \"You can reuse this function to efficiently solve the upcoming exercises.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now it's time to define the date range over which we want to obtain historical data for our assets. To do this, we first need to specify the time window considered in each time step (`dt`). This is important because we need, at a minimum, the closing prices for ``(nt + 1) * dt`` days, where `nt` is the number of time steps in our portfolio optimization problem.\\n\",\n \"\\n\",\n \"The time window (`dt`) we use is one month (30 days). Since our problem has 3 time steps (`nt = 3`), we need data covering 4 months in total. For example, we collect data from July 1, 2022, to November 1, 2022.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define the list of asset symbols \\n\",\n \"symbols = [\\n\",\n \" \\\"META\\\", \\\"AAPL\\\", \\\"GOOGL\\\", \\n\",\n \"]\\n\",\n \"# Define the start and end dates for the portfolio data\\n\",\n \"start_date = \\\"2024-07-01\\\"\\n\",\n \"end_date = \\\"2024-11-01\\\"\\n\",\n \"\\n\",\n \"# get the asset data dictionary\\n\",\n \"assets = load_asset_data(symbols, start_date, end_date)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, we need to define the maximum amount to invest at each time step. Since we are using a 2-bit resolution, the maximum investment amount per time step cannot exceed `(2**(nq) - 1) * n_assets = 9`. So for this case we fix the maximum amount to 7 (i.e., we allow for a maximum investment per asset of 7/9 ~ 77%).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# define max investment parameter\\n\",\n \"max_investment = 7\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since we use the Differential Evolution algorithm as our classical optimizer, we need to define the number of generations and the population size (number of individuals) for the optimization process.\\n\",\n \"\\n\",\n \"Note that the total amount of circuits is ``(num_generations + 1) * population_size``. In this case, to avoid taking large computation time, we execute 60 circuits.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# define the number of generations and the population size\\n\",\n \"num_generations = 5\\n\",\n \"population_size = 10\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we just need to set all the required parameters and pass them appropriately into the quantum portfolio optimizer function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"nt = 3 # Define the number of time steps\\n\",\n \"nq = 2 # Define the number of resolution bits\\n\",\n \"dt = 30 # Define the time window size\\n\",\n \"\\n\",\n \"max_parallel_jobs = 3 # Define the amount of parallel jobs executed in the QPU. Maximum parallel jobs available for open plan is 3.\\n\",\n \"max_batchsize = 4 # Define the number of circuits per job. Note that estimator_shots*max_batchsize should be less than 10_000_000.\\n\",\n \"\\n\",\n \"estimator_shots = 5_000 # Define the number of shots for the estimator. \\n\",\n \"sampler_shots = 10_000 # Define the number of samples of the optimized circuit.\\n\",\n \"\\n\",\n \"ansatz = 'real_amplitudes' # Define the ansatz to be used in the optimization\\n\",\n \"multiple_passmanager = False # Specify not using multiple passmanager option \\n\",\n \"\\n\",\n \"apply_postprocess = True # Specify if apply SQD-Based postprocess. \\n\",\n \"\\n\",\n \"backend_name = None # Chooses the least busy backend available for the instance.\\n\",\n \"\\n\",\n \"qubo_settings = {\\n\",\n \" 'nt': nt,\\n\",\n \" 'nq': nq,\\n\",\n \" 'dt': dt,\\n\",\n \" 'max_investment': max_investment,\\n\",\n \"}\\n\",\n \"\\n\",\n \"optimizer_settings = {\\n\",\n \" 'de_optimizer_settings': {\\n\",\n \" 'num_generations': num_generations,\\n\",\n \" 'population_size': population_size,\\n\",\n \" 'max_parallel_jobs': max_parallel_jobs, \\n\",\n \" 'max_batchsize': max_batchsize,\\n\",\n \" },\\n\",\n \" 'optimizer': 'differential_evolution', \\n\",\n \" 'primitive_settings': {\\n\",\n \" 'estimator_shots': estimator_shots,\\n\",\n \" 'sampler_shots': sampler_shots,\\n\",\n \" } \\n\",\n \"}\\n\",\n \"\\n\",\n \"ansatz_settings = {\\n\",\n \" 'ansatz': ansatz,\\n\",\n \" 'multiple_passmanager': multiple_passmanager,\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 3 minutes 50 seconds (based on tests on `ibm_brussels`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dpo_job = dpo_solver.run(\\n\",\n \" assets=assets, \\n\",\n \" qubo_settings=qubo_settings, \\n\",\n \" optimizer_settings=optimizer_settings, \\n\",\n \" ansatz_settings=ansatz_settings, \\n\",\n \" backend_name=backend_name, \\n\",\n \" apply_postprocess=apply_postprocess\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get the results of the job\\n\",\n \"dpo_result = dpo_job.result()\\n\",\n \"\\n\",\n \"# Show the solution strategy\\n\",\n \"dpo_result['result']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next we show how to access the metrics associated with the solution of the job. Specifically, we access the following metrics: Deviation from maximum investment, Sharpe ratio, and investment return.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Convert metadata to a DataFrame, excluding 'session_id'\\n\",\n \"df = pd.DataFrame(dpo_result['metadata']['all_samples_metrics'])\\n\",\n \"\\n\",\n \"# Find the minimum objective cost\\n\",\n \"min_cost = df['objective_costs'].min()\\n\",\n \"\\n\",\n \"# Extract the row with the lowest cost\\n\",\n \"best_row = df[df['objective_costs'] == min_cost].iloc[0]\\n\",\n \"\\n\",\n \"# Display the results associated with the best investment\\n\",\n \"print(\\\"Best Investment Strategy:\\\")\\n\",\n \"print(f\\\" - Deviation from maximum investment: {best_row['rest_breaches']}%\\\")\\n\",\n \"print(f\\\" - Sharpe Ratio: {best_row['sharpe_ratios']:.2f}\\\")\\n\",\n \"print(f\\\" - Return: {best_row['returns']}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exercise 1: DPO Job Execution\\n\",\n \"In this exercise, we perform a portfolio optimization using three financial assets, four time steps of 30 days of time window, and a 2-bit resolution. This time, we use the Optimized Real Amplitudes Ansatz. The population size and number of generations are chosen to ensure that a total of 70 quantum circuits are executed during the optimization process. Additionally, we allow for a maximum investment per asset of 6/9 (approximately 66%).\\n\",\n \"\\n\",\n \"The portfolio selected for this exercise are:\\n\",\n \"- [NVIDIA Corporation](https://finance.yahoo.com/quote/NVDA/)\\n\",\n \"- [Tesla, Inc.](https://finance.yahoo.com/quote/TSLA/)\\n\",\n \"- [Amazon.com, Inc.](https://finance.yahoo.com/quote/AMZN/)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Exercise 1: Follow the instructions in the cells below to perform portfolio optimization.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Tips: \\n\",\n \"\\n\",\n \"Visit the links to the asset pages on Yahoo Finance to check the ticker names.\\n\",\n \"\\n\",\n \"
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### TODO: Write your code below here ###\\n\",\n \"\\n\",\n \"# Fill the missing asset tickers of the portfolio\\n\",\n \"symbols = []\\n\",\n \"\\n\",\n \"# Define the QUBO problem specification parameters (nt, nq, dt, )\\n\",\n \"# - Set max_investment to approximately 66% of maximum investment per asset. (See example above)\\n\",\n \"# - Set nt to four time steps.\\n\",\n \"# - Set nq to two resolution bits.\\n\",\n \"# - Set dt to 30 days.\\n\",\n \"\\n\",\n \"qubo_settings = {\\n\",\n \" 'nt': , # Number of time steps\\n\",\n \" 'nq': , # Number of resolution bits\\n\",\n \" 'dt': , # Time window size in days\\n\",\n \" 'max_investment': , # Set max investment to approximately 66% of maximum investment per asset\\n\",\n \"}\\n\",\n \"\\n\",\n \"# Define the end dates for the portfolio data so that it fills the required amount of days according to the time window size and the number of time steps.\\n\",\n \"start_date = \\\"2024-07-01\\\"\\n\",\n \"end_date = \\\"\\\"\\n\",\n \"\\n\",\n \"# get the asset data dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Knowing that we want to run at most 70 quantum circuits:\\n\",\n \"# set the population size and the number of generations for the Differential Evolution algorithm accordingly. \\n\",\n \"# remember that the number of circuits is calculated as (num_generations + 1) * population_size.\\n\",\n \"\\n\",\n \"### TODO: Write your code below here ###\\n\",\n \"\\n\",\n \"num_generations = \\n\",\n \"population_size = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"max_parallel_jobs = 3 # Define the amount of parallel jobs executed in the QPU. Maximum parallel jobs available for open plan is 3.\\n\",\n \"max_batchsize = 4 # Define the number of circuits per job.\\n\",\n \"\\n\",\n \"estimator_shots = 25_000 # Define the number of shots for the estimator. \\n\",\n \"sampler_shots = 100_000 # Define the number of samples of the optimized circuit.\\n\",\n \"\\n\",\n \"# Now complete the configuration by defining the remaining parameters. \\n\",\n \"# Remember We use:\\n\",\n \"# - Optimized Real Amplitudes ansatz. \\n\",\n \"# - Not enable the multiple passmanager option. \\n\",\n \"# - Apply Postprocessing based on SQD. \\n\",\n \"# - The backend with the least load available to ensure more efficient execution.\\n\",\n \"\\n\",\n \"### TODO: Write your code below here ###\\n\",\n \"\\n\",\n \"optimizer_settings_ex1 = {\\n\",\n \" 'de_optimizer_settings': {\\n\",\n \" 'num_generations': ,\\n\",\n \" 'population_size': ,\\n\",\n \" 'max_parallel_jobs': , \\n\",\n \" 'max_batchsize': ,\\n\",\n \" },\\n\",\n \" 'optimizer': 'differential_evolution', \\n\",\n \" 'primitive_settings': {\\n\",\n \" 'estimator_shots': ,\\n\",\n \" 'sampler_shots': ,\\n\",\n \" } \\n\",\n \"}\\n\",\n \"\\n\",\n \"ansatz_settings = {\\n\",\n \" 'ansatz': ,\\n\",\n \" 'multiple_passmanager': ,\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_ex1a(qubo_settings, optimizer_settings_ex1, ansatz_settings, apply_postprocess, assets)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"catalog = QiskitFunctionsCatalog(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"dpo_solver = catalog.load(\\\"global-data-quantum/quantum-portfolio-optimizer\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 4 minutes 20 seconds (based on tests on `ibm_brussels`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Execute the job.\\n\",\n \"\\n\",\n \"### TODO: Write your code below here ###\\n\",\n \"\\n\",\n \"dpo_job = dpo_solver.run(\\n\",\n \" assets= ,\\n\",\n \" qubo_settings= , \\n\",\n \" optimizer_settings= , \\n\",\n \" ansatz_settings= , \\n\",\n \" backend_name= , \\n\",\n \" apply_postprocess= ,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dpo_job.status() # Check the status is DONE before getting the result\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, let's display the following key performance metrics: return, Sharpe ratio, deviation from the investment restriction, and transaction costs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### TODO: Write your code below here ###\\n\",\n \"\\n\",\n \"# Get the results of the job\\n\",\n \"dpo_result = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_ex1b(dpo_result)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# Convert metadata to a DataFrame, excluding 'session_id'\\n\",\n \"df = pd.DataFrame(dpo_result['metadata']['all_samples_metrics'])\\n\",\n \"\\n\",\n \"# Find the minimum objective cost\\n\",\n \"min_cost = df['objective_costs'].min()\\n\",\n \"\\n\",\n \"# Extract the row with the lowest cost\\n\",\n \"best_row = df[df['objective_costs'] == min_cost].iloc[0]\\n\",\n \"\\n\",\n \"# Display the results associated with the best investment\\n\",\n \"print(\\\"Best Investment Strategy:\\\")\\n\",\n \"print(f\\\" - Deviation from maximum investment: {best_row['rest_breaches']}%\\\")\\n\",\n \"print(f\\\" - Sharpe Ratio: {best_row['sharpe_ratios']:.2f}\\\")\\n\",\n \"print(f\\\" - Transaction Costs: {best_row['transaction_costs']}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exercise 2: Resuming Job Execution\\n\",\n \"This function allows you to resume a previous execution (either because it was interrupted or because you want to perform additional runs to improve the result). In this exercise, we resume the previous optimization and add two more generations to continue refining the portfolio solution\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To do this, we use the argument `previous_session_id`, which is a list of session IDs from which the execution is being resumed. Then, we need to provide exactly the same parameters as in the previous function call, but with two additional generations compared to the original example.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Exercise 2: Modify the number of generations to perform a warm restart and extend the execution.\\n\",\n \"\\n\",\n \"
\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"num_generations_ex1 = optimizer_settings_ex1['de_optimizer_settings']['num_generations']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# First we take the session id from the previous job.\\n\",\n \"session_id = dpo_result['metadata']['session_id']\\n\",\n \"\\n\",\n \"# Change the number of generations by adding 2 to the previous number of generations.\\n\",\n \"\\n\",\n \"### TODO: Write your code below here ###\\n\",\n \"\\n\",\n \"optimizer_settings = optimizer_settings_ex1\\n\",\n \"optimizer_settings['de_optimizer_settings']['num_generations'] = \\n\",\n \"\\n\",\n \"# Execute the job again introducing the new number of generations and the session id in the `previous_session_id` list.\\n\",\n \"\\n\",\n \"### TODO: Write your code below here ###\\n\",\n \"\\n\",\n \"previous_session_id = # Load session id from the output of the previous exercise dpo_job.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_ex2(qubo_settings, optimizer_settings, ansatz_settings, apply_postprocess, assets, num_generations_ex1, previous_session_id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 1 minutes 30 seconds (based on tests on `ibm_brussels`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dpo_job = dpo_solver.run(\\n\",\n \" assets=assets, \\n\",\n \" qubo_settings=qubo_settings, \\n\",\n \" optimizer_settings=optimizer_settings, \\n\",\n \" ansatz_settings=ansatz_settings, \\n\",\n \" backend_name=backend_name, \\n\",\n \" apply_postprocess=apply_postprocess,\\n\",\n \" previous_session_id=previous_session_id\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dpo_job.status() # Check the status is DONE \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feedback Survey\\n\",\n \"\\n\",\n \"We’d love to hear about your experience using the Qiskit Function! Your feedback is valuable and will help Qiskit Function providers enhance their tools and services. Please take a moment to share your thoughts by completing our short 2 min [feedback survey](https://airtable.com/app6VujlNUHZuOnAF/pagpw6TgP9UEt4TAT/form).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# References\\n\",\n \"\\n\",\n \"1. [Quantum Portfolio Optimizater Tutorial](https://quantum.cloud.ibm.com/docs/en/tutorials/global-data-quantum-optimizer)\\n\",\n \"2. [Quantum Portfolio Optimizer Documentation](https://quantum.cloud.ibm.com/docs/en/guides/global-data-quantum-optimizer)\\n\",\n \"3. [Scaling the Variational Quantum Eigensolver for Dynamic Portfolio Optimization](https://arxiv.org/abs/2412.19150)\\n\",\n \"4. [Qiskit Serverless Documentation](https://qiskit.github.io/qiskit-serverless/index.html)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Additional Information\\n\",\n \"**Created by**: Manuel Martín-Cordero, Álvaro Nodar \\n\",\n \"**Advised by**: Junye Huang\\n\",\n \"\\n\",\n \"**Version**: 1.1.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskit packages versions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_catalog\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit IBM Catalog: {qiskit_ibm_catalog.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "functions-labs/global-data-quantum/grader.py",
"content": "def grade_ex1a(qubo_settings: dict, optimizer_settings: dict, ansatz_settings: dict, apply_postprocess: bool, assets: dict) -> None:\n \"\"\"\n Grades Exercise 1a by checking:\n - Correct asset keys and data length\n - Correct QUBO settings: nt, nq, dt, max_investment\n - Correct ansatz settings\n - Postprocessing and optimizer configuration\n \"\"\"\n\n # Check asset keys\n expected_assets = {'NVDA', 'TSLA', 'AMZN'}\n if set(assets.keys()) != expected_assets:\n print(f\"❌ Incorrect asset keys. Expected: {sorted(expected_assets)}.\")\n return\n\n # Check data length\n min_points = (4 + 1) * 30\n for asset, prices in assets.items():\n if len(prices) <= min_points:\n print(f\"❌ Asset {asset} has insufficient data. Requires more than {min_points} data points.\")\n return\n\n # QUBO settings\n if qubo_settings.get('nt') != 4:\n print(\"❌ Incorrect value for 'nt'. Expected: 4.\")\n return\n\n if qubo_settings.get('nq') != 2:\n print(\"❌ Incorrect value for 'nq'. Expected: 2.\")\n return\n\n if qubo_settings.get('dt') != 30:\n print(\"❌ Incorrect value for 'dt'. Expected: 30.\")\n return\n\n if qubo_settings.get('max_investment') != 6:\n print(\"❌ Incorrect value for 'max_investment'. Expected: 6.\")\n return\n\n # Ansatz settings\n if ansatz_settings.get('ansatz') != 'optimized_real_amplitudes':\n print(\"❌ Incorrect 'ansatz'. Expected: 'optimized_real_amplitudes'.\")\n return\n\n if ansatz_settings.get('multiple_passmanager') is not False:\n print(\"❌ 'multiple_passmanager' must be set to False.\")\n return\n\n # Postprocess flag\n if apply_postprocess is not True:\n print(\"❌ 'apply_postprocess' must be set to True.\")\n return\n\n # Optimizer constraints\n de_settings = optimizer_settings.get('de_optimizer_settings', {})\n num_gens = de_settings.get('num_generations', 0)\n pop_size = de_settings.get('population_size', 0)\n if (num_gens + 1) * pop_size > 70:\n print(\"❌ Too many circuits. Ensure (num_generations + 1) * population_size ≤ 70.\")\n return\n\n primitive_settings = optimizer_settings.get('primitive_settings', {})\n if primitive_settings.get('estimator_shots') != 25_000:\n print(\"❌ 'estimator_shots' must be set to 25_000.\")\n return\n\n if primitive_settings.get('sampler_shots') != 100_000:\n print(\"❌ 'sampler_shots' must be set to 100_000.\")\n return\n\n print(\"✅ Exercise 1a solution is correct!\")\n\n\ndef grade_ex1b(dpo_result: dict) -> None:\n \"\"\"\n Grades Exercise 1b by checking how close the minimum objective cost is\n to the known global minimum (-3.51097567).\n \"\"\"\n\n try:\n min_cost = min(dpo_result['metadata']['all_samples_metrics']['objective_costs'])\n except (KeyError, TypeError):\n print(\"❌ Invalid format in 'dpo_result'. Could not find objective costs.\")\n return\n\n global_min = -3.51097567\n tolerance = 0.1\n\n # Check closeness to global minimum\n if abs(min_cost - global_min) < tolerance:\n print(\"✅ The minimum cost is very close to the global minimum! Your implementation is correct.\")\n return\n\n # Check for suspiciously low value\n if min_cost < global_min:\n print(\"❌ The minimum cost is lower than the known global minimum. There may be an issue with your implementation.\")\n return\n\n # Report relative deviation\n relative_deviation = abs((min_cost - global_min) / global_min) * 100\n print(f\"⚠️ The relative deviation from the global minimum is {relative_deviation:.2f}%. Please review your implementation.\")\n\n\ndef grade_ex2(\n qubo_settings: dict,\n optimizer_settings: dict,\n ansatz_settings: dict,\n apply_postprocess: bool,\n assets: dict,\n num_generations_ex_1: int,\n previous_session_id: list\n) -> None:\n \"\"\"\n Grades Exercise 2 by verifying:\n - Warm restart from previous session\n - Correct increment in number of generations\n - Asset keys and data size\n - QUBO, ansatz, optimizer, and postprocessing settings\n \"\"\"\n\n # Check previous_session_id\n if not isinstance(previous_session_id, list) or len(previous_session_id) != 1 or not isinstance(previous_session_id[0], str):\n print(\"❌ 'previous_session_id' must be a list containing a single string.\")\n return\n\n # Check number of generations increased by 2\n expected_generations = num_generations_ex_1 + 2\n actual_generations = optimizer_settings.get('de_optimizer_settings', {}).get('num_generations')\n if actual_generations != expected_generations:\n print(f\"❌ Incorrect number of generations. Expected: {expected_generations}, got: {actual_generations}.\")\n return\n\n # Check asset keys\n expected_assets = {'NVDA', 'TSLA', 'AMZN'}\n if set(assets.keys()) != expected_assets:\n print(f\"❌ Incorrect asset keys. Expected: {sorted(expected_assets)}.\")\n return\n\n # Check asset data length\n min_points = (4 + 1) * 30\n for asset, prices in assets.items():\n if len(prices) <= min_points:\n print(f\"❌ Asset {asset} has insufficient data. Requires more than {min_points} data points.\")\n return\n\n # QUBO settings\n if qubo_settings.get('nt') != 4:\n print(\"❌ Incorrect 'nt'. Expected: 4.\")\n return\n\n if qubo_settings.get('nq') != 2:\n print(\"❌ Incorrect 'nq'. Expected: 2.\")\n return\n\n if qubo_settings.get('dt') != 30:\n print(\"❌ Incorrect 'dt'. Expected: 30.\")\n return\n\n if qubo_settings.get('max_investment') != 6:\n print(\"❌ Incorrect 'max_investment'. Expected: 6.\")\n return\n\n # Ansatz settings\n if ansatz_settings.get('ansatz') != 'optimized_real_amplitudes':\n print(\"❌ Incorrect 'ansatz'. Expected: 'optimized_real_amplitudes'.\")\n return\n\n if ansatz_settings.get('multiple_passmanager') is not False:\n print(\"❌ 'multiple_passmanager' must be set to False.\")\n return\n\n # Postprocessing flag\n if apply_postprocess is not True:\n print(\"❌ 'apply_postprocess' must be set to True.\")\n return\n\n # Primitive shots\n primitive_settings = optimizer_settings.get('primitive_settings', {})\n if primitive_settings.get('estimator_shots') != 25_000:\n print(\"❌ 'estimator_shots' must be set to 25_000.\")\n return\n\n if primitive_settings.get('sampler_shots') != 100_000:\n print(\"❌ 'sampler_shots' must be set to 100_000.\")\n return\n\n print(\"✅ Exercise 2 solution is correct!\")\n"
},
{
"path": "functions-labs/kipu/data/ibm_washington.txt",
"content": "([{1: -1, 3: 1, 5: 1, 7: -1, 9: 1, 11: -1, 13: 1, 14: -1, 15: -1, 16: -1, 17: 1, 19: -1, 21: -1, 23: 1, 25: 1, 27: 1, 29: 1, 31: 1, 33: -1, 34: 1, 35: 1, 36: -1, 38: 1, 40: 1, 42: -1, 44: 1, 46: 1, 48: 1, 50: 1, 52: -1, 53: 1, 54: 1, 55: -1, 57: -1, 59: -1, 61: -1, 63: 1, 65: 1, 67: 1, 69: -1, 71: -1, 72: -1, 73: -1, 74: -1, 76: -1, 78: -1, 80: -1, 82: 1, 84: 1, 86: -1, 88: -1, 90: -1, 91: 1, 92: -1, 93: 1, 95: -1, 97: 1, 99: -1, 101: -1, 103: -1, 105: 1, 107: -1, 109: -1, 110: -1, 111: 1, 112: 1, 113: -1, 115: -1, 117: -1, 119: -1, 121: -1, 123: 1, 125: 1}, {0: -1, 2: -1, 4: -1, 6: -1, 8: -1, 10: 1, 12: 1, 18: 1, 20: -1, 22: -1, 24: -1, 26: 1, 28: 1, 30: -1, 32: 1, 37: -1, 39: -1, 41: -1, 43: -1, 45: -1, 47: 1, 49: 1, 51: 1, 56: -1, 58: 1, 60: 1, 62: -1, 64: -1, 66: 1, 68: 1, 70: 1, 75: 1, 77: -1, 79: 1, 81: -1, 83: 1, 85: 1, 87: 1, 89: 1, 94: 1, 96: 1, 98: -1, 100: 1, 102: -1, 104: 1, 106: -1, 108: -1, 114: -1, 116: -1, 118: -1, 120: -1, 122: 1, 124: 1, 126: -1}], [{(72, 62): -1, (61, 60): -1, (63, 64): -1, (80, 81): 1, (82, 83): -1, (53, 41): 1, (59, 58): 1, (65, 66): 1, (54, 45): -1, (78, 79): 1, (91, 98): 1, (84, 85): 1, (92, 102): 1, (40, 39): -1, (42, 43): -1, (57, 56): -1, (71, 77): 1, (67, 68): -1, (46, 47): -1, (76, 75): -1, (99, 100): -1, (97, 96): -1, (86, 87): 1, (103, 104): 1, (38, 37): 1, (33, 20): -1, (34, 24): -1, (69, 70): -1, (55, 49): 1, (35, 28): -1, (90, 94): 1, (110, 118): -1, (88, 89): 1, (93, 106): 1, (111, 122): 1, (21, 22): -1, (19, 18): -1, (25, 26): -1, (50, 51): 1, (29, 30): 1, (117, 116): 1, (119, 120): -1, (107, 108): -1, (123, 124): -1, (15, 4): 1, (14, 0): -1, (16, 8): -1, (36, 32): -1, (17, 12): 1, (115, 114): -1, (112, 126): 1, (3, 2): 1, (5, 6): -1, (11, 10): -1}, {(61, 62): -1, (72, 81): -1, (53, 60): -1, (65, 64): 1, (80, 79): -1, (84, 83): -1, (40, 41): -1, (57, 58): 1, (67, 66): -1, (46, 45): -1, (78, 77): 1, (73, 85): -1, (44, 43): -1, (99, 98): -1, (101, 102): 1, (38, 39): 1, (52, 56): -1, (69, 68): -1, (35, 47): -1, (48, 49): -1, (90, 75): 1, (110, 100): -1, (109, 96): -1, (88, 87): 1, (111, 104): -1, (21, 20): 1, (23, 24): -1, (74, 70): -1, (27, 28): 1, (95, 94): -1, (105, 106): -1, (117, 118): 1, (121, 122): 1, (15, 22): -1, (14, 18): 1, (16, 26): 1, (36, 51): 1, (31, 30): 1, (115, 116): -1, (112, 108): 1, (125, 124): 1, (3, 4): 1, (1, 0): -1, (7, 8): -1, (11, 12): 1, (113, 114): -1, (9, 10): -1}, {(63, 62): 1, (82, 81): -1, (59, 60): -1, (54, 64): -1, (91, 79): 1, (92, 83): 1, (42, 41): -1, (71, 58): -1, (73, 66): 1, (44, 45): -1, (76, 77): 1, (97, 98): 1, (86, 85): -1, (103, 102): 1, (33, 39): -1, (34, 43): 1, (55, 68): 1, (48, 47): 1, (101, 100): 1, (52, 37): 1, (95, 96): 1, (93, 87): 1, (105, 104): 1, (19, 20): -1, (25, 24): -1, (50, 49): 1, (29, 28): 1, (23, 22): -1, (74, 89): 1, (27, 26): -1, (119, 118): -1, (107, 106): 1, (123, 122): 1, (17, 30): -1, (121, 120): 1, (31, 32): -1, (5, 4): -1, (13, 12): -1, (125, 126): 1, (1, 2): 1, (7, 6): -1}], [{(1, 0, 2): -1, (14, 0, 18): -1, (3, 2, 4): -1, (5, 4, 6): 1, (15, 4, 22): 1, (7, 6, 8): 1, (16, 8, 26): -1, (11, 10, 12): -1, (17, 12, 30): -1, (19, 18, 20): -1, (21, 20, 22): -1, (33, 20, 39): -1, (23, 22, 24): -1, (25, 24, 26): -1, (34, 24, 43): -1, (27, 26, 28): 1, (29, 28, 30): 1, (35, 28, 47): -1, (31, 30, 32): -1, (36, 32, 51): 1, (38, 37, 39): 1, (52, 37, 56): -1, (40, 39, 41): -1, (42, 41, 43): -1, (53, 41, 60): -1, (44, 43, 45): -1, (46, 45, 47): 1, (54, 45, 64): 1, (48, 47, 49): -1, (50, 49, 51): 1, (55, 49, 68): -1, (57, 56, 58): -1, (59, 58, 60): 1, (71, 58, 77): -1, (61, 60, 62): 1, (63, 62, 64): 1, (72, 62, 81): 1, (65, 64, 66): -1, (67, 66, 68): 1, (73, 66, 85): -1, (69, 68, 70): -1, (74, 70, 89): 1, (76, 75, 77): -1, (90, 75, 94): -1, (78, 77, 79): -1, (80, 79, 81): 1, (91, 79, 98): 1, (82, 81, 83): 1, (84, 83, 85): -1, (92, 83, 102): -1, (86, 85, 87): -1, (88, 87, 89): -1, (93, 87, 106): 1, (95, 94, 96): 1, (97, 96, 98): -1, (99, 98, 100): 1, (101, 100, 102): 1, (110, 100, 118): 1, (103, 102, 104): 1, (105, 104, 106): -1, (111, 104, 122): 1, (107, 106, 108): -1, (112, 108, 126): -1, (115, 114, 116): 1, (117, 116, 118): -1, (119, 118, 120): -1, (121, 120, 122): -1, (123, 122, 124): 1, (125, 124, 126): -1}])"
},
{
"path": "functions-labs/kipu/grader_iskay.py",
"content": "import networkx as nx\n\n# Constants for the lab assignment\nNUM_NODES = 50\nSEED = 0\n\ndef print_results(results: bool):\n \"\"\"\n Print the results of the grading.\n \"\"\"\n if results:\n print(\"\\nCongratulations 🎉! Your answer is correct.\")\n else:\n print(\"Oops 😕! Your answer is incorrect\")\n\n\ndef grade_lab_iskay_ex1a(G: nx.Graph) -> bool:\n \"\"\"\n Return True if G matches the expected random 3-regular graph defined by:\n nx.random_regular_graph(3, NUM_NODES, seed=SEED)\n Checks:\n 1. G has exactly NUM_NODES nodes.\n 2. Every node has degree 3.\n 3. The edge set matches the graph generated with the fixed seed.\n \"\"\"\n # 1. Check node count\n if G.number_of_nodes() != NUM_NODES:\n return print_results(False)\n\n # 2. Check regularity (degree 3)\n if any(deg != 3 for _, deg in G.degree()):\n return print_results(False)\n\n # 3. Re-generate and compare\n try:\n expected = nx.random_regular_graph(3, NUM_NODES, seed=SEED)\n except Exception:\n return print_results(False)\n \n return print_results(set(G.edges()) == set(expected.edges()))\n\n\ndef grade_lab_iskay_ex1b(objective_func: dict, graph:nx.Graph) -> bool:\n \"\"\"\n Returns True if `objective_func` correctly encodes the Ising MaxCut objective for graph G:\n - One entry per edge (i, j) in G, with key \"(i, j)\"\n - Each coefficient value is exactly 0.5\n \"\"\"\n # Must be a dict with one entry per edge\n if not isinstance(objective_func, dict):\n return print_results(False)\n if len(objective_func) != graph.number_of_edges():\n return print_results(False)\n\n # Check each edge\n for i, j in graph.edges():\n key = f\"({i}, {j})\"\n # Ensure the key exists and the value is 0.5\n if objective_func.get(key) != 0.5:\n return print_results(False)\n return print_results(True)\n\n\ndef grade_lab_iskay_ex1c(arguments: dict) -> bool:\n \"\"\"\n Returns True if `arguments` correctly prepares the optimizer inputs:\n - Contains exactly the keys: 'problem', 'problem_type', 'instance', 'backend_name', 'options'\n - 'problem' matches `expected_problem`\n - 'problem_type' is 'spin'\n - 'instance' is 'project-based/kipu/main'\n - 'backend_name' matches `expected_backend_name`\n - 'options' matches `expected_options`\n \"\"\"\n # Must be a dict\n if not isinstance(arguments, dict):\n return print_results(False)\n\n # Check all required keys are present (and no extras)\n required_keys = {'problem', 'problem_type', 'instance', 'backend_name', 'options'}\n if set(arguments.keys()) != required_keys:\n return print_results(False)\n\n # 1. problem\n graph = nx.random_regular_graph(3, NUM_NODES, seed=SEED)\n # Create the objective function for MaxCut in Ising formulation\n def graph_to_ising_maxcut(G):\n # Initialize the linear and quadratic coefficients\n objective_func = {}\n for i, j in G.edges:\n objective_func[f\"({i}, {j})\"] = 0.5 \n return objective_func\n \n objective_func = graph_to_ising_maxcut(graph)\n if arguments['problem'] != objective_func:\n print(\"Problem does not match expected objective function.\")\n return print_results(False)\n\n # 2. problem_type\n if arguments['problem_type'] != 'spin':\n print(\"Problem type is not 'spin'.\")\n return print_results(False)\n\n available_backends = [\n 'ibm_aachen',\n 'ibm_brisbane',\n 'ibm_brussels',\n 'ibm_fez',\n 'ibm_marrakesh',\n 'ibm_sherbrooke',\n 'ibm_strasbourg',\n 'ibm_torino',\n ]\n\n # 3. backend_name\n if arguments['backend_name'] not in available_backends:\n print(\"Backend name does not match expected.\")\n return print_results(False)\n\n # 4. options\n expected_options = {\n \"shots\": 2_500,\n \"num_iterations\": 10,\n \"use_session\": True\n }\n if arguments['options'] != expected_options:\n print(\"Options do not match expected.\")\n return print_results(False)\n\n return print_results(True)\n\n\ndef grade_lab_iskay_ex1d(result: dict) -> bool:\n \"\"\"\n \"\"\"\n # Must be a dict\n if not isinstance(result, dict):\n return print_results(False)\n\n # Check all required keys are present (and no extras)\n required_keys = {'solution', 'solution_info', 'prob_type'}\n if set(result.keys()) != required_keys:\n return print_results(False)\n\n optimal_cost = -29.5\n\n obtained_cost = result.get('solution_info', {}).get('cost')\n if obtained_cost is None:\n print(\"Solution info does not contain 'cost'.\")\n return print_results(False)\n elif obtained_cost != optimal_cost:\n print(f\"Obtained cost {obtained_cost} does not match expected optimal cost {optimal_cost}. But it is not a failure. Try another backend or increase the number of shots.\")\n return print_results(True)\n elif obtained_cost == optimal_cost:\n print(f\"Obtained cost {obtained_cost} matches expected optimal cost {optimal_cost}. \")\n return print_results(True)\n \n\ndef grade_lab_iskay_ex1e(cut: int) -> bool:\n \"\"\"\n \"\"\"\n if not isinstance(cut, int):\n return print_results(False)\n\n if cut==67:\n return print_results(True)\n \n\ndef grade_lab_iskay_ex2a(idx: int) -> bool:\n \"\"\"\n \"\"\"\n if not isinstance(idx, int):\n return print_results(False)\n\n if idx==5:\n return print_results(True)\n \n\ndef grade_lab_iskay_ex2b(one_local_terms:int, two_local_terms:int, three_local_terms:int) -> bool:\n \"\"\"\n \"\"\"\n if not isinstance(one_local_terms, int) and not isinstance(two_local_terms, int) and not isinstance(three_local_terms, int):\n return print_results(False)\n\n if one_local_terms == 127 and two_local_terms == 142 and three_local_terms == 69:\n return print_results(True)\n \n\ndef grade_lab_iskay_ex2c(arguments: dict) -> bool:\n \"\"\"\n Returns True if `arguments` correctly prepares the optimizer inputs:\n - Contains exactly the keys: 'problem', 'problem_type', 'instance', 'backend_name', 'options'\n - 'problem' matches `expected_problem`\n - 'problem_type' is 'spin'\n - 'instance' is 'project-based/kipu/main'\n - 'backend_name' matches `expected_backend_name`\n - 'options' matches `expected_options`\n \"\"\"\n # Must be a dict\n if not isinstance(arguments, dict):\n return print_results(False)\n\n # Check all required keys are present (and no extras)\n required_keys = {'problem', 'problem_type', 'instance', 'backend_name', 'options'}\n if set(arguments.keys()) != required_keys:\n return print_results(False)\n\n # 1. problem\n with open(f'data/ibm_washington.txt', 'r') as file:\n content = file.read()\n\n objective_func = {}\n\n for list_of_fields in eval(content):\n for field in list_of_fields:\n for key,val in field.items():\n if isinstance(key, tuple):\n objective_func[str(tuple(sorted(key)))] = val\n elif isinstance(key, int):\n objective_func[str((key,))] = val\n \n if arguments['problem'] != objective_func:\n print(\"Problem does not match expected objective function.\")\n return print_results(False)\n\n # 2. problem_type\n if arguments['problem_type'] != 'spin':\n print(\"Problem type is not 'spin'.\")\n return print_results(False)\n\n available_backends = [\n 'ibm_aachen',\n 'ibm_brisbane',\n 'ibm_brussels',\n 'ibm_fez',\n 'ibm_marrakesh',\n 'ibm_sherbrooke',\n 'ibm_strasbourg',\n 'ibm_torino',\n ]\n\n # 3. backend_name\n if arguments['backend_name'] not in available_backends:\n print(\"Backend name does not match expected.\")\n return print_results(False)\n\n # 4. options\n expected_options = {\n \"shots\": 10000,\n \"num_iterations\": 10,\n \"use_session\": True\n }\n if arguments['options'] != expected_options:\n print(\"Options do not match expected.\")\n return print_results(False)\n\n return print_results(True)\n"
},
{
"path": "functions-labs/kipu/kipu_iskay_quantum_optimizer.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"aabd40ba\",\n \"metadata\": {},\n \"source\": [\n \"# Lab : Iskay Quantum Optimizer - A Qiskit Function by Kipu Quantum\\n\",\n \"\\n\",\n \"In this lab we will learn how to use the Iskay optimizer by Kipu Quantum in a practical and simple manner. We will need the basic tools explained during the first few lessons and labs of the QGSS.\\n\",\n \"\\n\",\n \"Iskay leverages Kipu's cutting-edge BF-DCQO algorithm [[1](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.L022010),[2](https://doi.org/10.48550/arXiv.2409.04477)], to tackle unconstrained binary optimization problems. For example, in the commonly used Quadratic Unconstrained Binary Optimization (QUBO) formulation, as well as higher-order (HUBO). BF-DCQO is a non-variational quantum algorithm, which requires fewer computational resources than common variational algorithms, such as QAOA.\\n\",\n \"\\n\",\n \"In Iskay, only the objective function is needed as input to automatically deliver problem solutions. It can handle optimization problems involving up to 156 qubits, enabling the use of all qubits of the IBM quantum devices. The Optimizer uses a 1-to-1 mapping between classical variables and qubits, which allows us to tackle optimization problems with up to 156 binary variables.\\n\",\n \"\\n\",\n \"This lab is structure as follows\\n\",\n \"\\n\",\n \"0. [Setup](#setup)\\n\",\n \"1. [Introduction](#introduction) \\n\",\n \" - [How does the Quantum Optimizer work?](#how_it_works)\\n\",\n \" - [Workflow](#workflow) \\n\",\n \"2. [Getting familiar with Iskay](#getting_familiar)\\n\",\n \" * [Exercise 1a: Define problem graph](#exercise_1a)\\n\",\n \" * [Exercise 1b: Extract the Ising fields](#exercise_1b)\\n\",\n \" * [Exercise 1c/1d: Accessing Iskay](#exercise_1c)\\n\",\n \" * [Exercise 1e: Retrieve and analyze results](#exercise_1e)\\n\",\n \" * [Exercise 1f: Verifying optimality](#exercise_1f)\\n\",\n \"3. [HUBO problems](#hubo) \\n\",\n \" * [Exercise 2a: Find the challenging instance](#exercise_2a) \\n\",\n \" * [Exercise 2b: Characterizing the instance](#exercise_2b) \\n\",\n \" * [Exercise 2c: Solving it with Iskay](#exercise_2c)\\n\",\n \"4. [Concluding remarks](#conclusion)\\n\",\n \"---\\n\",\n \"\\n\",\n \" Qiskit Functions are an experimental feature available only to IBM Quantum™ Premium and Flex Plan users. They are in preview release status and subject to change.\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"44ee6116\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 0. Setup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cf0d3fcc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Install dependencies\\n\",\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"%pip install \\\"qiskit[visualization]\\\" qiskit-ibm-runtime qiskit-ibm-catalog networkx\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fd9ba366\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f338ea0\",\n \"metadata\": {},\n \"source\": [\n \"You should have Grader `>=0.22.11`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"92a20e3f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"\\n\",\n \"import networkx as nx\\n\",\n \"from rustworkx.visualization import mpl_draw as draw_graph\\n\",\n \"from qiskit_ibm_catalog import QiskitFunctionsCatalog\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"import time\\n\",\n \"\\n\",\n \"from grader_iskay import (\\n\",\n \" grade_lab_iskay_ex1a,\\n\",\n \" grade_lab_iskay_ex1b,\\n\",\n \" grade_lab_iskay_ex1c,\\n\",\n \" grade_lab_iskay_ex1d,\\n\",\n \" grade_lab_iskay_ex1e,\\n\",\n \" grade_lab_iskay_ex2a,\\n\",\n \" grade_lab_iskay_ex2b,\\n\",\n \" grade_lab_iskay_ex2c\\n\",\n \")\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_kipu_function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"18ab46d1\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**Exclusive Access to Qiskit Functions**\\n\",\n \"\\n\",\n \"As part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\\n\",\n \"\\n\",\n \"If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`. in the cell below, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\\n\",\n \"\\n\",\n \"**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9c5aa61c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Qiskit Functions Catalog\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"catalog = QiskitFunctionsCatalog(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"# You should see a list of Qiskit Functions available to you\\n\",\n \"# If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`,\\n\",\n \"# it means your access is not yet active\\n\",\n \"catalog.list()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"53482e73\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" Load Qiskit Function\\n\",\n \"\\n\",\n \"Find the correct function name from the list above, or refer to the [Qiskit Functions Catalog](https://quantum.cloud.ibm.com/functions) to locate the appropriate function name string. The name should follow the format: `\\\"[provider]/[title]\\\"`.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"26d66199\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load Kipu Iskay Quantum Optimizer function\\n\",\n \"\\n\",\n \"function_name = \\\"\\\" # TODO\\n\",\n \"optimizer = catalog.load(function_name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5bf46779\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_kipu_function(optimizer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86275eb0\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 1. Introduction\\n\",\n \"\\n\",\n \"The Optimizer is a ready-to-use implementation of cutting-edge quantum optimization algorithms. It solves optimization problems by running highly-compressed quantum circuits on quantum hardware. This compression is achieved by introducing **counterdiabatic** terms into the underlying time evolution of the quantum system. The algorithm executes several iterations of hardware runs to obtain the final solutions and combines it with post-processing. These steps are seamlessly integrated into the Optimizer's workflow and are executed automatically.\\n\",\n \"\\n\",\n \"![Workflow](https://docs.quantum.ibm.com/images/guides/kipu-optimization/workflow.svg \\\"Workflow of the Quantum Optimizer\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"### 1.1 How does the Quantum Optimizer work?\\n\",\n \"This section outlines the basics of the implemented BF-DCQO algorithm. An introduction to the algorithm can also be found on the [Qiskit YouTube channel.](https://www.youtube.com/watch?v=33QmsXhIlpU&t=1223s)\\n\",\n \"\\n\",\n \"The algorithm is based on the time evolution of a quantum system which is transformed over time, where the problem solution is encoded in the ground state of the quantum system at the end of the evolution. According to the [adiabatic theorem](https://en.wikipedia.org/wiki/Adiabatic_theorem), this evolution has to be slow to ensure the system remains in its ground state. Digitizing this evolution is the basis of digitized quantum adiabatic computation (DQA) and the infamous QAOA algorithm. However, the required slow evolution is not feasible for increasing problem sizes since it results in an increasing circuit depth. By using counterdiabatic protocols, we can suppress unwanted excitations occurring during short evolution times while remaining in the ground state. Here, digitizing this shorter evolution time results in quantum circuits with shorter depth and fewer entangling gates.\\n\",\n \"\\n\",\n \"The circuits of the BF-DCQO algorithms typically use up to ten times fewer entangling gates than DQA, and three to four times fewer entangling gates than standard QAOA implementations. Because of the smaller number of gates, fewer errors occur during the circuit execution on hardware. Hence, the optimizer does not require using techniques like error suppression or error mitigation. Implementing them in future versions can enhance the solution quality even further.\\n\",\n \"\\n\",\n \"Although the BF-DCQO algorithm uses iterations, it is non-variational. After each iteration of the algorithm, the distribution of states is measured. The obtained distribution is used to calculate a so-called bias-field. The bias-field allows starting the next iteration from an energy state near the previously found solution. In this way, the algorithm moves with each iteration to solutions of lower energy. Typically, approximately ten iterations are sufficient to converge to a solution, in total requiring a much lower number of iterations than variational algorithms, which is on the order of approximately 100 iterations.\\n\",\n \"\\n\",\n \"The optimizer combines the BF-DCQO algorithm with classical post-processing. After measuring the distribution of states, a local search is performed. During the local search, the bits of the measured solution are randomly flipped. After the flip, the energy of the new bitstring is evaluated. If the energy is lower, the bitstring is kept as the new solution. The local search only scales linearly with the number of qubits; hence, it is computationally cheap. Since the post-processing corrects local bitflips, it compensates for bit-flip errors that often are the result of hardware imperfections and readout errors.\\n\",\n \"\\n\",\n \"\\n\",\n \"### 1.2 Workflow\\n\",\n \"\\n\",\n \"A schematic of the workflow of the Quantum Optimizer follows.\\n\",\n \"\\n\",\n \"By using the Quantum Optimizer, solving an optimization problem on quantum hardware can be reduced to\\n\",\n \"* Formulate the objective function of the problem\\n\",\n \"* Access the Optimizer via Qiskit Functions\\n\",\n \"* Run the Optimizer and collect the result\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"82fc2474\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 2. Getting familiar with Iskay\\n\",\n \"\\n\",\n \"First, we will tackle Max-Cut, a problem we already know from the first week of QGSS. In simple words, the Max-Cut problem asks: given a graph, how can we partition its vertices into two sets so that the total weight of edges between the sets is as large as possible? The problem is NP-hard in general.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b68837d8\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1a: Define problem graph \\n\",\n \"\\n\",\n \"**Your Goal:** Build a graph that represents the combinatorial optimization problem\\n\",\n \"\\n\",\n \"In this exercise you must create a 50 qubit 3-regular graph. You can take as template the labs from the first week. Use the ```networkx.random_regular_graph``` function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9419a719\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a random 3-regular graph. Do not change the seed or number of nodes.\\n\",\n \"seed = 0\\n\",\n \"num_nodes = 50\\n\",\n \"\\n\",\n \"# ---- TODO : Task 1 ---\\n\",\n \"# Use NetworkX to create a random 3-regular graph with the specified number of nodes and seed.\\n\",\n \"graph = \\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"nx.draw(\\n\",\n \" graph,\\n\",\n \" with_labels=True,\\n\",\n \" edge_color='gray',\\n\",\n \" node_size=100\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2ebb8cd9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_lab_iskay_ex1a(graph)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8c12dbd1\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1b: Extract the Ising fields \\n\",\n \"\\n\",\n \"**Your Goal:** Build the dictionary of the Ising fields, which represent the objective function. Use 0.5 as the coupling stengths.\\n\",\n \"\\n\",\n \"In this exercise you must obtain a dictionary of tuples and coefficients, which represents the problem. For example, \\n\",\n \"\\n\",\n \"```python \\n\",\n \"'(0,)': 1.5,\\n\",\n \"'(1,)': 2,\\n\",\n \"'(2,)': 1.3,\\n\",\n \"'(0, 3)': 2.5,\\n\",\n \"'(1, 4)': 3.5,\\n\",\n \"``` \\n\",\n \"\\n\",\n \"represents a graph of five nodes, where nodes 0,1,2 have specific weights and both (0,3) and (1,4) are connected. Note that these fields can also be used to easily build the Hamiltonian.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"64f19c89\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create the objective function for MaxCut in Ising formulation\\n\",\n \"def graph_to_ising_maxcut(graph):\\n\",\n \" # Initialize the linear and quadratic coefficients\\n\",\n \" objective_func = {}\\n\",\n \"\\n\",\n \" # ---- TODO : Task 2 ---\\n\",\n \"\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \" \\n\",\n \" return objective_func\\n\",\n \"\\n\",\n \"objective_func = graph_to_ising_maxcut(graph)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"14b9fff3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_lab_iskay_ex1b(objective_func, graph)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cab2b861\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1c: Accessing Iskay \\n\",\n \"\\n\",\n \"**Your Goal:** Solve the problem using Iskay\\n\",\n \"\\n\",\n \"In this exercise you must\\n\",\n \"- Get the name of the least busy backend\\n\",\n \"- Create a job \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0e0d06a5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"\\n\",\n \"# ---- TODO : Task 3 ---\\n\",\n \"least_busy_backend = \\n\",\n \"least_busy_backend_name = \\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"print(f\\\"Least busy backend: {least_busy_backend_name}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"95a0c82c\",\n \"metadata\": {},\n \"source\": [\n \"We are going to do 10 iterations with 2500 shots each. This gives a total of 25000 shots. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bdcfac97\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Setup options to run the optimizer\\n\",\n \"options = {\\\"shots\\\": 2_500, \\\"num_iterations\\\": 10, \\\"use_session\\\": True}\\n\",\n \"\\n\",\n \"# ---- TODO : Task 4 ---\\n\",\n \"# Prepare the arguments for the optimizer\\n\",\n \"arguments = {\\n\",\n \" 'problem': ,\\n\",\n \" 'problem_type': ,\\n\",\n \" 'instance': ,\\n\",\n \" 'backend_name': ,\\n\",\n \" 'options': ,\\n\",\n \"}\\n\",\n \"# --- End of TODO ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e442744f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_lab_iskay_ex1c(arguments)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"24628c38\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 2 minutes 35 seconds (based on tests on `ibm_brussels`)\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1d: Retrieve and analyze results \\n\",\n \"\\n\",\n \"**Your Goal:** Understand the structure from the results and plot the optimal solution found\\n\",\n \"\\n\",\n \"In this exercise you must\\n\",\n \"- Use the job id to retrieve the results. The status of the task can be checked with ```job.status()```\\n\",\n \"- Get the max-cut solution bitstring as well as its cost\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0a55f4a0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the job from the job ID\\n\",\n \"# job = catalog.job(job_id)\\n\",\n \"\\n\",\n \"while True:\\n\",\n \" print(f\\\"Waiting for job {job_id} to complete... (status: {job.status()})\\\", end='\\\\r', flush=True)\\n\",\n \" if job.status() in ['DONE', 'CANCELED', 'ERROR']:\\n\",\n \" print(f\\\"Job {job_id} completed with status: {job.status()}\\\")\\n\",\n \" break\\n\",\n \" time.sleep(30)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6df659c3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Retrieve the results\\n\",\n \"# ---- TODO : Task 5 ---\\n\",\n \"result = \\n\",\n \"maxcut_solution = \\n\",\n \"cost = \\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"print(\\\"Max-Cut solution:\\\", maxcut_solution)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"25bc3258\",\n \"metadata\": {},\n \"source\": [\n \"The solution dictionary indicates the orientation of each qubits. In terms of the graph, it indicates the bipartition: all nodes pointing up (+1) are part of one partition, whereas the ones point down (-1) are part of the other partition. Since the goal of the max-cut problem is to increate the number of connections between these two partitions, we can nicely visualize it. But first, let's check that the cost to the associated Hamiltonian is the right one.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"800021bf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_lab_iskay_ex1d(result)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1fe5bb8f\",\n \"metadata\": {},\n \"source\": [\n \"Use the following code to plot the solution obtained from hardware using BF-DCQO\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b9f650e4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Choose which nodes to highlight\\n\",\n \"special_nodes = {idx for idx, s in enumerate(maxcut_solution_bitstring) if s == \\\"1\\\"}\\n\",\n \"\\n\",\n \"# Build a list of colors, one per node, in graph.nodes() order\\n\",\n \"color_map = []\\n\",\n \"for node in graph.nodes():\\n\",\n \" if node in special_nodes:\\n\",\n \" color_map.append('red')\\n\",\n \" else:\\n\",\n \" color_map.append('skyblue')\\n\",\n \"\\n\",\n \"# Draw, passing the color list\\n\",\n \"nx.draw(\\n\",\n \" graph,\\n\",\n \" with_labels=True,\\n\",\n \" node_color=color_map,\\n\",\n \" edge_color='gray',\\n\",\n \" node_size=100\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28e3f16a\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"[Optional] Exercise 1e: Verifying optimality \\n\",\n \"\\n\",\n \"**Your Goal:** Find the cut value and check the optimality of the obtained solution\\n\",\n \"\\n\",\n \"In this exercise you must\\n\",\n \"- Use the graph to find the cut value, i.e. how many edges are connecting the two partitions\\n\",\n \"- Verify optimality of the obtained solution. \\n\",\n \"\\n\",\n \"
\\n\",\n \" ⚠️ Warning: Problem beyond brute force\\n\",\n \"\\n\",\n \"Despite this is an example problem of relatively small size (50 qubits), it is already beyond brute force approach. Therefore, do not search all possible combinations. Instead, rely on heuristics or exact solvers.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"eac7e997\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Calculate the cut value\\n\",\n \"\\n\",\n \"def cut_value(graph: nx.Graph, x: dict) -> int:\\n\",\n \"\\n\",\n \" # ---- TODO : Task 6 ---\\n\",\n \" return \\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \"\\n\",\n \"cut = cut_value(graph, maxcut_solution)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0280ac1f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_lab_iskay_ex1e(cut)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"07bf5000\",\n \"metadata\": {},\n \"source\": [\n \"Now we can verify the optimality of the solution. This part is free and will not be graded. Are you able to verify the solution without brute-force?\\n\",\n \"
\\n\",\n \"\\n\",\n \" Hint 💡: (Click to expand)\\n\",\n \" Check qiskit optimization at https://qiskit-community.github.io/qiskit-optimization/\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d2c65c4b\",\n \"metadata\": {},\n \"source\": [\n \"## 3. HUBO problems \\n\",\n \"\\n\",\n \"Higher order binary unconstrained optimization (HUBO) problems extend the familiar QUBO problems by allowing objective functions to include terms that involve interactions among three or more binary variables. Formally, a HUBO problem aims to optimize the cost function\\n\",\n \"\\n\",\n \"$$\\n\",\n \"f(x) = \\\\sum_i a_i x_i + \\\\sum_{i\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2a: Find the challenging instance \\n\",\n \"\\n\",\n \"**Your Goal:** Search for the challenging HUBO instance and provide the index\\n\",\n \"\\n\",\n \"In this exercise you must go to [this paper](https://www.nature.com/articles/s41534-024-00825-w) and find the challenging instance, where no quantum algorithm could find the optimal solution. \\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \" Hint 💡: (Click to expand)\\n\",\n \" See Table 4 \\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9725b8dc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Find the index of the challenging instance\\n\",\n \"# ---- TODO : Task 7 ---\\n\",\n \"idx = \\n\",\n \"# --- End of TODO ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"662a9469\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_lab_iskay_ex2a(idx)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"44124ba2\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2b: Characterizing the instance \\n\",\n \"\\n\",\n \"**Your Goal:** Check the amount of Hamiltonian terms and their locality\\n\",\n \"\\n\",\n \"In this exercise you must provide how many one-, two-, and three-local terms are present in the Hamiltonian (cost function). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"84d415bc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the instance as an objective function \\n\",\n \"\\n\",\n \"with open(f'data/ibm_washington.txt', 'r') as file:\\n\",\n \" content = file.read()\\n\",\n \"\\n\",\n \"objective_func = {}\\n\",\n \"\\n\",\n \"for list_of_fields in eval(content):\\n\",\n \" for field in list_of_fields:\\n\",\n \" for key,val in field.items():\\n\",\n \" if isinstance(key, tuple):\\n\",\n \" objective_func[str(tuple(sorted(key)))] = val\\n\",\n \" elif isinstance(key, int):\\n\",\n \" objective_func[str((key,))] = val\\n\",\n \"\\n\",\n \"\\n\",\n \"# ---- TODO : Task 8 ---\\n\",\n \"one_local_terms = \\n\",\n \"two_local_terms = \\n\",\n \"three_local_terms = \\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"print(f\\\"One-local terms: {one_local_terms}\\\")\\n\",\n \"print(f\\\"Two-local terms: {two_local_terms}\\\")\\n\",\n \"print(f\\\"Three-local terms: {three_local_terms}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ab53aaa6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_lab_iskay_ex2b(one_local_terms, two_local_terms, three_local_terms)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"061b9377\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2c: Solving it with Iskay \\n\",\n \"\\n\",\n \"**Your Goal:** Use the Iskay optimizer to tackle this instance\\n\",\n \"\\n\",\n \"Particularly, set the parameters\\n\",\n \"- shots per iteration : 10000\\n\",\n \"- number of iterations : 10\\n\",\n \"- problem type : spin\\n\",\n \"- backend : Use the best backend available in your plan for better results. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9512058c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Setup options to run the optimizer\\n\",\n \"options = {\\\"shots\\\": 10_000, \\\"num_iterations\\\": 10, \\\"use_session\\\": True}\\n\",\n \"\\n\",\n \"# ---- TODO : Task 9 ---\\n\",\n \"# Prepare the arguments for the optimizer\\n\",\n \"arguments = {\\n\",\n \" 'problem': ,\\n\",\n \" 'problem_type': ,\\n\",\n \" 'instance': ,\\n\",\n \" 'backend_name': ,\\n\",\n \" 'options': ,\\n\",\n \"}\\n\",\n \"# --- End of TODO ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ed6d69f9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_lab_iskay_ex2c(arguments)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5ac27441\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 7 minutes 1 seconds (based on tests on `ibm_brussles`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a467d61c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run the optimizer\\n\",\n \"\\n\",\n \"# ---- TODO : Task 10 ---\\n\",\n \"job = \\n\",\n \"job_id = \\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"print(f\\\"Job ID: {job_id}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"61c123fd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the job from the job ID \\n\",\n \"# job = catalog.job(job_id)\\n\",\n \"\\n\",\n \"while True:\\n\",\n \" print(f\\\"Waiting for job {job_id} to complete... (status: {job.status()})\\\", end='\\\\r', flush=True)\\n\",\n \" if job.status() in ['DONE', 'CANCELED', 'ERROR']:\\n\",\n \" print(f\\\"Job {job_id} completed with status: {job.status()}\\\")\\n\",\n \" break\\n\",\n \" time.sleep(30)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d955c137\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cost = job.result()[\\\"solution_info\\\"][\\\"cost\\\"]\\n\",\n \"print(\\\"Obtained best cost:\\\", cost)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5385294b\",\n \"metadata\": {},\n \"source\": [\n \"How do your results compare to this small benchmark? Add a new column with your results! The obtained results depend on the Hardware used. Here we also include our internal test on different IBM devices, compared to the ones from Table 4 from the reference paper. \\n\",\n \"\\n\",\n \"| | Reference | Iskay (IBM Torino) | Iskay (IBM Fez) | Iskay (IBM Marrakesh) | Quantum Annealing | QAOA | Iskay (QGSS lab)\\n\",\n \"|----------|----------|----------|----------|----------|----------|----------|----------|\\n\",\n \"| Energy | -198 | -194 | -196 | -198 | -194 | -128 | Your result here | \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e46819e8\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# 4. Concluding remarks\\n\",\n \"\\n\",\n \"Congratulations that you have made it to this point of the lab. \\n\",\n \"\\n\",\n \"We have learned how to use Iskay Optimizer at the basic level and even tackle slightly challenging problems. Iskay optimizer is based on BF-DCQO, which is an algorithm developed by Kipu Quantum to tackle unconstrained binary optimization problems. This algortihm has been fine tuned as a problem- and hardware- specific method, such that it has shown evidence of [runtime advantage](https://arxiv.org/abs/2505.08663) with respect to specific classical solvers. Additionally, it has been [tested on all-to-all](https://arxiv.org/abs/2506.07866) connected quantum devices as well as [combined with analog devices](https://arxiv.org/abs/2506.20655), such as quantum annealers. As next steps, we encourage you to test other types of problems and give us feedback through Discord.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"39cc9c50\",\n \"metadata\": {},\n \"source\": [\n \"# Feedback Survey\\n\",\n \"\\n\",\n \"We’d love to hear about your experience using the Qiskit Function! Your feedback is valuable and will help Qiskit Function providers enhance their tools and services. Please take a moment to share your thoughts by completing our short 2 min [feedback survey](https://airtable.com/app6VujlNUHZuOnAF/pagpw6TgP9UEt4TAT/form).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"50f67774\",\n \"metadata\": {},\n \"source\": [\n \"# References\\n\",\n \"\\n\",\n \"[1] Cadavid AG, Dalal A, Simen A, Solano E, Hegade NN. Bias-field Digitized Counterdiabatic Quantum Optimization. Phys. Rev. Research 7:L022010. 2025 Apr 9.\\n\",\n \"\\n\",\n \"[2] Romero SV, Visuri AM, Cadavid AG, Solano E, Hegade NN. Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization. arXiv preprint arXiv:2409.04477. 2024 Sep 5.\\n\",\n \"\\n\",\n \"[3] Chandarana P, Cadavid AG, Romero SV, Simen A, Solano E, Hegade NN. Runtime Quantum Advantage with Digital Quantum Optimization. arXiv preprint arXiv:2505.08663. 2025 May 13.\\n\",\n \"\\n\",\n \"[4] Romero SV, Cadavid AG, Solano E, Hegade NN. Sequential Quantum Computing. arXiv preprint arXiv:2506.20655. 2025 Jun 25.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"73240d8b\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Alejandro Gomez Cadavid\\n\",\n \"\\n\",\n \"**Advised by:** Junye Huang\\n\",\n \"\\n\",\n \"**Version:** 1.1.0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9cbb026b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_runtime\\n\",\n \"import qiskit_ibm_catalog\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit IBM Runtime: {qiskit_ibm_runtime.__version__}')\\n\",\n \"print(f'Qiskit IBM Catalog: {qiskit_ibm_catalog.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "functions-labs/multiverse/data/grid_stability/test.csv",
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},
{
"path": "functions-labs/multiverse/grader.py",
"content": "def grade_exercise_1(optimizer_options_ex1: dict) -> None:\n \"\"\"\n Grades Exercise 1 by checking:\n - 'simulator' key exists in optimizer_options_ex1\n - Its value is set to True\n \"\"\"\n\n if \"simulator\" not in optimizer_options_ex1:\n print(\"❌ Missing key: 'simulator'\")\n return\n\n if optimizer_options_ex1[\"simulator\"] is not True:\n print(\"❌ 'simulator' must be set to True.\")\n return\n\n print(\"✅ Exercise 1 solution is correct!\")\n\ndef grade_exercise_2(learners_types: list, learners_proportions: list) -> None:\n \"\"\"\n Grades Exercise 2 by verifying:\n - learners_types contains 2 unique valid classifiers.\n - learners_proportions contains 2 values, both 0.5, summing to 1.0.\n \"\"\"\n\n allowed_learners = {\n \"DecisionTreeClassifier\",\n \"GaussianNB\",\n \"KNeighborsClassifier\",\n \"MLPClassifier\",\n \"LogisticRegression\"\n }\n\n # Check learners_types\n if not isinstance(learners_types, list):\n print(\"❌ 'learners_types' must be a list.\")\n return\n if len(learners_types) != 2:\n print(\"❌ 'learners_types' must contain exactly 2 elements.\")\n return\n if len(set(learners_types)) != 2:\n print(\"❌ All learners in 'learners_types' must be unique.\")\n return\n invalid = [l for l in learners_types if l not in allowed_learners]\n if invalid:\n print(f\"❌ Invalid learner types detected: {invalid}\")\n print(f\"✅ Allowed types are: {sorted(allowed_learners)}\")\n return\n\n # Check learners_proportions\n if not isinstance(learners_proportions, list):\n print(\"❌ 'learners_proportions' must be a list.\")\n return\n if len(learners_proportions) != 2:\n print(\"❌ 'learners_proportions' must contain exactly 2 elements.\")\n return\n if not all(p == 0.5 for p in learners_proportions):\n print(\"❌ All proportions in 'learners_proportions' must be 0.5.\")\n return\n if not abs(sum(learners_proportions) - 1.0) < 1e-8:\n print(\"❌ The proportions must sum up to 1.0.\")\n return\n\n # All checks passed\n print(\"✅ Exercise 2 solution is correct!\")\n\n\ndef grade_exercise_3(optimizer_options_solution: dict) -> None:\n \"\"\"\n Grades Exercise 3 by validating the contents of optimizer_options_solution.\n Ensures:\n - 'num_solutions' is >= 100000\n - 'simulator' is False\n - 'classical_optimizer_options[\"maxiter\"]' is 10\n Prints detailed feedback based on correctness.\n \"\"\"\n # Required keys\n expected_keys = {\"num_solutions\", \"simulator\", \"classical_optimizer_options\"}\n\n missing_keys = expected_keys - optimizer_options_solution.keys()\n if missing_keys:\n print(f\"❌ Missing keys in optimizer_options_solution: {missing_keys}\")\n return\n\n # Check num_solutions is >= 100000\n if not isinstance(optimizer_options_solution[\"num_solutions\"], int) or optimizer_options_solution[\"num_solutions\"] < 100000:\n print(\"❌ 'num_solutions' must be an integer >= 100000.\")\n return\n\n # Check simulator is False\n if optimizer_options_solution[\"simulator\"] is not False:\n print(\"❌ 'simulator' must be set to False to use quantum optimization.\")\n return\n\n # Check classical_optimizer_options[\"maxiter\"] is 10\n classical_opts = optimizer_options_solution.get(\"classical_optimizer_options\", {})\n if not isinstance(classical_opts, dict) or classical_opts.get(\"maxiter\") != 10:\n print(\"❌ 'classical_optimizer_options[\\\"maxiter\\\"]' must be set to 10.\")\n return\n\n # All checks passed\n print(\"✅ Exercise 3 solution is correct!\")\n\n\n\n"
},
{
"path": "functions-labs/multiverse/multiverse_singularity.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d76b4b60-19a6-486a-af14-db875411b6db\",\n \"metadata\": {},\n \"source\": [\n \"# Lab 1: 'Singularity Machine Learning - Classification' by Multiverse Computing\\n\",\n \"\\n\",\n \"Welcome to this hands-on lab exploring the power of the 'Singularity Machine Learning - Classification' function. In this session, you'll walk through a real-world machine learning task—grid stability classification—leveraging both classical and quantum-enhanced techniques. You'll gain experience using a hybrid workflow that seamlessly integrates scikit-learn preprocessing with quantum ensemble classifiers hosted on IBM's serverless infrastructure. Whether running simulations or real quantum hardware, this lab gives you a practical glimpse into how scalable quantum machine learning can accelerate real-world applications. Beyond just building models, the goal of this notebook is to familiarize you with the capabilities of the QuantumEnhancedEnsembleClassifier and help you develop intuition around its features and advantages. You'll begin by establishing a classical benchmark using AdaBoost, then proceed through three exercises that demonstrate key functionalities: running the quantum classifier with a simulated quantum backend, simulating a heterogeneous ensemble with diverse learners, and finally executing the classifier on real quantum hardware. Each step is designed to build your confidence with the tool and deepen your understanding of where and how quantum optimization can offer meaningful benefits. Let’s dive in!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"558ef023-4465-4b0e-b1b7-dd185e6aff9b\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"* [Setup](#setup)\\n\",\n \"* [Imports](#imports)\\n\",\n \"* [Initialize the 'Singularity Machine Learning - Classification' Function](#initialize-the-singularity-machine-learning---classification-function)\\n\",\n \" * [Set Static Variables and IBM Token](#set-static-variables-and-ibm-token)\\n\",\n \" * [Load the 'Singularity Machine Learning - Classification' Function and Client](#load-the-singularity-machine-learning---classification-function-and-client)\\n\",\n \"* [Define Helper Functions](#define-helper-functions)\\n\",\n \"* [Load the Dataset](#load-the-dataset)\\n\",\n \"* [AdaBoostClassifier](#adaboostclassifier)\\n\",\n \"* [QuantumEnhancedEnsembleClassifier](#quantumenhancedensembleclassifier)\\n\",\n \" * [Introduction](#introduction)\\n\",\n \" * [Understanding the Optimization Parameters](#understanding-the-optimization-parameters)\\n\",\n \"* [Part 1: Simulated Quantum Classifier](#part-1-simulated-quantum-classifier)\\n\",\n \" * [Execute the Job](#execute-the-job)\\n\",\n \" * [Monitor Job Execution](#monitor-job-execution)\\n\",\n \" * [Retrieve and Analyze the Results](#retrieve-and-analyze-the-results)\\n\",\n \"* [Part 2: Simulated Heterogenous Quantum Classifier](#part-2-simulated-heterogenous-quantum-classifier)\\n\",\n \" * [Execute the Job](#execute-the-job)\\n\",\n \" * [Monitor Job Execution](#monitor-job-execution)\\n\",\n \" * [Retrieve and Analyze the Results](#retrieve-and-analyze-the-results)\\n\",\n \"* [Part 3: Quantum Classifier on Real Quantum Hardware](#part-3-quantum-classifier-on-real-quantum-hardware)\\n\",\n \" * [Execute the Job](#execute-the-job)\\n\",\n \" * [Monitor Job Execution](#monitor-job-execution)\\n\",\n \" * [Retrieve and Analyze the Results](#retrieve-and-analyze-the-results)\\n\",\n \" * [Visualization: Classical vs Quantum F1 Score](#visualization-classical-vs-quantum-f1-score)\\n\",\n \"* [Clean up Shared Directory](#clean-up-shared-directory)\\n\",\n \"* [Scaling Benefits and Enhancements](#scaling-benefits-and-enhancements)\\n\",\n \"* [References](#references)\\n\",\n \"* [Additional information](#additional-information)\\n\",\n \"* [Qiskit packages versions](#qiskit-packages-versions)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8384bcd7-8360-45e7-a237-25c2e643c962\",\n \"metadata\": {},\n \"source\": [\n \"# Setup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e4bbb589-d6aa-4131-85b3-fc9b9147bac1\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Install dependencies\\n\",\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"%pip install \\\"qiskit[visualization]~=2.1.0\\\" \\\"qiskit-serverless~=0.24.0\\\" \\\"qiskit-ibm-catalog~=0.8.0\\\" \\\"scikit-learn==1.5.2\\\" \\\"pandas>=2.0.0,<3.0.0\\\" \\\"imbalanced-learn~=0.12.3\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0c736bc9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fafb5664\",\n \"metadata\": {},\n \"source\": [\n \"You should have Grader `>=0.22.11`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7be03466-6b0e-4f79-9998-47923c7133af\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"\\n\",\n \"import sys\\n\",\n \"import os\\n\",\n \"import tarfile\\n\",\n \"import time\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from qiskit_serverless import IBMServerlessClient, QiskitFunction\\n\",\n \"from typing import Tuple\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from imblearn.over_sampling import RandomOverSampler\\n\",\n \"from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\\n\",\n \"from sklearn.ensemble import AdaBoostClassifier\\n\",\n \"from grader import grade_exercise_1, grade_exercise_2, grade_exercise_3\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_multiverse_function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d888e7b0\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**Exclusive Access to Qiskit Functions**\\n\",\n \"\\n\",\n \"As part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\\n\",\n \"\\n\",\n \"If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`. in the cell below, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\\n\",\n \"\\n\",\n \"**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0ced1098\",\n \"metadata\": {},\n \"source\": [\n \"# Initialize the 'Singularity Machine Learning - Classification' Function\\n\",\n \"\\n\",\n \"We will start by initializing the IBM Qiskit Serverless client and retrieve the function from the Multiverse Computing workspace. Qiskit Serverless offers a flexible and scalable way to run Qiskit programs without worrying about the underlying infrastructure. It allows you to offload complex quantum-classical workflows to the cloud, automatically managing resources across CPUs, GPUs, and QPUs. This makes it easier to focus on your algorithm logic while benefiting from remote, managed execution on the IBM Quantum Platform.\\n\",\n \"\\n\",\n \"By loading the 'Singularity Machine Learning - Classification' function, we gain access to a powerful quantum-enhanced classifier that can be used for hybrid workflows. This function encapsulates both classical and quantum components, allowing us to train, optimize, and predict with ensemble methods enhanced by quantum processing. For full details, please refer to our documentation: .\\n\",\n \"\\n\",\n \"Let’s get started by connecting to the serverless environment and loading the function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5afcc794\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(\\\"Using IBM Qiskit Serverless...\\\\n\\\")\\n\",\n \"\\n\",\n \"# Load the IBM Serverless Client\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"client = IBMServerlessClient(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"# You should see a list of Qiskit Functions available to you\\n\",\n \"# If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`,\\n\",\n \"# it means your access is not yet active\\n\",\n \"client.list()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf012b33\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" Load Qiskit Function\\n\",\n \"\\n\",\n \"Find the correct function name from the list above, or refer to the [Qiskit Functions Catalog](https://quantum.cloud.ibm.com/functions) to locate the appropriate function name string. The name should follow the format: `\\\"[provider]/[title]\\\"`.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bc3b277f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Multiverse Singularity function\\n\",\n \"\\n\",\n \"function_name = \\\"\\\" # TODO\\n\",\n \"singularity = client.get(function_name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"859e3f9a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_multiverse_function(singularity)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0ca209ef-508d-4917-a4a7-e2c187817aab\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define static variables\\n\",\n \"\\n\",\n \"RANDOM_STATE: int = 123\\n\",\n \"TRAIN_PATH = \\\"data/grid_stability/train.csv\\\"\\n\",\n \"TEST_PATH = \\\"data/grid_stability/test.csv\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ddbb1cc7-ff26-45e3-9da3-d14c9e04334e\",\n \"metadata\": {},\n \"source\": [\n \"# Define Helper Functions\\n\",\n \"\\n\",\n \"To streamline the workflow, we define a set of helper functions used for data handling and evaluation throughout this lab. These functions enable a smooth interface between local data processing and remote execution with Qiskit Serverless.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1d0eb03d-d530-4da2-9314-f4305daded2e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def load_data(data_path: str) -> Tuple[np.ndarray, np.ndarray]:\\n\",\n \" \\\"\\\"\\\"Load data from the given path to X and y arrays.\\\"\\\"\\\"\\n\",\n \" df: pd.DataFrame = pd.read_csv(data_path)\\n\",\n \" return df.iloc[:, :-1].values, df.iloc[:, -1].values\\n\",\n \"\\n\",\n \"\\n\",\n \"def make_tarfile(file_path, tar_file_name):\\n\",\n \" \\\"\\\"\\\"Create a tar file from the given file.\\\"\\\"\\\"\\n\",\n \" with tarfile.open(tar_file_name, \\\"w\\\") as tar:\\n\",\n \" tar.add(file_path, arcname=os.path.basename(file_path))\\n\",\n \"\\n\",\n \"\\n\",\n \"def upload_data(name: str, data: np.ndarray, client: IBMServerlessClient, function: QiskitFunction):\\n\",\n \" \\\"\\\"\\\"Save the data to a file, create a tar file, upload it, and remove the files.\\\"\\\"\\\"\\n\",\n \" np.save(name, data)\\n\",\n \" make_tarfile(name, f\\\"{name}.tar\\\")\\n\",\n \" client.file_upload(f\\\"{name}.tar\\\", function)\\n\",\n \"\\n\",\n \" \\n\",\n \"def evaluate_predictions(predictions, y_true, start_time=None, end_time=None):\\n\",\n \" accuracy = accuracy_score(y_true, predictions)\\n\",\n \" precision = precision_score(y_true, predictions)\\n\",\n \" recall = recall_score(y_true, predictions)\\n\",\n \" f1 = f1_score(y_true, predictions)\\n\",\n \" if start_time is not None and end_time is not None:\\n\",\n \" print(\\\"Time taken (s):\\\", end_time - start_time)\\n\",\n \" print(\\\"Accuracy:\\\", accuracy)\\n\",\n \" print(\\\"Precision:\\\", precision)\\n\",\n \" print(\\\"Recall:\\\", recall)\\n\",\n \" print(\\\"F1:\\\", f1)\\n\",\n \" return accuracy, precision, recall, f1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"11248574-3d1d-42c7-8cc2-df567361a3dd\",\n \"metadata\": {},\n \"source\": [\n \"# Load the Dataset \\n\",\n \"\\n\",\n \"Next, we load and preprocess the dataset used for training and evaluating our quantum-enhanced classifier. The dataset consists of labeled samples for a grid stability classification task, where the goal is to predict system stability based on sensor inputs.\\n\",\n \"\\n\",\n \"We begin by reading the training and test data from CSV files and then split the training set further to create a validation set. To address class imbalance—a common issue in real-world datasets—we apply random over-sampling to augment the minority class, ensuring that the classifier is not biased toward the dominant label.\\n\",\n \"\\n\",\n \"Once prepared, the datasets are serialized as NumPy arrays and uploaded to the Qiskit Serverless environment. This allows the remote 'Singularity Machine Learning - Classification' function to access and operate on the data during distributed execution.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b65a37e9-85cd-444a-bffe-b0e5fe885aaf\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Load and upload the data\\n\",\n \"X_train, y_train = load_data(TRAIN_PATH)\\n\",\n \"X_test, y_test = load_data(TEST_PATH)\\n\",\n \"X_train, X_val, y_train, y_val = train_test_split(\\n\",\n \" X_train, y_train, test_size=0.2, random_state=RANDOM_STATE\\n\",\n \")\\n\",\n \"# Balance the dataset through over-sampling of the positive class\\n\",\n \"ros = RandomOverSampler(random_state=RANDOM_STATE)\\n\",\n \"X_train, y_train = ros.fit_resample(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2b4a1baf-ced0-4934-8d13-91c665525ec0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(\\\"-- Uploading data --\\\")\\n\",\n \"upload_data(\\\"grid_stability_X_train.npy\\\", X_train, client, singularity)\\n\",\n \"upload_data(\\\"grid_stability_y_train.npy\\\", y_train, client, singularity)\\n\",\n \"upload_data(\\\"grid_stability_X_test.npy\\\", X_test, client, singularity)\\n\",\n \"print(\\\"Data uploaded!\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b4d2d1a1-0943-486e-9b14-997d66b58666\",\n \"metadata\": {},\n \"source\": [\n \"# AdaBoostClassifier\\n\",\n \"\\n\",\n \"Now that the data is ready, let's begin by using the AdaBoostClassifier, a well-established ensemble method in classical machine learning. AdaBoost works by combining multiple weak learners into a single strong classifier, iteratively improving performance by focusing more on difficult-to-classify samples.\\n\",\n \"\\n\",\n \"In this lab, we train the AdaBoost model using the balanced training set and evaluate it on the test set. This provides a strong baseline for comparison with the quantum-enhanced classifier we'll use later. The number of estimators is set to 75 to allow the ensemble to generalize well without overfitting.\\n\",\n \"\\n\",\n \"After training, we generate predictions on the test set and compute key performance metrics—accuracy, precision, recall, and F1-score—to assess how well the model performs on this grid stability classification task.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1678bce5-e4e4-4836-b8f1-37f332ce5157\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"classifier = AdaBoostClassifier(n_estimators=75, random_state=RANDOM_STATE)\\n\",\n \"classifier.fit(X_train, y_train)\\n\",\n \"predictions = classifier.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"89cef1ac-af1c-4b7a-b313-0077afd5603a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"evaluate_predictions(predictions, y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"70ff8fc5-90ec-42a2-91fa-71f0d507ec1d\",\n \"metadata\": {},\n \"source\": [\n \"# QuantumEnhancedEnsembleClassifier\\n\",\n \"\\n\",\n \"### Introduction\\n\",\n \"\\n\",\n \"The `QuantumEnhancedEnsembleClassifier` is a hybrid machine learning model from Multiverse Computing's 'Singularity Machine Learning - Classification' Function, combining classical ensemble methods—like boosting and bagging—with quantum optimization algorithms such as QAOA. Unlike classical ensembles, which become increasingly expensive to train as the number of learners grows, this quantum classifier demonstrates favorable scalability properties: training time remains comparatively stable as the number of learners (and thus qubits) increases. This makes it especially well-suited for problems that require large ensembles—where classical models tend to struggle with optimization costs that scale exponentially. Additionally, unlike traditional quantum models such as QSVMs, which are often constrained by dataset size, this classifier is designed to operate independently of the number of data points and features—limited primarily by available hardware infrastructure—making it well-suited for processing large-scale, high-dimensional datasets. As quantum hardware scales, so too will the accuracy and performance of this model—offering a compelling trajectory for future real-world quantum advantage. In this notebook, we demonstrate its use in both classical simulation and real quantum execution, showcasing its potential and flexibility.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"---\\n\",\n \"\\n\",\n \"## Understanding the Optimization Parameters\\n\",\n \"\\n\",\n \"To effectively use the `QuantumEnhancedEnsembleClassifier`, it's important to understand the role of several key parameters that influence model training and optimization. Please refer to the [documentation](https://docs.quantum.ibm.com/guides/multiverse-computing-singularity) for details on the usage and parameters for this function. \\n\",\n \"\\n\",\n \"Some of the key parameters, also used in this lab, are explained below:\\n\",\n \"\\n\",\n \"- **`regularization`** \\n\",\n \" This parameter controls the complexity of the final ensemble. A higher value penalizes large or complex learner combinations, promoting simpler models and reducing the risk of overfitting. \\n\",\n \"\\n\",\n \"- **`num_solutions`** \\n\",\n \" Defines the number of candidate ensemble configurations that the optimizer evaluates. Higher values lead to more exhaustive searches and can uncover better-performing ensembles, but at the cost of increased runtime. For demonstration, values around `100,000` balance exploration and performance.\\n\",\n \"\\n\",\n \"- **`simulator`** \\n\",\n \" Setting this to `True` enables classical optimization—faster and well-suited for quick experimentation but does not benefit from the scalability advantages of quantum computation. Setting it to `False` activates quantum optimization via QAOA, ideal for larger ensembles.\\n\",\n \"\\n\",\n \"- **`classical_optimizer_options`** \\n\",\n \" These options define the behavior of the underlying classical optimizer used in QAOA. For instance, `{\\\"maxiter\\\": 10}` limits the number of optimization iterations—useful for short demos, but convergence usually improves with `60+` iterations in real applications.\\n\",\n \"\\n\",\n \"These parameters give you flexibility to balance speed, accuracy, and compute resource use. In the following sections, you'll experiment with both classical and quantum setups to see these trade-offs in action.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2f72e8b3-9e7a-41ab-a6b0-de00c0547697\",\n \"metadata\": {},\n \"source\": [\n \"# Part 1: Simulated Quantum Classifier\\n\",\n \"\\n\",\n \"### Execute the Job\\n\",\n \"\\n\",\n \"Now let’s begin experimenting with the `QuantumEnhancedEnsembleClassifier` provided by the Singularity Qiskit Function. This classifier integrates classical ensemble learning with quantum-inspired optimization to intelligently combine multiple learners.\\n\",\n \"\\n\",\n \"In this exercise, we configure the model to use classical optimization using our simulator. This allows you to test the optimization logic without invoking real quantum hardware. For demonstration purposes, we set `num_learners` to `15`, which controls how many weak learners the ensemble will combine. Note that the simulator mode is not designed for scalability—larger values of `num_learners` may significantly increase runtime or hit performance limits.\\n\",\n \"\\n\",\n \"This setup provides a quick, low-cost way to validate the workflow and gain insights into the expected performance before scaling up with quantum optimization.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"626fb97a-a5e1-4c30-b313-8ba9914107f0\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" Exercise 1: \\n\",\n \" Fill in the #TODO sections to enable the simulator for classical optimization.\\n\",\n \"
\\n\",\n \" ⚠️ Warning: Use a simulator for this exercise to avoid consuming QPU time.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f75ad675-1458-4a45-b285-8b9b699a09fa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"optimizer_options_ex1 = {\\n\",\n \" ### TODO: Write your code below here ###\\n\",\n \" \\n\",\n \" ### Don't change any code past this line ###\\n\",\n \" \\\"num_solutions\\\": 100000,\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0d538220-5bd5-4660-8a0b-2defe2b03e52\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_exercise_1(optimizer_options_ex1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b56a4247-8ab0-49b6-aca1-d32816dd6040\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create, fit, and predict\\n\",\n \"print(\\\"-- Creating, fitting, and predicting --\\\")\\n\",\n \"start = time.time()\\n\",\n \"job = singularity.run(\\n\",\n \" action=\\\"create_fit_predict\\\",\\n\",\n \" name=\\\"classifier_for_grid_stability\\\",\\n\",\n \" quantum_classifier=\\\"QuantumEnhancedEnsembleClassifier\\\",\\n\",\n \" num_learners=15, \\n\",\n \" regularization=15,\\n\",\n \" optimizer_options=optimizer_options_ex1,\\n\",\n \" backend_name= None, \\n\",\n \" instance=your_crn,\\n\",\n \" random_state=RANDOM_STATE,\\n\",\n \" X_train=\\\"grid_stability_X_train.npy\\\",\\n\",\n \" y_train=\\\"grid_stability_y_train.npy\\\",\\n\",\n \" X_test=\\\"grid_stability_X_test.npy\\\",\\n\",\n \" fit_params={\\n\",\n \" \\\"validation_data\\\": (X_val, y_val),\\n\",\n \" },\\n\",\n \" options={\\n\",\n \" \\\"save\\\": False,\\n\",\n \" }\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bc7455a7-d994-461e-8d39-b8925f88d3b6\",\n \"metadata\": {},\n \"source\": [\n \"### Monitor Job Execution\\n\",\n \"\\n\",\n \"Now that the job has been submitted, we can print `job` to save the JobID, and monitor the status of the execution using `job.status()`. The cell can be re-ran again to ping for the latest status. If the job fails, we can obtain information about the reason it did so using `job.error_message()`. \\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7c4d238e-8aeb-47af-97cc-70589bd6bbf1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"efcb8447-9fbc-4e0a-a08b-8e668f1008f4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9aa6e435-8447-4ac5-8c83-17f7a99ca113\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"error_message = job.error_message()\\n\",\n \"print(error_message)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8c871230-39c9-4f0a-934b-b5f849e42cc1\",\n \"metadata\": {},\n \"source\": [\n \"### Retrieve and Analyze the Results\\n\",\n \"\\n\",\n \"Once the job has been submitted and completed, we can retrieve the results using `job.result()`. We can then evaluate the results using our previously defined helper function and ensure it matches our expectation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1fa3df38-30f5-4c8d-b96c-0278e56b5e13\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Get the job status, logs, and result\\n\",\n \"result = job.result()\\n\",\n \"status = job.status()\\n\",\n \"error_message = job.error_message()\\n\",\n \"end = time.time()\\n\",\n \"\\n\",\n \"print(\\\"Job status: \\\", status)\\n\",\n \"print(\\\"Error message: \\\", error_message)\\n\",\n \"print(\\\"Action status: \\\", result[\\\"status\\\"])\\n\",\n \"print(\\\"Action message: \\\", result[\\\"message\\\"])\\n\",\n \"print(\\\"Action result: \\\", result[\\\"data\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5971254a-a095-47c6-a670-924fca44e776\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"predictions = result[\\\"data\\\"][\\\"predictions\\\"]\\n\",\n \"evaluate_predictions(predictions, y_test, start, end)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3bbd8fc4-e538-40a0-8f9f-0aa0ce58ce49\",\n \"metadata\": {},\n \"source\": [\n \"# Part 2: Simulated Heterogenous Quantum Classifier\\n\",\n \"\\n\",\n \"### Execute the Job\\n\",\n \"\\n\",\n \"In this section, we simulate a heterogeneous quantum ensemble using classical resources. Unlike the standard simulated classifier, the heterogeneous variant allows us to define a mix of different learner types within the ensemble—such as decision trees, k-nearest neighbors, or logistic regression—each contributing a unique inductive bias. This strategy can enhance model diversity and improve generalization, particularly on complex datasets with varied feature relationships.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ec7a62df-c085-4901-9355-c3a4349de517\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" Exercise 2: \\n\",\n \" Fill in the #TODO sections to create a job that uses two unique learners, with an equal proportion distributed between them. \\n\",\n \"
\\n\",\n \" 💡 Tip: Refer to the documentation \\n\",\n \" here on how to specify different learners. Observe how this influenced the results. Once you have passed the exercise grader, feel free to experiment with different learners to learn how this impacts the performance.\\n\",\n \"
\\n\",\n \" ⚠️ Warning: Note that for this exercise, we again use a simulator to avoid consuming QPU time.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"78685382-aaa2-4e54-a1c5-7ecaae8bca9a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create, fit, and predict\\n\",\n \"print(\\\"-- Creating, fitting, and predicting --\\\")\\n\",\n \"start = time.time()\\n\",\n \"job = singularity.run(\\n\",\n \" action=\\\"create_fit_predict\\\",\\n\",\n \" name=\\\"classifier_for_grid_stability\\\",\\n\",\n \" quantum_classifier=\\\"QuantumEnhancedEnsembleClassifier\\\",\\n\",\n \" num_learners=15, \\n\",\n \" regularization=15,\\n\",\n \" learners_types=learners_types_solution,\\n\",\n \" learners_proportions=learners_proportions_solution,\\n\",\n \" optimizer_options={\\n\",\n \" \\\"num_solutions\\\": 100000,\\n\",\n \" \\\"simulator\\\": True, \\n\",\n \" },\\n\",\n \" backend_name= None, \\n\",\n \" instance=your_crn,\\n\",\n \" random_state=RANDOM_STATE,\\n\",\n \" X_train=\\\"grid_stability_X_train.npy\\\",\\n\",\n \" y_train=\\\"grid_stability_y_train.npy\\\",\\n\",\n \" X_test=\\\"grid_stability_X_test.npy\\\",\\n\",\n \" fit_params={\\n\",\n \" \\\"validation_data\\\": (X_val, y_val),\\n\",\n \" },\\n\",\n \" options={\\n\",\n \" \\\"save\\\": False,\\n\",\n \" }\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5163dd55-b248-4a18-a7ed-14d74d6ca717\",\n \"metadata\": {},\n \"source\": [\n \"### Monitor Job Execution\\n\",\n \"\\n\",\n \"Now that the job has been submitted, we can print `job` to save the JobID, and monitor the status of the execution using `job.status()`. The cell can be re-ran again to ping for the latest status. If the job fails, we can obtain information about the reason it did so using `job.error_message()`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f3e2de58-f4bf-4c19-bff9-0ecf329207f4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ccf9c752-bd6e-4b52-8fbf-738c46e90d48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2c48ebb4-ea21-400c-a0cd-cb623311689a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"error_message = job.error_message()\\n\",\n \"print(error_message)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"93d57f45-b6bc-4b0a-bb65-7938729c068a\",\n \"metadata\": {},\n \"source\": [\n \"### Retrieve and Analyze the Results\\n\",\n \"\\n\",\n \"Once the job has been submitted and completed, we can retrieve the results using `job.result()`. We can then evaluate the results using our previously defined helper function and ensure it matches our expectation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5c36bb11-99fa-42ef-9b5c-848807d2df5d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get the job status, error affecting the job (if any), and result\\n\",\n \"result = job.result()\\n\",\n \"status = job.status()\\n\",\n \"error_message = job.error_message()\\n\",\n \"end = time.time()\\n\",\n \"\\n\",\n \"print(\\\"Job status: \\\", status)\\n\",\n \"print(\\\"Error message: \\\", error_message)\\n\",\n \"print(\\\"Action status: \\\", result[\\\"status\\\"])\\n\",\n \"print(\\\"Action message: \\\", result[\\\"message\\\"])\\n\",\n \"print(\\\"Action result: \\\", result[\\\"data\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fa057161-7912-4159-8465-f654f1a57925\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"predictions = result[\\\"data\\\"][\\\"predictions\\\"]\\n\",\n \"evaluate_predictions(predictions, y_test, start, end)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4ce0ffe5-5a6e-4122-95e1-2e8da7711671\",\n \"metadata\": {},\n \"source\": [\n \"# Part 3: Quantum Classifier on Real Quantum Hardware\\n\",\n \"\\n\",\n \"### Execute the Job\\n\",\n \"\\n\",\n \"Next, let's use real quantum hardware to perform the evaluation. This approach offers multiple advantages, the most significant being better scalability as the number of learners increases. Unlike the simulator mode, which uses classical resources, real quantum optimization can efficiently handle larger ensembles and more complex search spaces.\\n\",\n \"\\n\",\n \"To enable quantum optimization, set the `simulator` flag to `False` in the `optimizer_options`. Also include a `classical_optimizer_options` parameter to control the convergence behavior. While quantum algorithms like QAOA generally benefit from `maxiter` values of 60 or more, for demonstration purposes we limit it to `10` to ensure shorter runtimes—approximately 10 minutes is expected.\\n\",\n \"\\n\",\n \"This section showcases how hybrid quantum-classical workflows can be executed at scale using Qiskit Serverless, bridging the gap between research and production-ready quantum applications.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fe9b2fc7-7fb5-4bd0-8077-f08ed4ccd328\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" Exercise 3: \\n\",\n \" Fill in the #TODO sections to specify the optimizer options, for a job that runs the QuantumEnhancedEnsembleClassifier on real quantum hardware. Limit the number of iterations in the optimization to 10 to ensure it finishes in a reasonable amount of time (expected: ~10 minutes).\\n\",\n \"
\\n\",\n \" 💡 Tip: Use a similar configuration as the one used in Part 1: Simulated Quantum Classifier. Once you have passed the exercise, feel free to experiment with different parameters such as regularization, num_learners, or num_solutions to explore how they influence performance and optimization time. Refer to the documentation \\n\",\n \" here for examples.\\n\",\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 2 minutes 47 seconds (based on tests on `ibm_brisbane`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d05c8e89-bf02-425b-93c9-dfca650aceb8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create, fit, and predict\\n\",\n \"print(\\\"-- Creating, fitting, and predicting --\\\")\\n\",\n \"start = time.time()\\n\",\n \"job = singularity.run(\\n\",\n \" action=\\\"create_fit_predict\\\",\\n\",\n \" name=\\\"classifier_for_grid_stability\\\",\\n\",\n \" quantum_classifier=\\\"QuantumEnhancedEnsembleClassifier\\\",\\n\",\n \" num_learners=75,\\n\",\n \" regularization=15,\\n\",\n \" optimizer_options=optimizer_options_ex3,\\n\",\n \" backend_name=None,\\n\",\n \" instance=your_crn,\\n\",\n \" random_state=RANDOM_STATE,\\n\",\n \" X_train=\\\"grid_stability_X_train.npy\\\",\\n\",\n \" y_train=\\\"grid_stability_y_train.npy\\\",\\n\",\n \" X_test=\\\"grid_stability_X_test.npy\\\",\\n\",\n \" fit_params={\\n\",\n \" \\\"validation_data\\\": (X_val, y_val),\\n\",\n \" },\\n\",\n \" options={\\n\",\n \" \\\"save\\\": False,\\n\",\n \" }\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0eae92b1-ae88-46f9-a920-53dc21a38966\",\n \"metadata\": {},\n \"source\": [\n \"### Monitor Job Execution\\n\",\n \"\\n\",\n \"Now that the job has been submitted, we can print `job` to save the JobID, and monitor the status of the execution using `job.status()`. The cell can be re-ran again to ping for the latest status. If the job fails, we can obtain information about the reason it did so using `job.error_message()`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3cecdfaa-7f92-4f87-8785-bfda37d5cdf7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f54c55a5-47a1-475e-8aad-5cbf43221d90\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ab06aca1-a44a-4ff5-89ff-cfd4c10eda27\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"error_message = job.error_message()\\n\",\n \"print(error_message)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f473f36b-9492-4408-972a-c36579ba70c1\",\n \"metadata\": {},\n \"source\": [\n \"### Retrieve and Analyze the Results\\n\",\n \"\\n\",\n \"Once the job has been submitted and completed, we can retrieve the results using `job.result()`. We can then evaluate the results using our previously defined helper function and ensure it matches our expectation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"527bd350-d77f-4965-9608-910597e639a3\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Get the job status, logs, and result\\n\",\n \"result = job.result()\\n\",\n \"status = job.status()\\n\",\n \"error_message = job.error_message()\\n\",\n \"end = time.time()\\n\",\n \"\\n\",\n \"print(\\\"Job status: \\\", status)\\n\",\n \"print(\\\"Error message: \\\", error_message)\\n\",\n \"print(\\\"Action status: \\\", result[\\\"status\\\"])\\n\",\n \"print(\\\"Action message: \\\", result[\\\"message\\\"])\\n\",\n \"print(\\\"Action result: \\\", result[\\\"data\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"575e80bf-2582-4440-b106-244e9a8b1d3d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"predictions = result[\\\"data\\\"][\\\"predictions\\\"]\\n\",\n \"evaluate_predictions(predictions, y_test, start, end)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8edfc660-a359-4bf3-be15-34d8b953d169\",\n \"metadata\": {},\n \"source\": [\n \"### Visualization: Classical vs Quantum F1 Score\\n\",\n \"\\n\",\n \"To summarize our experiments, let’s compare the F1 scores of the classical and quantum classifiers. F1 score is a balanced metric that considers both precision and recall, making it especially useful in imbalanced classification tasks like this one. This visualization helps highlight the performance gains introduced by quantum optimization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"73385e5e-c705-4b9d-9e5e-fb7b2d529401\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Collect F1 scores from both experiments\\n\",\n \"labels = [\\\"AdaBoost (Classical)\\\", \\\"Quantum Classifier\\\"]\\n\",\n \"f1_scores = []\\n\",\n \"\\n\",\n \"# Recalculate F1 for AdaBoost\\n\",\n \"_, _, _, f1_ada = evaluate_predictions(classifier.predict(X_test), y_test)\\n\",\n \"f1_scores.append(f1_ada)\\n\",\n \"\\n\",\n \"# Use F1 score from the quantum job\\n\",\n \"_, _, _, f1_quantum = evaluate_predictions(predictions, y_test)\\n\",\n \"f1_scores.append(f1_quantum)\\n\",\n \"\\n\",\n \"# Create the bar chart\\n\",\n \"plt.figure(figsize=(8, 5))\\n\",\n \"plt.bar(labels, f1_scores, color=[\\\"#808080\\\", \\\"#8A2BE2\\\"])\\n\",\n \"plt.ylabel(\\\"F1 Score\\\")\\n\",\n \"plt.title(\\\"F1 Score Comparison: Classical vs Quantum Classifier\\\")\\n\",\n \"plt.ylim(0, 1.0)\\n\",\n \"for i, f1 in enumerate(f1_scores):\\n\",\n \" plt.text(i, f1 + 0.01, f\\\"{f1:.3f}\\\", ha='center', va='bottom', fontweight='bold')\\n\",\n \"plt.grid(axis=\\\"y\\\", linestyle=\\\"--\\\", alpha=0.7)\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2b0f0e89-0a1e-4dab-8086-d007da0f5038\",\n \"metadata\": {},\n \"source\": [\n \"# Clean up Shared Directory\\n\",\n \"\\n\",\n \"After completing all remote executions, it's important to clean up any temporary files that were uploaded to the Qiskit Serverless environment. This helps maintain a tidy workspace and avoids unnecessary storage usage in shared or limited environments.\\n\",\n \"\\n\",\n \"In this step, we remove the uploaded `.tar` archives corresponding to the training and test datasets using the `client.file_delete()` method. These files are no longer needed after the jobs have completed and results have been retrieved.\\n\",\n \"\\n\",\n \"Cleaning up is a good practice, especially when working with multiple users or when automating workflows in production.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f28012db-3530-4e01-9975-39504f15e5b6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Clean up the shared data directory\\n\",\n \"file_names = [\\n\",\n \" \\\"grid_stability_X_train.npy\\\",\\n\",\n \" \\\"grid_stability_y_train.npy\\\",\\n\",\n \" \\\"grid_stability_X_test.npy\\\",\\n\",\n \"]\\n\",\n \"\\n\",\n \"for name in file_names:\\n\",\n \" tar_name = f\\\"{name}.tar\\\"\\n\",\n \" client.file_delete(tar_name, singularity)\\n\",\n \" if os.path.exists(name):\\n\",\n \" os.remove(name)\\n\",\n \" if os.path.exists(tar_name):\\n\",\n \" os.remove(tar_name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bc04cbda-fd17-4463-85b5-18275a666cc4\",\n \"metadata\": {},\n \"source\": [\n \"# Scaling Benefits and Enhancements\\n\",\n \"\\n\",\n \"The true power of the `QuantumEnhancedEnsembleClassifier` comes into play in scenarios that involve **large ensemble sizes** or **high-dimensional optimization**. While in this lab we limited the number of learners and iterations (`maxiter = 10`) to ensure the job completes in under 30 minutes, it's important to note that quantum optimization algorithms like QAOA typically require 60 or more iterations to converge effectively. This setting provides only a glimpse of the potential performance gains.\\n\",\n \"\\n\",\n \"In classical ensemble optimization, the training time scales poorly—often exponentially—as the number of learners increases. This makes it impractical for large ensemble configurations due to the combinatorial nature of selecting optimal learners. On the other hand, quantum optimization is expected to scale much more favorably as you increase the number of learners or the problem size (e.g., qubits). In practice, this means quantum methods can remain efficient even when classical methods become infeasible.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ee0bd48b\",\n \"metadata\": {},\n \"source\": [\n \"# Feedback Survey\\n\",\n \"\\n\",\n \"We’d love to hear about your experience using the Qiskit Function! Your feedback is valuable and will help Qiskit Function providers enhance their tools and services. Please take a moment to share your thoughts by completing our short 2 min [feedback survey](https://airtable.com/app6VujlNUHZuOnAF/pagpw6TgP9UEt4TAT/form).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5907411a-bd3e-4dca-b81b-4ab1d666a6ec\",\n \"metadata\": {},\n \"source\": [\n \"# References\\n\",\n \"\\n\",\n \"1. Qiskit Function:\\n\",\n \" - [Qiskit Function Tutorial](https://docs.quantum.ibm.com/guides/functions)\\n\",\n \" - [Qiskit Serverless Documentation](https://qiskit.github.io/qiskit-serverless/index.html)\\n\",\n \"\\n\",\n \"2. Papers:\\n\",\n \" - [Neven et al. 2008: Training a Binary Classifier with the Quantum Adiabatic Algorithm](https://arxiv.org/abs/0811.0416)\\n\",\n \" - [Neven et al. 2012: QBoost: Large Scale Classifier Training with Adiabatic Quantum Optimization](http://proceedings.mlr.press/v25/neven12/neven12.pdf)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74708eab-9f0d-4c41-900e-726c2ce399a6\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Sepehr Hosseini, Pablo Lauret\\n\",\n \"\\n\",\n \"**Advised by:** Junye Huang\\n\",\n \"\\n\",\n \"**Version:** 1.1.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"04c58c5f-e471-4fc8-8318-1c60f46b05ee\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit packages versions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1de25001-bad2-4b1c-a0a9-8da0cfced98d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_serverless\\n\",\n \"import qiskit_ibm_catalog\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit Serverless: {qiskit_serverless.__version__}')\\n\",\n \"print(f'Qiskit IBM Catalog: {qiskit_ibm_catalog.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "functions-labs/q-ctrl/grader.py",
"content": "from qiskit import QuantumCircuit\nfrom qiskit.quantum_info import SparsePauliOp\n\ncorrect_message = \"Congratulations! 🎉 Your answer is correct.\"\nincorrect_message = \"Sorry, your answer is incorrect. Please try again.\"\n\ndef grade_ex1(circuit_width: int, hidden_bitstring: str, shot_count: int, options: dict):\n if not isinstance(circuit_width, int) or circuit_width <= 0:\n return \"The circuit width must be a positive integer. Please try again.\"\n\n if circuit_width > 40:\n return \"The circuit width must not exceed 40 for best results. Please refer to our benchmarks for more information: https://quantum.cloud.ibm.com/docs/en/guides/q-ctrl-performance-management#benchmarks.\"\n\n if not isinstance(hidden_bitstring, str) or len(hidden_bitstring) != circuit_width:\n return f\"The hidden bitstring must be a string of length {circuit_width}. Please try again.\"\n\n if not all(bit in '01' for bit in hidden_bitstring):\n return \"The hidden bitstring must only contain '0' and '1'. Please try again.\"\n\n if not isinstance(shot_count, int) or shot_count <= 0:\n return \"The shot count must be a positive integer. Please try again.\"\n\n if shot_count < 4096:\n return \"The shot count must be at least 4096 for reliable results. Please try again.\"\n\n if circuit_width > 20 and shot_count < 10000:\n return \"The shot count must be at least 10000 for larger circuits for reliable results. Please try again.\"\n\n if options.get(\"primitive\") != \"sampler\":\n return \"The primitive must be 'sampler'. Please try again.\"\n\n if options.get(\"pubs\") is None or not isinstance(options.get(\"pubs\"), list):\n return \"The pubs must be a list of Sampler PUBs. Please try again.\"\n\n if len(options.get(\"pubs\")) != 1:\n return \"The pubs list should contain only one PUB. Please try again.\"\n\n pub = options.get(\"pubs\")[0]\n\n if not isinstance(pub, tuple):\n return \"The pub must be a tuple. Please try again.\"\n if len(pub) != 1:\n return \"The pub tuple should contain exactly one element. Please try again.\"\n if not isinstance(pub[0], QuantumCircuit):\n return \"The pub must contain a QuantumCircuit. Please try again.\"\n\n if options.get(\"backend_name\") is None or not isinstance(options.get(\"backend_name\"), str):\n return \"The backend name must be a non-empty string. Please try again.\"\n\n if options.get(\"options\") is None or not isinstance(options.get(\"options\"), dict):\n return \"The options must be a dictionary. Please try again.\"\n\n if options.get(\"options\").get(\"default_shots\") != shot_count:\n return f\"The options dictionary must contain 'default_shots' equal to `shot_count`. Please try again.\"\n\n return correct_message\n\n\ndef grade_ex2(n_qubits: int, observable: SparsePauliOp, options: dict):\n if not isinstance(n_qubits, int) or n_qubits <= 0:\n return \"The number of qubits must be a positive integer. Please try again.\"\n\n if not isinstance(observable, SparsePauliOp):\n return \"The observable must be a SparsePauliOp. Please try again.\"\n\n pauli_strings = observable.to_list()\n if not pauli_strings:\n return \"The observable must contain at least one Pauli string. Please try again.\"\n\n for pauli_string, _ in pauli_strings:\n if len(pauli_string) != n_qubits:\n return f\"Each Pauli string must have length {n_qubits}. Please try again.\"\n\n groups = observable.group_commuting(qubit_wise=True)\n if len(groups) != 1:\n return \"The observable must be a single commuting group. Please try again.\"\n\n if options.get(\"primitive\") != \"estimator\":\n return \"The primitive must be 'estimator'. Please try again.\"\n\n if options.get(\"pubs\") is None or not isinstance(options.get(\"pubs\"), list):\n return \"The pubs must be a list of Estimator PUBs. Please try again.\"\n\n if len(options.get(\"pubs\")) != 1:\n return \"The pubs list should contain only one PUB. Please try again.\"\n\n pub = options.get(\"pubs\")[0]\n\n if not isinstance(pub, tuple):\n return \"The pub must be a tuple. Please try again.\"\n if len(pub) != 2:\n return \"The pub tuple must contain exactly two elements. Please try again.\"\n if not isinstance(pub[0], QuantumCircuit) or not isinstance(pub[1], SparsePauliOp):\n return \"The pub must contain a QuantumCircuit and a SparsePauliOp. Please try again.\"\n\n if options.get(\"backend_name\") is None or not isinstance(options.get(\"backend_name\"), str):\n return \"The backend name must be a non-empty string. Please try again.\"\n\n return correct_message\n"
},
{
"path": "functions-labs/q-ctrl/q-ctrl_performance_management.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Lab: Experience Q-CTRL's Performance Management Qiskit Functions!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"* [Function description and details](#function-description-and-details)\\n\",\n \"* [Setup](#setup)\\n\",\n \"* [Part 1: Try out the Sampler](#exercise1)\\n\",\n \"* [Part 2: Try out the Estimator](#exercise2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Welcome to Qiskit Global Summer School. We’re delighted that you are interested in using Q-CTRL’s Fire Opal Performance Management Function. \\n\",\n \"\\n\",\n \"[Fire Opal Performance Management](https://docs.quantum.ibm.com/guides/q-ctrl-performance-management) makes it simple for anyone to achieve meaningful results from quantum computers at scale without needing to be quantum hardware experts. When running circuits with Fire Opal Performance Management, AI-driven error suppression techniques are automatically applied, enabling the scaling of larger problems with more gates and qubits. This approach reduces the number of shots required to reach the correct answer, with no added overhead — resulting in significant savings in both compute time and cost. \\n\",\n \"\\n\",\n \"Performance Management suppresses errors and increases the probability of getting the correct answer on noisy hardware. In other words, it increases the signal-to-noise ratio, allowing for faster convergence on the correct result with less shots. By getting the right answer faster, you save significant compute runtime. Learn more [here](https://docs.quantum.ibm.com/guides/q-ctrl-performance-management).\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Function description and details\\n\",\n \"\\n\",\n \"Fire Opal Performance Management has two options for execution that are similar to the Qiskit Runtime primitives, so you can easily swap in the Q-CTRL Sampler and Estimator. The general workflow for using the Performance Management function is:\\n\",\n \"\\n\",\n \"Define your circuit (and operators in the case of the Estimator).\\n\",\n \"1. Run the circuit.\\n\",\n \"2. Retrieve the results.\\n\",\n \"3. To reduce hardware noise, Fire Opal employs a range of AI-driven error suppression techniques depicted in the following image. With Fire Opal, the entire pipeline is completely automated with zero need for configuration.\\n\",\n \"\\n\",\n \"Fire Opal's pipeline eliminates the need for additional overhead, such as increased quantum runtime or extra physical qubits. Note that classical processing time remains a factor (refer to the [Benchmarks](https://docs.quantum.ibm.com/guides/q-ctrl-performance-management#benchmarks) section for estimates, where \\\"Total time\\\" reflects both classical and quantum processing). In contrast to error mitigation, which requires overhead in the form of sampling, Fire Opal's error suppression works at the gate level to address various sources of noise and to prevent the likelihood of an error occurring. By suppressing errors, the need for expensive post-processing is eliminated.\\n\",\n \"\\n\",\n \"The function offers two primitives, Sampler and Estimator, and the inputs and outputs of both extend the implemented spec for [Qiskit Runtime V2 primitives](https://docs.quantum.ibm.com/guides/primitive-input-output). For details on Estimator inputs and outputs, refer to details [here](https://docs.quantum.ibm.com/guides/q-ctrl-performance-management#estimator-inputs). For details on Sampler inputs and outputs, refer to details [here](https://docs.quantum.ibm.com/guides/q-ctrl-performance-management#sampler-inputss). \\n\",\n \"\\n\",\n \"For details on the full Performance Management pipeline, refer to the [published paper](https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.20.024034). \\n\",\n \"\\n\",\n \"For details on benchmarks, refer to details [here](https://docs.quantum.ibm.com/guides/q-ctrl-performance-management#benchmarks). \\n\",\n \"\\n\",\n \"Additional details in [Q-CTRL's Performance Management Qiskit Functions documentation](https://docs.quantum.ibm.com/guides/q-ctrl-performance-management#benchmarks)
\\n\",\n \"\\n\",\n \"**Use Qiskit v1.4.3**\\n\",\n \"\\n\",\n \"Q-CTRL Performance Management function currently only supports Qiskit version up to Qiskit `1.4.3`. If you are using Qiskit v2, please downgrade to `1.4.3` or create a fresh Python environment and install Qiskit `1.4.3`. Support for Qiskit v2 will be added after Qiskit Global Summer School.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Install dependencies\\n\",\n \"%pip install \\\"qc-grader @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"%pip install --force-reinstall \\\"qiskit[visualization]\\\"~=1.4.3 qiskit-aer qiskit-ibm-catalog qiskit-ibm-runtime\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `==1.4.3` and Grader `>=0.22.11`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"\\n\",\n \"# Import common packages first\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"# Import qiskit classes\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.circuit.library import iqp\\n\",\n \"from qiskit.quantum_info import random_hermitian, SparsePauliOp\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"\\n\",\n \"# Import qiskit ecosystems\\n\",\n \"from qiskit_ibm_catalog import QiskitFunctionsCatalog\\n\",\n \"from qiskit_ibm_runtime import SamplerV2 as Sampler\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"\\n\",\n \"# Grader\\n\",\n \"from grader import grade_ex1, grade_ex2\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_qctrl_function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**Exclusive Access to Qiskit Functions**\\n\",\n \"\\n\",\n \"As part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\\n\",\n \"\\n\",\n \"If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`. in the cell below, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\\n\",\n \"\\n\",\n \"**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Qiskit Functions Catalog\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"catalog = QiskitFunctionsCatalog(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"# You should see a list of Qiskit Functions available to you\\n\",\n \"# If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`,\\n\",\n \"# it means your access is not yet active\\n\",\n \"catalog.list()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" Load Qiskit Function\\n\",\n \"\\n\",\n \"Find the correct function name from the list above, or refer to the [Qiskit Functions Catalog](https://quantum.cloud.ibm.com/functions) to locate the appropriate function name string. The name should follow the format: `\\\"[provider]/[title]\\\"`.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use Fire Opal Performance Management's Sampler primitive to run a Bernstein–Vazirani circuit. This algorithm, used to find a hidden string from the outputs of a black box function, is a common benchmarking algorithm because there is a single correct answer.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1. Create the circuit\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Getting the best results: \\n\",\n \"\\n\",\n \"Quantum hardware performance can vary due to device fluctuations and architecture. To maximize your experience in this exercise:\\n\",\n \"* **Start small**: Try ~20 qubits to explore Fire Opal's capabilities\\n\",\n \"* **Scale thoughtfully**: Larger circuits (30+ qubits) may require 10k+ shots for greater reliability\\n\",\n \"* **Device matters**: Performance may differ by device. The latest Heron devices often provide greater stability and accuracy.\\n\",\n \"\\n\",\n \"Check our [benchmarks](https://docs.quantum.ibm.com/guides/q-ctrl-performance-management#benchmarks) for detailed performance guidance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Note: The Q-CTRL Performance Management function accepts abstract circuits, in contrast to the native Qiskit Runtime primitives, which only accept circuits that are written in the target backend’s Instruction Set Architecture (ISA). For best results, circuits should *not* be transpiled before submitting via the Performance Management function.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO: Exercise 1.1 ----\\n\",\n \"\\n\",\n \"circuit_width =\\n\",\n \"hidden_bitstring =\\n\",\n \"shot_count =\\n\",\n \"\\n\",\n \"# ---- End of TODO ----\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create Bernstein-Vazirani (BV) circuit and Sampler PUB\\n\",\n \"\\n\",\n \"# Create circuit, reserving one ancilla qubit for BV oracle\\n\",\n \"bv_circuit = QuantumCircuit(circuit_width + 1, circuit_width)\\n\",\n \"bv_circuit.x(circuit_width)\\n\",\n \"bv_circuit.h(range(circuit_width + 1))\\n\",\n \"for input_qubit, bit in enumerate(reversed(hidden_bitstring)):\\n\",\n \" if bit == \\\"1\\\":\\n\",\n \" bv_circuit.cx(input_qubit, circuit_width)\\n\",\n \"bv_circuit.barrier()\\n\",\n \"bv_circuit.h(range(circuit_width + 1))\\n\",\n \"bv_circuit.barrier()\\n\",\n \"for input_qubit in range(circuit_width):\\n\",\n \" bv_circuit.measure(input_qubit, input_qubit)\\n\",\n \"\\n\",\n \"# Create PUB tuple\\n\",\n \"sampler_pubs = [(bv_circuit,)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now try it out yourself in real-time by running the circuit in the below steps!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. Run the circuit\\n\",\n \"Run the circuit and optionally define the backend and number of shots.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO: Exercise 1.2 ----\\n\",\n \"backend_name = \\\"\\\"\\n\",\n \"\\n\",\n \"options_ex1 = {\\n\",\n \" 'primitive': ,\\n\",\n \" 'pubs': ,\\n\",\n \" 'backend_name': backend_name,\\n\",\n \" 'options': ,\\n\",\n \"}\\n\",\n \"\\n\",\n \"# ---- End of TODO ----\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check your answer using following code\\n\",\n \"grade_ex1(circuit_width, hidden_bitstring, shot_count, options_ex1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 15 seconds (based on tests on `ibm_fez`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run the circuit using the sampler\\n\",\n \"\\n\",\n \"qctrl_sampler_job = perf_mgmt.run(**options_ex1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Tips: \\n\",\n \"\\n\",\n \"* Check your Qiskit Function workload's [status](https://quantum.cloud.ibm.com/docs/en/guides/functions#check-job-status) or return [results](https://quantum.cloud.ibm.com/docs/en/guides/functions#retrieve-results) with: `qctrl_sampler_job.status()`\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qctrl_sampler_job.status()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3. Retrieve the result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Retrieve the job results\\n\",\n \"sampler_result = qctrl_sampler_job.result()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get results for the first (and only) PUB\\n\",\n \"pub_result = sampler_result[0]\\n\",\n \"counts = pub_result.data.c.get_counts()\\n\",\n \"\\n\",\n \"print(f\\\"Counts for the meas output register: {counts}\\\")\\n\",\n \"\\n\",\n \"if hidden_bitstring not in counts:\\n\",\n \" print(\\\"The hidden_bitstring has 0% probability.\\\")\\n\",\n \"else:\\n\",\n \" print(\\n\",\n \" f\\\"Success probability: {(100 * counts.get(hidden_bitstring, 0) / shot_count):.2f}%\\\"\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"(Optional) Plot the bitstring with the highest counts to see if the hidden bitstring was the mode.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def plot_top_bitstrings(counts_dict, hidden_bitstring=None):\\n\",\n \" # Sort and take the top 100 bitstrings\\n\",\n \" top_100 = sorted(counts_dict.items(), key=lambda x: x[1], reverse=True)[\\n\",\n \" :100\\n\",\n \" ]\\n\",\n \" if not top_100:\\n\",\n \" print(\\\"No bitstrings found in the input dictionary.\\\")\\n\",\n \" return\\n\",\n \"\\n\",\n \" # Unzip the bitstrings and their counts\\n\",\n \" bitstrings, counts = zip(*top_100)\\n\",\n \"\\n\",\n \" # Assign colors: purple if the bitstring matches hidden_bitstring, otherwise gray\\n\",\n \" colors = [\\n\",\n \" \\\"#680CE9\\\" if bit == hidden_bitstring else \\\"gray\\\" for bit in bitstrings\\n\",\n \" ]\\n\",\n \"\\n\",\n \" # Create the bar plot\\n\",\n \" plt.figure(figsize=(15, 8))\\n\",\n \" plt.bar(\\n\",\n \" range(len(bitstrings)), counts, tick_label=bitstrings, color=colors\\n\",\n \" )\\n\",\n \"\\n\",\n \" # Rotate the bitstrings for better readability\\n\",\n \" plt.xticks(rotation=90, fontsize=8)\\n\",\n \" plt.xlabel(\\\"Bitstrings\\\")\\n\",\n \" plt.ylabel(\\\"Counts\\\")\\n\",\n \" plt.title(\\\"Top 100 Bitstrings by Counts\\\")\\n\",\n \"\\n\",\n \" # Show the plot\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The hidden bitstring is highlighted in purple, and it should be the bitstring with the highest number of counts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_top_bitstrings(counts, hidden_bitstring)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Optional: Compare Fire Opal to IBM Qiskit Runtime\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you are seeking a comparison, you may run the same program using Qiskit, without realizing the error suppression benefits Fire Opal includes. The code below uses Qiskit on the same IBM backend as previously to obtain this one-to-one comparison. Note that this job too is subject to the device queue and therefore may take anywhere from seconds to potentially hours.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"backend = service.backend(backend_name)\\n\",\n \"sampler = Sampler(backend)\\n\",\n \"\\n\",\n \"pass_manager = generate_preset_pass_manager(backend=backend)\\n\",\n \"isa_circuit = pass_manager.run(bv_circuit)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ibm_sampler_job = sampler.run([isa_circuit], shots=shot_count)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Tips: \\n\",\n \"\\n\",\n \"* Check your IBM job's [status](https://quantum.cloud.ibm.com/docs/en/guides/monitor-job#monitor-a-job) or return [results](https://quantum.cloud.ibm.com/docs/en/guides/monitor-job#monitor-a-job) with: `ibm_sampler_job.status()`\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Retrieve the job results\\n\",\n \"ibm_sampler_result = ibm_sampler_job.result()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get results for the first (and only) PUB\\n\",\n \"ibm_pub_result = ibm_sampler_result[0]\\n\",\n \"ibm_counts = ibm_pub_result.data.c.get_counts()\\n\",\n \"\\n\",\n \"if hidden_bitstring not in ibm_counts:\\n\",\n \" print(\\\"The hidden_bitstring has 0% probability.\\\")\\n\",\n \"else:\\n\",\n \" print(\\n\",\n \" f\\\"Success probability: {(100 * ibm_counts.get(hidden_bitstring, 0) / shot_count):.2f}%\\\"\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot the top bitstrings for comparison\\n\",\n \"plot_top_bitstrings(ibm_counts, hidden_bitstring)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use Fire Opal Performance Management's Estimator primitive to determine the expectation value of a single circuit-observable pair.\\n\",\n \"\\n\",\n \"In addition to the `qiskit-ibm-catalog` and `qiskit` packages, you will also use the numpy package to run this example. You can install this package by uncommenting the following cell if you are running this example in a notebook using the IPython kernel.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1. Create the circuit\\n\",\n \"\\n\",\n \"The Estimator has efficient grouping of observables - it tries to minimize the number of circuits sent for execution, by grouping qubit-wise commuting observables together.\\n\",\n \"\\n\",\n \"Commuting observables are quantum operators (represented here as Pauli strings) that can be measured together because the order in which you apply them doesn’t matter - their measurements don’t interfere with each other. Mathematically, two observables `A` and `B` commute if `AB = BA`.\\n\",\n \"\\n\",\n \"For example, the Pauli operators \\\"Z\\\" and \\\"I\\\" commute, so observables like \\\"ZIII\\\" and \\\"IZIZ\\\" commute with each other. This property allows quantum algorithms to group and measure them efficiently in a single circuit.\\n\",\n \"\\n\",\n \"In the below exercise, try creating a circuit with `n_qubits`, and create a set of commuting observables using `SparsePauliOp`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO: Exercise 2.1 ----\\n\",\n \"\\n\",\n \"n_qubits =\\n\",\n \"seed =\\n\",\n \"observable =\\n\",\n \"\\n\",\n \"# ---- End of TODO ----\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create the estimator circuit and estimator PUB\\n\",\n \"\\n\",\n \"mat = np.real(random_hermitian(n_qubits, seed=seed))\\n\",\n \"circuit = iqp(mat)\\n\",\n \"\\n\",\n \"# Create PUB tuple\\n\",\n \"estimator_pubs = [(circuit, observable)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now try it out yourself in real-time by running the circuit in the below steps!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. Run the circuit\\n\",\n \"Run the circuit and optionally define the backend and number of shots.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO: Exercise 2.2 ----\\n\",\n \"# Create options for the Estimator function\\n\",\n \"\\n\",\n \"options_ex2 = {\\n\",\n \" 'primitive': ,\\n\",\n \" 'pubs': ,\\n\",\n \" 'backend_name': ,\\n\",\n \"}\\n\",\n \"\\n\",\n \"# ---- End of TODO ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check your answer using following code\\n\",\n \"grade_ex2(n_qubits, observable, options_ex2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 17 seconds (based on tests on `ibm_fez`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run the circuit using the estimator\\n\",\n \"\\n\",\n \"qctrl_estimator_job = perf_mgmt.run(**options_ex2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Tips: \\n\",\n \"\\n\",\n \"* Check your Qiskit Function workload's [status](https://quantum.cloud.ibm.com/docs/en/guides/functions#check-job-status) or return [results](https://quantum.cloud.ibm.com/docs/en/guides/functions#retrieve-results) with: `qctrl_estimator_job.status()`\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qctrl_estimator_job.status()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3. Retrieve the result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Retrieve the counts from the result list\\n\",\n \"result = qctrl_estimator_job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The results have the same format as an [Estimator result](https://docs.quantum.ibm.com/guides/primitive-input-output#estimator-output):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(\\n\",\n \" f\\\"The result of the submitted job had {len(result)} PUB and has a value:\\\\n {result}\\\\n\\\"\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The associated PubResult of this job has the following DataBins:\\\\n {result[0].data}\\\\n\\\"\\n\",\n \")\\n\",\n \"print(f\\\"And this DataBin has attributes: {result[0].data.keys()}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The expectation values measured from this PUB are: \\\\n{result[0].data.evs}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feedback Survey\\n\",\n \"\\n\",\n \"We’d love to hear about your experience using the Qiskit Function! Your feedback is valuable and will help Qiskit Function providers enhance their tools and services. Please take a moment to share your thoughts by completing our short 2 min [feedback survey](https://airtable.com/app6VujlNUHZuOnAF/pagpw6TgP9UEt4TAT/form).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Get support\\n\",\n \"For any questions or issues, contact the Q-CTRL team at [support@q-ctrl.com](mailto:support@q-ctrl.com?subject=QGSS:%20requesting%20support).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# References\\n\",\n \"\\n\",\n \"1. Learn more with [Q-CTRL's Performance Management function documentation](https://docs.quantum.ibm.com/guides/q-ctrl-performance-management)\\n\",\n \"2. For a detailed summary of the full Optimization Solver workflow, refer to the [published paper](https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.20.024034).\\n\",\n \"3. Learn more about the underlying technology at [Q-CTRL Fire Opal](https://q-ctrl.com/fire-opal).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Alex Shih, You Quan Chong\\n\",\n \"\\n\",\n \"**Advised by:** Junye Huang\\n\",\n \"\\n\",\n \"**Version:** 1.1.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit packages versions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_catalog\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit IBM Catalog: {qiskit_ibm_catalog.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "functions-labs/qedma/grader.py",
"content": "from qiskit import QuantumCircuit\nfrom qiskit.quantum_info.operators.base_operator import BaseOperator\nfrom qiskit.quantum_info import SparsePauliOp, Operator\nimport numpy as np\nimport networkx as nx\nimport qiskit\n\ncorrect_message = \"Congratulations! 🎉 Your answer is correct.\"\nincorrect_message = \"Sorry, your answer is incorrect. Please try again.\"\n\n\ndef kicked_ising_1D(num_qubits: int, theta_x: float, theta_zz: float, s: int) -> QuantumCircuit:\n \"\"\"\n Parameters:\n num_qubits (int): number of qubits on chain. \n theta_x (float): Angle for RX gates.\n theta_zz (float): Angle for RZZ gates.\n s (int): Number of steps.\n \n Returns:\n QuantumCircuit: The resulting quantum circuit.\n \"\"\"\n graph = nx.path_graph(num_qubits)\n qc = QuantumCircuit(num_qubits)\n\n # Precompute edge layers (alternating non-overlapping pairs)\n edges = list(graph.edges())\n even_edges = [(u, v) for (u, v) in edges if u % 2 == 0]\n odd_edges = [(u, v) for (u, v) in edges if u % 2 == 1]\n\n for step in range(s):\n # RX on all qubits\n for q in range(num_qubits):\n qc.rx(theta_x, q)\n \n # Apply even and odd layers separately\n for edge_layer in [even_edges, odd_edges]:\n for u, v in edge_layer:\n qc.rzz(theta_zz, u, v)\n\n if step < s-1:\n qc.barrier()\n \n return qc\n\n\ndef grade_ex2(pubs_ex2:list, backend_name_ex2:str, options_ex2:dict):\n \n \n # check types:\n if not isinstance(pubs_ex2,list) or not isinstance(backend_name_ex2, str) or not isinstance(options_ex2,dict):\n return \"Type error in input parameters\"\n \n \n circ = QuantumCircuit(4)\n circ.ry(0.927*2, 0) \n circ.cx(0, 1)\n circ.cx(1, 2)\n circ.cx(2, 3)\n \n avg_magnetization = SparsePauliOp.from_sparse_list(\n [(\"Z\", [q], 1 / 4) for q in range(4)], num_qubits=4)\n \n all_z = SparsePauliOp.from_sparse_list(\n [(\"Z\"*4, range(4),1)], 4)\n \n obs_list = [avg_magnetization, all_z]\n\n # test backend name\n if backend_name_ex2 !='fake_fez':\n return \"Bad backend name. \" +backend_name_ex2\n\n # test options\n if \"default_precision\" not in options_ex2 or \"max_execution_time\" not in options_ex2 or len(options_ex2)!=2:\n return \"Options should have two entries 'default_precision' and 'max_execution_time'\" \n\n if options_ex2['default_precision']!=0.1 or options_ex2[\"max_execution_time\"]!=900:\n return \"Value in options field is wrong. Try again\"\n \n # checks pubs\n if len(pubs_ex2)!=1:\n return \"pubs format is [(QuantumCircuit, [obs1,obs2,...]), [parameters], precision]\\n For this exercise, use pubs=[(QuantumCircuit, [obs1,obs2,...])]\" \n\n pair = pubs_ex2[0]\n \n if not (isinstance(pair, tuple)) or not len(pair) == 2:\n return \"pubs format is [(QuantumCircuit, [obs1,obs2,...]), [parameters], precision]\\n For this exercise, use pubs=[(QuantumCircuit, [obs1,obs2,...])]\" \n\n circ_ex2 = pair[0]\n obs_list_ex2 = pair[1]\n \n if not (isinstance(circ_ex2, QuantumCircuit)):\n return \"pubs format is [(QuantumCircuit, [obs1,obs2,...]), [parameters], precision]\\n For this exercise, use pubs=[(QuantumCircuit, [obs1,obs2,...])]\" \n\n\n # check circuit\n if not Operator(circ).equiv(Operator(circ_ex2)):\n return \"Different circuit specified.\"\n\n # check observable list\n if not isinstance(obs_list_ex2, list):\n return \"pubs[0][1] is not a list of observables\"\n\n if len (obs_list_ex2)!=2:\n return \"pubs[0][1] is not a list of the two observables specified\"\n\n if obs_list_ex2[0]==obs_list_ex2[1]:\n return \"pubs[0][1] is not a list of the two observables specified\"\n \n \n for en, obs in enumerate(obs_list_ex2):\n if not (isinstance(obs, SparsePauliOp)):\n return (f\"observable {en} is not a SparsePauliOp\")\n if obs not in obs_list:\n return (f\"observable {en} is not one of the two observables specified\")\n\n return correct_message\n\n\n \n \ndef grade_ex3(options_ex3:dict):\n \n \n # check types:\n if not isinstance(options_ex3,dict):\n return \"Type error in input parameter\"\n \n\n # test options\n if \"default_precision\" not in options_ex3 or \"estimate_time_only\" not in options_ex3 or len(options_ex3)!=2:\n return \"Options should have two entries 'default_precision' and 'estimate_time_only'\" \n\n if options_ex3['default_precision']!=0.02 or options_ex3[\"estimate_time_only\"]!=\"analytical\":\n return \"Value in options field is wrong. Try again\" \n\n return correct_message\n\n \n\n\ndef grade_ex_4_1(circuit: QuantumCircuit):\n \n if not isinstance(circuit, QuantumCircuit):\n return \"Circuit should be a QuantumCircuit object. Please try again.\"\n \n if circuit.num_qubits != 5:\n return \"Circuit should have 5 qubits (n_qubits_ex4 = 5). Please try again.\"\n \n expected_2q_layers = 20\n actual_2q_layers = circuit.depth(filter_function=lambda instr: len(instr.qubits) == 2)\n \n if actual_2q_layers != expected_2q_layers:\n return f\"Circuit should have {expected_2q_layers} two-qubit gate layers (10 steps × 2 layers per step). Found {actual_2q_layers}. Please try again.\"\n \n expected_barriers = 9\n actual_barriers = sum(1 for instr in circuit.data if instr.operation.name == 'barrier')\n \n if actual_barriers != expected_barriers:\n return f\"Circuit should have {expected_barriers} barriers (one between each of the 10 steps). Found {actual_barriers}. Please try again.\"\n \n expected_theta_x = np.pi/6\n expected_theta_zz = np.pi/3\n \n for instr in circuit.data:\n if instr.operation.name == 'rx':\n if not np.isclose(instr.operation.params[0], expected_theta_x):\n return f\"RX gate angles should be π/6 (theta_x_ex4). Found {instr.operation.params[0]}. Please try again.\"\n elif instr.operation.name == 'rzz':\n if not np.isclose(instr.operation.params[0], expected_theta_zz):\n return f\"RZZ gate angles should be π/3 (theta_zz_ex4). Found {instr.operation.params[0]}. Please try again.\"\n \n return correct_message\n\n\n\n\ndef grade_ex_4_2(precision_ex4, pubs_ex4, backend_name_ex4, options_ex4):\n \n # Expected values for Exercise 4.2\n n_qubits_ex4 = 5\n n_steps_ex4 = 10\n theta_x_ex4 = np.pi/6\n theta_zz_ex4 = np.pi/3\n \n circ_ex4 = kicked_ising_1D(n_qubits_ex4, theta_x_ex4, theta_zz_ex4, n_steps_ex4)\n observable_ex4 = qiskit.quantum_info.SparsePauliOp.from_sparse_list([(\"Z\"*n_qubits_ex4, range(n_qubits_ex4),1)], n_qubits_ex4) # Global Z measurement\n \n # Check precision\n if not isinstance(precision_ex4, (int, float)):\n return \"Precision should be a number (int or float). Please try again.\"\n \n if precision_ex4 <= 0.03:\n return \"Precision should be greater than 0.03. Please try again.\"\n \n # Check backend name\n if not isinstance(backend_name_ex4, str):\n return \"Backend name should be a string. Please try again.\"\n \n if \"ibm\" not in backend_name_ex4.lower():\n return \"Backend name should contain 'ibm'. Please try again.\"\n \n # Check pubs format\n if not isinstance(pubs_ex4, list):\n return \"Pubs should be a list. Please try again.\"\n \n if len(pubs_ex4) != 1:\n return \"Pubs should contain exactly one publication tuple. Please try again.\"\n \n pub = pubs_ex4[0]\n if not isinstance(pub, tuple) or len(pub) != 4:\n return \"Each pub should be a tuple with 4 elements: (circuit, observables, params, precision). Please try again.\"\n \n circuit, observables, params, precision = pub\n \n if not isinstance(circuit, QuantumCircuit):\n return \"First element of pub should be a QuantumCircuit (use circ_ex4). Please try again.\"\n \n # Check if the circuit matches the expected circ_ex4\n if not Operator(circuit).equiv(Operator(circ_ex4)):\n return \"Circuit should be circ_ex4 created with kicked_ising_1D. Please try again.\"\n \n if not isinstance(observables, list) or len(observables) != 1:\n return \"Second element of pub should be a list with one observable. Please try again.\"\n \n if not isinstance(observables[0], SparsePauliOp):\n return \"Observable should be a SparsePauliOp (use observable_ex4). Please try again.\"\n \n # Check if the observable matches the expected observable_ex4\n if not observables[0].equiv(observable_ex4):\n return \"Observable should be the global Z measurement (observable_ex4). Please try again.\"\n \n if params != []:\n return \"Third element of pub should be an empty list []. Please try again.\"\n \n if precision != precision_ex4:\n return \"Fourth element of pub should match your precision_ex4 parameter. Please try again.\"\n \n # Check options\n if not isinstance(options_ex4, dict):\n return \"Options should be a dictionary. Please try again.\"\n \n required_keys = [\"estimate_time_only\", \"max_execution_time\", \"execution_mode\"]\n \n for key in required_keys:\n if key not in options_ex4:\n return f\"Options missing required key '{key}'. Please try again.\"\n \n if len(options_ex4) != len(required_keys):\n return f\"Options should contain exactly {len(required_keys)} keys: {required_keys}. Please try again.\"\n \n # Check estimate_time_only\n if options_ex4[\"estimate_time_only\"] != \"empirical\":\n return \"Option 'estimate_time_only' should be 'empirical'. Please try again.\"\n \n # Check max_execution_time (up to 8 minutes = 480 seconds)\n if not isinstance(options_ex4[\"max_execution_time\"], (int, float)) or options_ex4[\"max_execution_time\"] > 480:\n return \"Option 'max_execution_time' should be a number ≤ 480 seconds (8 minutes). Please try again.\"\n \n # Check execution_mode (can be \"batch\" or \"session\")\n if options_ex4[\"execution_mode\"] not in [\"batch\", \"session\"]:\n return \"Option 'execution_mode' should be either 'batch' or 'session'. Please try again.\"\n \n return correct_message\n\n\n\n\ndef grade_ex_4_3(precision_ex4, pubs_ex4, backend_name_ex4, options_ex4):\n \n # Expected values for Exercise 4.2\n n_qubits_ex4 = 5\n n_steps_ex4 = 10\n theta_x_ex4 = np.pi/6\n theta_zz_ex4 = np.pi/3\n \n circ_ex4 = kicked_ising_1D(n_qubits_ex4, theta_x_ex4, theta_zz_ex4, n_steps_ex4)\n observable_ex4 = qiskit.quantum_info.SparsePauliOp.from_sparse_list([(\"Z\"*n_qubits_ex4, range(n_qubits_ex4),1)], n_qubits_ex4) # Global Z measurement\n \n # Check precision\n if not isinstance(precision_ex4, (int, float)):\n return \"Precision should be a number (int or float). Please try again.\"\n \n if precision_ex4 <= 0.03:\n return \"Precision should be at least 0.03. Please try again.\"\n \n # Check backend name\n if not isinstance(backend_name_ex4, str):\n return \"Backend name should be a string. Please try again.\"\n \n if \"ibm\" not in backend_name_ex4.lower():\n return \"Backend name should contain 'ibm'. Please try again.\"\n \n # Check pubs format\n if not isinstance(pubs_ex4, list):\n return \"Pubs should be a list. Please try again.\"\n \n if len(pubs_ex4) != 1:\n return \"Pubs should contain exactly one publication tuple. Please try again.\"\n \n pub = pubs_ex4[0]\n if not isinstance(pub, tuple) or len(pub) != 4:\n return \"Each pub should be a tuple with 4 elements: (circuit, observables, params, precision). Please try again.\"\n \n circuit, observables, params, precision = pub\n \n if not isinstance(circuit, QuantumCircuit):\n return \"First element of pub should be a QuantumCircuit (use circ_ex4). Please try again.\"\n \n # Check if the circuit matches the expected circ_ex4\n if not Operator(circuit).equiv(Operator(circ_ex4)):\n return \"Circuit should be circ_ex4 created with kicked_ising_1D. Please try again.\"\n \n if not isinstance(observables, list) or len(observables) != 1:\n return \"Second element of pub should be a list with one observable. Please try again.\"\n \n if not isinstance(observables[0], SparsePauliOp):\n return \"Observable should be a SparsePauliOp (use observable_ex4). Please try again.\"\n \n # Check if the observable matches the expected observable_ex4\n if not observables[0].equiv(observable_ex4):\n return \"Observable should be the global Z measurement (observable_ex4). Please try again.\"\n \n if params != []:\n return \"Third element of pub should be an empty list []. Please try again.\"\n \n if precision != precision_ex4:\n return \"Fourth element of pub should match your precision_ex4 parameter. Please try again.\"\n \n # Check options\n if not isinstance(options_ex4, dict):\n return \"Options should be a dictionary. Please try again.\"\n \n required_keys = [\"max_execution_time\", \"execution_mode\"]\n \n for key in required_keys:\n if key not in options_ex4:\n return f\"Options missing required key '{key}'. Please try again.\"\n \n if len(options_ex4) != len(required_keys):\n return f\"Options should contain exactly {len(required_keys)} keys: {required_keys}. Please try again.\"\n \n # Check max_execution_time (up to 8 minutes = 480 seconds)\n if not isinstance(options_ex4[\"max_execution_time\"], (int, float)) or options_ex4[\"max_execution_time\"] > 480:\n return \"Option 'max_execution_time' should be a number ≤ 480 seconds (8 minutes). Please try again.\"\n \n # Check execution_mode (can be \"batch\" or \"session\")\n if options_ex4[\"execution_mode\"] not in [\"batch\", \"session\"]:\n return \"Option 'execution_mode' should be either 'batch' or 'session'. Please try again.\"\n \n return correct_message\n\n\n "
},
{
"path": "functions-labs/qedma/qedma_qesem.ipynb",
"content": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Abstract\\n\",\n \"In this lab, you will learn about **Quantum Error Suppression and Error Mitigation (QESEM)** through hands-on experiments. \\n\",\n \"With QESEM, users can run quantum circuits on noisy QPUs and obtain highly accurate, error-free results with minimal QPU time overhead, close to theoretical limits. \\n\",\n \"When executing a circuit, QESEM runs a device characterization protocol tailored to your circuit, yielding a reliable noise model for the errors occurring in the circuit. Based on the characterization, QESEM first implements noise-aware transpilation to map the input circuit onto a set of physical qubits and gates, which minimizes the noise affecting the target observable. These include the natively available gates (CX/CZ on IBM® devices), as well as additional gates optimized by QESEM, forming QESEM’s extended gate set. QESEM then runs a set of characterization-based error suppression (ES) and error mitigation (EM) circuits on the QPU and collects their measurement outcomes. These are then classically post-processed to provide an unbiased expectation value and an error bar for each observable, corresponding to the requested accuracy.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"**Exclusive Access to Qiskit Functions**\\n\",\n \"\\n\",\n \"As part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\\n\",\n \"\\n\",\n \"If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`. in the cell below, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\\n\",\n \"\\n\",\n \"**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Qiskit Functions Catalog\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"catalog = QiskitFunctionsCatalog(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"# You should see a list of Qiskit Functions available to you\\n\",\n \"# If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`,\\n\",\n \"# it means your access is not yet active\\n\",\n \"catalog.list()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" Load Qiskit Function\\n\",\n \"\\n\",\n \"Find the correct function name from the list above, or refer to the [Qiskit Functions Catalog](https://quantum.cloud.ibm.com/functions) to locate the appropriate function name string. The name should follow the format: `\\\"[provider]/[title]\\\"`.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load Qedma QESEM function\\n\",\n \"\\n\",\n \"function_name = \\\"\\\" # TODO\\n\",\n \"qesem_function = catalog.load(function_name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_qedma_function(qesem_function)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Learning Objectives\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll explore:\\n\",\n \"\\n\",\n \"1. **Why do we need error mitigation?** - See how quantum noise affects different types of measurements\\n\",\n \"2. **Kicked Ising circuits** - Our main example is the Kicked Ising chain, a benchmark model in quantum dynamics\\n\",\n \"3. **Using QESEM** - Use QESEM to mitigate the noise\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"1. [Section 1: Understanding the Importance of Error Mitigation](#Section-1:-Understanding-the-Importance-of-Error-Mitigation)\\n\",\n \" - [1.1 Use Case: Kicked Ising](#1.1-Use-Case:-Kicked-Ising)\\n\",\n \" - [1.2 Exercise 1: Comparing Ideal and Noisy Values](#1.2-Exercise-1:-Comparing-Ideal-and-Noisy-Values)\\n\",\n \" \\n\",\n \"2. [Section 2: Introduction to QESEM](#Section-2:-Introduction-to-QESEM)\\n\",\n \" - [QESEM Function Parameters Reference](#QESEM-Function-Parameters-Reference)\\n\",\n \" - [2.1 Exercise 2: Applying QESEM on a simple circuit](#2.1-Exercise-2:-Applying-QESEM-on-a-simple-circuit)\\n\",\n \" \\n\",\n \" - [2.2 Key Concepts](#2.2-Key-Concepts)\\n\",\n \" - [2.2.1 Volume and Active Volume](#2.2.1-Volume-and-Active-Volume)\\n\",\n \" - [2.2.2 QESEM Runtime Overhead](#2.2.2-QESEM-Runtime-Overhead)\\n\",\n \" - [2.3 Exercise 3: QPU time vs. Active Volume](#2.3-Exercise-3:-QPU-time-vs.-Active-Volume)\\n\",\n \" \\n\",\n \" - [2.4 Exercise 4: Exploring the $T\\\\propto\\\\frac{1}{\\\\varepsilon^2}$ Relationship](#2.4-Exercise-4:-Exploring-the-$T-\\\\propto-\\\\frac{1}{\\\\varepsilon^2}$-Relationship)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Section 1: Understanding the Importance of Error Mitigation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Quantum computers are inherently noisy devices. Even small amounts of noise can significantly affect quantum computations, especially as circuit depth increases. In this section, we'll:\\n\",\n \"\\n\",\n \"1. **Build a circuit** for the Kicked Ising model\\n\",\n \"2. **Learn the effects of noise** on different types of quantum observables\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.1 Use Case: Kicked Ising\\n\",\n \"\\n\",\n \"The **Kicked Ising Model** is a paradigmatic quantum spin chain model used to study quantum chaos, entanglement, and non-equilibrium dynamics. It consists of spin-1/2 particles (qubits) with nearest-neighbor interactions on a graph, and a periodically applied \\\"kick\\\".\\n\",\n \"\\n\",\n \"### Model Definition\\n\",\n \"\\n\",\n \"The time evolution of the kicked Ising model is governed by a periodically time-dependent Hamiltonian. The unitary evolution over one period $T$, on a 1D lattice of size $n$, is given by:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"U = \\\\exp\\\\left(-i J \\\\sum_{j=0}^{n-2} \\\\sigma_j^z \\\\sigma_{j+1}^z \\\\right) \\\\exp\\\\left(-i h_x \\\\sum_{j=0}^{n-1} \\\\sigma_j^x \\\\right)\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $\\\\sigma_j^x, \\\\sigma_j^z$ are Pauli matrices acting on site $j$, and $J, h_x$ are constants.\\n\",\n \"\\n\",\n \"### Physical Significance\\n\",\n \"\\n\",\n \"- **Quantum Chaos:** The kicked Ising model exhibits rich quantum chaotic behavior, making it a benchmark for studies of thermalization and information scrambling in many-body quantum systems.\\n\",\n \"- **Floquet Systems:** Since the system is driven periodically, it is a canonical example of a **Floquet system**, where stroboscopic (periodic) dynamics are studied.\\n\",\n \"- **Entanglement & Quantum Information:** The model is widely used to investigate entanglement growth, operator spreading, and out-of-time-ordered correlators (OTOCs).\\n\",\n \"\\n\",\n \"### Applications\\n\",\n \"\\n\",\n \"- Modeling non-equilibrium quantum dynamics.\\n\",\n \"- Studying quantum information scrambling and entanglement spreading.\\n\",\n \"- Exploring the transition between integrability and chaos in quantum systems.\\n\",\n \"\\n\",\n \"---\\n\",\n \"\\n\",\n \"**References:**\\n\",\n \"- Prosen, T. (2007). \\\"Chaos and complexity in a kicked Ising spin chain.\\\" *Phys. Rev. E* 65, 036208.\\n\",\n \"- [Wikipedia: Kicked Ising Model](https://en.wikipedia.org/wiki/Kicked_Ising_model)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Note:\\n\",\n \"\\n\",\n \"While in this lab we focus on exploring a 1D Kicked Ising model for simplicity, the QESEM qiskit function can work on arbitrary circuits defined on the heavy-hex lattice connectivity\\n\",\n \"available on IBM systems.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.2 Exercise 1: Comparing Ideal and Noisy Values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This exercise demonstrates how quantum noise degrades computation results by comparing three scenarios:\\n\",\n \"\\n\",\n \"**What We'll Do:**\\n\",\n \"- **Build Kicked Ising Circuits**: Create quantum circuits simulating time evolution with increasing number of Trotter steps \\n\",\n \"- **Measure Observables**: \\n\",\n \" - **Z Correlation Operators**: Evaluate $\\\\left\\\\langle Z_0 Z_1 \\\\dots Z_{n-1} \\\\right\\\\rangle$ and $\\\\left\\\\langle Z_0 Z_1 \\\\dots Z_{4} \\\\right\\\\rangle$ to probe global multi-qubit correlations \\n\",\n \"- **Compare Results**: Ideal expectation values to noisy ones\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 1: Preparing Kicked-Ising circuit \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"raw\"\n }\n },\n \"source\": [\n \"kicked_ising_1D: Creates a quantum circuit that implements the Kicked Ising model. It applies alternating layers of RX gates (transverse field) and RZZ gates (interaction terms) to simulate the time evolution of the system. \\n\",\n \"\\n\",\n \"remove_idle_qubits: Removes unused qubits from a quantum circuit\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def kicked_ising_1D(num_qubits: int, theta_x: float, theta_zz: float, s: int) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Parameters:\\n\",\n \" num_qubits (int): number of qubits on chain. \\n\",\n \" theta_x (float): Angle for RX gates.\\n\",\n \" theta_zz (float): Angle for RZZ gates.\\n\",\n \" s (int): Number of steps.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" QuantumCircuit: The resulting quantum circuit.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" graph = nx.path_graph(num_qubits)\\n\",\n \" qc = QuantumCircuit(num_qubits)\\n\",\n \"\\n\",\n \" # Precompute edge layers (alternating non-overlapping pairs)\\n\",\n \" edges = list(graph.edges())\\n\",\n \" even_edges = [(u, v) for (u, v) in edges if u % 2 == 0]\\n\",\n \" odd_edges = [(u, v) for (u, v) in edges if u % 2 == 1]\\n\",\n \"\\n\",\n \" for step in range(s):\\n\",\n \" # RX on all qubits\\n\",\n \" for q in range(num_qubits):\\n\",\n \" qc.rx(theta_x, q)\\n\",\n \" \\n\",\n \" # Apply even and odd layers separately\\n\",\n \" for edge_layer in [even_edges, odd_edges]:\\n\",\n \" for u, v in edge_layer:\\n\",\n \" qc.rzz(theta_zz, u, v)\\n\",\n \"\\n\",\n \" if step < s-1:\\n\",\n \" qc.barrier()\\n\",\n \" \\n\",\n \" return qc\\n\",\n \"\\n\",\n \"def remove_idle_qubits(qc:QuantumCircuit):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Removes qubits that are idle throughout the circuit\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" used = ({q for op in qc for q in op.qubits})\\n\",\n \" dag = circuit_to_dag(qc)\\n\",\n \" for q in set (qc.qubits) - used:\\n\",\n \" dag.remove_qubits (q)\\n\",\n \" return dag_to_circuit(dag)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 2: Kicked Ising Circuit Visualization \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"raw\"\n }\n },\n \"source\": [\n \"Here we set up the circuit experiment parameters and create a visualization of the circuit.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"n_qubits_ex1 = 20\\n\",\n \"n_steps = 3 \\n\",\n \"\\n\",\n \"circ = kicked_ising_1D(n_qubits_ex1, theta_x=np.pi/6, theta_zz=np.pi/3, s=n_steps)\\n\",\n \"\\n\",\n \"print(f\\\"Circuit 2q layers: {circ.depth(filter_function=lambda instr: len(instr.qubits) == 2)}\\\") \\n\",\n \"print(f\\\"\\\\nCircuit structure:\\\")\\n\",\n \"\\n\",\n \"circ.draw('mpl', scale=0.8, fold=-1)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 3: Simulation Parameters\\n\",\n \"\\n\",\n \"Here we define: \\n\",\n \"\\n\",\n \"1. A list of circuits to simulate (corresponding to the different number of time steps)\\n\",\n \"2. A list of observables to measure: $\\\\langle Z_0...Z_4 \\\\rangle$ and $\\\\langle Z_0...Z_{n-1} \\\\rangle$ and their labels \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"steps_range = range(1,9)\\n\",\n \"\\n\",\n \"circs_ex1=[] \\n\",\n \"for n_steps in (steps_range): \\n\",\n \" circs_ex1.append(kicked_ising_1D(n_qubits_ex1, theta_x=np.pi*0.14, theta_zz=np.pi*0.05, s=n_steps)) \\n\",\n \"\\n\",\n \"# Prepare pairs of (observables , labels)\\n\",\n \"observable_label_pairs = [\\n\",\n \" (SparsePauliOp.from_sparse_list([(\\\"Z\\\"*5, range(5),1)], n_qubits_ex1), r\\\"$Z_0Z_1...Z_{4}$)\\\"),\\n\",\n \" (SparsePauliOp.from_sparse_list([(\\\"Z\\\"*n_qubits_ex1, range(n_qubits_ex1),1)], n_qubits_ex1), r\\\"$Z_0Z_1...Z_{n-1}$\\\")\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 4: Compute ideal values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we compute the ideal expectation values for each observable using exact simulation with statevectors. \\n\",\n \"This gives us the theoretical values that we would obtain on a perfect quantum computer without any noise.\\n\",\n \"\\n\",\n \"The following dictionary holds the ideal/noisy expectation values for every observable per step: \\n\",\n \"graphs[\\\"ideal\\\" or \\\"noisy\\\"][observable label] = [Expectation of observable at steps_range[0], Expectation of observable at steps_range[1], ....]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"[Ungraded] Exercise 1.1:\\n\",\n \"\\n\",\n \"Complete the code for computing the ideal expectation value of `obs` after running the circuit `circ`\\n\",\n \"\\n\",\n \"Hint: [Statevector Documentation](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.quantum_info.Statevector)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"graphs={'ideal':{}}\\n\",\n \"graphs['ideal']['steps_range'] = steps_range\\n\",\n \"for circ in tqdm(circs_ex1): # loop over circuits [one step circuit, two steps circuit ,....]\\n\",\n \" for obs, label in observable_label_pairs: # loop over observables and their label\\n\",\n \" if label not in graphs['ideal']: # create list of ideal expectation values for every circuit\\n\",\n \" graphs['ideal'][label]=[]\\n\",\n \" # ---- TODO: Exercise 1.1 ---- \\n\",\n \" ideal_value = \\n\",\n \" # ---- End of TODO ----\\n\",\n \" graphs['ideal'][label].append(ideal_value)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Function to draw graphs from \\\"graphs\\\" object\\n\",\n \"\\n\",\n \"def graph_plots(graphs):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Input: graphs object (dictionary)\\n\",\n \" Display graphs of noisy (optional) and ideal values vs step \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" fig, axs = plt.subplots(1,2 ,figsize=(15, 4))\\n\",\n \" fig.subplots_adjust(wspace=0.5, hspace=0.3)\\n\",\n \" \\n\",\n \" j=0 # axis index\\n\",\n \" for obs, obs_label in observable_label_pairs:\\n\",\n \" ax=axs[j]\\n\",\n \" ax.set_title(\\\"Kicked Ising Model - ideal vs noisy\\\")\\n\",\n \" ax.set_ylabel(r\\\"$\\\\langle$\\\"+obs_label+r\\\"$\\\\rangle$\\\")\\n\",\n \" ax.xaxis.set_major_locator(MaxNLocator(integer=True)) # Makes x axis integers\\n\",\n \" ax.set_xlabel(\\\"Steps\\\")\\n\",\n \" ax.plot(graphs['ideal']['steps_range'], graphs['ideal'][obs_label], label='Ideal values', marker='.', color='black')\\n\",\n \" if 'noisy' in graphs: # Plot the noisy graph if it exists.\\n\",\n \" ax.errorbar(graphs['noisy']['steps_range'], graphs['noisy'][obs_label], np.array(graphs['noisy_std'][obs_label])**0.5, label='Noisy values', marker='.', capsize=3)\\n\",\n \" ax.legend()\\n\",\n \" ax.grid()\\n\",\n \" j=j+1\\n\",\n \" \\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"# Running the function\\n\",\n \"graph_plots(graphs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Build Intuition:\\n\",\n \" \\n\",\n \"Which of the two observables will be more affected by noise?\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 5: Compute noisy values with error bars\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's add **realistic quantum noise** to our simulation. We'll use an AerSimulator based on IBM's Fake Fez backend, which includes realistic noise models based on actual quantum hardware. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"GPU = 'GPU' in AerSimulator().available_devices() # Use GPU if available.\\n\",\n \"fake_backend = FakeFez()\\n\",\n \"basis_gates = fake_backend.configuration().basis_gates\\n\",\n \"\\n\",\n \"noisy_backend = AerSimulator(\\n\",\n \" method='matrix_product_state',\\n\",\n \" noise_model=NoiseModel.from_backend(fake_backend),\\n\",\n \" basis_gates=basis_gates,\\n\",\n \" coupling_map=fake_backend.configuration().coupling_map,\\n\",\n \" properties=fake_backend.properties(),\\n\",\n \" device='GPU'*GPU + 'CPU'*(1-GPU),\\n\",\n \")\\n\",\n \"\\n\",\n \"num_shots = 1000 #### <------ Edit if too slow\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's compute the noisy expectation values by running the circuits on the noisy simulator with finite shots. We calculate both the expectation values and their standard deviations to understand the statistical uncertainty in our measurements.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"[Ungraded] Exercise 1.2:\\n\",\n \"\\n\",\n \"Complete the code for running \\\"num_shots\\\" shots of \\\"transpiled_circ\\\" on \\\"noisy_backend\\\" and extract the \\\"counts\\\" dictionary from the result.\\n\",\n \"\\n\",\n \"Change the number of shots if the simulation is too slow.\\n\",\n \" \\n\",\n \"**Hint:** [AerSimulator Documentation](https://qiskit.github.io/qiskit-aer/tutorials/1_aersimulator.html)\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"lines_to_next_cell\": 2\n },\n \"outputs\": [],\n \"source\": [\n \"graphs['noisy'] = {'steps_range':steps_range}\\n\",\n \"graphs['noisy_std'] = {}\\n\",\n \"\\n\",\n \"for circ in tqdm(circs_ex1): \\n\",\n \" transpiled_circ = transpile(circ, basis_gates=basis_gates, coupling_map=fake_backend.coupling_map) \\n\",\n \" transpiled_circ = remove_idle_qubits(transpiled_circ)\\n\",\n \"\\n\",\n \" transpiled_circ.measure_all()\\n\",\n \" # ---- TODO: Exercise 1.2 ---- \\n\",\n \" counts = \\n\",\n \" # ---- End of TODO ----\\n\",\n \"\\n\",\n \" for obs, label in observable_label_pairs:\\n\",\n \" if label not in graphs['noisy']:\\n\",\n \" graphs['noisy'][label]=[]\\n\",\n \" graphs['noisy_std'][label]=[]\\n\",\n \" \\n\",\n \" noisy_obs_expectation = qiskit.result.sampled_expectation_value(counts,obs) # Convert counts to expectation value\\n\",\n \" noisy_obs_variance = (1-qiskit.result.sampled_expectation_value(counts,obs)**2)/num_shots # Only true for Paulis: P^2=I\\n\",\n \" graphs['noisy'][label].append(noisy_obs_expectation)\\n\",\n \" graphs['noisy_std'][label].append((noisy_obs_variance)**0.5) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 6: Plot the results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we visualize the results by plotting both the ideal and noisy expectation values as a function of the number of Trotter steps.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"graph_plots(graphs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Build Intuition:\\n\",\n \" \\n\",\n \"- As circuits get deeper, quantum noise accumulates and the gap between ideal and noisy values grows. QESEM allows to run deeper circuits and still get meaningful results.\\n\",\n \"- The decay of the noisy value is controlled by the number of noisy operations the observable is sensitive to. The $\\\\langle Z0...Z19 \\\\rangle$ observable decays much more than $\\\\langle Z0...Z4 \\\\rangle$ because its lightcone spans the entire circuit.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Section 2: Introduction to QESEM\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we've seen how dramatically noise affects quantum computations, let's explore **QESEM** - a powerful technique to reduce these errors.\\n\",\n \"\\n\",\n \"### How QESEM Works\\n\",\n \"\\n\",\n \"QESEM combines two approaches:\\n\",\n \"\\n\",\n \"1. **Error Suppression**: Reduce the unitary part of the noise. It is based on QESEM's characterization.\\n\",\n \"2. **Error Mitigation**: Use classical post-processing to estimate what the results would have been without noise.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### QESEM Function Parameters Reference\\n\",\n \"During this section we will guide you through the main function parameters and explain each one. \\n\",\n \"There are more advanced parameters which we won't cover, see an elaborate explanation about all the function parameters [here](https://docs.quantum.ibm.com/guides/qedma-qesem#function-parameters).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.1 Exercise 2: Applying QESEM on a simple circuit\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 1: Initialize QESEM Function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Qiskit Functions Catalog\\n\",\n \"# your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"# your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"# catalog = QiskitFunctionsCatalog(\\n\",\n \"# channel=\\\"ibm_quantum_platform\\\",\\n\",\n \"# token=your_api_key,\\n\",\n \"# instance=your_crn,\\n\",\n \"# )\\n\",\n \"\\n\",\n \"qesem_function = catalog.load(\\\"qedma/qesem\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 2: Prepare Circuit and Observables\\n\",\n \"The following circuit prepares the state $|\\\\psi\\\\rangle = 0.6|0000\\\\rangle+0.8|1111\\\\rangle $\\n\",\n \"\\n\",\n \"We will measure two observables: \\n\",\n \"avg_magnetization_ex2 = $\\\\frac{1}{4} \\\\sum_j Z_j$ \\n\",\n \"all_z_ex2 = $Z_0...Z_3$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"circ_ex2 = qiskit.QuantumCircuit(4)\\n\",\n \"circ_ex2.ry(0.927*2, 0) \\n\",\n \"circ_ex2.cx(0, 1)\\n\",\n \"circ_ex2.cx(1, 2)\\n\",\n \"circ_ex2.cx(2, 3)\\n\",\n \"\\n\",\n \"circ_ex2.draw(\\\"mpl\\\", scale=0.7)\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"avg_magnetization_ex2 = SparsePauliOp.from_sparse_list(\\n\",\n \" [(\\\"Z\\\", [q], 1 / 4) for q in range(4)], num_qubits=4)\\n\",\n \"\\n\",\n \"all_z_ex2 = SparsePauliOp.from_sparse_list(\\n\",\n \" [(\\\"Z\\\"*4, range(4),1)], 4)\\n\",\n \"\\n\",\n \"obs_list_ex2 = [avg_magnetization_ex2, all_z_ex2]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 3: Empirical Time Estimation\\n\",\n \"\\n\",\n \"Users would typically want to know how much QPU time is required for their experiment.\\n\",\n \"However, this is considered a hard problem for classical computers. \\n\",\n \"QESEM offers two modes of time estimation to inform users about the feasibility of their experiments:\\n\",\n \"1. Analytical time estimation - gives a very rough estimation and requires no QPU time. This can be used to test if a transpilation pass would potentially reduce the QPU time. \\n\",\n \"2. Empirical time estimation (demonstrated here) - gives a pretty good estimation and uses a few minutes of QPU time.\\n\",\n \"\\n\",\n \"In both cases, QESEM outputs the time estimation for reaching the required precision for all observables. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below may submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed. For learning purposes, you can use a fake QPU.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 0 minutes 0 seconds (based on tests on `fake_fez`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Start a job for empirical time estimation\\n\",\n \"\\n\",\n \"estimation_job_ex2 = qesem_function.run(\\n\",\n \" pubs=[(circ_ex2, obs_list_ex2)],\\n\",\n \" instance=your_crn,\\n\",\n \" backend_name='fake_fez', # E.g. \\\"ibm_brisbane\\\"\\n\",\n \" options={\\n\",\n \" \\\"estimate_time_only\\\": \\\"empirical\\\", # \\\"empirical\\\" - gets actual time estimates without running full mitigation\\n\",\n \" \\\"default_precision\\\": 0.01, # Default precision for all observable. Precision per observable can be defined in the pubs parameter.\\n\",\n \" \\\"max_execution_time\\\": 120, # Limits the QPU time, specified in seconds.\\n\",\n \" }\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get the result object (blocking method). Use job.status() in a loop for non-blocking. \\n\",\n \"# This takes a 1-3 minutes\\n\",\n \"result = estimation_job_ex2.result() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print (f\\\"Empirical time estimation (sec): {result[0].metadata['time_estimation_sec']}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"raw\"\n }\n },\n \"source\": [\n \"### Step 4: Use QESEM to estimate the expectation values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 2:\\n\",\n \"\\n\",\n \"Use QESEM to estimate the expectation values of `avg_magnetization_ex2` and `all_z_ex2` observables on the state generated by `circ_ex2`.\\n\",\n \"Set the precision to `0.1`, use `fake_fez` as the backend, and set the maximum QPU time to `15` minutes (`900` seconds).\\n\",\n \"\\n\",\n \"**Hint:** Remove the options key `estimate_time_only`\\n\",\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below may submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed. For learning purposes, you can use a fake QPU.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 0 minutes 0 seconds (based on tests on `fake_fez`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if message == correct_message: # Run the job only if parameters are correct\\n\",\n \" # Start a job\\n\",\n \" job_ex2 = qesem_function.run(\\n\",\n \" pubs=pubs_ex2,\\n\",\n \" instance=instance_ex2,\\n\",\n \" backend_name=backend_name_ex2, \\n\",\n \" options= options_ex2\\n\",\n \" )\\n\",\n \" print (\\\"Job sent.\\\")\\n\",\n \"else:\\n\",\n \" print (\\\"Parameter check failed, job did not start.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 5: Reading the results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"lines_to_next_cell\": 2\n },\n \"outputs\": [],\n \"source\": [\n \"result = job_ex2.result() # Blocking - takes 3-5 minutes\\n\",\n \"noisy_results = result[0].metadata[\\\"noisy_results\\\"]\\n\",\n \"for en,obs in enumerate(obs_list_ex2):\\n\",\n \" print (\\\"-\\\"*10)\\n\",\n \" print (\\\"Observable: \\\"+['Average Magnetization','ZZZZ'][en])\\n\",\n \" print (f\\\"Ideal: {Statevector(circ_ex2).expectation_value(obs).real}\\\")\\n\",\n \" print (f\\\"Noisy: {noisy_results.evs[en]} \\\\u00B1 {noisy_results.stds[en]}\\\")\\n\",\n \" print (f\\\"QESEM: {result[0].data.evs[en]} \\\\u00B1 {result[0].data.stds[en]}\\\")\\n\",\n \" \\n\",\n \"print (\\\"-\\\"*10)\\n\",\n \"print (f\\\"Gate fidelities found: {result[0].metadata['gate_fidelities']}\\\") # Some of the data gathered during a QESEM run.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Results as a graph\\n\",\n \"x = np.arange(2) # Column position\\n\",\n \"width = 0.06 \\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(figsize=(4,2.5*1.5))\\n\",\n \"\\n\",\n \"# Plot the bars side by side\\n\",\n \"ax.bar(x - width, [Statevector(circ_ex2).expectation_value(obs).real for obs in [avg_magnetization_ex2, all_z_ex2]], width, label='Ideal')\\n\",\n \"ax.bar(x, noisy_results.evs, width, label='Noisy', yerr=noisy_results.stds, capsize=6)\\n\",\n \"ax.bar(x + width, result[0].data.evs, width, label='QESEM', yerr=result[0].data.stds, capsize=6)\\n\",\n \"\\n\",\n \"ax.set_xticks(x)\\n\",\n \"ax.set_xticklabels(['Average Magnetization','ZZZZ'])\\n\",\n \"ax.set_title(r\\\"Comparing Ideal, Noisy and QESEM for $|\\\\psi\\\\rangle = 0.6|0000\\\\rangle+0.8|1111\\\\rangle$ \\\")\\n\",\n \"ax.legend()\\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.grid(axis='y')\\n\",\n \"plt.axhline(0, color='black', linewidth=2.5) # y=0, bold black line\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2 Key Concepts\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2.1 Volume and Active Volume\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A key figure of merit for quantifying the hardness of both error mitigation and classical simulation for a given circuit and observable is **active volume**: The number of two-qubit gates affecting the observable in the circuit. Different circuits and observables have different **active volumes**, which affects how hard they are to error-mitigate.\\n\",\n \"\\n\",\n \"The active volume depends on:\\n\",\n \"- Circuit depth and width\\n\",\n \"- Observable weight (number of non-identity Pauli operators)\\n\",\n \"- Circuit structure (light cone of the observable)\\n\",\n \"\\n\",\n \"For further details, see the talk from the [2024 IBM Quantum Summit](https://www.youtube.com/watch?v=Hd-IGvuARfE&t=1730s&ab_channel=IBMResearch).\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2.2 QESEM Runtime Overhead\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The QPU time behaves roughly like:\\n\",\n \"$$T_{QPU} \\\\approx \\\\left(\\\\frac{A}{\\\\epsilon^2}\\\\right) \\\\times e^{C \\\\times IF \\\\times V_{active}} + B$$ \\n\",\n \"- B overhead of gate optimization and error characterization\\n\",\n \"- precision $\\\\epsilon$ absolute error in expectation value\\n\",\n \"- $ IF \\\\times V_{active} $ infidelity-per-gate times active volume. The active volume only includes gates within the active light-cone.\\n\",\n \"\\n\",\n \"The **overhead** of error mitigation tells us how many additional quantum resources we need to achieve a target precision.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.3 Exercise 3: QPU time vs. Active Volume\\n\",\n \"In this section we'll fix the number of steps and the precision, and compare the QPU time for the two observables in Exercise 1. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 3:\\n\",\n \"\\n\",\n \"Use QESEM's **analytical** time estimation for measuring the expectation of $\\\\langle Z_0...Z_4\\\\rangle$ and $\\\\langle Z_0...Z_{n-1}\\\\rangle$ of the state generated by a few steps of the Kicked Ising model.\\n\",\n \"\\n\",\n \"Set the `default_precision` to `0.02`.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set the options parameter for the QESEM job by the instruction:\\n\",\n \"\\n\",\n \"# ---- TODO: Exercise 3 ---- \\n\",\n \"options_ex3={\\n\",\n \" \\\"estimate_time_only\\\": ,\\n\",\n \" \\\"default_precision\\\": ,\\n\",\n \"}\\n\",\n \"# ---- End of TODO ----\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test parameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"message = grade_ex3(options_ex3)\\n\",\n \"print (message)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Start a jobs only if parameter check passed\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below may submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed. For learning purposes, you can use a fake QPU.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 0 minutes 0 seconds (based on tests on `fake_fez`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if message == correct_message:\\n\",\n \" print(\\\"Starting jobs\\\")\\n\",\n \" selected_step = 6 # number of steps in the circuit. \\n\",\n \" num_qubits_ex3 = 20\\n\",\n \" \\n\",\n \" \\n\",\n \" circ = kicked_ising_1D(num_qubits_ex3, theta_x=np.pi*0.14, theta_zz=np.pi*0.05, s=selected_step)\\n\",\n \" \\n\",\n \" # Prepare pairs of (observables , labels)\\n\",\n \" observable_label_pairs_ex3 = [\\n\",\n \" (SparsePauliOp.from_sparse_list([(\\\"Z\\\"*5, range(5),1)], num_qubits_ex3), r\\\"$Z_0Z_1...Z_{4}$\\\"),\\n\",\n \" (SparsePauliOp.from_sparse_list([(\\\"Z\\\"*num_qubits_ex3, range(num_qubits_ex3),1)], num_qubits_ex3), r\\\"$Z_0Z_1...Z_{n-1}$\\\")\\n\",\n \" ] \\n\",\n \" \\n\",\n \" volume_jobs = []\\n\",\n \" for observable, obs_label in observable_label_pairs_ex3:\\n\",\n \" # run analytical time estimation on each observable separately\\n\",\n \" job = qesem_function.run(\\n\",\n \" pubs=[(circ, [observable])],\\n\",\n \" instance=your_crn,\\n\",\n \" backend_name=\\\"fake_fez\\\", \\n\",\n \" options=options_ex3\\n\",\n \" )\\n\",\n \" volume_jobs.append(job)\\n\",\n \"else:\\n\",\n \" print (\\\"Parameters check failed. Jobs did not start\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Print the analytical time estimation. Takes about 3 minutes\\n\",\n \"job_index=0\\n\",\n \"for observable, obs_label in observable_label_pairs_ex3:\\n\",\n \" # Get the result and extract time estimation\\n\",\n \" result = volume_jobs[job_index].result()\\n\",\n \" job_index+=1\\n\",\n \" print (f\\\"Analytical time estimation for observable {obs_label}: {result[0].metadata['time_estimation_sec']/60} min\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Build Intuition: \\n\",\n \"\\n\",\n \"While the analytical time estimation is very rough (resolution of 30 minutes) and pessimistic, it still captures the difference in the active volumes of the two observables fairly quickly, and without using any QPU time.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.4 Exercise 4: Exploring the $T \\\\propto \\\\frac{1}{\\\\varepsilon^2}$ Relationship\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this exercise, we will investigate **how QPU time scales with precision requirements**.\\n\",\n \"### Step 1: Create and visualize the test circuit\\n\",\n \"\\n\",\n \"We'll create a single Kicked Ising circuit to test the precision-time relationship. This circuit will have enough complexity to show clear scaling behavior while remaining manageable for the experiment.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 4.1:\\n\",\n \"\\n\",\n \"Complete the code. Use the function `kicked_ising_1D()` defined in Exercise 1 to create the circuit `circ_ex4`. Be sure to use the variables defined below `n_qubits_ex4`, `n_steps_ex4`, `theta_x_ex4`, `theta_zz_ex4`.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"n_qubits_ex4 = 5 # Number of qubits (enough to see scaling effects)\\n\",\n \"n_steps_ex4 = 10 # Number of Trotter steps (creates sufficient circuit depth)\\n\",\n \"theta_x_ex4 = np.pi/6 # Rotation angle for X gates (transverse field strength)\\n\",\n \"theta_zz_ex4 = np.pi/3 # Rotation angle for ZZ gates (interaction strength)\\n\",\n \"\\n\",\n \"# Create the Kicked Ising circuit that will be used for precision testing\\n\",\n \"# ---- TODO: Exercise 4.1 ---- \\n\",\n \"circ_ex4 = \\n\",\n \"# ---- End of TODO ----\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"message = grade_ex_4_1(circ_ex4)\\n\",\n \"print (message)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(f\\\"Circuit 2q layers: {circ_ex4.depth(filter_function=lambda instr: len(instr.qubits) == 2)}\\\") \\n\",\n \"print(f\\\"\\\\nCircuit structure:\\\")\\n\",\n \"\\n\",\n \"circuit_drawer = circ_ex4.draw('mpl', scale=1, fold=-1)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 2: Submit time estimation jobs with different precision values\\n\",\n \"\\n\",\n \"We'll submit multiple QESEM jobs, each with a different precision requirement (ε). The range from 0.005 to 0.06 covers both high-precision (expensive) and moderate-precision (cheaper) regimes.\\n\",\n \"\\n\",\n \"The observable: $\\\\langle Z_0...Z_4 \\\\rangle$. \\n\",\n \"\\n\",\n \"\\n\",\n \"**Empirical time estimation**: We will use \\\"empirical\\\" time estimation this setting performs a small execution on the QPU without running full mitigation. This usually takes a few minutes per estimation and is more reliable than the \\\"analytical\\\" estimation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"backend_name_fake = \\\"fake_fez\\\"\\n\",\n \"precisions_ex4 = [0.005, 0.01, 0.03, 0.05] \\n\",\n \"observable_ex4 = qiskit.quantum_info.SparsePauliOp.from_sparse_list([(\\\"Z\\\"*n_qubits_ex4, range(n_qubits_ex4),1)], n_qubits_ex4) # Z_0..Z_{n-1}\\n\",\n \"\\n\",\n \"# Initialize list to store job IDs\\n\",\n \"jobs_ex4 = []\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below may submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed. For learning purposes, you can use a fake QPU.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 0 minutes 0 seconds (based on tests on `fake_fez`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if message == correct_message:\\n\",\n \" # Loop through each precision value\\n\",\n \" for precision in precisions_ex4:\\n\",\n \" print(f\\\"Submitting job with precision: {precision:.3f}\\\")\\n\",\n \" \\n\",\n \" # Start a job with current precision\\n\",\n \" job = qesem_function.run(\\n\",\n \" pubs=[(circ_ex4, [observable_ex4], [], precision)],\\n\",\n \" instance=your_crn,\\n\",\n \" backend_name=backend_name_fake, # E.g. \\\"ibm_brisbane\\\"\\n\",\n \" options={\\n\",\n \" \\\"estimate_time_only\\\": \\\"empirical\\\", # \\\"empirical\\\" - gets actual time estimates without running full mitigation\\n\",\n \" \\\"max_execution_time\\\": 240, # limits the QPU time, specified in seconds.\\n\",\n \" }\\n\",\n \" )\\n\",\n \" \\n\",\n \" # Store the job ID\\n\",\n \" jobs_ex4.append(job)\\n\",\n \" print(f\\\"Job submitted with ID: {job.job_id}\\\")\\n\",\n \" \\n\",\n \" print(f\\\"\\\\nAll jobs submitted!\\\")\\n\",\n \" \\n\",\n \" start_time = time.time()\\n\",\n \"else:\\n\",\n \" print (\\\"Parameter check failed. Jobs did not start\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 3: Monitor job progress\\n\",\n \"\\n\",\n \"Since time estimation jobs can take several minutes to complete, we'll monitor their progress. This gives us real-time feedback on when all jobs are finished. Might take approximately 14 minutes (tested on fake_fez).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(\\\"Monitoring job completion... (this takes around 14 minutes, tested on fake_fez, you may break and return later)\\\")\\n\",\n \"print(f\\\"Total jobs: {len(jobs_ex4)}\\\")\\n\",\n \"print(\\\"-\\\" * 30)\\n\",\n \"\\n\",\n \"while True: # Each job performs device characterization to estimate QPU time requirements\\n\",\n \" # Count how many jobs are done\\n\",\n \" done_count = 0\\n\",\n \" for job in jobs_ex4: # Jobs with smaller ε values may take longer to process\\n\",\n \" if job.status() == \\\"DONE\\\":\\n\",\n \" done_count += 1\\n\",\n \" \\n\",\n \" # Show current status\\n\",\n \" elapsed_time = time.time() - start_time\\n\",\n \" timestamp = datetime.now().strftime('%H:%M:%S')\\n\",\n \" print(f\\\"[{timestamp}] {done_count}/{len(jobs_ex4)} jobs completed (elapsed: {elapsed_time/60:.1f} min)\\\")\\n\",\n \" \\n\",\n \" # Check if all jobs are done\\n\",\n \" if done_count == len(jobs_ex4):\\n\",\n \" print(f\\\"\\\\nAll jobs completed!\\\")\\n\",\n \" break\\n\",\n \" \\n\",\n \" # Wait 1 minute before next check\\n\",\n \" time.sleep(60)\\n\",\n \"\\n\",\n \"end_time = time.time()\\n\",\n \"print(f\\\"Total time: {(end_time - start_time)/60:.1f} minutes\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"raw\"\n }\n },\n \"source\": [\n \"### Step 4: Extract time estimation results\\n\",\n \"\\n\",\n \"Once all jobs are complete, we extract the QPU time estimates from each job's metadata. These time estimates represent how long QESEM would need to achieve the requested precision.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Once jobs are complete, extract time estimations\\n\",\n \"time_estimations = []\\n\",\n \"\\n\",\n \"print(\\\"\\\\nExtracting time estimations from jobs...\\\")\\n\",\n \"for i, job in enumerate(jobs_ex4): # Loop through all completed jobs\\n\",\n \" print(f\\\"Getting time estimation for job {i+1}\\\")\\n\",\n \" \\n\",\n \" # Get the result and extract time estimation\\n\",\n \" time_estimate_result = job.result()\\n\",\n \" time_estimation_sec = time_estimate_result[0].metadata['time_estimation_sec']\\n\",\n \" \\n\",\n \" # Store the time estimation\\n\",\n \" time_estimations.append(time_estimation_sec)\\n\",\n \" print(f\\\"Time estimation: {time_estimation_sec} seconds\\\")\\n\",\n \"\\n\",\n \"print(f\\\"\\\\nAll time estimations extracted!\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"vscode\": {\n \"languageId\": \"raw\"\n }\n },\n \"source\": [\n \"\\n\",\n \"### Step 5: Visualize the results\\n\",\n \"\\n\",\n \"We'll create a standard linear plot to visualize how QPU time varies with precision. This gives us an intuitive view of the relationship.\\n\",\n \"\\n\",\n \"**Expected Pattern**: Time estimates should increase dramatically as precision requirements become more stringent (smaller ε).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define the plotting function to visualize QPU time vs precision\\n\",\n \"def plot_qpu_time_vs_precision(precisions, time_estimations_sec, title_suffix=\\\"\\\"):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Plot QPU time estimation vs precision with curve fitting\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" precisions: Array of precision values (ε)\\n\",\n \" time_estimations_sec: Array of time estimations in seconds\\n\",\n \" title_suffix: Optional suffix to add to the plot title\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Convert time estimations from seconds to minutes\\n\",\n \" time_estimations_min = np.array(time_estimations_sec) / 60\\n\",\n \"\\n\",\n \" # Define the fitting function: T = A/precision^2 + B\\n\",\n \" def fit_function(precision, A, B):\\n\",\n \" return A / (precision**2) + B\\n\",\n \"\\n\",\n \" # Perform the curve fit using time\\n\",\n \" popt, pcov = curve_fit(fit_function, precisions, time_estimations_min)\\n\",\n \" A_fit, B_fit = popt\\n\",\n \"\\n\",\n \" # Create a smooth curve for plotting the fit\\n\",\n \" precision_smooth = np.linspace(min(precisions), max(precisions), 100)\\n\",\n \" time_fit = fit_function(precision_smooth, A_fit, B_fit)\\n\",\n \"\\n\",\n \" plt.figure(figsize=(8, 5))\\n\",\n \" plt.plot(precisions, time_estimations_min, 'o', markersize=8, color='blue', label='Data')\\n\",\n \" plt.plot(precision_smooth, time_fit, '-', linewidth=2, color='red', label=f'Fit: T = {A_fit:.3f}/ε² + {B_fit:.1f}')\\n\",\n \" plt.xlabel('Precision (ε)', fontsize=12)\\n\",\n \" plt.ylabel('QPU Time Estimation (minutes)', fontsize=12)\\n\",\n \" plt.title(f'QESEM QPU Time Estimation vs Precision{title_suffix}', fontsize=14)\\n\",\n \"\\n\",\n \" plt.locator_params(axis='y', nbins=10) # Increase number of y-axis ticks\\n\",\n \" plt.grid(True, alpha=0.3)\\n\",\n \" plt.legend()\\n\",\n \"\\n\",\n \" # Display fit parameters\\n\",\n \" print(f\\\"Fitted parameters:\\\")\\n\",\n \" print(f\\\"A = {A_fit:.3f}\\\")\\n\",\n \" print(f\\\"B = {B_fit:.2f}\\\")\\n\",\n \" print(f\\\"Function: T = {A_fit:.3f}/ε² + {B_fit:.2f}\\\")\\n\",\n \" print(f\\\"Data points: {len(precisions)}\\\")\\n\",\n \"\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"# Create the initial plot with Steps 2-5 data\\n\",\n \"plot_qpu_time_vs_precision(precisions_ex4, time_estimations)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And indeed we can see the expected functional dependence of:\\n\",\n \"$$T_{QPU} \\\\approx \\\\frac{\\\\tilde{A}}{\\\\epsilon^2} + B$$\\n\",\n \"Where:\\n\",\n \"- $\\\\tilde A={A} \\\\times e^{C \\\\times IF \\\\times V_{active}}$\\n\",\n \"- $A$ is a constant that depends on the circuit and the expectation value\\n\",\n \"- $B$ is overhead due to gate optimization and error characterization\\n\",\n \"- $C$ is a constant between 2-4 that depends on the details of the error mitigation method. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 6: Execute with error mitigation on a real hardware\\n\",\n \"\\n\",\n \"Now let's run the actual QESEM error mitigation on our circuit.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" Exercise 4.2:\\n\",\n \"\\n\",\n \"Look at the results of the QPU time vs precision graph above and think which precision you would like for your execution on real quantum hardware.\\n\",\n \"Complete the QESEM job parameters accordingly.\\n\",\n \"\\n\",\n \"We will run an empirical time estimation for your desired precision to predict QPU resource requirements before running the full QESEM mitigation. This will take less than 5 minutes QPU time.\\n\",\n \" \\n\",\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"This exercise should use use a real QPU (e.g. `ibm_fez). Please ensure you intend to proceed. \\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 1 minutes 45 seconds (based on tests on `ibm_fez`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO: Exercise 4.2 ---- \\n\",\n \"precision_ex4 =\\n\",\n \"pubs_ex4 = [( , , [], precision_ex4)] # Complete the pubs\\n\",\n \"instance_ex4 =\\n\",\n \"backend_name_ex4 =\\n\",\n \"options_ex4 = {\\n\",\n \" \\\"estimate_time_only\\\": ,\\n\",\n \" \\\"max_execution_time\\\": ,\\n\",\n \" \\\"execution_mode\\\": # \\\"batch\\\" or \\\"session\\\" for less QPU time or faster execution\\n\",\n \"}\\n\",\n \"# ---- End of TODO ----\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test parameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"message = grade_ex_4_2(\\n\",\n \" precision_ex4= precision_ex4,\\n\",\n \" pubs_ex4= pubs_ex4,\\n\",\n \" backend_name_ex4=backend_name_ex4,\\n\",\n \" options_ex4=options_ex4\\n\",\n \")\\n\",\n \"print(message)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Start a job only if parameter check passed\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if message == correct_message: # Run the job only if parameters are correct\\n\",\n \" # Start a job\\n\",\n \" # Get time estimation first (quick empirical check)\\n\",\n \" time_est_job = qesem_function.run(\\n\",\n \" pubs = pubs_ex4,\\n\",\n \" instance = instance_ex4,\\n\",\n \" backend_name = backend_name_ex4,\\n\",\n \" options = options_ex4\\n\",\n \" )\\n\",\n \"\\n\",\n \" print(f\\\"Time estimation job submitted: {time_est_job.job_id}\\\")\\n\",\n \" print(f\\\"with precision: {precision_ex4}\\\")\\n\",\n \" \\n\",\n \"else:\\n\",\n \" print (message)\\n\",\n \" print (\\\"Parameter check failed. Job did not start.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Tip: Use this code cell to check your job's status.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"time_est_job.status()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Extract the time estimation from Step 6a and add it to our precision vs. QPU time graph to see how the new data point fits the T ∝ 1/ε² relationship.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get the time estimation result\\n\",\n \"time_est_result = time_est_job.result()\\n\",\n \"step6_time_estimation = time_est_result[0].metadata['time_estimation_sec']\\n\",\n \"\\n\",\n \"print(f\\\"Step 6 time estimation: {step6_time_estimation} seconds\\\")\\n\",\n \"\\n\",\n \"# Add the new data point to existing arrays\\n\",\n \"precisions_updated = np.append(precisions_ex4, precision_ex4)\\n\",\n \"time_estimations_updated = np.append(time_estimations, step6_time_estimation)\\n\",\n \"\\n\",\n \"# Plot the updated graph with the new data point\\n\",\n \"print(\\\"\\\\nUpdated graph with Step 6 data point:\\\")\\n\",\n \"plot_qpu_time_vs_precision(precisions_updated, time_estimations_updated, \\\" (Including Step 6)\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" Exercise 4.3: \\n\",\n \"\\n\",\n \"If you are satisfied with the time estimation go ahead and run the full QESEM error mitigation on our 5-qubit, 10-step Kicked Ising circuit with precision ε. This will take QPU time according to your estimation result.\\n\",\n \"Complete the QESEM job parameters accordingly. \\n\",\n \" \\n\",\n \"
\\n\",\n \" \\n\",\n \" Tip: Use `max_execution_time` option, this allows you to limit the QPU time, specified in seconds. After the time limit is reached, QESEM stops sending new circuits. Circuits that have already been sent continue running, you can see detailed explanation [here](https://docs.quantum.ibm.com/guides/qedma-qesem#function-parameters).\\n\",\n \"\\n\",\n \"Note: QESEM will end its run when it reaches the target precision or when it reaches `max_execution_time`, whichever comes first.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"precision_ex4_miti = precision_ex4\\n\",\n \"pubs_ex4_miti = pubs_ex4\\n\",\n \"instance_ex4_miti = instance_ex4\\n\",\n \"backend_name_ex4_miti = backend_name_ex4\\n\",\n \"\\n\",\n \"# ---- TODO: Exercise 4.3 ---- \\n\",\n \"options_ex4_miti = {\\n\",\n \" \\\"max_execution_time\\\": , # In seconds\\n\",\n \" \\\"execution_mode\\\": # \\\"batch\\\" or \\\"session\\\" for less QPU time or faster execution\\n\",\n \"}\\n\",\n \"# ---- End of TODO ----\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Test parameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"message = grade_ex_4_3(\\n\",\n \" precision_ex4= precision_ex4_miti,\\n\",\n \" pubs_ex4= pubs_ex4_miti,\\n\",\n \" backend_name_ex4=backend_name_ex4_miti,\\n\",\n \" options_ex4=options_ex4_miti\\n\",\n \")\\n\",\n \"print(message)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Start a job only if parameter check passed\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below may submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 3 minutes 0 seconds (based on tests on `ibm_fez`)\\n\",\n \"\\n\",\n \"
\\n\",\n \"Warning: The QPU time estimation changes from one backend to another. Therefore, when executing the QESEM function, make sure to run it on the same backend that was selected when obtaining the QPU time estimation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if message == correct_message: # Run the job only if parameters are correct\\n\",\n \" # Start a job\\n\",\n \" # Submit actual mitigation job\\n\",\n \" mitigation_job = qesem_function.run(\\n\",\n \" pubs=pubs_ex4_miti,\\n\",\n \" instance=instance_ex4_miti,\\n\",\n \" backend_name=backend_name_ex4_miti,\\n\",\n \" options=options_ex4_miti\\n\",\n \" )\\n\",\n \" print(f\\\"QESEM mitigation job submitted: {mitigation_job.job_id}\\\")\\n\",\n \" print(\\\"Job is running... This may take several minutes.\\\")\\n\",\n \" \\n\",\n \"else:\\n\",\n \" print (message)\\n\",\n \" print (\\\"Parameter check failed, job did not start.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Tip: Use this code cell to check your job's status.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mitigation_job.status()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Extract and analyze the QESEM results, comparing ideal, noisy, and error-mitigated expectation values to demonstrate the effectiveness of QESEM.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Read and analyze results\\n\",\n \"print(\\\"Reading QESEM results...\\\")\\n\",\n \"\\n\",\n \"# Get the mitigated results\\n\",\n \"qesem_result = mitigation_job.result()\\n\",\n \"print(f\\\"Job completed successfully!\\\")\\n\",\n \"\\n\",\n \"# Extract results for our observable\\n\",\n \"pub_result = qesem_result[0]\\n\",\n \"mitigated_expectation = pub_result.data.evs[0] \\n\",\n \"mitigated_std = pub_result.data.stds[0]\\n\",\n \"\\n\",\n \"# Get noisy results for comparison\\n\",\n \"noisy_results = pub_result.metadata[\\\"noisy_results\\\"]\\n\",\n \"noisy_expectation = noisy_results.evs[0]\\n\",\n \"noisy_std = noisy_results.stds[0]\\n\",\n \"\\n\",\n \"# Calculate ideal value for comparison\\n\",\n \"ideal_expectation = Statevector(circ_ex4).expectation_value(observable_ex4).real\\n\",\n \"\\n\",\n \"# Display comprehensive results summary\\n\",\n \"print(\\\"\\\\n\\\" + \\\"=\\\"*60)\\n\",\n \"print(\\\"EXERCISE 4 - QESEM RESULTS SUMMARY\\\")\\n\",\n \"print(\\\"=\\\"*60)\\n\",\n \"print(f\\\"Observable: Global Z measurement (Z^⊗{n_qubits_ex4})\\\")\\n\",\n \"print(f\\\"Circuit: {n_steps_ex4}-step Kicked Ising, {n_qubits_ex4} qubits\\\")\\n\",\n \"print(f\\\"Precision target: {precision_ex4}\\\")\\n\",\n \"print(\\\"-\\\"*60)\\n\",\n \"print(f\\\"Ideal value: {ideal_expectation:.6f}\\\")\\n\",\n \"print(f\\\"Noisy value: {noisy_expectation:.6f} ± {noisy_std:.6f}\\\")\\n\",\n \"print(f\\\"QESEM value: {mitigated_expectation:.6f} ± {mitigated_std:.6f}\\\")\\n\",\n \"print(\\\"-\\\"*60)\\n\",\n \"print(f\\\"Noisy error: {abs(noisy_expectation - ideal_expectation):.6f}\\\")\\n\",\n \"print(f\\\"QESEM error: {abs(mitigated_expectation - ideal_expectation):.6f}\\\")\\n\",\n \"print(f\\\"Error reduction: {abs(noisy_expectation - ideal_expectation)/abs(mitigated_expectation - ideal_expectation if mitigated_expectation != ideal_expectation else 1):.1f}x\\\")\\n\",\n \"print(\\\"-\\\"*60)\\n\",\n \"print(f\\\"QESEM within target precision: {'✓' if abs(mitigated_expectation - ideal_expectation) <= precision_ex4 else '✗'}\\\")\\n\",\n \"print(\\\"-\\\"*60)\\n\",\n \"# Additional detailed metrics\\n\",\n \"print(f\\\"Total QPU time: \\\\n {pub_result.metadata['total_qpu_time']:.6f}\\\")\\n\",\n \"print(f\\\"Gates fidelity measured during the experiment: \\\\n {pub_result.metadata['gate_fidelities']}\\\")\\n\",\n \"print(f\\\"Total shots / mitigation shots: \\\\n {pub_result.metadata['total_shots']} / {pub_result.metadata['mitigation_shots']}\\\")\\n\",\n \"print(\\\"=\\\"*60)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feedback Survey\\n\",\n \"\\n\",\n \"We’d love to hear about your experience using the Qiskit Function! Your feedback is valuable and will help Qiskit Function providers enhance their tools and services. Please take a moment to share your thoughts by completing our short 2 min [feedback survey](https://airtable.com/app6VujlNUHZuOnAF/pagpw6TgP9UEt4TAT/form).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Environment Check: Package Versions\\n\",\n \"\\n\",\n \"Let's verify that all required packages are installed and check their versions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_runtime\\n\",\n \"import qiskit_ibm_catalog\\n\",\n \"import qiskit_aer\\n\",\n \"import matplotlib\\n\",\n \"import networkx\\n\",\n \"import tqdm\\n\",\n \"import numpy\\n\",\n \"import scipy\\n\",\n \"import pylatexenc\\n\",\n \"import IPython\\n\",\n \"\\n\",\n \"print(\\\"Main Qiskit Packages:\\\")\\n\",\n \"print(f\\\"Qiskit: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Qiskit IBM Runtime: {qiskit_ibm_runtime.__version__}\\\")\\n\",\n \"print(f\\\"Qiskit IBM Catalog: {qiskit_ibm_catalog.__version__}\\\")\\n\",\n \"print(f\\\"Qiskit Aer: {qiskit_aer.__version__}\\\")\\n\",\n \"\\n\",\n \"print(\\\"\\\\nScientific:\\\")\\n\",\n \"print(f\\\"NumPy: {numpy.__version__}\\\")\\n\",\n \"print(f\\\"SciPy: {scipy.__version__}\\\")\\n\",\n \"print(f\\\"Matplotlib: {matplotlib.__version__}\\\")\\n\",\n \"\\n\",\n \"print(\\\"\\\\nUtilities:\\\")\\n\",\n \"print(f\\\"NetworkX: {networkx.__version__}\\\")\\n\",\n \"print(f\\\"tqdm: {tqdm.__version__}\\\")\\n\",\n \"print(f\\\"pylatexenc: {pylatexenc.__version__}\\\")\\n\",\n \"print(f\\\"IPython/Jupyter: {IPython.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Yosi Atia, Tali Shnaider\\n\",\n \"\\n\",\n \"**Advised by:** Ori Alberton, Eyal Bairey, Asaf Berkovitch, Assaf Zubida\\n\",\n \"\\n\",\n \"**Version:** 1.2.0\"\n ]\n }\n ],\n \"metadata\": {\n \"jupytext\": {\n \"formats\": \"ipynb,py:percent\"\n },\n \"kernelspec\": {\n \"display_name\": \"summer school 3\",\n \"language\": \"python\",\n \"name\": \"your-poetry-env\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "functions-labs/qunova/grader.py",
"content": "from qiskit import QuantumCircuit\nfrom qiskit.quantum_info.operators.base_operator import BaseOperator\n\ncorrect_message = \"Congratulations! 🎉 Your answer is correct.\"\nincorrect_message = \"Sorry, your answer is incorrect. Please try again.\"\n\ndef grade_ex1a(ex1a_answer: dict):\n if ex1a_answer.get(\"basis\") != \"ccpvdz\":\n return \"Wrong basis. Please try again.\"\n if ex1a_answer.get(\"active_orbitals\") != list(range(2,14,1)):\n return \"Wrong active orbitals. Please try again.\"\n if ex1a_answer.get(\"frozen_orbitals\") != list(range(2)):\n return \"Wrong frozen orbitals. Please try again.\"\n return correct_message\n\ndef grade_ex1b(ex1b_answer: dict):\n if ex1b_answer.get(\"hivqe_energy\") > -78.0396545776652 or ex1b_answer.get(\"hivqe_energy\") < -78.0788722041575:\n return \"Wrong hivqe energy. Please try again.\"\n return correct_message\n\ndef grade_ex2a(ex2_answer: dict):\n if type(ex2_answer.get(\"max_states_list\")) is not list:\n return \"max_states_list is not a list. Please try again.\"\n max_states_list = ex2_answer.get(\"max_states_list\")\n if not any(2000 <= x < 5000 for x in max_states_list) or max(max_states_list) < 5000:\n return \"max_states_list does not include any number strictly between 2000 and 5000. Please try again.\"\n if ex2_answer.get(\"basis\") != \"ccpvdz\":\n return \"Wrong basis. Please try again.\"\n if ex2_answer.get(\"active_orbitals\") != list(range(2,14,1)):\n return \"Wrong active orbitals. Please try again.\"\n if ex2_answer.get(\"frozen_orbitals\") != list(range(2)):\n return \"Wrong frozen orbitals. Please try again.\"\n return correct_message\n\ndef grade_ex2b(ex2_answer: dict):\n if ex2_answer.get(\"max_states\") < 3000:\n return \"Too small max_states. Please try again.\"\n if ex2_answer.get(\"hivqe_energy\") > (-78.0788722041575 + 0.0016):\n return \"hivqe energy would not be accurate enough. Please try again.\"\n return correct_message\n\ndef grade_ex3(ex3_answer: float):\n print(f\"Note: Maximum energy difference is: {ex3_answer*1000} mHa\")\n if ex3_answer > 0.0016:\n return \"At least one of the energy difference is greater than 1.6 mHa. Please try again.\"\n return correct_message"
},
{
"path": "functions-labs/qunova/qunova_hi-vqe.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit Function - Practical Application of Handover Iterative VQE (HI-VQE) Chemistry for C=C torsional deformation of $C_{2}H_{4}$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"* [Overview](#overview)\\n\",\n \"* [Setup](#setup)\\n\",\n \"* [Part 1: Introduction to HI-VQE](#part-1-introduction-to-hi-vqe)\\n\",\n \" * [Exercise 1](#exercise1)\\n\",\n \"* [Part 2: Accurate Ground State Energy](#part-2-accurate-ground-state-energy)\\n\",\n \" * [Exercise 2](#exercise2)\\n\",\n \"* [Part 3: Torsional Potential Energy](#part-3-torsional-potential-energy)\\n\",\n \" * [Exercise 3](#exercise3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Overview\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The **Handover Iterative VQE (HI-VQE)** method is designed to efficiently build accurate ground state subspace Hamiltonian to avoid diagonalization of full space Hamiltonian matrix, leveraging significant sparsity of full space Hamiltonian observed in quantum chemistry systems. The handover approach ensure the accurate energy estimation sampled from quantum circuits as important and core states of electronic configurations. The quantum circuit parameters are also iteratively revised to navigate to core states enabling robust and reliable calculations even in the presence of quantum noise. \\n\",\n \"\\n\",\n \"This notebook guides you how to utilize HI-VQE Chemistry;you'll learn how the HI-VQE function works with the input parameters and setup to explore its application to chemically intriguing molecules. \\n\",\n \"\\n\",\n \"The notebook is structured as follows:\\n\",\n \"\\n\",\n \"- **Part 1: Introduction to HI-VQE**\\n\",\n \" In Part 1, you will learn about the HI-VQE algorithm, covering how it works, what problems it's useful for, and what makes it unique.\\n\",\n \"\\n\",\n \"- **Part 2: Ground State Energy calculation** \\n\",\n \" In Part 2, you will learn how to use the HI-VQE Chemistry to set up and execute the Quantum Chemistry calculations with the $C_2H_4$ (Ethylene) molecule in a given basis. This section will provide you with hands-on experience for HI-VQE Chemistry, focusing on **energy estimation** based on the subspace obtained from quantum samples and energy obtained from classical solvers as handover process. You will be tasked with tuning parameters to improve the performance of HI-VQE and achieve a closer approximation of the molecule's ground state energy. **Your goal** is to refine the parameters to enhance accuracy while considering noise and other quantum device constraints.\\n\",\n \"\\n\",\n \"- **Part 3: Torsion Potential Energy calculations** \\n\",\n \" Once you've completed the **Part 2**, you'll move on to calculation of torsional deformation and corresponding energy profile of $C_2H_4$. \\n\",\n \" \\n\",\n \" You will be tasked with tuning parameters to improve the performance of HI-VQE and achieve a closer approximation of the molecule's ground state energy and potential energy surface upon torsion deformation .\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Setup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Install dependencies\\n\",\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"%pip install \\\"qiskit[visualization]\\\" qiskit_ibm_runtime qiskit_ibm_catalog py3Dmol ipywidgets \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You should have Grader `>=0.22.11`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"\\n\",\n \"# Import common libraries\\n\",\n \"import reprlib\\n\",\n \"import py3Dmol\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import ipywidgets as widgets\\n\",\n \"from IPython.display import display, clear_output\\n\",\n \"\\n\",\n \"# Import qiskit ecosystems\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from qiskit_ibm_catalog import QiskitFunctionsCatalog\\n\",\n \"\\n\",\n \"# Grader\\n\",\n \"from grader import grade_ex1a, grade_ex1b, grade_ex2a, grade_ex2b, grade_ex3\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_qunova_function\\n\",\n \"\\n\",\n \"# Define the function to visualize the geometries\\n\",\n \"def VISUALIZE_GEOMETRIES(structure_names, xyz_strings):\\n\",\n \" mol_selector = widgets.SelectionSlider(\\n\",\n \" options=[(name, idx) for idx, name in enumerate(structure_names)],\\n\",\n \" description='Geometry:',\\n\",\n \" style={'description_width': '150px'},\\n\",\n \" layout=widgets.Layout(\\n\",\n \" width='500px',\\n\",\n \" border='2px solid black',\\n\",\n \" padding='10px'\\n\",\n \" ),\\n\",\n \" continuous_update=False\\n\",\n \" )\\n\",\n \" output = widgets.Output()\\n\",\n \" output.layout = widgets.Layout(\\n\",\n \" border='2px solid black', \\n\",\n \" padding='5px', \\n\",\n \" width='500px', height='320px'\\n\",\n \" )\\n\",\n \"\\n\",\n \" def show_molecule(index):\\n\",\n \" with output:\\n\",\n \" clear_output(wait=True)\\n\",\n \" viewer = py3Dmol.view(width=400, height=300)\\n\",\n \" viewer.layout.border = '10px solid black'\\n\",\n \" viewer.addModel(xyz_strings[index], 'xyz')\\n\",\n \" viewer.setStyle({}, {'stick': {}, 'sphere': {'radius': 0.4}})\\n\",\n \" viewer.zoom(2)\\n\",\n \" viewer.show()\\n\",\n \"\\n\",\n \" def on_value_change(change):\\n\",\n \" if change['name'] == 'value' and change['type'] == 'change':\\n\",\n \" index = change['new']\\n\",\n \" show_molecule(index)\\n\",\n \"\\n\",\n \" mol_selector.observe(on_value_change)\\n\",\n \"\\n\",\n \" display(mol_selector, output)\\n\",\n \" show_molecule(0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**Exclusive Access to Qiskit Functions**\\n\",\n \"\\n\",\n \"As part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\\n\",\n \"\\n\",\n \"If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`. in the cell below, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\\n\",\n \"\\n\",\n \"**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Qiskit Functions Catalog\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"catalog = QiskitFunctionsCatalog(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"# You should see a list of Qiskit Functions available to you\\n\",\n \"# If you encounter the error `QiskitServerlessException: Credentials couldn't be verified`,\\n\",\n \"# it means your access is not yet active\\n\",\n \"catalog.list()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \" Load Qiskit Function\\n\",\n \"\\n\",\n \"Find the correct function name from the list above, or refer to the [Qiskit Functions Catalog](https://quantum.cloud.ibm.com/functions) to locate the appropriate function name string. The name should follow the format: `\\\"[provider]/[title]\\\"`.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load Qunova HI-VQE function\\n\",\n \"\\n\",\n \"function_name = \\\"\\\" # TODO\\n\",\n \"function = catalog.load(function_name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_qunova_function(function)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# You'll need to specify the credentials when initializing QiskitRuntimeService, if they were not previously saved.\\n\",\n \"service = QiskitRuntimeService(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \")\\n\",\n \"backend = service.least_busy(operational=True, simulator=False)\\n\",\n \"backend_name = backend.name\\n\",\n \"print(f\\\"Using the least busy backend: {backend_name}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 1: Introduction to HI-VQE\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"HI‑VQE (Handover Iterative Variational Quantum Eigensolver) is a hybrid quantum-classical algorithm designed to help solve one of the most important problems in quantum chemistry: finding a chemical system's lowest energy state, which we call the ground state.\\n\",\n \"This is essential for predicting how molecules behave, how they bond, and how chemical reactions happen. But exactly calculating the ground state is extremely hard for big molecules, because the number of possible ways electrons can arrange themselves (called electron configurations) grows exponentially as systems get larger.\\n\",\n \"\\n\",\n \"Instead of trying every possible configuration (which would be too slow and costly), HI‑VQE cleverly **focuses only on the important ones**, as only a subset of configurations are crucial for modelling the ground state of many chemical systems. It does this using both classical and quantum computing:\\n\",\n \"\\n\",\n \"1. Specify the desired chemical system using some chemistry software on a classical computer.\\n\",\n \"\\n\",\n \"2. Use a quantum computer to generate promising electron configurations, essentially asking the quantum device to identify the subset of configurations that are important for the ground state.\\n\",\n \"\\n\",\n \"3. Check and filter the sampled configurations using classical tools to remove invalid or unimportant ones.\\n\",\n \"\\n\",\n \"4. Build a simplified model (a “subspace”) from the best configurations found so far.\\n\",\n \"\\n\",\n \"5. Solve this simplified problem classically by calculating the energy for just this smaller group of configurations.\\n\",\n \"\\n\",\n \"6. Repeat the process from Step 2, gradually improving the accuracy by updating the quantum circuit and refining the subspace, until the energy estimate stops changing much.\\n\",\n \"\\n\",\n \"By focusing on a small number of meaningful configurations, HI‑VQE avoids wasting time and resources on unimportant ones and can handle larger molecules than many other quantum chemistry methods. Users can choose different quantum circuits and tune how the algorithm behaves based on the system they’re studying. Additionally, even though today’s quantum devices are noisy, HI‑VQE is designed to handle errors and still produce useful results.\\n\",\n \"\\n\",\n \"HI‑VQE differs from other methods through the novel combination of the following techniques:\\n\",\n \"\\n\",\n \"- **Subspace construction:** It builds and updates a compact “subspace” of only the most relevant configurations.\\n\",\n \"\\n\",\n \"- **Screening and pruning:** It estimates which configurations matter the most and keeps the \\\"subspace\\\" lean.\\n\",\n \"\\n\",\n \"- **Hybrid iteration:** It alternates between quantum and classical steps, letting each type of computer do what it’s best at.\\n\",\n \"\\n\",\n \"- **Configuration expansion:** It can generate new candidate configurations classically, adding variety to the quantum-generated ones.\\n\",\n \"\\n\",\n \"Once HI‑VQE finishes, you get:\\n\",\n \"\\n\",\n \"- An approximate ground state wave function,\\n\",\n \"\\n\",\n \"- The corresponding ground state energy, and\\n\",\n \"\\n\",\n \"- An estimate of how accurate the result is.\\n\",\n \"\\n\",\n \"These results are useful for studying chemical systems, mapping potential energy surfaces, and exploring reactions and material behavior.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Define all geometries and reference data for $C_{2}H_{4}$ ground state and torsional deformation to be utilized in the following tasks. In this practice, you can obtain ground state energy using 12 electrons with 12 orbitals to utilize 24 qubits.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rhf_energies = {\\n\",\n \" \\\"Optimized geometry\\\":-78.0396545776652,\\n\",\n \" \\\"30deg-torsion\\\": -78.0192613144388,\\n\",\n \" \\\"45deg-torsion\\\": -77.9941686599885,\\n\",\n \" \\\"60deg-torsion\\\": -77.9598374283669,\\n\",\n \" \\\"90deg-torsion\\\": -77.8675828498107,\\n\",\n \"}\\n\",\n \"\\n\",\n \"exact_energies_24qubits = {\\n\",\n \" \\\"Optimized geometry\\\":-78.0788722041575,\\n\",\n \" \\\"30deg-torsion\\\": -78.0602772472799,\\n\",\n \" \\\"45deg-torsion\\\": -78.03806395985646,\\n\",\n \" \\\"60deg-torsion\\\": -78.0094172772050,\\n\",\n \" \\\"90deg-torsion\\\": -77.9637383376237,\\n\",\n \"}\\n\",\n \"\\n\",\n \"xyz_strings = [\\n\",\n \"\\\"\\\"\\\"6\\n\",\n \"Optimized geometry\\n\",\n \"C -0.000000 0.000000 0.666700\\n\",\n \"C 0.000000 0.000000 -0.666700\\n\",\n \"H -0.000000 0.931265 1.241297\\n\",\n \"H -0.000000 -0.931265 1.241297\\n\",\n \"H -0.000000 -0.931265 -1.241297\\n\",\n \"H 0.000000 0.931265 -1.241297\\\"\\\"\\\",\\n\",\n \"\\\"\\\"\\\"6\\n\",\n \"30deg-torsion\\n\",\n \"C 0.000000 -0.000000 0.670241\\n\",\n \"C 0.000000 -0.000000 -0.670241\\n\",\n \"H -0.240927 0.899153 1.248661\\n\",\n \"H 0.240927 -0.899153 1.248661\\n\",\n \"H -0.240927 -0.899153 -1.248661\\n\",\n \"H 0.240927 0.899153 -1.248661\\\"\\\"\\\",\\n\",\n \"\\\"\\\"\\\"6\\n\",\n \"45deg-torsion\\n\",\n \"C 0.000000 -0.000000 0.674960\\n\",\n \"C 0.000000 -0.000000 -0.674960\\n\",\n \"H -0.355980 0.859414 1.257953\\n\",\n \"H 0.355980 -0.859414 1.257953\\n\",\n \"H -0.355980 -0.859414 -1.257953\\n\",\n \"H 0.355980 0.859414 -1.257953\\\"\\\"\\\",\\n\",\n \"\\\"\\\"\\\"6\\n\",\n \"60deg-torsion\\n\",\n \"C 0.000000 -0.000000 0.682204\\n\",\n \"C 0.000000 -0.000000 -0.682204\\n\",\n \"H -0.464596 0.804705 1.270962\\n\",\n \"H 0.464596 -0.804705 1.270962\\n\",\n \"H -0.464596 -0.804705 -1.270962\\n\",\n \"H 0.464596 0.804705 -1.270962\\\"\\\"\\\",\\n\",\n \"\\\"\\\"\\\"6\\n\",\n \"90deg-torsion\\n\",\n \"C -0.000000 0.000000 0.708799\\n\",\n \"C 0.000000 0.000000 -0.708799\\n\",\n \"H -0.655845 0.655846 1.306020\\n\",\n \"H 0.655845 -0.655846 1.306020\\n\",\n \"H -0.655845 -0.655846 -1.306020\\n\",\n \"H 0.655845 0.655846 -1.306020\\\"\\\"\\\",\\n\",\n \"]\\n\",\n \"\\n\",\n \"structure_names = []\\n\",\n \"molecular_geometries = []\\n\",\n \"for xyz_string in xyz_strings:\\n\",\n \" structure_name = xyz_string.split(\\\"\\\\n\\\")[1]\\n\",\n \" structure_names.append(structure_name)\\n\",\n \" xyz_data = \\\";\\\".join(xyz_string.split(\\\"\\\\n\\\")[2:])\\n\",\n \" molecular_geometries.append(xyz_data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Exercise 1: \\n\",\n \"\\n\",\n \"Submit the first $C{_2}H{_4}$ geometry for 24 qubits and 12 electrons with `ccpvdz` and obtain ground state energy using HI-VQE Chemistry function. You will use 6 occupied spatial orbitals out of 8 occupied orbitals with 6 virtual spatial orbitals. `active orbital` and `frozen orbital` should be supplied as orbital indices in zero base indexing.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Write your code below here ###\\n\",\n \"molecule_options = {\\n\",\n \" \\\"basis\\\": ..., # TODO set basis\\n\",\n \" \\\"active_orbitals\\\": ..., # TODO set active orbitals\\n\",\n \" \\\"frozen_orbitals\\\": ..., # TODO set frozen orbitals\\n\",\n \"}\\n\",\n \"\\n\",\n \"### Don't change any code past this line ###\\n\",\n \"\\n\",\n \"hivqe_options = {\\\"shots\\\": 1000, \\\"max_iter\\\": 40}\\n\",\n \"max_states = 2000\\n\",\n \"classical_expansion_states = 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_ex1a(molecule_options)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit a job to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 1 minutes 22 seconds (based on tests on `ibm_fez`)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run HI-VQE\\n\",\n \"job = function.run(\\n\",\n \" geometry=molecular_geometries[0],\\n\",\n \" backend_name=backend_name,\\n\",\n \" max_states=max_states,\\n\",\n \" max_expansion_states=classical_expansion_states,\\n\",\n \" molecule_options=molecule_options,\\n\",\n \" hivqe_options=hivqe_options,\\n\",\n \" use_session=True,\\n\",\n \")\\n\",\n \"\\n\",\n \"job_id = job.job_id\\n\",\n \"job_status = job.status()\\n\",\n \"print(f\\\"Optimized geometry HI-VQE Job ID: {job_id}, status: {job_status}\\\")\\n\",\n \"\\n\",\n \"ex1_job_id = {\\\"ex1b_job_id\\\": job_id}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job_id = ex1_job_id[\\\"ex1b_job_id\\\"]\\n\",\n \"job = catalog.job(job_id)\\n\",\n \"job_status = job.status()\\n\",\n \"\\n\",\n \"if job_status == 'DONE':\\n\",\n \" job_result = job.result()\\n\",\n \" print(f\\\"Optimized geometry HI-VQE energy: {job_result['energy']}\\\")\\n\",\n \"else:\\n\",\n \" print(f\\\"Waiting for job to complete: {job_id}, status: {job_status}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"ex1b_answer = {\\n\",\n \" \\\"hivqe_energy\\\": ..., # TODO get hivqe energy\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_ex1b(ex1b_answer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 2: Accurate Ground State Energy\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Ethylene ($C_{2}H_{4}$) is a fundamental molecule in organic and theoretical chemistry. With 16 electrons and a simple planar structure, it provides a clean system to study π-bonding, electronic excitation, and torsional barriers using quantum chemical methods.\\n\",\n \"\\n\",\n \"Geometry and Bonding\\n\",\n \"\\n\",\n \"- **Geometry**: Planar molecule with sp² hybridized carbon atoms.\\n\",\n \"- **Bonding**:\\n\",\n \" - Each carbon forms 3 σ-bonds:\\n\",\n \" - 2 with hydrogen atoms.\\n\",\n \" - 1 with the other carbon atom.\\n\",\n \" - The **π bond** between carbons arises from sideways overlap of unhybridized 2p orbitals.\\n\",\n \"- **Electronic Structure**\\n\",\n \" - The π and π* orbitals are well-separated from the σ-framework.\\n\",\n \" - Ideal for studying **active space** approaches in multireference methods.\\n\",\n \" - The π → π* excitation is a **benchmark** low-lying singlet excited state. This makes ethylene highly valuable for benchmarking excited-state computational methods.\\n\",\n \"\\n\",\n \"- **Torsional Barrier**\\n\",\n \" - Rotation around the C=C bond breaks the π-overlap, creating a **torsional barrier**.\\n\",\n \" - Useful for exploring **correlation effects** and **orbital symmetry breaking**.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Exercise 2: \\n\",\n \"\\n\",\n \"Submit calculations with several `max_states` value to obtain chemically accurate result (less than 1.6 mHa difference from exact result) and submit the best `max_states` as the answer of the exercise2. Note that you should use the same `molecule_options` obtained from the exercise1.\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Write your code below here ###\\n\",\n \"\\n\",\n \"max_states_list = [ , , , ] #TODO use max_states list between 2000 and 5000\\n\",\n \"molecule_options = {\\n\",\n \" \\\"basis\\\": ..., # TODO set basis\\n\",\n \" \\\"active_orbitals\\\": ..., # TODO set active orbitals\\n\",\n \" \\\"frozen_orbitals\\\": ..., # TODO set frozen orbitals\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ex2a_answer = {\\n\",\n \" \\\"max_states_list\\\": max_states_list,\\n\",\n \" \\\"basis\\\": molecule_options[\\\"basis\\\"],\\n\",\n \" \\\"active_orbitals\\\": molecule_options[\\\"active_orbitals\\\"],\\n\",\n \" \\\"frozen_orbitals\\\": molecule_options[\\\"frozen_orbitals\\\"],\\n\",\n \"}\\n\",\n \"\\n\",\n \"grade_ex2a(ex2a_answer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit multiple jobs to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 6 minutes 2 seconds (based on tests on `ibm_fez`)\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Tips: \\n\",\n \"\\n\",\n \"The `max_states` variable defines the maximum number of states retained in the subspace for diagonalization. It also serves as a threshold for selecting and screening the most relevant states. While smaller values reduce memory usage and computational cost, setting it too low may exclude important states, so it should be chosen carefully based on the problem size and accuracy requirements.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_hivqe_energies = {}\\n\",\n \"\\n\",\n \"for max_state, job_id in ex2_job_ids.items():\\n\",\n \" job = catalog.job(job_id)\\n\",\n \" job_status = job.status()\\n\",\n \" print(f\\\"Optimized geometry HI-VQE Job ID: {job_id}, status: {job_status}\\\")\\n\",\n \" if job_status == 'DONE':\\n\",\n \" result = job.result()\\n\",\n \" energy = result['energy']\\n\",\n \" print(f\\\"HI-VQE energy: {energy}\\\")\\n\",\n \" opt_hivqe_energies[max_state]= energy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_hivqe_energy_values = list(opt_hivqe_energies.values())\\n\",\n \"\\n\",\n \"if len(ex2_job_ids) == len(opt_hivqe_energies):\\n\",\n \" print(\\\"All jobs are done\\\")\\n\",\n \" # Plot results for the optimized geometry\\n\",\n \" rhf_energy = rhf_energies[\\\"Optimized geometry\\\"]\\n\",\n \" exact_energy = exact_energies_24qubits[\\\"Optimized geometry\\\"]\\n\",\n \" fig,axs = plt.subplots(1,2,figsize=(10, 4))\\n\",\n \"\\n\",\n \" axs[0].plot(max_states_list, opt_hivqe_energy_values, 'o-', label='HI-VQE final energy', color='blue')\\n\",\n \" axs[0].set_xlabel('max_states', fontsize=16)\\n\",\n \" axs[0].set_ylabel('Energy (Ha)', fontsize=16)\\n\",\n \" axs[0].set_title('HI-VQE energy vs max_states', fontsize=16)\\n\",\n \" axs[0].legend(fontsize=16)\\n\",\n \" axs[0].grid(color='gray', linestyle='--', linewidth=0.7, alpha=0.7)\\n\",\n \"\\n\",\n \" axs[1].plot(max_states_list, np.array(opt_hivqe_energy_values)-exact_energy, 'o-', label='Exact - HI-VQE Error', color='green')\\n\",\n \" axs[1].set_xlabel('max_states', fontsize=16)\\n\",\n \" axs[1].set_ylabel('Error (Ha)', fontsize=16)\\n\",\n \" axs[1].set_title('HI-VQE error vs max_states', fontsize=16)\\n\",\n \" axs[1].axhline(y=0.0016, color='red', linestyle='--', linewidth=3.0, alpha=0.7)\\n\",\n \" axs[1].legend(fontsize=16)\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.grid(color='gray', linestyle='--', linewidth=0.7, alpha=0.7)\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"ex2b_answer = {\\n\",\n \" \\\"max_states\\\": ..., #TODO set max_states\\n\",\n \" \\\"hivqe_energy\\\": ... #TODO set hivqe energy\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# grader set up\\n\",\n \"grade_ex2b(ex2b_answer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 3: Torsional Potential Energy\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What is the torsional potential energy surface (PES) \\n\",\n \"\\n\",\n \"The torsional PES of ethylene serves as a textbook example of π-bonding and conjugation effects in unsaturated hydrocarbons. The following image shows that in the 0° conformation, the p-orbitals on each carbon overlap maximally, forming a strong π bond. Upon 90° rotation, the p-orbitals become orthogonal, and the overlap vanishes, effectively breaking the π bond. This results in a significant energy barrier, which is clearly seen on the torsional PES.\\n\",\n \"\\n\",\n \"Accurately predicting the torsional PES of simple systems like ethylene also holds significant industrial value, as it lays the groundwork for:\\n\",\n \"\\n\",\n \"\\n\",\n \"- **Polymer and material design:** Ethylene is the monomeric unit of polyethylene. Understanding the torsional behavior informs flexibility, crystallinity, and thermal properties of the polymer chains.\\n\",\n \"- **Catalysis and reaction mechanisms:** Ethylene is a key feedstock in petrochemical industries. In transition metal catalysis, such as Ziegler–Natta or metallocene catalysts, the ability of ethylene to rotate (or resist rotation) upon coordination impacts the stereochemistry and activity of the polymerization reaction.\\n\",\n \"- **Surface chemistry and adsorption modeling:** In catalysis on metal surfaces or zeolites, ethylene’s torsion affects how it binds and reacts. Accurate PES modeling is vital in surface science and heterogeneous catalysis.\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Examine the molecular geometry with torsion of C=C bond\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"VISUALIZE_GEOMETRIES(structure_names, xyz_strings)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Exercise 3: \\n\",\n \"\\n\",\n \"Apply max_states value to obtain from the previous HI-VQE exercise calculation and run with 5 different molecular geometry calculations to get torsional deformation PES curve for C=C of $C_{2}H_{4}$\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Write your code below here ###\\n\",\n \"\\n\",\n \"max_states = ... #TODO set max_states\\n\",\n \"molecule_options = {\\n\",\n \" \\\"basis\\\": ..., #TODO set basis\\n\",\n \" \\\"active_orbitals\\\": ..., #TODO set active orbitals\\n\",\n \" \\\"frozen_orbitals\\\": ..., #TODO set frozen orbitals\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"**⚠️ Warning: QPU Time Consumption**\\n\",\n \"\\n\",\n \"Running the cell below will submit multiple jobs to a QPU and consume real QPU time. Please ensure you intend to proceed.\\n\",\n \"\\n\",\n \"**Estimated QPU runtime:** 5 minutes 50 seconds (based on tests on `ibm_fez`)\\n\",\n \"\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### Don't change any code past this line ###\\n\",\n \"classical_expansion_states = 10\\n\",\n \"\\n\",\n \"ex3_job_ids = {}\\n\",\n \"for structure_name, molecular_geometry in zip(structure_names, molecular_geometries):\\n\",\n \" \\n\",\n \" hivqe_options = {\\\"shots\\\": 1000, \\\"max_iter\\\": 40}\\n\",\n \"\\n\",\n \" # Run HI-VQE\\n\",\n \" job = function.run(\\n\",\n \" geometry=molecular_geometry,\\n\",\n \" backend_name=backend_name,\\n\",\n \" max_states=max_states,\\n\",\n \" max_expansion_states=classical_expansion_states,\\n\",\n \" molecule_options=molecule_options,\\n\",\n \" hivqe_options=hivqe_options,\\n\",\n \" use_session=True,\\n\",\n \" )\\n\",\n \" print(f\\\"{structure_name} HI-VQE Job id: {job.job_id} and status: {job.status()}\\\")\\n\",\n \" ex3_job_ids[structure_name] = job.job_id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hivqe_energies_24qubits = {}\\n\",\n \"\\n\",\n \"for structure_name, job_id in ex3_job_ids.items():\\n\",\n \" job = catalog.job(job_id)\\n\",\n \" job_status = job.status()\\n\",\n \" print(f\\\"{structure_name} HI-VQE Job status:\\\", job_status)\\n\",\n \" if job_status == 'DONE':\\n\",\n \" result = job.result()\\n\",\n \" energy = result['energy']\\n\",\n \" print(f\\\"HI-VQE energy: {energy}\\\")\\n\",\n \" hivqe_energies_24qubits[structure_name]= energy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"rhf_energy_min = min([v for k,v in rhf_energies.items()])\\n\",\n \"rhf_energy_rel = np.array([v for k,v in rhf_energies.items()]) - rhf_energy_min\\n\",\n \"rhf_energy_rel_kcalmol = rhf_energy_rel * 627.509\\n\",\n \"\\n\",\n \"exact_energies_24qubits_min = min([v for k,v in exact_energies_24qubits.items()])\\n\",\n \"exact_energies_24qubits_rel = np.array([v for k,v in exact_energies_24qubits.items()]) - exact_energies_24qubits_min\\n\",\n \"exact_energies_24qubits_rel_kcalmol = exact_energies_24qubits_rel * 627.509\\n\",\n \"\\n\",\n \"num_hivqe_energies = len([v for k,v in hivqe_energies_24qubits.items()])\\n\",\n \"num_rhf_energies = len([v for k,v in rhf_energies.items()])\\n\",\n \"num_exact_energies = len([v for k,v in exact_energies_24qubits.items()])\\n\",\n \"\\n\",\n \"if num_hivqe_energies == num_exact_energies:\\n\",\n \" hivqe_energies_24qubits_min = min([v for k,v in hivqe_energies_24qubits.items()])\\n\",\n \" hivqe_energies_24qubits_rel = np.array([v for k,v in hivqe_energies_24qubits.items()]) - hivqe_energies_24qubits_min\\n\",\n \" hivqe_energies_24qubits_rel_kcalmol = hivqe_energies_24qubits_rel * 627.509\\n\",\n \"else:\\n\",\n \" print(\\\"Error: Number of structures in hivqe_energies_24qubits and rhf_energies do not match\\\")\\n\",\n \" \\n\",\n \"structure_names_symmetric = structure_names + structure_names[-2::-1]\\n\",\n \"rhf_energy_symmetric = np.concatenate([rhf_energy_rel_kcalmol, rhf_energy_rel_kcalmol[-2::-1]])\\n\",\n \"exact_energy_symmetric = np.concatenate([exact_energies_24qubits_rel_kcalmol, exact_energies_24qubits_rel_kcalmol[-2::-1]])\\n\",\n \"if num_hivqe_energies == num_exact_energies:\\n\",\n \" hivqe_energy_symmetric = np.concatenate([hivqe_energies_24qubits_rel_kcalmol, hivqe_energies_24qubits_rel_kcalmol[-2::-1]])\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \"plt.plot(structure_names_symmetric, rhf_energy_symmetric, 'o-', label=\\\"RHF\\\", color=\\\"blue\\\", linewidth=2)\\n\",\n \"plt.plot(structure_names_symmetric, exact_energy_symmetric, 'o-', label=\\\"Exact\\\", color=\\\"green\\\", linewidth=2)\\n\",\n \"if num_hivqe_energies == num_exact_energies:\\n\",\n \" plt.plot(structure_names_symmetric, hivqe_energy_symmetric, 'o-', label=\\\"HI-VQE\\\", color=\\\"red\\\", linewidth=2)\\n\",\n \"\\n\",\n \"plt.xlabel(\\\"Torsion angle (deg)\\\", fontsize=16)\\n\",\n \"plt.ylabel(\\\"Relative energy (kcal/mol)\\\", fontsize=16)\\n\",\n \"plt.title(\\\"Torsional PES curve for $C_{2}H_{4}$, C=C bond\\\",fontsize=16)\\n\",\n \"plt.xticks(rotation=30, fontsize=14)\\n\",\n \"plt.grid(color='gray', linestyle='--', linewidth=0.7, alpha=0.7) # Add this line\\n\",\n \"plt.legend(fontsize=16)\\n\",\n \"plt.savefig(\\\"Torsional_PES_curve_C2H4_C=C_bond.png\\\", dpi=300, bbox_inches=\\\"tight\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"hivqe_energies_24qubits = ... #TODO provide above results for hivqe energies in dictionary format\\n\",\n \"\\n\",\n \"en_diff = []\\n\",\n \"for k,v in hivqe_energies_24qubits.items():\\n\",\n \" en_diff.append(v-exact_energies_24qubits[k])\\n\",\n \"max_en_diff = max(en_diff)\\n\",\n \"\\n\",\n \"ex3_answer = max_en_diff\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grade_ex3(ex3_answer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Tips: \\n\",\n \"\\n\",\n \"You may notice that the solution to Exercise 2 can be directly leveraged to address Exercise 3, even when the system involves inaccuracies in torsional geometry. This is because changes in molecular geometry lead to variations in correlation energies, which in turn require different values for `max_state`. The `max_state` parameter in HI-VQE limits the size of the subspace matrix, enabling efficient selection of the most relevant states from the full Hilbert space. However, it is not intended to be fixed across all systems. Therefore, the convergence tests performed with various `max_state` values in Exercise 2 provide valuable insight for tackling new chemical systems, helping ensure accurate results despite geometric perturbations.\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feedback Survey\\n\",\n \"\\n\",\n \"We’d love to hear about your experience using the Qiskit Function! Your feedback is valuable and will help Qiskit Function providers enhance their tools and services. Please take a moment to share your thoughts by completing our short 2 min [feedback survey](https://airtable.com/app6VujlNUHZuOnAF/pagpw6TgP9UEt4TAT/form).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# References\\n\",\n \"\\n\",\n \"1. HI-VQE Chemistry Qiskit Function Tutorial: https://docs.quantum.ibm.com/guides/qunova-chemistry\\n\",\n \"2. Arxiv paper describing HI-VQE function: https://arxiv.org/abs/2503.06292\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Pilsun Yoo\\n\",\n \"\\n\",\n \"**Advised by:** Junye Huang\\n\",\n \"\\n\",\n \"**Version:** 1.1.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit packages versions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_runtime\\n\",\n \"import qiskit_ibm_catalog\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit IBM Runtime: {qiskit_ibm_runtime.__version__}')\\n\",\n \"print(f'Qiskit IBM Catalog: {qiskit_ibm_catalog.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "lab-0/lab0-ko.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"551a947d\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 0: Hello Quantum World!\\n\",\n \"\\n\",\n \"# 목차\\n\",\n \"\\n\",\n \"* [Qiskit Global Summer School 2025에 오신 것을 환영합니다!](#welcome)\\n\",\n \" - [Lab 0 개요](#overview)\\n\",\n \" - [Qiskit 설치하기](#install)\\n\",\n \" - [오류 발생 시](#troubleshooting)\\n\",\n \" - [IBM Cloud 계정 세팅](#setting-ibm-cloud)\\n\",\n \" - [불러오기](#imports)\\n\",\n \" - [기능 점검](#sanity-check)\\n\",\n \"* [Qiskit pattern에 따라 3-큐빗 GHZ 상태를 만들어봅시다](#ghz) \\n\",\n \" - [Step 1. 정의](#map)\\n\",\n \" - [Step 2. 최적화](#optimize)\\n\",\n \" - [Step 3. 실행](#execute)\\n\",\n \" - [Step 4. 후처리](#post-process)\\n\",\n \"* [축하합니다!](#congratulations)\\n\",\n \" - [보너스 챌린지: 실제 하드웨어에서 GHZ 회로를 실행해봅시다](#bonus)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2144e6fe\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit Global Summer School 2025에 오신 것을 환영합니다! \\n\",\n \"\\n\",\n \"안녕하세요, 올해도 새로운 Qiskit Global Summer School(QGSS)으로 여러분을 만나볼 수 있어 정말 행복하네요. 이 2주간의 여름 학교에서는 처음 시작하는 학생과 연구자, 이미 Qiskit에 익숙한 전문가까지 다양한 커뮤니티의 사람들이 양자 컴퓨팅과 양자 프로그래밍 지식을 배울 수 있도록 이론 강의와 실습 문제를 제공하고 있습니다.\\n\",\n \"\\n\",\n \"특히, 이 여름 학교의 실습 부분에서는 여러분이 몇 가지 흥미로운 주제들을 만나볼 수 있도록 만들어진 주피터 노트북(\\\"lab\\\")들을 공부하고 풀어보게 됩니다.\\n\",\n \"\\n\",\n \"각각의 랩은 앞의 이론 강의에서 배운 내용을 보완하고, 관련 문서, 튜토리얼, 참고 자료 등을 확인할 수 있는 링크도 기재되어 있습니다. 여기 양자 컴퓨팅에 대한 수많은 교육자료들을 찾을 수 있는 [IBM Quantum Learning](https://quantum.cloud.ibm.com/learning) 플랫폼처럼요.\\n\",\n \"\\n\",\n \"## Lab 0 개요 \\n\",\n \"\\n\",\n \"이번 입문용 랩의 목표는 여러분께 랩 노트북을 사용하는 법과 답안을 채점하는 법을 보여주고, 여러분이 앞으로 풀어야 할 양자 코드를 실행할 수 있도록 컴퓨터를 잘 세팅하셨는지 확인하는 것입니다.\\n\",\n \"\\n\",\n \"QGSS의 랩을 완료하기 위해서는, 모든 노트북에 수정해서는 안 되는 미리 작성된 코드와 직접 채워야 하는 챌린지 코드 블록이 있다는 점을 확인해주세요. 특히, `### 아래에 코드를 작성해주세요 ###`라는 주석을 찾으신다면 그 아래에 필요한 코드는 직접 작성해야 합니다. 만약 노트북 커널을 재시작하시는 경우에는 모든 셀을 처음부터 순서대로 실행하여 노트북이 올바르게 실행되고 채점이 되도록 해야 합니다. 이렇게 함으로써 모든 코드가 수정된 내용에 따라 원활하게 실행되고 있다는 것을 확인할 수 있습니다. 물론, 패키지를 설치하는 셀이나 계정을 저장하는 셀과 같은 몇 가지 예외는 있을 수 있습니다 - 이런 셀들은 다시 실행하지 않아도 괜찮습니다.\\n\",\n \"\\n\",\n \"이번 랩에서는 Qiskit pattern에 따라 GHZ 상태를 만들고, 양자 회로를 최적화하는 법을 중점적으로 배웁니다. 마지막 부분은 여러분의 코드를 실제 양자 컴퓨터에서 실행할 수 있는 보너스 문제입니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b3df2ea\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskit 설치하기 \\n\",\n \"양자 컴퓨터는 암호를 푸는 쇼어 알고리즘, 더 빠른 검색을 가능하게 하는 그로버 알고리즘, 배터리 설계 등에 적용되는 양자 위상 측정 등, 기존 계산 방식의 혁신을 가져다 줄 기술로 자리매김하고 있습니다. 이 모든 것들의 첫 단계는 이러한 알고리즘들을 구현하고 실행하기 위한 소프트웨어 도구를 선택하는 것입니다. 이 여름 학교 챌린지에서는 양자 회로를 설계하고 구현하여 시뮬레이터와 실제 하드웨어에서 실행하기 위해 Qiskit이라는 도구를 사용하겠습니다. 다음 코드를 따라 Qiskit 2.0 버전을 설치해봅시다.\\n\",\n \"\\n\",\n \"첫 번째로, 현재 사용 중인 파이썬의 버전이 최신 Qiskit 버전과 호환 가능한 3.10 버전 이상이 맞는지 확인해주세요:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a35543d4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from platform import python_version\\n\",\n \"\\n\",\n \"print(python_version())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47d097d3\",\n \"metadata\": {},\n \"source\": [\n \"만약 3.10 이전 버전을 사용하고 계시다면 각자의 환경에 맞는 방법으로 파이썬 버전을 업데이트 해주세요. 아래에 여러분이 시도해볼 수 있는 몇 가지 방법을 링크해두었습니다:\\n\",\n \"\\n\",\n \"- MacOS: [Homebrew](https://brew.sh/)\\n\",\n \"- Linux: `sudo apt-get update `\\n\",\n \"\\n\",\n \"각 OS별 파이썬 업그레이드에 관한 자세한 가이드는 여기에서 찾아보실 수 있습니다: [How to update Python](https://4geeks.com/how-to/how-to-update-python-version)\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **공지:** 이번 QGSS의 Lab 3는 윈도우 환경에서는 실행이 되지 않습니다. 따라서 여러분이 윈도우를 사용하고 계신다면 [온라인 랩 환경](https://docs.quantum.ibm.com/guides/online-lab-environments)을 이용하시길 추천드립니다. + qBraid 에서 안내하는 QGSS 2025 가이드도 참고해주세요: [qBraid QGSS 2025](https://docs.qbraid.com/lab/user-guide/qgss-2025)\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"QGSS 2025 위키에서 이와 관련한 추가적인 안내를 확인하실 수 있습니다: https://github.com/qiskit-community/qgss-2025/wiki/Jupyter-Notebook-Environment-(Local-and-Online)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74f76ade\",\n \"metadata\": {},\n \"source\": [\n \"환경 설정이 마무리되었으면 이제 이번 여름 학교에서 사용할 그레이더와 Qiskit, 그 외 필수적인 라이브러리를 설치해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b89c3cd1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1b216728\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d9f8ae4\",\n \"metadata\": {},\n \"source\": [\n \"Qiskit 버전 `>=2.0.0` 그레이더 버전 `>=0.22.9` 이상이 나오면 되겠습니다. 만약 버전이 다르게 표시된다면 커널을 재시작해보시고, 그래도 안 된다면 그레이더를 다시 설치해야 할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c34209af\",\n \"metadata\": {},\n \"source\": [\n \"## 오류 발생 시 \\n\",\n \"\\n\",\n \"만약 앞의 셀에서 버전이 표시되지 않거나 계속해서 오류가 발생하는 경우 아래의 방법을 참고해 가상환경을 설정하거나 앞에서 안내한 온라인 랩 환경을 이용해주세요. 에러 없이 잘 실행이 됐다면 이 챕터는 넘어가셔도 됩니다.\\n\",\n \"\\n\",\n \"다음은 Qiskit을 사용하기 위한 가상환경을 설정하는 두 가지 방법입니다.\\n\",\n \"1. [venv](https://docs.python.org/3/library/venv.html)를 사용하여 [Qiskit installation guide](https://docs.quantum.ibm.com/guides/install-qiskit)의 안내를 따르기 \\n\",\n \"2. [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)를 사용하여 [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4) 영상의 안내를 따르기\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e8e89851\",\n \"metadata\": {},\n \"source\": [\n \"## IBM Cloud 계정 세팅 \\n\",\n \"\\n\",\n \"여름 학교 랩에서 그레이더로 채점을 하고 양자 회로를 실제 하드웨어에서 실행하기 위해서는 IBM Cloud 계정을 설정해야 합니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03041cd7\",\n \"metadata\": {},\n \"source\": [\n \"다음의 순서를 따라 계정을 설정해주세요:\\n\",\n \"\\n\",\n \"1. [새로운 IBM Quantum® 플랫폼](https://quantum.cloud.ibm.com/)에 로그인하세요.\\n\",\n \"2. 위 그림과 같은 화면에서 오른쪽 위의 **Create API key** 버튼을 누르고 안내를 따라 키를 생성한 뒤 안전하게 저장해두세요.\\n\",\n \"3. 다음 셀로 가서 `이문장을지우고API키를붙여넣으세요` 부분에 API 키를 붙여넣으세요.\\n\",\n \"4. 다시 플랫폼의 위 그림과 같은 화면에서 **Create an instance** 버튼을 누르고 안내를 따라 open plan으로 인스턴스를 생성하세요.\\n\",\n \"5. 인스턴스가 생성되었다면, 인스턴스의 CRN 코드를 복사하세요. 인스턴스가 보이지 않는 경우에는 화면을 새로고침 해주세요.\\n\",\n \"6. 다음 셀로 가서 `이문장을지우고CRN을붙여넣으세요` 부분에 CRN 코드를 붙여넣으세요.\\n\",\n \"\\n\",\n \"IBM Cloud® 계정 설정에 관한 자세한 사항은 [이 가이드](https://quantum.cloud.ibm.com/docs/guides/cloud-setup)를 참고하세요.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **공지:** 여러분의 API 키는 비밀번호와 같이 안전하게 다뤄야 합니다. 안전한 환경 또는 신뢰할 수 없는 환경에서 API 키를 사용하는 자세한 방법은 [클라우드 설정 가이드](https://quantum.cloud.ibm.com/docs/guides/cloud-setup#cloud-save)를 참고해주세요.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"52cfa1be\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# API 키를 저장하여 답안을 제출하고 양자컴퓨터에 접속하는 데에 사용합니다\\n\",\n \"\\n\",\n \"your_api_key = \\\"이문장을지우고API키를붙여넣으세요\\\"\\n\",\n \"your_crn = \\\"이문장을지우고CRN을붙여넣으세요\\\"\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"QiskitRuntimeService.save_account(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \" name=\\\"qgss-2025\\\",\\n\",\n \" overwrite=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"92766751\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 계정이 잘 저장되었는지 확인합니다\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c94c720c\",\n \"metadata\": {},\n \"source\": [\n \"## 불러오기 \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3952087b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit, generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.quantum_info import SparsePauliOp\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler, EstimatorV2 as Estimator\\n\",\n \"\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_lab0_ex1, grade_lab0_ex2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"282b4699\",\n \"metadata\": {},\n \"source\": [\n \"## 기능 점검 \\n\",\n \"\\n\",\n \"모든 설정이 완료되었으면 이제 간단한 양자 회로를 만들어 모든 기능이 정상적으로 동작하는지 확인해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"840ba2c5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 2-큐빗 회로를 만듭니다\\n\",\n \"qc = QuantumCircuit(2)\\n\",\n \"# 0번 큐빗에 H 게이트를 가합니다\\n\",\n \"qc.h(0)\\n\",\n \"# 0번 큐빗에서 1번 큐빗으로 CNOT 게이트를 가합니다\\n\",\n \"qc.cx(0, 1)\\n\",\n \"# MatPlotLib (\\\"mpl\\\")으로 회로를 그립니다\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed117c17\",\n \"metadata\": {},\n \"source\": [\n \"보고 계신 양자 회로는 Bell 상태를 만드는 양자 회로에 해당합니다:\\n\",\n \"\\n\",\n \"$$|Bell\\\\rangle=\\\\frac{|00\\\\rangle+|11\\\\rangle}{\\\\sqrt{2}}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf861730\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit pattern에 따라 3-큐빗 GHZ 상태를 만들어봅시다 \\n\",\n \"\\n\",\n \"자 이제 [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4) 영상을 따라 [Qiskit pattern](https://quantum.cloud.ibm.com/docs/en/guides/intro-to-patterns)을 이용해 3-큐빗 GHZ 상태를 만드는 과정을 알아봅시다.\\n\",\n \"\\n\",\n \"Qiskit 패턴은 여러 응용 분야의 문제를 쪼개고 단계별로 필요한 기능을 설계하는 보편적인 프레임워크입니다. 이를 통해 IBM Quantum의 연구원을 포함한 다양한 전문가들이 만든 기능을 보다 쉽게 이해하고 원활하게 사용할 수 있으며, 미래의 CPU/GPU/QPU를 결합한 컴퓨팅 작업도 설계할 수 있습니다.\\n\",\n \"\\n\",\n \"Qiskit 패턴의 4단계는 다음과 같습니다:\\n\",\n \"\\n\",\n \"1. 문제를 양자 회로와 관측가능량으로 **정의**합니다.\\n\",\n \"2. 사용하는 하드웨어에 맞게 **최적화**합니다.\\n\",\n \"3. 하드웨어에서 **실행**합니다.\\n\",\n \"4. 결과를 **후처리**합니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"## Step 1. 정의 \\n\",\n \"\\n\",\n \"Greenberger–Horne–Zeilinger(GHZ) 상태는 위에서 다뤘던 Bell 상태와 같이 최대로 얽힌 상태를 3개 또는 그 이상의 큐빗으로 확장한 것입니다. 예를 들어, 3-큐빗 GHZ 상태는 다음과 같이 정의할 수 있습니다:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"|GHZ\\\\rangle = \\\\frac{|000\\\\rangle+|111\\\\rangle}{\\\\sqrt{2}}.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"GHZ 상태의 흥미로운 성질 중 하나는 이것을 여러 가지 방법으로 양자 회로에서 구현할 수 있다는 것입니다. 연습문제 1에서는 그 중 가장 일반적인 방법으로 GHZ 상태를 만들어보겠습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ca23b8d3\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 1: GHZ 상태 만들기 \\n\",\n \"\\n\",\n \"자 드디어 올해 여름 학교의 첫 번째 연습문제입니다~ 빠밤!\\n\",\n \"\\n\",\n \"문제입니다, 다음의 단계를 따라 GHZ 상태를 만들어주세요:\\n\",\n \"\\n\",\n \"1. 0번 큐빗에 Hadamard 게이트를 가해 중첩 상태를 만들어주세요. \\n\",\n \"2. 0번 큐빗에서 1번 큐빗으로 CNOT 게이트를 가하세요.\\n\",\n \"3. 1번 큐빗에서 2번 큐빗으로 CNOT 게이트를 가하세요.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1c389cbe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 3-큐빗 회로를 만듭니다\\n\",\n \"qc = QuantumCircuit(3)\\n\",\n \"\\n\",\n \"### 아래에 코드를 작성해주세요 ###\\n\",\n \"# 0번 큐빗에 H 게이트를 가하세요\\n\",\n \"\\n\",\n \"# 0번 큐빗에서 1번 큐빗으로 CNOT 게이트를 가하세요\\n\",\n \"\\n\",\n \"# 1번 큐빗에서 2번 큐빗으로 CNOT 게이트를 가하세요\\n\",\n \"\\n\",\n \"### 코드 작성이 완료되었습니다 ###\\n\",\n \"\\n\",\n \"# MatPlotLib (\\\"mpl\\\")으로 회로를 그립니다\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a29c6fe7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab0_ex1(qc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"66a3ed14\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2. 최적화 \\n\",\n \"\\n\",\n \"훌륭하게 해내셨군요!\\n\",\n \"\\n\",\n \"다음 단계는 최적화입니다. 지금은 회로가 매우 짧아서 회로를 더 단순화하거나 필요한 게이트의 수를 줄이기는 어렵습니다. 하지만 다른 여러 상황에서는 회로를 최적화하는 것이 중요하게 작용하는 경우가 많습니다.\\n\",\n \"\\n\",\n \"실제 양자 회로를 구성할 때에는 여러 가지 제약이 발생할 수 있습니다. 예를 들어, 사용하는 하드웨어에 따라 큐빗 간의 연결이 제약될 수 있죠. 이 경우, 일부 큐빗들은 사용자가 원하는 대로 연결되어 있지 않으며, 따라서 원하는 양자 상태를 구현하기 위해 좀 더 현명한 방법을 생각해봐야 할 수 있습니다. 다행히도, 여기서 Qiskit 트랜스파일러가 여러분을 도와주게 됩니다! Qiskit에서 제공하는 프리셋 패스매니저에 하드웨어의 제약 조건을 넣어주고 원하는 회로를 트랜스파일하도록 하면 이러한 최적화를 알아서 처리해줍니다.\\n\",\n \"\\n\",\n \"자 이제 GHZ 상태로 돌아와서, 큐빗 0과 1, 0과 2는 연결되어 있지만, 큐빗 1과 2는 연결되어 있지 않은 상황을 생각해봅시다. 이러한 제약 조건은 `generate_preset_pass_manager` 함수에 전달하여 최적화에 적용할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"38782ae6\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 2: GHZ 상태 트랜스파일 하기 \\n\",\n \"\\n\",\n \"두 번째 연습문제에서는 앞에서 만든 GHZ 상태를 주어진 연결 조건에 따라 트랜스파일을 해보도록 하겠습니다.\\n\",\n \"\\n\",\n \"- 큐빗 0 <---> 큐빗 1\\n\",\n \"- 큐빗 0 <---> 큐빗 2\\n\",\n \"- ~~큐빗 1 <---> 큐빗 2~~\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"222b26f8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### 아래에 코드를 작성해주세요 ###\\n\",\n \"# 큐빗 0과 1, 0과 2가 연결되어 있음을 나타내는 coupling map을 다음과 같이 짝지어서 리스트로 작성해주세요: [[0,1],...]\\n\",\n \"coupling_map =\\n\",\n \"\\n\",\n \"# `generate_preset_pass_manager` 함수와 coupling map을 이용해 패스매니저를 만들고 양자 회로 `qc`를 트랜스파일 해주세요\\n\",\n \"pm =\\n\",\n \"qc_transpiled =\\n\",\n \"### 코드 작성이 완료되었습니다 ###\\n\",\n \"\\n\",\n \"qc_transpiled.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5d860929\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab0_ex2(qc_transpiled)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e2aa24a5\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3. 실행 \\n\",\n \"\\n\",\n \"다음 단계로 넘어가볼까요? 이제 만들어진 양자 회로를 Qiskit Runtime을 통해 실행해볼 차례입니다!\\n\",\n \"\\n\",\n \"실행 단계에서는 두 가지의 [Qiskit primitive](https://quantum.cloud.ibm.com/docs/guides/primitives)를 사용해 양자 회로를 실행하게 됩니다:\\n\",\n \"1. **Sampler**는 양자 회로를 실행하여 얻게 되는 레지스터의 결괏값을 샘플링합니다. Sampler의 출력값은 각 shot에서 얻은 레지스터 결괏값의 카운트입니다.\\n\",\n \"2. **Estimator**는 양자 회로가 만든 상태에서 특정 관측가능량을 측정하여 그 기댓값을 계산합니다. Estimator의 출력값은 설정된 관측가능량의 기댓값과 표준 편차로 이루어집니다.\\n\",\n \"\\n\",\n \"먼저 Sampler를 사용하여 회로를 실행하고, 결과를 `results_sampler` 변수에 저장해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"345c5084\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 백엔드를 설정합니다\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"# Sampler를 설정합니다\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"\\n\",\n \"# Sampler로 회로를 실행합니다 (Sampler를 실행할 때에는 측정 부분을 꼭 포함해주세요)\\n\",\n \"pm = generate_preset_pass_manager(backend=backend)\\n\",\n \"job = sampler.run([pm.run(qc.measure_all(inplace=False))])\\n\",\n \"\\n\",\n \"# 결과를 가져옵니다\\n\",\n \"results_sampler = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1a8e7991\",\n \"metadata\": {},\n \"source\": [\n \"이번에는 Estimator를 사용하여 회로를 실행하고, 결과를 `results_estimator` 변수에 저장해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f2354c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Estimator를 설정합니다\\n\",\n \"estimator = Estimator(mode=backend)\\n\",\n \"\\n\",\n \"# 관측가능량을 정의합니다\\n\",\n \"ZZZ = SparsePauliOp(\\\"ZZZ\\\")\\n\",\n \"ZZX = SparsePauliOp(\\\"ZZX\\\")\\n\",\n \"ZII = SparsePauliOp(\\\"ZII\\\")\\n\",\n \"XXI = SparsePauliOp(\\\"XXI\\\")\\n\",\n \"ZZI = SparsePauliOp(\\\"ZZI\\\")\\n\",\n \"III = SparsePauliOp(\\\"III\\\")\\n\",\n \"observables = [ZZZ, ZZX, ZII, XXI, ZZI, III]\\n\",\n \"\\n\",\n \"# Estimator로 회로를 실행합니다\\n\",\n \"pm = generate_preset_pass_manager(backend=backend)\\n\",\n \"pub = (pm.run(qc), observables)\\n\",\n \"job = estimator.run(pubs=[pub])\\n\",\n \"\\n\",\n \"# 결과를 가져옵니다\\n\",\n \"results_estimator = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"08df17b9\",\n \"metadata\": {},\n \"source\": [\n \"다음은 Qiskit 패턴의 마지막 단계입니다. 여기서 얻은 결과를 출력해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03ccb1ce\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4. 후처리 \\n\",\n \"\\n\",\n \"Qiskit 패턴의 마지막 단계는 회로를 실행하여 얻은 정보를 후처리하는 것입니다.\\n\",\n \"\\n\",\n \"먼저 Sampler에서 얻은 결과를 출력해봅시다. 히스토그램 그래프를 이용하면 Sampler에서 얻은 결괏값의 카운트를 시각화하여, 두 개의 양자 상태가 50% 확률로 관측되는 것을 쉽게 확인할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2cb9ba4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"counts_list = results_sampler[0].data.meas.get_counts()\\n\",\n \"print(f\\\" Outcomes : {counts_list}\\\")\\n\",\n \"display(plot_histogram(counts_list, title=\\\"GHZ state\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86748b30\",\n \"metadata\": {},\n \"source\": [\n \"Estimator의 결과도 출력해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"983608f7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"exp_values = results_estimator[0].data.evs\\n\",\n \"observables_list = [\\\"ZZZ\\\", \\\"ZZX\\\", \\\"ZII\\\", \\\"XXI\\\", \\\"ZZI\\\", \\\"III\\\"]\\n\",\n \"print(f\\\"Expectation values: {list(zip(observables_list, exp_values))}\\\")\\n\",\n \"\\n\",\n \"# 그래프를 그립니다\\n\",\n \"container = plt.bar(observables_list, exp_values, width=0.8)\\n\",\n \"# x축과 y축 이름을 설정합니다\\n\",\n \"plt.xlabel(\\\"Observables\\\")\\n\",\n \"plt.ylabel(\\\"Values\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e5b49810\",\n \"metadata\": {},\n \"source\": [\n \"여기서는 관측가능량 $ZZI$와 $III$가 기댓값 1을 갖는 것을 확인하실 수 있습니다. $ZZI$를 관측할 때에는 (-) 부호가 항상 두 개씩 나와 서로 상쇄되고, $III$는 항등원이어서 그렇죠. 그 외의 관측가능량은 기댓값이 0인 것을 볼 수 있는데, 이는 $Z$ 연산자의 개수가 홀수개여서 (-) 부호가 상쇄되지 않거나, $X$ 연산자가 큐빗을 뒤집어 두 상태가 수직이 되도록 만들기 때문입니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e905aea1\",\n \"metadata\": {},\n \"source\": [\n \"# 축하합니다! \\n\",\n \"\\n\",\n \"여러분은 성공적으로 lab 0를 마치고 Qiskit Global Summer School 2025에 참여할 준비가 되었습니다!\\n\",\n \"\\n\",\n \"이번 랩에서 여러분은 Qiskit v2.x를 실행하기 위한 환경을 설정하였으며, 여름 학교의 진도를 저장하고 양자 컴퓨터에서 양자 회로를 실행하기 위한 IBM Cloud 계정을 설정하였습니다. 또한, GHZ 회로를 만들고 최적화하는 예시를 통해 Qiskit 패턴에 대해 공부하였습니다. GHZ 회로를 시뮬레이터를 이용해 실행하였고, Sampler의 관측 결과가 이론값과 같이 $|000\\\\rangle$과 $|111\\\\rangle$ 각 50% 확률로 관측되는 것을 확인하였습니다.\\n\",\n \"\\n\",\n \"아래의 셀을 실행하면 이번 여름 학교 랩의 전체 진도 상황을 확인할 수 있습니다:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"677398c7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"83692cb4\",\n \"metadata\": {},\n \"source\": [\n \"보너스 문제로, 같은 실험을 실제 하드웨어에서 실행해보고 노이즈가 어떻게 결과에 영향을 주는지, 이론값과 어느 정도 일치하는지 한 번 확인해봅시다.\\n\",\n \"\\n\",\n \"준비되셨나요? 시작해볼까요?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c5e04fb1\",\n \"metadata\": {},\n \"source\": [\n \"## 보너스 챌린지: 실제 하드웨어에서 GHZ 회로를 실행해봅시다 \\n\",\n \"\\n\",\n \"Qiskit으로 양자 컴퓨터에서 양자 회로를 실행하려면 먼저 백엔드를 설정해야 합니다. IBM Quantum의 양자 컴퓨터 중 원하는 것을 직접 선택할 수도 있고요, 어떤 하드웨어를 사용하는지가 큰 상관이 없을 때에는 현재 가장 대기열이 적은 것을 고르는 것이 편할 수도 있습니다. 바로 이럴 때에 `least_busy` 매소드를 사용할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ef101f81\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 서비스를 불러와 IBM QPU에 접속합니다\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"\\n\",\n \"# 대기열이 가장 적은 백엔드를 불러옵니다\\n\",\n \"backend = service.least_busy(operational=True, simulator=False)\\n\",\n \"print(f\\\"We are using the {backend.name} quantum computer\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b94e6ec7\",\n \"metadata\": {},\n \"source\": [\n \"위의 셀은 `QiskitRuntimeService`를 통해 양자 컴퓨터를 얼마나 쉽게 불러올 수 있는지 보여줍니다. 앞의 과정을 통해 백엔드를 골랐다면, 이제 앞에서 시뮬레이터로 실행했던 것과 같은 코드를 그대로 가져와 실제 하드웨어에서 양자 회로를 실행할 수 있습니다. 후처리 과정의 시각화 코드도 포함해서 말이죠.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c50b3aa1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### 아래에 코드를 작성해주세요 ###\\n\",\n \"# Step 1. 정의\\n\",\n \"# 위에서 `qc`라는 변수에 GHZ 회로를 할당하였던 것을 기억해보세요\\n\",\n \"\\n\",\n \"# Step 2. 최적화\\n\",\n \"pm = \\n\",\n \"qc_transpiled = \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b90f52a1\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"주의: 대기 시간과 10분 사용 시간 제한\\n\",\n \"\\n\",\n \"이 회로는 실제 하드웨어에서 약 10초의 실행 시간을 갖습니다. 하지만 회로를 실행하기 위해서는 대기하는 시간이 필요하며, 이로 인해 노트북 셀이 실행되는 시간은 대기 시간을 포함해 10초보다 훨씬 길어질 수 있습니다.\\n\",\n \"\\n\",\n \"별도의 약정이 없는 open plan의 경우 실제 하드웨어를 최대 10분까지 사용할 수 있습니다. 이후의 랩에서 실제 하드웨어를 사용하는 실험이 더 있을 예정이므로, 너무 많은 회로를 실행하여 사용 시간이 부족해지지 않도록 주의해주세요.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2ba1eb48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 3. 실행\\n\",\n \"sampler = \\n\",\n \"job = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fb229e5e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 4. 후처리\\n\",\n \"results = \\n\",\n \"counts_list = \\n\",\n \"### 코드 작성이 완료되었습니다 ###\\n\",\n \"\\n\",\n \"print(f\\\"Outcomes : {counts_list}\\\")\\n\",\n \"plot_histogram(counts_list,title='GHZ state')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d830731e\",\n \"metadata\": {},\n \"source\": [\n \"훌륭합니다! \\n\",\n \"\\n\",\n \"여러분은 양자 회로를 실제 양자 컴퓨터에서 실행하였으며 좋은 결과도 얻어내셨습니다. 가장 많이 관측된 상태는 $|000\\\\rangle$과 $|111\\\\rangle$ 상태이며, 50%에 약간 못 미치는 확률을 얻으셨을 것입니다. 그러나 이번에는 실제 하드웨어의 노이즈로 인해, 관측될 확률이 0이어야 할 다른 상태들도 약간씩 관측된 것도 보이실 것입니다. 이것은 자연스러운 현상이며, 이렇게 발생하는 노이즈를 없애거나 완화하는 방법을 다음 랩에서 보게 될 것입니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bafe6e0d\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Jorge Martínez de Lejarza\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Junye Huang\\n\",\n \"\\n\",\n \"**Translated by:** Boseong Kim\\n\",\n \"\\n\",\n \"**Version:** 1.0.0\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"qgss-ea\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-0/lab0.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"551a947d\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 0: Hello Quantum World!\\n\",\n \"\\n\",\n \"# Table of Contents\\n\",\n \"\\n\",\n \"* [Welcome to the Qiskit Global Summer School 2025!](#welcome)\\n\",\n \" - [Lab 0 overview](#overview)\\n\",\n \" - [Install Qiskit](#install)\\n\",\n \" - [Troubleshooting](#troubleshooting)\\n\",\n \" - [Setting API token](#setting-ibm-cloud)\\n\",\n \" - [Imports](#imports)\\n\",\n \" - [Sanity check](#sanity-check)\\n\",\n \"* [Generate a three-qubit GHZ state using Qiskit patterns](#ghz) \\n\",\n \" - [Step 1. Map](#map)\\n\",\n \" - [Step 2. Optimize](#optimize)\\n\",\n \" - [Step 3. Execute](#execute)\\n\",\n \" - [Step 4. Post-process](#post-process)\\n\",\n \"* [Congratulations!](#congratulations)\\n\",\n \" - [Bonus challenge: Run GHZ on hardware](#bonus)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2144e6fe\",\n \"metadata\": {},\n \"source\": [\n \"# Welcome to the Qiskit Global Summer School 2025! \\n\",\n \"\\n\",\n \"We are thrilled to welcome you to another year of the Qiskit Global Summer School (QGSS). This two-week summer program combines theoretical lectures with hands-on exercises to expand the knowledge of quantum computing and quantum programming among the community of students, researchers, and professionals that use Qiskit in their everyday quantum journey. \\n\",\n \"\\n\",\n \"The hands-on component of this summer school consists of a series of Jupyter notebooks (\\\"labs\\\") designed to guide you through different topics of interest.\\n\",\n \"\\n\",\n \"Each lab complements the corresponding theoretical lectures and includes helpful links to documentation, tutorials, and references to the lectures. Furthermore, you can also find many useful resources in [IBM Quantum Learning](https://quantum.cloud.ibm.com/learning).\\n\",\n \"\\n\",\n \"## Lab 0 overview \\n\",\n \"\\n\",\n \"The main goal of this introductory lab is to show you how to use the challenge notebooks, grade your solutions, and verify that your computer is correctly set up for executing the quantum codes that you will be asked to complete.\\n\",\n \"\\n\",\n \"To complete the different labs of the QGSS, you need to know that every notebook will contain some predefined cells that you should not modify, and some challenge code blocks that you will need to fill with your own code. In particular, you will need to write the required code below each line that has the `### WRITE YOUR CODE BELOW HERE ###` comment. Make sure that every time you restart the kernel, you execute every cell in order, so that the challenge notebooks execute and are graded properly. This will guarantee that all the code is updated and everything runs smoothly. There may be a few exceptions, such as installation cells or cells where you save your account credentials. These will likely only need to be run once.\\n\",\n \"\\n\",\n \"The content of this lab focuses on using Qiskit patterns to produce a GHZ state, emphasizing the importance of designing optimized quantum circuits. At the end is a bonus exercise where you can execute your code on a real quantum computer.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b3df2ea\",\n \"metadata\": {},\n \"source\": [\n \"## Install Qiskit \\n\",\n \"Quantum computers stand as a technology that promises to disrupt how some calculations are done - from breaking encryption with Shor's algorithm, to enabling faster searches with Grover's algorithm, to designing better batteries with quantum phase estimation. A first step is choosing a software tool for designing and executing such algorithms. In these challenges you will use Qiskit for the design and implementation of quantum circuits in simulators and real hardware. The following will help you install Qiskit v2.0.\\n\",\n \"\\n\",\n \"First, check that the version of Python you are using in your environment is python>=3.10, to make sure that it is compatible with the latest Qiskit version we will use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a35543d4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from platform import python_version\\n\",\n \"\\n\",\n \"print(python_version())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47d097d3\",\n \"metadata\": {},\n \"source\": [\n \"If that is not the case, you can upgrade it using your preferred tool. If you are unsure how to do it, some recommended options are:\\n\",\n \"\\n\",\n \"- MacOS: [Homebrew](https://brew.sh/)\\n\",\n \"- Linux: `sudo apt-get update `\\n\",\n \"\\n\",\n \"A detailed guide on how to upgrade Python depending on your OS is detailed here: [How to update Python](https://4geeks.com/how-to/how-to-update-python-version)\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** You cannot run Lab 3 of the QGSS using Windows. Hence, if you are using Windows, we recommend you use [an online lab environment.](https://docs.quantum.ibm.com/guides/online-lab-environments) \\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"For more information take a look at the wiki: https://github.com/qiskit-community/qgss-2025/wiki/Jupyter-Notebook-Environment-(Local-and-Online)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74f76ade\",\n \"metadata\": {},\n \"source\": [\n \"Now let's install the grader that will install Qiskit 2.x and the necessary libraries we will need for the summer school.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b89c3cd1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1b216728\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d9f8ae4\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.9`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c34209af\",\n \"metadata\": {},\n \"source\": [\n \"## Troubleshooting \\n\",\n \"\\n\",\n \"If the previous cell raised an error, you can opt to install Qiskit in a virtual environment (two suggested methods follow). If you have no errors, you can ignore this cell and proceed to the next one.\\n\",\n \"\\n\",\n \"Here we propose two different methods to set up a virtual environment to install Qiskit.\\n\",\n \"1. Using [venv](https://docs.python.org/3/library/venv.html), as explained in the [Qiskit installation guide](https://docs.quantum.ibm.com/guides/install-qiskit). \\n\",\n \"2. Using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html), as explained in this video of [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e8e89851\",\n \"metadata\": {},\n \"source\": [\n \"## Set up your IBM Cloud account \\n\",\n \"\\n\",\n \"You must set up an IBM Cloud account in order to track progress with the grader, and to execute quantum circuits on real hardware.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03041cd7\",\n \"metadata\": {},\n \"source\": [\n \"Set up your account as follows:\\n\",\n \"\\n\",\n \"1. Go to the [upgraded IBM Quantum® Platform](https://quantum.cloud.ibm.com/).\\n\",\n \"2. Go to the top right corner (as shown in the above picture), create your API token, and copy it to a secure location.\\n\",\n \"3. In the next cell, replace `deleteThisAndPasteYourAPIKeyHere` with your API key.\\n\",\n \"4. Go to the bottom left corner (as shown in the above picture) and **create your instance**. Make sure to choose the open plan.\\n\",\n \"5. After the instance is created, copy its associated CRN code. You may need to refresh to see the instance.\\n\",\n \"6. In the cell below, replace `deleteThisAndPasteYourCRNHere` with your CRN code.\\n\",\n \"\\n\",\n \"See [this guide](https://quantum.cloud.ibm.com/docs/guides/cloud-setup) for more details on how to set up your IBM Cloud® account.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** Treat your API key as you would a secure password. See the [Cloud setup](https://quantum.cloud.ibm.com/docs/guides/cloud-setup#cloud-save) guide for more information about using your API key in both secure and untrusted environments.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"52cfa1be\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Save your API key to track your progress and have access to the quantum computers\\n\",\n \"\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"QiskitRuntimeService.save_account(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \" name=\\\"qgss-2025\\\",\\n\",\n \" overwrite=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"92766751\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c94c720c\",\n \"metadata\": {},\n \"source\": [\n \"## Imports \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3952087b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit, generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.quantum_info import SparsePauliOp\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler, EstimatorV2 as Estimator\\n\",\n \"\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_lab0_ex1, grade_lab0_ex2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"282b4699\",\n \"metadata\": {},\n \"source\": [\n \"## Sanity check \\n\",\n \"\\n\",\n \"Let's now create a very simple quantum circuit to check that everything is working as expected.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"840ba2c5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a new circuit with a single qubit\\n\",\n \"qc = QuantumCircuit(2)\\n\",\n \"# Add a H gate to qubit 0\\n\",\n \"qc.h(0)\\n\",\n \"# Add a CNOT gate to qubit 1\\n\",\n \"qc.cx(0, 1)\\n\",\n \"# Return a drawing of the circuit using MatPlotLib (\\\"mpl\\\").\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed117c17\",\n \"metadata\": {},\n \"source\": [\n \"The output you see represents a quantum circuit that produces a Bell state:\\n\",\n \"\\n\",\n \"$$|Bell\\\\rangle=\\\\frac{|00\\\\rangle+|11\\\\rangle}{\\\\sqrt{2}}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf861730\",\n \"metadata\": {},\n \"source\": [\n \"# Generate a three-qubit GHZ state using Qiskit patterns \\n\",\n \"\\n\",\n \"Now, we will follow this episode of [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4) to guide you through the process of generating a three-qubit GHZ state using [Qiskit patterns](https://quantum.cloud.ibm.com/docs/en/guides/intro-to-patterns). \\n\",\n \"\\n\",\n \"A Qiskit pattern is a general framework for breaking down domain-specific problems and contextualizing required capabilities in stages. This allows for the seamless composability of new capabilities developed by IBM Quantum researchers (and others) and enables a future in which quantum computing tasks are performed by powerful heterogenous (CPU/GPU/QPU) computing infrastructure. \\n\",\n \"\\n\",\n \"The four steps of a Qiskit pattern are as follows:\\n\",\n \"\\n\",\n \"1. **Map** problem to quantum circuits and operators\\n\",\n \"2. **Optimize** for target hardware\\n\",\n \"3. **Execute** on target hardware\\n\",\n \"4. **Post-process** results\\n\",\n \"\\n\",\n \"\\n\",\n \"## Step 1. Map \\n\",\n \"\\n\",\n \"The Greenberger–Horne–Zeilinger (GHZ) state is the extension to three (or more) qubits to the maximally entangled state characteristic of the Bell state depicted above. That means that the GHZ state is:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"|GHZ\\\\rangle = \\\\frac{|000\\\\rangle+|111\\\\rangle}{\\\\sqrt{2}}.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"One of the interesting features of the GHZ state is that there are different and equivalent ways to build it using a quantum circuit. In Exercise 1 you will do it in one of the most common ways.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ca23b8d3\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1: Design a GHZ state \\n\",\n \"\\n\",\n \"This is your first exercise of the Summer School! Exciting, huh?\\n\",\n \"\\n\",\n \"In this exercise, you will design a GHZ state following the steps below:\\n\",\n \"\\n\",\n \"1. Apply a Hadamard gate to qubit 0, putting it into a superposition. \\n\",\n \"2. Apply a CNOT gate between qubits 0 and 1.\\n\",\n \"3. Apply a CNOT gate between qubits 1 and 2.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1c389cbe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a new circuit with three qubits\\n\",\n \"qc = QuantumCircuit(3)\\n\",\n \"\\n\",\n \"### WRITE YOUR CODE BELOW HERE ###\\n\",\n \"# Add a H gate to qubit 0\\n\",\n \"\\n\",\n \"# Add a CNOT gate to qubits 0 and 1\\n\",\n \"\\n\",\n \"# Add a CNOT gate to qubits 1 and 2\\n\",\n \"\\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"\\n\",\n \"# Return a drawing of the circuit using MatPlotLib (\\\"mpl\\\").\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a29c6fe7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab0_ex1(qc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"66a3ed14\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2. Optimize \\n\",\n \"\\n\",\n \"Well done designing the circuit!\\n\",\n \"\\n\",\n \"In this case, the circuit is very shallow, and it is not possible to further simplify it or reduce the required number of gates that are needed to build the GHZ state. However, there are other scenarios in which optimizing the circuit is key.\\n\",\n \"\\n\",\n \"There may be situations where you face restrictions in how your quantum circuit can be designed. For example, when running circuits on quantum hardware, you might find connectivity constraints between qubits. This means that some qubits may not be physically connected, so you will need to think of a smart way to implement gates that produce the desired quantum state. Luckily for us, this is where the Qiskit pass manager comes to the rescue! You can provide the desired circuit along with the device's constraints to the pass manager and it will transpile the circuit and handle the optimization for you.\\n\",\n \"\\n\",\n \"Let us consider, for the GHZ state, a situation in which we are limited to interactions only between qubits 0 and 1 and qubits 0 and 2, but not between qubits 1 and 2. We can introduce these constraints to the paass manager using the `generate_preset_pass_manager` function.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"38782ae6\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2: Transpile a GHZ state \\n\",\n \"\\n\",\n \"In this second exercise you are asked to transpile the previous GHZ state with the mentioned connectivity constraints:\\n\",\n \"\\n\",\n \"- Qubit 0 <---> Qubit 1\\n\",\n \"- Qubit 0 <---> Qubit 2\\n\",\n \"- ~~Qubit 1 <---> Qubit 2~~\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"222b26f8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### WRITE YOUR CODE BELOW HERE ###\\n\",\n \"# Write the coupling map of connections between qubits 0 and 1 and 0 and 2 as a list of pairs: [[0,1],...]\\n\",\n \"coupling_map =\\n\",\n \"\\n\",\n \"# Transpile the quantum circuit `qc` using the `generate_preset_pass_manager` function and the `coupling_map` list\\n\",\n \"pm = generate_preset_pass_manager()\\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"qc_transpiled = pm.run(qc)\\n\",\n \"\\n\",\n \"qc_transpiled.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5d860929\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab0_ex2(qc_transpiled)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e2aa24a5\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3. Execute \\n\",\n \"\\n\",\n \"The next step is exciting - we are going to run the quantum circuit using Qiskit Runtime! \\n\",\n \"\\n\",\n \"We will do that using the two [Qiskit primitives](https://quantum.cloud.ibm.com/docs/guides/primitives):\\n\",\n \"1. **Sampler** samples the output register from the execution of one or more quantum circuits. Its output is counts on per-shot measurements. \\n\",\n \"2. **Estimator** computes the expectation value of one or more observables with respect to the states generated by the quantum circuit. Its output consist of the expectation values along with their standard errors.\\n\",\n \"\\n\",\n \"First, we execute our circuit using the Sampler, then save the results as the variable `results_sampler`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"345c5084\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Add measurement operations\\n\",\n \"qc.measure_all()\\n\",\n \"\\n\",\n \"# Set up the backend\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"# Set up the sampler\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"\\n\",\n \"# Submit the circuit to Sampler\\n\",\n \"pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\\n\",\n \"job = sampler.run(pm.run([qc]))\\n\",\n \"\\n\",\n \"# Get the results\\n\",\n \"results_sampler = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1a8e7991\",\n \"metadata\": {},\n \"source\": [\n \"Now, we run our circuit using the Estimator primitive, then save the results as the variable `results_estimator`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f2354c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set up the Estimator\\n\",\n \"estimator = Estimator(mode=backend)\\n\",\n \"\\n\",\n \"# Define some observables\\n\",\n \"ZZZ = SparsePauliOp(\\\"ZZZ\\\")\\n\",\n \"ZZX = SparsePauliOp(\\\"ZZX\\\")\\n\",\n \"ZII = SparsePauliOp(\\\"ZII\\\")\\n\",\n \"XXI = SparsePauliOp(\\\"XXI\\\")\\n\",\n \"ZZI = SparsePauliOp(\\\"ZZI\\\")\\n\",\n \"III = SparsePauliOp(\\\"III\\\")\\n\",\n \"observables = [ZZZ, ZZX, ZII, XXI, ZZI, III]\\n\",\n \"\\n\",\n \"# Submit the circuit to Estimator\\n\",\n \"pub = (qc, observables)\\n\",\n \"job = estimator.run(pubs=[pub])\\n\",\n \"\\n\",\n \"# Get the results\\n\",\n \"results_estimator = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"08df17b9\",\n \"metadata\": {},\n \"source\": [\n \"Next is the final step of Qiskit patterns, where we will visualize our results.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03ccb1ce\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4. Post-process \\n\",\n \"\\n\",\n \"Finally, the last step of Qiskit patterns is to post-process the information we have gathered from the execution of the quantum circuit.\\n\",\n \"\\n\",\n \"First we visualize the results from the Sampler. We can visualize the counts with a histogram plot and quickly see how the two possible quantum states are measured with a probability of 50%.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2cb9ba4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"counts_list = results_sampler[0].data.meas.get_counts()\\n\",\n \"print(f\\\" Outcomes : {counts_list}\\\")\\n\",\n \"display(plot_histogram(counts_list, title=\\\"GHZ state\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86748b30\",\n \"metadata\": {},\n \"source\": [\n \"Now we can visualize the results of the Estimator.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"983608f7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"exp_values = results_estimator[0].data.evs\\n\",\n \"observables_list = [\\\"ZZZ\\\", \\\"ZZX\\\", \\\"ZII\\\", \\\"XXI\\\", \\\"ZZI\\\", \\\"III\\\"]\\n\",\n \"print(f\\\"Expectation values: {list(zip(observables_list, exp_values))}\\\")\\n\",\n \"\\n\",\n \"# Set up our plot\\n\",\n \"container = plt.bar(observables_list, exp_values, width=0.8)\\n\",\n \"# Label each axis\\n\",\n \"plt.xlabel(\\\"Observables\\\")\\n\",\n \"plt.ylabel(\\\"Values\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e5b49810\",\n \"metadata\": {},\n \"source\": [\n \"We see that the observables $ZZI$ and $III$ have an expectation value of 1, since $ZZI$ introduces two minus signs that cancel out, and $III$ acts as the identity, leaving the GHZ state unchanged. The rest of the observables have an expectation value of 0, since their $Z$ operators introduce an odd number of minus signs, or the $X$ operators flip a number of qubits that make the overlapping states orthogonal.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e905aea1\",\n \"metadata\": {},\n \"source\": [\n \"# Congratulations! \\n\",\n \"\\n\",\n \"You have successfully completed Lab 0 and are now set to start the Quantum Global Summer School 2025!\\n\",\n \"\\n\",\n \"In this lab you have successfully set up your machine to execute Qiskit v2.x, and saved your IBM Cloud token to track your progress during the Summer School and execute quantum circuits in real hardware. You have also learned about Qiskit patterns by implementing a specific example of the GHZ circuit and designing an optimized quantum circuit. Finally, you have executed a GHZ circuit on a quantum simulator, and observed how the measurements of the Sampler are in good agreement with the theoretical probabilities of measuring the states $|000\\\\rangle$ and $|111\\\\rangle$ with 50% probability.\\n\",\n \"\\n\",\n \"You can check your progress with the labs using the cell below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"677398c7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"83692cb4\",\n \"metadata\": {},\n \"source\": [\n \"As a bonus exercise, it would be interesting to perform the same experiment on real hardware to see how the noise affects the outcomes, then compare the results to show that we will not have a perfect agreement between the theoretical probabilities and the outcomes. \\n\",\n \"\\n\",\n \"Are you ready? Let's dig in!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c5e04fb1\",\n \"metadata\": {},\n \"source\": [\n \"## Bonus challenge: Run GHZ on hardware \\n\",\n \"\\n\",\n \"To execute a quantum circuit on a quantum computer using Qiskit, we first need to define the quantum backend. We could manually select a specific quantum computer we want to use out of the ones available from IBM Quantum. However, sometimes it is more convenient to select the machine that is least busy at the moment to ensure a fast execution. That's where the method `least_busy` is helpful.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ef101f81\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define the service. This allows you to access IBM QPUs.\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"\\n\",\n \"# Get a backend\\n\",\n \"backend = service.least_busy(operational=True, simulator=False)\\n\",\n \"print(f\\\"We are using the {backend.name} quantum computer\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b94e6ec7\",\n \"metadata\": {},\n \"source\": [\n \"The next call illustrates how easy it is to execute quantum circuits on hardware with `QiskitRuntimeService`. Once we have selected the backend in the cell above, we can simply copy and paste the same lines of code that we wrote for the Sampler simulator, including post-processing and visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c50b3aa1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### WRITE YOUR CODE BELOW HERE ###\\n\",\n \"# Step 1. Map\\n\",\n \"# You should have created a GHZ circuit above and assigned with variable `qc`\\n\",\n \"\\n\",\n \"# Step 2. Optimize\\n\",\n \"pm = \\n\",\n \"qc_transpiled = \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b90f52a1\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Warning: Queue time and 10 minute limit\\n\",\n \"\\n\",\n \"This will roughly take 10s of calculation time on the real hardware, however, running this on the real hardware can lead to long queue times and will take a while, \\n\",\n \"and will block the jupyter notebook in the meantime. \\n\",\n \"\\n\",\n \"Please note that the open plan only includes 10 minutes time on the real hardware, and since we will need these minutes in the later labs, please make sure to save most of your time for these labs. \\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2ba1eb48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 3. Execute\\n\",\n \"sampler = \\n\",\n \"job = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fb229e5e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 4. Post-process\\n\",\n \"results = \\n\",\n \"counts_list = \\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"\\n\",\n \"print(f\\\"Outcomes : {counts_list}\\\")\\n\",\n \"plot_histogram(counts_list,title='GHZ state')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d830731e\",\n \"metadata\": {},\n \"source\": [\n \"Awesome! \\n\",\n \"\\n\",\n \"You have managed to run a circuit on a real quantum computer, and the results are very good! The most repeated states are $|000\\\\rangle$ and $|111\\\\rangle$, and they accumulate a probability just below 50%. However, in this case we observe that, due to noise from the quantum computer, some quantum states are measured, even if the theoretical probability of measuring is 0. This is indeed expected, and we will see in the next labs how we can try to correct or mitigate the errors that are introduced by the noisy nature of quantum computers.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bafe6e0d\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Jorge Martínez de Lejarza\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Junye Huang\\n\",\n \"\\n\",\n \"**Version:** 1.0.0\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-0/lab0_br.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"551a947d\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 0: Olá, Mundo Quântico!\\n\",\n \"\\n\",\n \"# Sumário\\n\",\n \"\\n\",\n \"* [Bem vindos ao Qiskit Global Summer School 2025!](#welcome)\\n\",\n \" - [Visão geral do Lab 0](#overview)\\n\",\n \" - [Instalando Qiskit](#install)\\n\",\n \" - [Resolução de problemas](#troubleshooting)\\n\",\n \" - [Configurando token da API](#setting-ibm-cloud)\\n\",\n \" - [Importações](#imports)\\n\",\n \" - [Verificação de sanidade](#sanity-check)\\n\",\n \"* [Gerar um estado GHZ de três qubits usando Qiskit patterns](#ghz) \\n\",\n \" - [Etapa 1. Mapeamento](#map)\\n\",\n \" - [Etapa 2. Otimização](#optimize)\\n\",\n \" - [Etapa 3. Execução](#execute)\\n\",\n \" - [Etapa 4. Pós-processamento](#post-process)\\n\",\n \"* [Parabéns!](#congratulations)\\n\",\n \" - [Desafio Bônus: Executar GHZ no hardware](#bonus)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2144e6fe\",\n \"metadata\": {},\n \"source\": [\n \"# Bem vindos ao Qiskit Global Summer School 2025! \\n\",\n \"\\n\",\n \"É com grande satisfação que lhe damos as boas-vindas a mais um ano do Qiskit Global Summer School (QGSS). Este programa de duas semanas combina aulas teóricas com exercícios práticos para expandir os conhecimentos de computação quântica e programação quântica entre a comunidade de estudantes, pesquisadores e profissionais que usam Qiskit em sua jornada quântica de cada dia.\\n\",\n \"\\n\",\n \"A vertente prática desse summer school consiste em uma série de Jupyner notebooks (\\\"labs\\\") elaborados para lhe guiar por diferentes tópicos de interesse.\\n\",\n \"\\n\",\n \"Cada lab complementa as aulas teóricas correspondentes e inclui links úteis para documentação, tutoriais e referências às aulas. Além disso, você também pode encontrar muitos recursos úteis em [IBM Quantum Learning (em inglês)](https://quantum.cloud.ibm.com/learning).\\n\",\n \"\\n\",\n \"## Visão geral do Lab 0 \\n\",\n \"\\n\",\n \"O objetivo principal deste lab introdutório é mostrar como usar os notebooks dos desafios, como avaliar suas soluções e verificar se o seu computador está devidamente configurado para executar os códigos quânticos que deverão ser completados.\\n\",\n \"\\n\",\n \"Para concluir os diferentes labs do QGSS, você precisa saber que cada notebook conterá células predefinidas que não deverão ser modificadas, e alguns blocos de código que você deverá preencher com seu próprio código. Especificamente, você deverá escrever o código necessário abaixo de cada linha que contiver o comentário `### ESCREVA O SEU CÓDIGO ABAIXO ###`. \\n\",\n \"\\n\",\n \"Certifique-se de, toda vez que reiniciar o kernel, executar todas as células em ordem, para que os notebooks do desafio sejam executados e avaliados corretamente. Isso garantirá que todo o código esteja atualizado e tudo funcione sem problemas. Pode haver algumas exceções, como em células de instalação ou células nas quais você salva as credenciais de sua conta: essas provavelmente só precisarão ser executadas uma vez.\\n\",\n \"\\n\",\n \"O conteúdo deste lab foca no uso de Qiskit patterns para produzir um estado GHZ, enfatizando a importância de projetar circuitos quânticos otimizados. Ao final, há um exercício bônus onde você pode executar seu código em um computador quântico real.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b3df2ea\",\n \"metadata\": {},\n \"source\": [\n \"## Instalando Qiskit \\n\",\n \"Computadores quânticos são uma tecnologia que promete revolucionar a forma como alguns cálculos são realizados - desde a quebra de criptografia com o algoritmo de Shor, passando por buscas mais rápidas com o algoritmo de Grover, até o projeto de baterias aprimoradas com a estimação de fase quântica. O primeiro passo é escolher uma ferramenta de software para desenvolver e executar esses algoritmos. Nestes desafios, você utilizará Qiskit para o design e implementação de circuitos quânticos em simuladores e em hardware real. As instruções a seguir ajudarão a instalar o Qiskit v2.0.\\n\",\n \"\\n\",\n \"Primeiro, verifique se a versão do Python que você está usando em seu ambiente é python>=3.10, para garantir compatibilidade com a versão mais recente do Qiskit que utilizaremos:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a35543d4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from platform import python_version\\n\",\n \"\\n\",\n \"print(python_version())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47d097d3\",\n \"metadata\": {},\n \"source\": [\n \"Se não for o caso, você pode atualizá-la com sua ferramenta de preferência. Caso não tenha certeza de como fazê-lo, algumas opções recomendadas são:\\n\",\n \"\\n\",\n \"- MacOS: [Homebrew](https://brew.sh/)\\n\",\n \"- Linux: `sudo apt-get update `\\n\",\n \"\\n\",\n \"Um guia detalhado sobre como atualizar o Python dependendo do seu sistema operacional está dispónivel aqui: [Como atualizar o Python (em inglês)](https://4geeks.com/how-to/how-to-update-python-version)\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Nota:** Você não conseguirá rodar o Lab 3 do QGSS usando Windows. Portanto, se estiver no Windows, recomendadmos que utilize um [ambiente de laboratório online (documentação em inglês).](https://docs.quantum.ibm.com/guides/online-lab-environments) \\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Para mais informações, confira a wiki (em inglês): https://github.com/qiskit-community/qgss-2025/wiki/Jupyter-Notebook-Environment-(Local-and-Online)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74f76ade\",\n \"metadata\": {},\n \"source\": [\n \"Agora vamos instalar o avaliador (grader), que instalará o Qiskit 2.x e as bibliotecas necessárias para este summer school.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b89c3cd1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1b216728\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d9f8ae4\",\n \"metadata\": {},\n \"source\": [\n \"Você deve ter o Qiskit em uma versão `>=2.0.0` e o Grader `>=0.22.9`. Se ver uma versão inferior, reinicie o kernel e reinstale o avaliador.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c34209af\",\n \"metadata\": {},\n \"source\": [\n \"## Resolução de problemas \\n\",\n \"\\n\",\n \"Se a célula anterior lançou um erro, você pode tentar instalar o Qiskit em um ambiente virtual (duas formas sugeridas a seguir). Caso não tenha erros, pode ignorar esta célula e continuar para a próxima.\\n\",\n \"\\n\",\n \"Aqui propomos dois métodos diferentes para configurar um ambiente virtual para instalar o Qiskit.\\n\",\n \"1. Usando [venv](http://docs.python.org/pt-br/3/library/venv.html), como descrito no [guia de instalação do Qiskit (em inglês)](https://docs.quantum.ibm.com/guides/install-qiskit). \\n\",\n \"2. Usando [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html), como explicado neste vídeo do canal [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4) (ambos recursos em inglês).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e8e89851\",\n \"metadata\": {},\n \"source\": [\n \"## Configurando sua conta da IBM Cloud \\n\",\n \"\\n\",\n \"Você deve configurar uma conta na IBM Cloud para acompanhar seu progresso com o avaliador e para poder executar circuitos em hardware real.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03041cd7\",\n \"metadata\": {},\n \"source\": [\n \"Para configurar sua conta, siga os passos abaixo:\\n\",\n \"\\n\",\n \"1. Acesse a [Plataforma IBM Quantum® atualizada](https://quantum.cloud.ibm.com/).\\n\",\n \"2. No canto superior direito (como na imagem acima), crie seu token da API, copie e cole-o em um lugar seguro\\n\",\n \"3. Na próxima célula, substitua `deleteIstoEColeSuaChaveDaAPIAqui` pela sua chave da API.\\n\",\n \"4. No canto inferior esquerdo (como na imagem acima), crie sua instância. Certifique-se de escolher o plano gratuito (open plan).\\n\",\n \"5. Após criar a instância, copie o código CRN associado a ela. Talvez seja necessário atualizar a página para conseguir ver a instância.\\n\",\n \"6. Na célula abaixo, substitua `deleteIstoEColeSeuCRNAqui` pelo seu código CRN.\\n\",\n \"\\n\",\n \"Consulte [este guia (em inglês)](https://quantum.cloud.ibm.com/docs/guides/cloud-setup) para mais detalhes sober como configurar sua conta IBM Cloud®.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** Trate sua chave da API como uma senha que deve ficar segura. Consulte o guia de [configuração da Cloud (em inglês)](https://quantum.cloud.ibm.com/docs/guides/cloud-setup#cloud-save) para mais informações sobre como usar sua chave da API tanto em ambientes seguros quanto em não confiáveis.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"52cfa1be\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Salve sua chave da API para acompanhar seu progresso e para ter acesso aos computadores quânticos\\n\",\n \"\\n\",\n \"your_api_key = \\\"deleteIstoEColeSuaChaveDaAPIAqui\\\"\\n\",\n \"your_crn = \\\"deleteIstoEColeSeuCRNAqui\\\"\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"QiskitRuntimeService.save_account(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \" name=\\\"qgss-2025\\\",\\n\",\n \" overwrite=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"92766751\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Confira se a conta foi salva corretamente\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c94c720c\",\n \"metadata\": {},\n \"source\": [\n \"## Importações \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3952087b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit, generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.quantum_info import SparsePauliOp\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler, EstimatorV2 as Estimator\\n\",\n \"\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_lab0_ex1, grade_lab0_ex2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"282b4699\",\n \"metadata\": {},\n \"source\": [\n \"## Verificação de sanidade \\n\",\n \"\\n\",\n \"Vamos criar um circuito quântico bem simples para verificar se está tudo funcionando como deveria.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"840ba2c5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Cria um novo circuito com um único qubit\\n\",\n \"qc = QuantumCircuit(2)\\n\",\n \"# Adiciona uma porta H ao qubit 0\\n\",\n \"qc.h(0)\\n\",\n \"# Adiciona uma porta CNOT ao qubit 1\\n\",\n \"qc.cx(0, 1)\\n\",\n \"# Retorna o desenho do circuito usando MatPlotLib (\\\"mpl\\\").\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed117c17\",\n \"metadata\": {},\n \"source\": [\n \"A saída que você vê representa um circuito quântico que produz um estado de Bell:\\n\",\n \"\\n\",\n \"$$|Bell\\\\rangle=\\\\frac{|00\\\\rangle+|11\\\\rangle}{\\\\sqrt{2}}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf861730\",\n \"metadata\": {},\n \"source\": [\n \"# Gerando um estado GHZ de três qubits usando Qiskit patterns \\n\",\n \"\\n\",\n \"Agora, seguiremos este episódio de [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4) para guiá-lo pelo processo de geração de um estado GHZ de três qubits usando os [Qiskit patterns](https://quantum.cloud.ibm.com/docs/en/guides/intro-to-patterns). \\n\",\n \"\\n\",\n \"\\n\",\n \"Um Qiskit pattern é uma estrutura geral para quebrar problemas específicos de domínio em partes e contextualizar as capacidades necesárias em estágios. Isso permite a perfeita composição de novas capacidades desenvolvidas pelos pesquisadores da IBM Quantum (e outros) e possibilita um futuro em que as tarefas de computação quântica são realizadas por uma poderosa infraestrutura de computação heterogênea (CPU/GPU/QPU).\\n\",\n \"\\n\",\n \"As quatro etapas de um Qiskit pattern são:\\n\",\n \"\\n\",\n \"1. **Mapear** o problema para circuitos e operadores quânticos\\n\",\n \"2. **Otimizar** para o hardware alvo\\n\",\n \"3. **Executar** no hardware alvo\\n\",\n \"4. **Pós-processar** os resultss\\n\",\n \"\\n\",\n \"\\n\",\n \"## Etapa 1. Mapeamento \\n\",\n \"\\n\",\n \"O estado de Greenberger–Horne–Zeilinger (GHZ) é a extensão para três (ou mais) qubits do estado de emaranhamento máximo característico do estado de Bell descrito acima. Isso significa que o estado GHZ é:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"|GHZ\\\\rangle = \\\\frac{|000\\\\rangle+|111\\\\rangle}{\\\\sqrt{2}}.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"Umas das características interessantes do estado GHZ é que existe formas diferentes e equivalentes de construí-lo usando um circuito quântico. No Exercício 1, você o fará de uma das maneiras mais comuns.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ca23b8d3\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercício 1: Projetar um estado GHZ \\n\",\n \"\\n\",\n \"Este é seu primeiro exercício do Summer School! Empolgante, né?\\n\",\n \"\\n\",\n \"Neste exercício, vocâ vai projetar um estado GHZ seguindo os passos abaixo:\\n\",\n \"\\n\",\n \"1. Aplique uma porta Hadamard ao qubit 0, colacando-o em superposição.\\n\",\n \"2. Aplique uma porta CNOT entre os qubits 0 e 1.\\n\",\n \"3. Aplique uma porta CNOT entre os qubits 1 e 2.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1c389cbe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Cria um novo circuito com três qubits\\n\",\n \"qc = QuantumCircuit(3)\\n\",\n \"\\n\",\n \"### ESCREVA O SEU CÓDIGO ABAIXO ###\\n\",\n \"# Adicione uma porta H ao qubit 0\\n\",\n \"\\n\",\n \"# Adicione uma porta CNOT aos qubits 0 e 1\\n\",\n \"\\n\",\n \"# Adicione uma porta CNOT aos qubits 1 e 2\\n\",\n \"\\n\",\n \"### SEU CÓDIGO TERMINA AQUI ###\\n\",\n \"\\n\",\n \"# Retorna o desenho do circuito usando MatPlotLib (\\\"mpl\\\").\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a29c6fe7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Envie sua resposta com o código a seguir\\n\",\n \"grade_lab0_ex1(qc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"66a3ed14\",\n \"metadata\": {},\n \"source\": [\n \"## Etapa 2. Otimização \\n\",\n \"\\n\",\n \"Muito bem ao projetar o circuito!\\n\",\n \"\\n\",\n \"Neste caso, o circuito é bem raso e não é possível simplificá-lo mais ou reduzir o número de portas necessárias para construir o estado GHZ. Porém, hś outros cenários em que otimizar o circuito é fundamental.\\n\",\n \"\\n\",\n \"Pode haver situiações nas quais você enfrenta restrições na forma como o seu circuito quântico pode ser projetado. Por exemplo, ao executar circuitos em hardware quântico, pode haver restrições de conectividade entre os qubits. Isso significa que alguns qubits podem não estar fisicamente conectados, então você precisa pensar em uma forma esperta para implementar portas que produzam o estado quântico desejado. Por sorte, é aí que o pass manager do Qiskit entra em ação! Você pode fornecer o circuito desejado junto das restrições do dispositivo ao pass manager e ele transpilará o circuito e lidará com a otimização para você.\\n\",\n \"\\n\",\n \"Vamos considerar, para o estado GHZ, uma situação em que estamos limitados a interações apenas entre os qubits 0 e 1 e entre os qubits 0 e 2, mas não entre os qubits 1 e 2. Podemos instroduzir essas restrições ao pass manager usando a função `generate_preset_pass_manager`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"38782ae6\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercício 2: Transpilar um estado GHZ \\n\",\n \"\\n\",\n \"Neste segundo exercício, você deverá transpilar o estado GHZ anterior com as restrições de conectividade mencionadas:\\n\",\n \"\\n\",\n \"- Qubit 0 <---> Qubit 1\\n\",\n \"- Qubit 0 <---> Qubit 2\\n\",\n \"- ~~Qubit 1 <---> Qubit 2~~\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"222b26f8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### ESCREVA O SEU CÓDIGO ABAIXO ###\\n\",\n \"# Escreva o mapa de acoplamento (coupling map) das conexões entre os qubits 0 e 1 e entre 0 e 2 como uma lista de pares: [[0,1],...]\\n\",\n \"coupling_map =\\n\",\n \"\\n\",\n \"# Transpile o circuito quântico `qc` usando a função `generate_preset_pass_manager` e a lista `coupling_map`\\n\",\n \"pm = generate_preset_pass_manager()\\n\",\n \"### SEU CÓDIGO TERMINA AQUI ###\\n\",\n \"qc_transpiled = pm.run(qc)\\n\",\n \"\\n\",\n \"qc_transpiled.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5d860929\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Envie sua resposta com o código a seguir\\n\",\n \"grade_lab0_ex2(qc_transpiled)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e2aa24a5\",\n \"metadata\": {},\n \"source\": [\n \"## Etapa 3. Execução \\n\",\n \"\\n\",\n \"A próxima etapa é empolgante - vamos executar o circuito quântico usando o Qiskit Runtime! \\n\",\n \"\\n\",\n \"O faremos utilizando as duas [primitivas do Qiskit (documentação em inglês)](https://quantum.cloud.ibm.com/docs/guides/primitives):\\n\",\n \"1. **Sampler** gera amostras do registrador de saída a partir da execução de um ou mais circuitos quânticos. Sua saída são as contagens das medições por _shot_.\\n\",\n \"2. **Estimator** calcula o valor esperado de um ou mais observáveis em relação aos estados gerados pelo circuito quântico. Sua saída consiste nos valores esperados, juntamente com seus erros padrão.\\n\",\n \"\\n\",\n \"Primeiro, executamos nosso circuito usando o Sampler e, então, salvamos o resultados na variável `results_sampler`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"345c5084\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Adiciona operações de medição\\n\",\n \"qc.measure_all()\\n\",\n \"\\n\",\n \"# Instancia o backend\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"# Instancia o sampler\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"\\n\",\n \"# Submete o circuito ao Sampler\\n\",\n \"pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\\n\",\n \"job = sampler.run(pm.run([qc]))\\n\",\n \"\\n\",\n \"# Recebe os resultados\\n\",\n \"results_sampler = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1a8e7991\",\n \"metadata\": {},\n \"source\": [\n \"Agora, executamos nosso circuito com a primitiva Estimator, então salvamos os resultados na variável `results_estimator`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f2354c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Instancia o Estimator\\n\",\n \"estimator = Estimator(mode=backend)\\n\",\n \"\\n\",\n \"# Define alguns observáveis\\n\",\n \"ZZZ = SparsePauliOp(\\\"ZZZ\\\")\\n\",\n \"ZZX = SparsePauliOp(\\\"ZZX\\\")\\n\",\n \"ZII = SparsePauliOp(\\\"ZII\\\")\\n\",\n \"XXI = SparsePauliOp(\\\"XXI\\\")\\n\",\n \"ZZI = SparsePauliOp(\\\"ZZI\\\")\\n\",\n \"III = SparsePauliOp(\\\"III\\\")\\n\",\n \"observables = [ZZZ, ZZX, ZII, XXI, ZZI, III]\\n\",\n \"\\n\",\n \"# Submete o circuito ao Estimator\\n\",\n \"pub = (qc, observables)\\n\",\n \"job = estimator.run(pubs=[pub])\\n\",\n \"\\n\",\n \"# Recebe os resultados\\n\",\n \"results_estimator = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"08df17b9\",\n \"metadata\": {},\n \"source\": [\n \"A seguir está a última estapa dos Qiskit patterns, onde visualizaremos nossos resultados.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03ccb1ce\",\n \"metadata\": {},\n \"source\": [\n \"## Etapa 4. Pós-processamento \\n\",\n \"\\n\",\n \"Finalmente, a etapa final dos Qiskit patterns é pós-processar as informações que coletamos da execução do circuito quântico.\\n\",\n \"\\n\",\n \"Primeiro, visualizamos os resultados do Sampler. Podemos ver as contagens em um histograma e rapidamente perceber como os estados quânticos possíveis são medidos com uma probabilidade de 50%.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2cb9ba4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"counts_list = results_sampler[0].data.meas.get_counts()\\n\",\n \"print(f\\\" Outcomes : {counts_list}\\\")\\n\",\n \"display(plot_histogram(counts_list, title=\\\"GHZ state\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86748b30\",\n \"metadata\": {},\n \"source\": [\n \"Agora, podemos visualizar os resultados do Estimator.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"983608f7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"exp_values = results_estimator[0].data.evs\\n\",\n \"observables_list = [\\\"ZZZ\\\", \\\"ZZX\\\", \\\"ZII\\\", \\\"XXI\\\", \\\"ZZI\\\", \\\"III\\\"]\\n\",\n \"print(f\\\"Expectation values: {list(zip(observables_list, exp_values))}\\\")\\n\",\n \"\\n\",\n \"# Configura nosso gráfico\\n\",\n \"container = plt.bar(observables_list, exp_values, width=0.8)\\n\",\n \"# Legenda cada eixo\\n\",\n \"plt.xlabel(\\\"Observables\\\")\\n\",\n \"plt.ylabel(\\\"Values\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e5b49810\",\n \"metadata\": {},\n \"source\": [\n \"Vemos que os observáveis $ZZI$ e $III$ têm valor esperado de 1, visto que $ZZI$ introduz dois sinais de menos que se cancelam, e $III$ atua como a identidade, deixando o estado GHZ inalterado. Os demais observáveis apresentam valor esperado de 0, já que seus operadores $Z$ introduzem um número ímpar de sinais de menos, ou os operadores $X$ invertem um número de qubits que tornam os estados justapostos ortogonais.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e905aea1\",\n \"metadata\": {},\n \"source\": [\n \"# Parabéns! \\n\",\n \"\\n\",\n \"Você concluiu com sucesso o Lab 0 e agora está pronto para iniciar o Quantum Global Summer School 2025!\\n\",\n \"\\n\",\n \"Neste lab, você configurou sua máquina para executar o Qiskit v2.x corretamente, além de salvar seu token da IBM Cloud para acompanhar seu progresso durante o Summer School e executar circuitos quânticos em hardware real. Também aprendeu sobre os Qiskit patterns ao implementar um exemplo específico do circuito GHZ e projetar um circuito quântico otimizado. Por fim, você executou um circuito GHZ em um simulador quântico e observou como as medições do Sampler estão em boa concordância com as probabilidades teóricas de medir os estados $|000\\\\rangle$ e $|111\\\\rangle$ com 50% de probabilidade.\\n\",\n \"\\n\",\n \"Você pode verificar seu progresso nos laboratórios com a célula abaixo:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"677398c7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"83692cb4\",\n \"metadata\": {},\n \"source\": [\n \"Como exercício bônus, seria interessante realizar o mesmo experimento em hardware real para observar como o ruído afeta os resultados e, então, comparar os resultados anteriores para mostrar que não haverá uma concordância perfeita entre as probabilidades teóricas e os resultados obtidos.\\n\",\n \"\\n\",\n \"Tudo certo? Vamos lá!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c5e04fb1\",\n \"metadata\": {},\n \"source\": [\n \"## Desafio Bônus: Executar GHZ no hardware \\n\",\n \"\\n\",\n \"Para executar um circuito quântico em um computador quântico com Qiskit, primeiro precisamos definir o backend quântico. Poderíamos selecionar manualmente um computador quântico em específico que gostaríamos de utilizar destre os disponíveis na IBM Quantum. No entanto, às vezes é mais conveniente escolher a máquina que está menos ocupada no momento para garantir uma execução rápida. É aí que o método `least_busy` se torna útil.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ef101f81\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define o serviço. Permite que você acesse as QPUs da IBM.\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"\\n\",\n \"# Obtém um backend\\n\",\n \"backend = service.least_busy(operational=True, simulator=False)\\n\",\n \"print(f\\\"We are using the {backend.name} quantum computer\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b94e6ec7\",\n \"metadata\": {},\n \"source\": [\n \"A próxima célula ilustra o quão fácil é executar circuitos quânticos em hardware com o `QiskitRuntimeService`. Uma vez que selecionamos o backend na célula acima, podemos simplesmente copiar e colar as mesmas linhas de código que escrevemos para o simulador Sampler, incluindo pós-processamento e visualização.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c50b3aa1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### ESCREVA O SEU CÓDIGO ABAIXO ###\\n\",\n \"# Etapa 1. Mapeamento\\n\",\n \"# Você deve ter criado um circuito GHZ anteriormente e atribuído à variável `qc`\\n\",\n \"\\n\",\n \"# Etapa 2. Otimização\\n\",\n \"pm = \\n\",\n \"qc_transpiled = \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b90f52a1\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Aviso: Tempo de fila e limite de 10 minutos\\n\",\n \"\\n\",\n \"Isso levará aproximadamente 10s de tempo de cálculo no hardware real, porém, executar no hardware real pode resultar em longos tempos de fila e demorar um tempo, bloqueando o jupyer notebook enquanto isso.\\n\",\n \"\\n\",\n \"Por favor, note que o plano gratuito inclui apenas 10 minutos de tempo no hardware real e, como precisaremos desses minutos nos labs posteriores, certifique-se de guardar a maior parte do seu tempo para eles.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2ba1eb48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Etapa 3. Executar\\n\",\n \"sampler = \\n\",\n \"job = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fb229e5e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Etapa 4. Pós-processamento\\n\",\n \"results = \\n\",\n \"counts_list = \\n\",\n \"### SEU CÓDIGO TERMINA AQUI ###\\n\",\n \"\\n\",\n \"print(f\\\"Outcomes : {counts_list}\\\")\\n\",\n \"plot_histogram(counts_list,title='GHZ state')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d830731e\",\n \"metadata\": {},\n \"source\": [\n \"Excelente! \\n\",\n \"\\n\",\n \"Você conseguiu executar um circuito em um computador quântico real, e os resultados são muito bons! Os estados mais repetidos são $|000\\\\rangle$ e $|111\\\\rangle$, e eles acumulam uma probabilidade um pouco abaixo de 50%. No entanto, nesse caso, observamos que, devido ao ruído do computador quântico, alguns estados quânticos são medidos, mesmo que a probabilidade teórica de suas medições seja 0. Isso é de fato esperado, e veremos nos próximos labs como podemos tentar corrigir ou mitigar os erros introduzidos pela natureza ruidosa dos computadores quânticos.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bafe6e0d\",\n \"metadata\": {},\n \"source\": [\n \"# Informação adicional\\n\",\n \"\\n\",\n \"**Criado por:** Jorge Martínez de Lejarza\\n\",\n \"\\n\",\n \"**Revisado por:** Marcel Pfaffhauser, Junye Huang\\n\",\n \"\\n\",\n \"**Traduzido por:** Kauê Miziara\\n\",\n \"\\n\",\n \"**Version:** 1.0.0\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-0/lab0_cn.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"551a947d\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# 实验0：你好，量子世界！\\n\",\n \"\\n\",\n \"# 目录\\n\",\n \"\\n\",\n \"* [欢迎参加Qiskit全球暑期学校2025！](#welcome)\\n\",\n \" - [实验0 概述](#overview)\\n\",\n \" - [安装Qiskit](#install)\\n\",\n \" - [故障排除](#troubleshooting)\\n\",\n \" - [设置API令牌](#setting-ibm-cloud)\\n\",\n \" - [导入模块](#imports)\\n\",\n \" - [环境检查](#sanity-check)\\n\",\n \"* [使用Qiskit模式生成三量子比特GHZ态](#ghz)\\n\",\n \" - [步骤1：映射](#map)\\n\",\n \" - [步骤2：优化](#optimize)\\n\",\n \" - [步骤3：执行](#execute)\\n\",\n \" - [步骤4：后期处理](#post-process)\\n\",\n \"* [恭喜完成！](#congratulations)\\n\",\n \" - [附加挑战：在真实量子硬件上运行GHZ实验](#bonus)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2144e6fe\",\n \"metadata\": {},\n \"source\": [\n \"# 欢迎参加Qiskit全球暑期学校2025！ \\n\",\n \"\\n\",\n \"我们非常高兴地欢迎您参加新一届Qiskit全球暑期学校(QGSS)。这个为期两周的暑期项目将理论讲座与实践练习相结合，旨在提升学生、研究人员和日常使用Qiskit的专业人士在量子计算和量子编程领域的知识水平。\\n\",\n \"\\n\",\n \"本次暑期学校的实践部分由一系列Jupyter笔记本(\\\"实验\\\")组成，这些实验将引导您探索量子计算的不同主题领域。\\n\",\n \"\\n\",\n \"每个实验都配有相应的理论讲座，并包含文档链接、教程资源以及相关讲座参考资料。此外，您还可以在[IBM Quantum Learning](https://quantum.cloud.ibm.com/learning)平台找到更多实用资源。\\n\",\n \"\\n\",\n \"## 实验0概述 \\n\",\n \"\\n\",\n \"本入门实验的主要目标是：\\n\",\n \"1. 指导您如何使用挑战性实验笔记本\\n\",\n \"2. 演示解决方案评分机制\\n\",\n \"3. 验证您的计算机环境是否已正确配置以运行量子代码\\n\",\n \"\\n\",\n \"在完成QGSS各项实验时，请注意每个笔记本都包含：\\n\",\n \"- 不应修改的预定义代码单元格\\n\",\n \"- 需要您自行编写代码的挑战区块（标有`### WRITE YOUR CODE BELOW HERE ###`注释）\\n\",\n \"\\n\",\n \"重要提示：\\n\",\n \"1. 每次重启内核后，请按顺序执行所有单元格\\n\",\n \"2. 确保挑战笔记本能正常执行和评分\\n\",\n \"3. 这将保证所有代码更新并顺利运行\\n\",\n \"（安装单元格和账户凭证保存单元格等少数例外情况可能只需执行一次）\\n\",\n \"\\n\",\n \"本实验重点介绍使用Qiskit模式生成GHZ态，强调设计优化量子电路的重要性。最后还包含一个可在真实量子计算机上运行代码的附加挑战练习。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b3df2ea\",\n \"metadata\": {},\n \"source\": [\n \"## 安装Qiskit \\n\",\n \"\\n\",\n \"量子计算机是一项具有颠覆性潜力的技术——从运用肖尔(Shor)算法破解加密，到利用格罗弗(Grover)算法实现更快的搜索，再到通过量子相位估计算法设计更优质的电池。要探索这些应用，首先需要选择合适的量子编程工具。在本课程中，您将使用Qiskit在模拟器和真实量子硬件上设计与实现量子电路。以下指南将帮助您安装Qiskit v2.0版本。\\n\",\n \"\\n\",\n \"**环境准备：**\\n\",\n \"首先请确保您的Python环境版本≥3.10，以保证与我们将使用的最新版Qiskit兼容。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a35543d4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from platform import python_version\\n\",\n \"\\n\",\n \"print(python_version())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47d097d3\",\n \"metadata\": {},\n \"source\": [\n \"若当前版本不符合要求，您可以通过以下方式升级Python环境（根据操作系统选择相应方法）：\\n\",\n \"\\n\",\n \"**各平台升级指南：**\\n\",\n \"- MacOS系统推荐使用[Homebrew](https://brew.sh/)包管理器\\n\",\n \"- Linux系统可执行终端命令：`sudo apt-get update && sudo apt-get upgrade python3`\\n\",\n \"\\n\",\n \"完整Python升级教程参考：[Python版本升级指南](https://4geeks.com/how-to/how-to-update-python-version)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2ba1eb48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 3. Execute\\n\",\n \"sampler = \\n\",\n \"job = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fb229e5e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 4. Post-process\\n\",\n \"results = \\n\",\n \"counts_list = \\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"\\n\",\n \"print(f\\\"Outcomes : {counts_list}\\\")\\n\",\n \"plot_histogram(counts_list,title='GHZ state')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d830731e\",\n \"metadata\": {},\n \"source\": [\n \"太棒了！\\n\",\n \"\\n\",\n \"您已成功在真实量子计算机上运行了量子线路，且结果相当理想！出现频率最高的状态确实是$|000\\\\rangle$和$|111\\\\rangle$，其累计概率略低于50%。然而我们也观察到，由于量子计算机的噪声影响，某些理论概率为0的量子态也被测量到了。这种现象完全在预期之中，在后续实验中我们将学习如何校正或缓解量子计算机噪声引入的各类误差。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bafe6e0d\",\n \"metadata\": {},\n \"source\": [\n \"# 附加信息\\n\",\n \"\\n\",\n \"**创建者:** Jorge Martínez de Lejarza\\n\",\n \"\\n\",\n \"**顾问:** Marcel Pfaffhauser, Junye Huang\\n\",\n \"\\n\",\n \"**翻译:** Ziqing Guo\\n\",\n \"\\n\",\n \"**版本:** 1.0.0\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-0/lab0_hindi.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"551a947d\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 0: हेलो क्वांटम वर्ल्ड!\\n\",\n \"\\n\",\n \"# सामग्री सूची\\n\",\n \"\\n\",\n \"* [Quantum Gobal Summer School 2025 में आपका स्वागत है!](#welcome)\\n\",\n \" - [Lab 0 का अवलोकन](#overview)\\n\",\n \" - [Qiskit इंस्टॉल करें](#install)\\n\",\n \" - [समस्याओं का समाधान](#troubleshooting)\\n\",\n \" - [API टोकन सेट करना](#setting-ibm-cloud)\\n\",\n \" - [इंपोर्ट्स](#imports)\\n\",\n \" - [सैनिटी चेक ](#sanity-check)\\n\",\n \"* [Qiskit पैटर्न का उपयोग करके तीन-क्वबिट GHZ स्टेट जनरेट करें](#ghz) \\n\",\n \" - [चरण 1: मैप करें](#map)\\n\",\n \" - [चरण 2: ऑप्टिमाइज़ करें](#optimize)\\n\",\n \" - [चरण 3: एक्जीक्यूट करें](#execute)\\n\",\n \" - [चरण 4: पोस्ट-प्रोसेस करें](#post-process)\\n\",\n \"* [बधाई हो!](#congratulations)\\n\",\n \" - [बोनस चुनौती: GHZ को हार्डवेयर पर रन करें](#bonus)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2144e6fe\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit ग्लोबल समर स्कूल 2025 में आपका स्वागत है!\\n\",\n \"\\n\",\n \"हमें आपको Qiskit Global Summer School (QGSS) के एक और नए संस्करण में स्वागत करते हुए अत्यंत खुशी हो रही है। यह दो सप्ताह का समर प्रोग्राम सिद्धांतात्मक व्याख्यानों और व्यावहारिक अभ्यासों का एक ऐसा संयोजन है, जिसका उद्देश्य छात्रों, शोधकर्ताओं और पेशेवरों के उस समुदाय का क्वांटम कंप्यूटिंग और क्वांटम प्रोग्रामिंग में ज्ञान बढ़ाना है, जो अपने दैनिक क्वांटम सफर में Qiskit का उपयोग करते हैं।\\n\",\n \"\\n\",\n \"इस समर स्कूल का व्यावहारिक भाग Jupyter नोटबुक्स (जिसे \\\"labs\\\" कहा गया है) की एक श्रृंखला है, जो आपको विभिन्न रुचिकर विषयों में मार्गदर्शन प्रदान करती है।\\n\",\n \"\\n\",\n \"प्रत्येक लैब संबंधित थ्योरी लेक्चर की पूरक होती है और उसमें उपयोगी डाक्यूमेंटेशन, ट्यूटोरियल्स और लेक्चर से जुड़ी अन्य संदर्भ सामग्री के लिंक भी शामिल होते हैं। इसके अतिरिक्त, आप [IBM Quantum Learning](https://quantum.cloud.ibm.com/learning)में कई उपयोगी संसाधन भी पा सकते हैं।.\\n\",\n \"\\n\",\n \"## Lab 0 का अवलोकन\\n\",\n \"\\n\",\n \"इस प्रारंभिक लैब का मुख्य उद्देश्य यह है कि आप यह सीख सकें कि: चैलेंज नोटबुक्स का सही ढंग से,उपयोग कैसे करें, अपने समाधानों को ग्रेड (मूल्यांकन) कैसे करें, और यह सुनिश्चित करें कि आपका कंप्यूटर क्वांटम कोड्स को चलाने के लिए सही तरीके से सेटअप हो चुका है।\\n\",\n \"\\n\",\n \"QGSS की विभिन्न लैब्स को पूरा करने के लिए आपको यह जानना आवश्यक है कि हर नोटबुक में कुछ पूर्व-निर्धारित सेल्स होंगे जिन्हें आपको बिल्कुल नहीं बदलना है, और कुछ चैलेंज कोड ब्लॉक्स होंगे जहाँ आपको अपना कोड भरना होगा। विशेष रूप से, आपको कोड उस लाइन के नीचे लिखना होगा जहाँ यह कमेंट लिखा हो: `### WRITE YOUR CODE BELOW HERE ###` हर बार जब आप कर्नेल रीस्टार्ट करें, तो यह सुनिश्चित करें कि आप हर सेल को क्रम से दोबारा चलाएं, ताकि नोटबुक्स सही ढंग से एक्जीक्यूट और ग्रेड हो सकें। इससे यह सुनिश्चित होगा कि आपका कोड अपडेट हो और सब कुछ सही से काम करे। कुछ अपवाद हो सकते हैं जैसे कि इंस्टॉलेशन या क्रेडेंशियल सेविंग वाली सेल्स, जिन्हें आमतौर पर सिर्फ एक बार ही चलाना होता है।\\n\",\n \"\\n\",\n \"इस लैब की सामग्री का फोकस Qiskit पैटर्न्स का उपयोग करके GHZ स्टेट बनाने पर है, जिससे यह समझने में मदद मिलती है कि क्वांटम सर्किट को ऑप्टिमाइज़ करना कितना महत्वपूर्ण है।\\n\",\n \"\\n\",\n \"लैब के अंत में एक बोनस अभ्यास है जहाँ आप अपने बनाए गए कोड को वास्तविक क्वांटम कंप्यूटर पर रन कर सकते हैं।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b3df2ea\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskit इंस्टॉल करें \\n\",\n \"क्वांटम कंप्यूटर एक ऐसी तकनीक के रूप में उभर रहे हैं जो गणनाओं की दुनिया में क्रांतिकारी बदलाव ला सकती है — चाहे वह Shor’s एल्गोरिदम द्वारा एन्क्रिप्शन तोड़ना हो, Grover’s एल्गोरिदम से तेज़ सर्च करना हो, या Quantum Phase Estimation से बेहतर बैटरियाँ डिज़ाइन करना हो। इस दिशा में पहला कदम है:\\n\",\n \"ऐसे सॉफ्टवेयर टूल को चुनना जो इन एल्गोरिदम को डिज़ाइन और कार्यान्वित करने के लिए सक्षम हो।\\n\",\n \"\\n\",\n \"इन चैलेंजों में आप Qiskit का उपयोग करेंगे ताकि आप क्वांटम सर्किट्स को सिम्युलेटर और रियल हार्डवेयर दोनों पर डिज़ाइन और रन कर सकें। iskit v2.0 को इंस्टॉल करने से पहले, सबसे पहले यह सुनिश्चित करें कि:\\n\",\n \"\\n\",\n \"आपके सिस्टम में जो Python वर्ज़न है, वह Python 3.10 या उससे ऊपर है।\\n\",\n \"\\n\",\n \"इसे चेक करने के लिए:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a35543d4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from platform import python_version\\n\",\n \"\\n\",\n \"print(python_version())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47d097d3\",\n \"metadata\": {},\n \"source\": [\n \"अगर आपका Python वर्ज़न पुराना है, तो आप इसे अपने ऑपरेटिंग सिस्टम के अनुसार अपडेट कर सकते हैं:\\n\",\n \"\\n\",\n \"- MacOS: [Homebrew](https://brew.sh/) का उपयोग करें\\n\",\n \"- Linux: `sudo apt-get update ` कमांड से अपडेट करें\\n\",\n \"\\n\",\n \"Python अपडेट करने की विस्तृत गाइड यहाँ उपलब्ध है: [How to update Python](https://4geeks.com/how-to/how-to-update-python-version)\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **नोट :** आप QGSS की Lab 3 को Windows ऑपरेटिंग सिस्टम पर नहीं चला सकते। इसलिए, यदि आप Windows का उपयोग कर रहे हैं, तो हम अनुशंसा करते हैं कि आप एक ऑनलाइन लैब वातावरण [an online lab environment.](https://docs.quantum.ibm.com/guides/online-lab-environments) का उपयोग करें।\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"अधिक जानकारी के लिए आप इस विकी पेज पर जा सकते हैं: https://github.com/qiskit-community/qgss-2025/wiki/Jupyter-Notebook-Environment-(Local-and-Online)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74f76ade\",\n \"metadata\": {},\n \"source\": [\n \"अब हम grader इंस्टॉल करेंगे, जो आपके सिस्टम में Qiskit v2.x और समर स्कूल में आवश्यक सभी लाइब्रेरीज़ को इंस्टॉल करेगा।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b89c3cd1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1b216728\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d9f8ae4\",\n \"metadata\": {},\n \"source\": [\n \"आपके पास निम्नलिखित वर्ज़न होने चाहिए Qiskit `>=2.0.0` और Grader `>=0.22.9`। यदि इनसे कम वर्ज़न दिखाई देता है, तो आपको अपना kernel restart करना होगा और फिर से grader को reinstall करना होगा।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c34209af\",\n \"metadata\": {},\n \"source\": [\n \"## त्रुटि समाधान \\n\",\n \"\\n\",\n \"अगर पिछली सेल को चलाते समय कोई त्रुटि (error) आई हो, तो आप Qiskit को एक वर्चुअल एनवायरनमेंट में इंस्टॉल करने का विकल्प चुन सकते हैं। (नीचे दो सुझाव दिए गए हैं)।\\n\",\n \"यदि कोई त्रुटि नहीं आई है, तो आप इस सेल को छोड़ सकते हैं और अगली सेल पर आगे बढ़ सकते हैं।\\n\",\n \"\\n\",\n \"\\n\",\n \"Qiskit को वर्चुअल एनवायरनमेंट में इंस्टॉल करने के दो तरीके:\\n\",\n \"1. [venv](https://docs.python.org/3/library/venv.html) का उपयोग करके – जैसा कि [Qiskit installation guide](https://docs.quantum.ibm.com/guides/install-qiskit) में समझाया गया है। \\n\",\n \"2. [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) का उपयोग करके – जैसा कि [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4) के वीडियो में बताया गया है।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e8e89851\",\n \"metadata\": {},\n \"source\": [\n \"## IBM Cloud अकाउंट सेट करें \\n\",\n \"\\n\",\n \"आपको एक IBM Cloud अकाउंट सेटअप करना अनिवार्य है ताकि:\\n\",\n \"\\n\",\n \"आप Grader के साथ अपनी प्रगति को ट्रैक कर सकें और वास्तविक क्वांटम हार्डवेयर पर क्वांटम सर्किट्स को एक्जीक्यूट कर सकें।\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03041cd7\",\n \"metadata\": {},\n \"source\": [\n \"अपना खाता इस प्रकार सेट करें:\\n\",\n \"\\n\",\n \"1. अपग्रेडेड [upgraded IBM Quantum® Platform](https://quantum.cloud.ibm.com/) पर जाएँ।\\n\",\n \"2. ऊपर दाएँ कोने में जाएँ (जैसा कि ऊपर चित्र में दिखाया गया है), अपना API टोकन बनाएं और उसे किसी सुरक्षित स्थान पर कॉपी कर लें।\\n\",\n \"3. अगली कोड सेल में, `deleteThisAndPasteYourAPIKeyHere` की जगह अपना API टोकन पेस्ट करें।\\n\",\n \"4. नीचे बाएँ कोने में जाएँ (जैसा कि चित्र में दिखाया गया है) और अपना इंस्टेंस **create your instance** बनाएं। ध्यान दें कि आप Open Plan को चुनें।\\n\",\n \"5. जब इंस्टेंस बन जाए, तो उससे जुड़ा CRN कोड कॉपी करें। इंस्टेंस को देखने के लिए पेज को refresh करने की आवश्यकता हो सकती है।\\n\",\n \"6. फिर कोड में `deleteThisAndPasteYourCRNHere` की जगह अपना CRN कोड पेस्ट करें।\\n\",\n \"\\n\",\n \" अधिक [this guide](https://quantum.cloud.ibm.com/docs/guides/cloud-setup) के लिए यह गाइड देखें: IBM Cloud® खाता कैसे सेट करें।\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **नोट :** अपने API टोकन को पासवर्ड की तरह गोपनीय रखें। सुरक्षित और असुरक्षित दोनों प्रकार के वातावरणों में API टोकन के उपयोग से संबंधित जानकारी के लिए [Cloud setup](https://quantum.cloud.ibm.com/docs/guides/cloud-setup#cloud-save) अवश्य पढ़ें।\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"52cfa1be\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Save your API key to track your progress and have access to the quantum computers\\n\",\n \"\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"QiskitRuntimeService.save_account(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \" name=\\\"qgss-2025\\\",\\n\",\n \" overwrite=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"92766751\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c94c720c\",\n \"metadata\": {},\n \"source\": [\n \"## इंपोर्ट्स \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3952087b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit, generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.quantum_info import SparsePauliOp\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler, EstimatorV2 as Estimator\\n\",\n \"\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_lab0_ex1, grade_lab0_ex2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"282b4699\",\n \"metadata\": {},\n \"source\": [\n \"## सैनिटी चेक \\n\",\n \"\\n\",\n \"आइए अब एक बहुत ही साधारण quantum circuit बनाते हैं ताकि यह जांचा जा सके कि सब कुछ उम्मीद के अनुसार कार्य कर रहा है या नहीं।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"840ba2c5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a new circuit with a single qubit\\n\",\n \"qc = QuantumCircuit(2)\\n\",\n \"# Add a H gate to qubit 0\\n\",\n \"qc.h(0)\\n\",\n \"# Add a CNOT gate to qubit 1\\n\",\n \"qc.cx(0, 1)\\n\",\n \"# Return a drawing of the circuit using MatPlotLib (\\\"mpl\\\").\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed117c17\",\n \"metadata\": {},\n \"source\": [\n \"आप जो आउटपुट देखेंगे, वह एक quantum circuit को दर्शाता है जो एक Bell state तैयार करता है।\\n\",\n \"\\n\",\n \"$$|Bell\\\\rangle=\\\\frac{|00\\\\rangle+|11\\\\rangle}{\\\\sqrt{2}}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf861730\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit पैटर्न का उपयोग करके तीन-क्वबिट GHZ स्टेट तैयार करें \\n\",\n \"\\n\",\n \"अब हम [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4) के इस एपिसोड को फॉलो करेंगे, जो आपको [Qiskit patterns](https://quantum.cloud.ibm.com/docs/en/guides/intro-to-patterns) का उपयोग करके तीन-क्वबिट GHZ state तैयार करने की प्रक्रिया में मार्गदर्शन देगा। \\n\",\n \"\\n\",\n \"Qiskit पैटर्न एक सामान्य ढांचा (framework) है जिसका उपयोग डोमेन-विशिष्ट समस्याओं को चरणों में विभाजित करके आवश्यक क्षमताओं को संदर्भित करने के लिए किया जाता है।\\n\",\n \"यह ढांचा IBM Quantum के शोधकर्ताओं (और अन्य) द्वारा विकसित नई क्षमताओं को आसानी से जोड़ने की सुविधा देता है, और भविष्य की उस दुनिया की ओर इशारा करता है जहाँ क्वांटम कंप्यूटिंग कार्य शक्तिशाली विविध कंप्यूटिंग इन्फ्रास्ट्रक्चर (जैसे CPU/GPU/QPU) पर किए जाएंगे।\\n\",\n \"\\n\",\n \"\\n\",\n \"Qiskit pattern के चार चरण इस प्रकार हैं:\\n\",\n \"\\n\",\n \"1. **Map** समस्या को क्वांटम सर्किट और ऑपरेटर में मैप करें\\n\",\n \"2. **Optimize** लक्षित हार्डवेयर के लिए ऑप्टिमाइज़ करें\\n\",\n \"3. **Execute** लक्षित हार्डवेयर पर एक्जीक्यूट करें\\n\",\n \"4. **Post-process** परिणामों को पोस्ट-प्रोसेस करें\\n\",\n \"\\n\",\n \"\\n\",\n \"## चरण 1. मैप करें (Map) \\n\",\n \"\\n\",\n \"Greenberger–Horne–Zeilinger (GHZ) स्टेट, Bell स्टेट का तीन (या उससे अधिक) क्वबिट्स में विस्तारित रूप है, जो अधिकतम रूप से उलझी हुई (maximally entangled) क्वांटम स्थिति को दर्शाता है। इसका अर्थ है कि GHZ स्टेट वह क्वांटम स्थिति है जिसमें सभी क्वबिट्स इस तरह से उलझे होते हैं कि उनमें से किसी एक की माप बाकी सभी को प्रभावित करती है — जैसा कि ऊपर Bell स्टेट के उदाहरण में दिखाया गया है :\\n\",\n \"\\n\",\n \"$$\\n\",\n \"|GHZ\\\\rangle = \\\\frac{|000\\\\rangle+|111\\\\rangle}{\\\\sqrt{2}} \\n\",\n \"$$\\n\",\n \"\\n\",\n \"GHZ स्टेट की एक रोचक विशेषता यह है कि इसे क्वांटम सर्किट का उपयोग करके बनाने के कई विभिन्न लेकिन समान रूप से प्रभावी तरीके होते हैं।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ca23b8d3\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1: एक GHZ स्टेट डिज़ाइन करें \\n\",\n \"\\n\",\n \"यह समर स्कूल का आपका पहला अभ्यास है! रोमांचक है, है ना?\\n\",\n \"\\n\",\n \"इस अभ्यास में, आप नीचे दिए गए चरणों का पालन करते हुए एक GHZ स्टेट डिज़ाइन करेंगे:\\n\",\n \"\\n\",\n \"1. Qubit 0 पर Hadamard गेट लागू करें, ताकि वह सुपरपोजिशन में चला जाए।\\n\",\n \"2. Qubit 0 और Qubit 1 के बीच एक CNOT गेट लागू करें।\\n\",\n \"3. Qubit 1 और Qubit 2 के बीच एक CNOT गेट लागू करें।\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1c389cbe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a new circuit with three qubits\\n\",\n \"qc = QuantumCircuit(3)\\n\",\n \"\\n\",\n \"### WRITE YOUR CODE BELOW HERE ###\\n\",\n \"# Add a H gate to qubit 0\\n\",\n \"\\n\",\n \"# Add a CNOT gate to qubits 0 and 1\\n\",\n \"\\n\",\n \"# Add a CNOT gate to qubits 1 and 2\\n\",\n \"\\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"\\n\",\n \"# Return a drawing of the circuit using MatPlotLib (\\\"mpl\\\").\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a29c6fe7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab0_ex1(qc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"66a3ed14\",\n \"metadata\": {},\n \"source\": [\n \"## चरण 2. ऑप्टिमाइज़ करें (Optimize) \\n\",\n \"\\n\",\n \"शानदार कार्य! आपने GHZ स्टेट का सर्किट अच्छी तरह डिज़ाइन किया है।\\n\",\n \"\\n\",\n \"इस विशेष मामले में, सर्किट बहुत ही सरल (shallow) है, और इसे और अधिक सरलीकृत (simplify) या गेट्स की संख्या को और कम करना संभव नहीं है।\\n\",\n \"हालांकि, कई ऐसे परिदृश्य होते हैं जहाँ सर्किट को ऑप्टिमाइज़ करना अत्यंत आवश्यक होता है।\\n\",\n \"\\n\",\n \"\\n\",\n \"ऐसी परिस्थितियाँ हो सकती हैं जहाँ आपके क्वांटम सर्किट के डिज़ाइन पर सीमाएँ हों।\\n\",\n \"उदाहरण के लिए, जब आप किसी क्वांटम हार्डवेयर पर सर्किट चला रहे हों, तो आपको क्वबिट्स के बीच कनेक्टिविटी की बाधाएं मिल सकती हैं।\\n\",\n \"इसका मतलब यह है कि कुछ क्वबिट्स शारीरिक (physically) रूप से एक-दूसरे से जुड़े नहीं होते,\\n\",\n \"इसलिए आपको एक स्मार्ट तरीका अपनाना होगा ताकि गेट्स को इस तरह लागू किया जाए कि वांछित क्वांटम स्टेट प्राप्त की जा सके।\\n\",\n \"\\n\",\n \"सौभाग्यवश, यही वह स्थान है जहाँ Qiskit का transpiler हमारी मदद करता है!\\n\",\n \"आप अपना डिज़ाइन किया हुआ सर्किट और डिवाइस की सीमाओं को transpiler को देंगे, और यह अपने आप सर्किट को ऑप्टिमाइज़ कर देगा।\\n\",\n \"\\n\",\n \"आइए GHZ स्टेट के लिए एक ऐसी स्थिति पर विचार करें जिसमें हमें केवल qubit 0 और 1 तथा qubit 0 और 2 के बीच ही इंटरैक्शन की अनुमति हो, लेकिन qubit 1 और 2 के बीच कोई इंटरैक्शन न हो।\\n\",\n \"हम इन सीमाओं को `transpile` फ़ंक्शन का उपयोग करके transpiler में शामिल कर सकते हैं।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"38782ae6\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2: एक GHZ स्टेट को Transpile करें \\n\",\n \"\\n\",\n \"इस दूसरे अभ्यास में, आपको पहले बनाए गए GHZ सर्किट को ऊपर बताए गए कनेक्टिविटी प्रतिबंधों के साथ transpile करने को कहा गया है:\\n\",\n \"\\n\",\n \"- Qubit 0 <---> Qubit 1\\n\",\n \"- Qubit 0 <---> Qubit 2\\n\",\n \"- ~~Qubit 1 <---> Qubit 2~~\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"222b26f8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### WRITE YOUR CODE BELOW HERE ###\\n\",\n \"# Write the coupling map of connections between qubits 0 and 1 and 0 and 2 as a list of pairs: [[0,1],...]\\n\",\n \"coupling_map =\\n\",\n \"\\n\",\n \"# Transpile the quantum circuit `qc` using the `transpile` function and the coupling map\\n\",\n \"from qiskit import transpile\\n\",\n \"qc_transpiled =\\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"\\n\",\n \"qc_transpiled.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5d860929\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab0_ex2(qc_transpiled)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e2aa24a5\",\n \"metadata\": {},\n \"source\": [\n \"## चरण 3. चलाना (Execute) \\n\",\n \"\\n\",\n \"अब बारी है सबसे रोमांचक चरण की —\\n\",\n \"हम अपने क्वांटम सर्किट को Qiskit Runtime का उपयोग करके चलाने जा रहे हैं!\\n\",\n \"\\n\",\n \"हम यह कार्य Qiskit की दो [Qiskit primitives](https://quantum.cloud.ibm.com/docs/guides/primitives) की सहायता से करेंगे:\\n\",\n \"1. **Sampler** Sampler क्वांटम सर्किट (या सर्किट्स) के एक्जीक्यूशन से उत्पन्न output register का सैंपल लेता है। इसका आउटपुट होता है: हर शॉट पर मापन के अनुसार प्राप्त count।\\n\",\n \"2. **Estimator** Estimator एक या अधिक observables के सापेक्ष क्वांटम सर्किट द्वारा उत्पन्न स्टेट्स के लिए expectation values की गणना करता है। इसका आउटपुट होता है: expectation values और उनके साथ जुड़ी standard errors।\\n\",\n \"\\n\",\n \"सबसे पहले, हम अपने सर्किट को Sampler का उपयोग करके चलाएंगे,\\n\",\n \"और फिर उसके परिणाम को एक वेरिएबल में `results_sampler` के रूप में सहेज लेंगे।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"345c5084\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Add measurement operations\\n\",\n \"qc.measure_all()\\n\",\n \"\\n\",\n \"# Set up the backend\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"# Set up the sampler\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"\\n\",\n \"# Submit the circuit to Sampler\\n\",\n \"pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\\n\",\n \"job = sampler.run(pm.run([qc]))\\n\",\n \"\\n\",\n \"# Get the results\\n\",\n \"results_sampler = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1a8e7991\",\n \"metadata\": {},\n \"source\": [\n \"अब हम अपने सर्किट को Estimator primitive का उपयोग करके चलाएंगे,\\n\",\n \"और उसके परिणामों को `results_estimator` नामक वेरिएबल में सहेजेंगे।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f2354c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set up the Estimator\\n\",\n \"estimator = Estimator(mode=backend)\\n\",\n \"\\n\",\n \"# Define some observables\\n\",\n \"ZZZ = SparsePauliOp(\\\"ZZZ\\\")\\n\",\n \"ZZX = SparsePauliOp(\\\"ZZX\\\")\\n\",\n \"ZII = SparsePauliOp(\\\"ZII\\\")\\n\",\n \"XXI = SparsePauliOp(\\\"XXI\\\")\\n\",\n \"ZZI = SparsePauliOp(\\\"ZZI\\\")\\n\",\n \"III = SparsePauliOp(\\\"III\\\")\\n\",\n \"observables = [ZZZ, ZZX, ZII, XXI, ZZI, III]\\n\",\n \"\\n\",\n \"# Submit the circuit to Estimator\\n\",\n \"pub = (qc, observables)\\n\",\n \"job = estimator.run(pubs=[pub])\\n\",\n \"\\n\",\n \"# Get the results\\n\",\n \"results_estimator = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"08df17b9\",\n \"metadata\": {},\n \"source\": [\n \"अब आता है Qiskit patterns का अंतिम चरण, जहाँ हम अपने परिणामों को विज़ुअलाइज़ (दृश्य रूप में प्रस्तुत) करेंगे। \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03ccb1ce\",\n \"metadata\": {},\n \"source\": [\n \"## चरण 4. पोस्ट-प्रोसेस (Post-process) \\n\",\n \"\\n\",\n \"अंततः, Qiskit pattern का अंतिम चरण है — पोस्ट-प्रोसेसिंग करना, यानी क्वांटम सर्किट के एक्जीक्यूशन से प्राप्त जानकारी को प्रसंस्कृत (process) करना।\\n\",\n \"\\n\",\n \"सबसे पहले, हम Sampler से प्राप्त परिणामों को विज़ुअलाइज़ करेंगे। हम मापन (measurement) के count को हिस्टोग्राम (Histogram) के रूप में प्रदर्शित कर सकते हैं, जिससे हमें तुरंत यह पता चलेगा कि दो संभावित क्वांटम स्टेट्स को लगभग 50% संभावना के साथ मापा गया है।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2cb9ba4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"counts_list = results_sampler[0].data.meas.get_counts()\\n\",\n \"print(f\\\" Outcomes : {counts_list}\\\")\\n\",\n \"display(plot_histogram(counts_list, title=\\\"GHZ state\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86748b30\",\n \"metadata\": {},\n \"source\": [\n \"अब हम Estimator से प्राप्त परिणामों को विज़ुअलाइज़ (दृश्य रूप में प्रदर्शित) कर सकते हैं।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"983608f7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"exp_values = results_estimator[0].data.evs\\n\",\n \"observables_list = [\\\"ZZZ\\\", \\\"ZZX\\\", \\\"ZII\\\", \\\"XXI\\\", \\\"ZZI\\\", \\\"III\\\"]\\n\",\n \"print(f\\\"Expectation values: {list(zip(observables_list, exp_values))}\\\")\\n\",\n \"\\n\",\n \"# Set up our plot\\n\",\n \"container = plt.bar(observables_list, exp_values, width=0.8)\\n\",\n \"# Label each axis\\n\",\n \"plt.xlabel(\\\"Observables\\\")\\n\",\n \"plt.ylabel(\\\"Values\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e5b49810\",\n \"metadata\": {},\n \"source\": [\n \"हम देखते हैं कि observables $ZZI$ और $III$ का expectation value 1 है, क्योंकि $ZZI$ दो माइनस साइन प्रस्तुत करता है जो एक-दूसरे को रद्द (cancel out) कर देते हैं, और $III$ एक identity की तरह कार्य करता है, जिससे GHZ स्टेट अपरिवर्तित (unchanged) रहती है। बाकी के observables का expectation value 0 होता है, क्योंकि उनके $Z$ ऑपरेटर एक विषम संख्या (odd number) में माइनस साइन लाते हैं, या फिर उनके $X$ ऑपरेटर क्वबिट्स को इस तरह पलटते हैं कि उत्पन्न अवस्थाएँ एक-दूसरे के लिए ऑर्थोगोनल (orthogonal) हो जाती हैं।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e905aea1\",\n \"metadata\": {},\n \"source\": [\n \"# बधाई हो! \\n\",\n \"\\n\",\n \"आपने Lab 0 को सफलतापूर्वक पूरा कर लिया है और अब आप Quantum Global Summer School 2025 शुरू करने के लिए तैयार हैं!\\n\",\n \"\\n\",\n \"इस प्रयोगशाला (lab) में, आपने सिस्टम को Qiskit v2.x चलाने के लिए सफलतापूर्वक सेटअप किया,\\n\",\n \"और अपने IBM Cloud टोकन को सेव किया ताकि समर स्कूल के दौरान अपनी प्रगति को ट्रैक कर सकें और असली क्वांटम हार्डवेयर पर सर्किट चला सकें। आपने Qiskit patterns के बारे में भी सीखा —\\n\",\n \"जहाँ आपने GHZ सर्किट का एक विशेष उदाहरण लागू किया और एक optimized quantum circuit डिज़ाइन किया।\\n\",\n \"अंततः, आपने GHZ सर्किट को एक क्वांटम सिम्युलेटर पर चलाया,\\n\",\n \"और देखा कि Sampler से प्राप्त मापन परिणाम सैद्धांतिक रूप से अपेक्षित $|000\\\\rangle$ और $|111\\\\rangle$ स्टेट्स के 50% संभावना वाले परिणामों से अच्छी तरह मेल खाते हैं।\\n\",\n \"\\n\",\n \"आप नीचे दिए गए सेल का उपयोग करके लैब्स में अपनी प्रगति की जांच कर सकते हैं:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"677398c7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"83692cb4\",\n \"metadata\": {},\n \"source\": [\n \"एक बोनस अभ्यास के रूप में, यही प्रयोग यदि हम असली क्वांटम हार्डवेयर पर करें,\\n\",\n \"तो यह देखना दिलचस्प होगा कि उसमें उपस्थित शोर (noise) परिणामों को किस प्रकार प्रभावित करता है।\\n\",\n \"इसके बाद, हम उन परिणामों की तुलना सैद्धांतिक संभावनाओं से कर सकते हैं,\\n\",\n \"जिससे यह स्पष्ट होगा कि थ्योरी और वास्तविक परिणामों में पूर्ण समानता नहीं होती।\\n\",\n \"\\n\",\n \"तैयार हैं? तो चलिए शुरू करते हैं!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c5e04fb1\",\n \"metadata\": {},\n \"source\": [\n \"## बोनस चुनौती: GHZ सर्किट को हार्डवेयर पर चलाना \\n\",\n \"\\n\",\n \"Qiskit का उपयोग करके किसी क्वांटम सर्किट को असली क्वांटम कंप्यूटर पर चलाने के लिए,\\n\",\n \"सबसे पहले हमें क्वांटम बैकएंड (quantum backend) को परिभाषित करना होता है।\\n\",\n \"\\n\",\n \"हम IBM Quantum द्वारा उपलब्ध क्वांटम कंप्यूटरों में से किसी एक को मैन्युअली (स्वतः चयन करके) चुन सकते हैं। हालाँकि, कई बार यह अधिक सुविधाजनक होता है कि हम उस मशीन को चुनें जो इस समय सबसे कम व्यस्त (least busy) हो, ताकि सर्किट को तेज़ी से निष्पादित (execute) किया जा सके। यहीं पर Qiskit की `least_busy` विधि (method) मददगार साबित होती है।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ef101f81\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define the service. This allows you to access IBM QPUs.\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"\\n\",\n \"# Get a backend\\n\",\n \"backend = service.least_busy(operational=True, simulator=False)\\n\",\n \"print(f\\\"We are using the {backend.name} quantum computer\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b94e6ec7\",\n \"metadata\": {},\n \"source\": [\n \"अगली कॉल यह दिखाती है कि `QiskitRuntimeService` के साथ क्वांटम सर्किट्स को हार्डवेयर पर चलाना कितना आसान है। एक बार जब हम ऊपर दिए गए सेल में backend का चयन कर लेते हैं, तो हम बस वही कोड की पंक्तियाँ कॉपी-पेस्ट कर सकते हैं जो हमने पहले Sampler सिम्युलेटर के लिए लिखी थीं — जिसमें post-processing और visualization भी शामिल है।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c50b3aa1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### WRITE YOUR CODE BELOW HERE ###\\n\",\n \"# Step 1. Map\\n\",\n \"# You should have created a GHZ circuit above and assigned with variable `qc`\\n\",\n \"\\n\",\n \"# Step 2. Optimize\\n\",\n \"pm = \\n\",\n \"qc_transpiled = \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b90f52a1\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"चेतावनी: कतार (Queue) का समय और 10 मिनट की सीमा\\n\",\n \"\\n\",\n \"यह प्रयोग वास्तविक क्वांटम हार्डवेयर पर लगभग 10 सेकंड का गणनात्मक समय लेगा, हालाँकि, वास्तविक हार्डवेयर पर इसे चलाने में लम्बी कतार का सामना करना पड़ सकता है, जिसके कारण प्रयोग पूरा होने में समय लग सकता है, और इस दौरान आपका Jupyter नोटबुक ब्लॉक हो जाएगा।\\n\",\n \"\\n\",\n \"कृपया ध्यान दें कि ओपन प्लान में आपको वास्तविक हार्डवेयर पर केवल 10 मिनट का समय मिलता है।\\n\",\n \"चूँकि हमें ये मिनट आगामी प्रयोगशालाओं (labs) में ज़रूरत होंगे,\\n\",\n \"इसलिए कृपया सुनिश्चित करें कि आप इस लैब में अपने समय का ज़्यादातर हिस्सा बचाकर रखें।\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2ba1eb48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 3. Execute\\n\",\n \"sampler = \\n\",\n \"job = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fb229e5e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 4. Post-process\\n\",\n \"results = \\n\",\n \"counts_list = \\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"\\n\",\n \"print(f\\\"Outcomes : {counts_list}\\\")\\n\",\n \"plot_histogram(counts_list,title='GHZ state')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d830731e\",\n \"metadata\": {},\n \"source\": [\n \"शानदार!\\n\",\n \"\\n\",\n \"आपने सफलतापूर्वक एक सर्किट को असली क्वांटम कंप्यूटर पर चलाया है, और परिणाम बहुत अच्छे हैं!\\n\",\n \"सबसे अधिक बार मापी गई स्टेट्स हैं $|000\\\\rangle$ और $|111\\\\rangle$ और इनकी कुल संभावनाएँ लगभग 50% के आसपास हैं। हालाँकि, इस मामले में हमने देखा कि क्वांटम कंप्यूटर में मौजूद शोर (noise) के कारण कुछ ऐसी क्वांटम स्टेट्स भी मापी गईं, जिनकी सैद्धांतिक संभावना (theoretical probability) शून्य (0) होनी चाहिए थी। यह पूरी तरह अपेक्षित (expected) है, और हम आने वाली लैब्स में देखेंगे कि हम इन त्रुटियों को कैसे सुधार सकते हैं या कम कर सकते हैं, जो क्वांटम कंप्यूटर की शोरयुक्त प्रकृति (noisy nature) के कारण उत्पन्न होती हैं।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bafe6e0d\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Jorge Martínez de Lejarza\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Junye Huang\\n\",\n \"\\n\",\n \"**Translated by:** Raja Babu Jamatia\\n\",\n \"\\n\",\n \"**Version:** 1.0.0\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-0/lab0_ja.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"551a947d\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 0: こんにちは、量子の世界!\\n\",\n \"\\n\",\n \"# 目次\\n\",\n \"\\n\",\n \"* [Qiskit Global Summer School 2025へようこそ！](#welcome)\\n\",\n \" - [Lab 0 概要](#overview)\\n\",\n \" - [Qiskitのインストール](#install)\\n\",\n \" - [トラブルシューティング](#troubleshooting)\\n\",\n \" - [APIトークンの設定](#setting-ibm-cloud)\\n\",\n \" - [インポート](#imports)\\n\",\n \" - [動作確認](#sanity-check)\\n\",\n \"* [Qiskitパターンを使った3量子ビットGHZ状態の生](#ghz) \\n\",\n \" - [ステップ1. マッピング](#map)\\n\",\n \" - [ステップ2. 最適化](#optimize)\\n\",\n \" - [ステップ3. 実行](#execute)\\n\",\n \" - [ステップ4. 後処理](#post-process)\\n\",\n \"* [おめでとうございます！](#congratulations)\\n\",\n \" - [ボーナスチャレンジ：GHZを実機で実行](#bonus)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2144e6fe\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit Global Summer School 2025へようこそ！ \\n\",\n \"Qiskit Global Summer Schoolの新たな年へようこそ！この2週間のサマープログラムは、理論講義とハンズオン演習を組み合わせ、Qiskitを日常的に使う学生・研究者・専門家コミュニティの量子コンピューティングと量子プログラミングの知識を広げます。\\n\",\n \"\\n\",\n \"ハンズオン部分は、一連のJupyterノートブック（「ラボ」）で構成され、様々なトピックをガイドします。\\n\",\n \"\\n\",\n \"各ラボは対応する理論講義を補完し、ドキュメントやチュートリアル、講義への参照リンクも含まれています。さらに、IBMの新しい量子教育ページ [IBM Quantum Learning](https://learning.quantum.ibm.com/) もご活用ください。\\n\",\n \"\\n\",\n \"## Lab 0 概要 \\n\",\n \"この入門ラボの主な目的は、チャレンジノートブックの使い方、解答の採点方法、そして量子コードの実行に必要な環境が正しくセットアップされているかを確認することです。\\n\",\n \"\\n\",\n \"各ノートブックには、変更不要な事前定義セルと、あなた自身でコードを記述するチャレンジ用コードブロックが含まれています。特に、`### WRITE YOUR CODE BELOW HERE ###` のコメントの下に必要なコードを書いてください。カーネルを再起動した場合は、すべてのセルを順番に実行してください。これにより、コードが最新の状態で正しく動作します。インストールセルやアカウント認証情報を保存するセルなど、一部例外もありますが、通常は一度だけ実行すれば十分です。\\n\",\n \"\\n\",\n \"このラボでは、Qiskitパターンを使ってGHZ状態を生成し、最適化された量子回路設計の重要性を学びます。最後に、実機でコードを実行するボーナス課題も用意しています！\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b3df2ea\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskitのインストール \\n\",\n \"量子コンピュータは、一部の計算方法を根本から変革する可能性を秘めた有望な技術です。Shor のアルゴリズムによる暗号解読、Grover のアルゴリズムによる高速探索、さらには量子位相推定によるより良いバッテリー設計など、さまざまな応用が期待されています。これらのアルゴリズムの設計や実行のための最初のステップはソフトウェアツールを選ぶことです。このチャレンジでは Qiskit を使って量子回路の設計や実装をシミュレータと実機で行います。以降では Qiskit v2.0 をインストールします。\\n\",\n \"\\n\",\n \"まず、使用している環境の Python バージョンが python>=3.10 であることを確認してください。これにより、私たちが使用する最新の Qiskit バージョンと互換性があることが保証されます。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a35543d4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from platform import python_version\\n\",\n \"\\n\",\n \"print(python_version())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47d097d3\",\n \"metadata\": {},\n \"source\": [\n \"もしあなたのPythonバージョンが条件を満たしていない場合は、お好みのツールを使ってアップグレードできます。やり方が分からない場合は、以下の方法がおすすめです：\\n\",\n \"\\n\",\n \"- MacOS: [Homebrew](https://brew.sh/)\\n\",\n \"- Linux: `sudo apt-get update`\\n\",\n \"\\n\",\n \"各OSごとの詳細なアップグレード方法については、こちらのガイドを参照してください: [How to update python](https://4geeks.com/how-to/how-to-update-python-version)\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"75977189\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 3. 実行\\n\",\n \"sampler = \\n\",\n \"job = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6778d5c8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 4. 後処理\\n\",\n \"results = \\n\",\n \"counts_list = \\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"\\n\",\n \"print(f\\\"Outcomes : {counts_list}\\\")\\n\",\n \"plot_histogram(counts_list,title='GHZ state')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d830731e\",\n \"metadata\": {},\n \"source\": [\n \"素晴らしい！\\n\",\n \"\\n\",\n \"あなたは実際の量子コンピュータ上で回路を実行することに成功しました。その結果は非常に良好です！最も多く観測された状態は $|000\\\\rangle$ と $|111\\\\rangle$ であり、それぞれの確率は50%弱となっています。しかし、この場合、量子マシンのノイズによって理論的には測定される確率が0であるはずの量子状態も一部観測されていることが分かります。これはまさに予想されることであり、今後のラボでは、量子コンピュータのノイズによって生じる誤りをどのように補正・軽減できるかについて学んでいきます。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bafe6e0d\",\n \"metadata\": {},\n \"source\": [\n \"# 追加情報\\n\",\n \"\\n\",\n \"**作成:** Jorge Martínez de Lejarza\\n\",\n \"\\n\",\n \"**監修:** Marcel Pfaffhauser, Junye Huang\\n\",\n \"\\n\",\n \"**翻訳:** Daiki Murata\\n\",\n \"\\n\",\n \"**Version:** 1.0.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1def2fac\",\n \"metadata\": {},\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-1/Supplemental_long_range_entanglement_with_limited_qubit_connectivity.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ba4719fa\",\n \"metadata\": {\n \"heading_collapsed\": true,\n \"id\": \"ba4719fa\"\n },\n \"source\": [\n \"# Long-range entanglement with limited qubit connectivity [Abstracted Version]\\n\",\n \"\\n\",\n \"This tutorial is based on the IBM Quantum Learning tutorial, \\\"[Long-range entanglement with dynamic circuits\\n\",\n \"](https://quantum.cloud.ibm.com/docs/en/tutorials/long-range-entanglement#long-range-entanglement-with-dynamic-circuits)\\\". It covers the core idea from the original: using a **measurement-based implementation with dynamic circuits** to entangle qubits, which helps optimize QPU (Quantum Processing Unit) usage. \\n\",\n \"\\n\",\n \"This notebook aims to provide a starting point for the Lab 1 open challenge by demonstrating how to create long-range entanglement between physically non-directly connected qubits. The ideas and results presented build upon the work in the paper by Elisa Bäumer et al. [1]. You can find a more high level explanation in [this blog post.](https://www.ibm.com/quantum/blog/long-range-entanglement)\\n\",\n \"\\n\",\n \"Usage estimate: ~30 seconds on ibm_torino. (NOTE: This is an estimate only. Your runtime may vary.)\\n\",\n \"\\n\",\n \"
\\n\",\n \"Resource limit\\n\",\n \"\\n\",\n \"The original tutorial consumes roughly 90 min QPU time accordingly. Please use this tutorial to save your resource.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1f0b8be3\",\n \"metadata\": {\n \"id\": \"1f0b8be3\"\n },\n \"source\": [\n \"## Requirements\\n\",\n \"\\n\",\n \"Before starting this tutorial, ensure that you have the following installed:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a56db8a0-9fc7-40e9-b72c-0dfaab0d6dbc\",\n \"metadata\": {\n \"id\": \"a56db8a0-9fc7-40e9-b72c-0dfaab0d6dbc\"\n },\n \"outputs\": [],\n \"source\": [\n \"!pip install -U 'qiskit[visualization]' matplotlib numpy qiskit-ibm-runtime\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c75dbecb-c273-4955-991e-557f9b5a41df\",\n \"metadata\": {\n \"hidden\": true,\n \"id\": \"c75dbecb-c273-4955-991e-557f9b5a41df\"\n },\n \"source\": [\n \"In this tutorial, you will run a gate teleportation circuit in three different setups. We will always assume a line of n qubits, where we want to apply a CNOT gate between the two end qubits, with n-2 ancilla qubits in between.\\n\",\n \"\\n\",\n \"The three approaches are:\\n\",\n \"* **Unitary-based implementation**: This method uses SWAP gates to bring the qubits to the middle to apply the CNOT.\\n\",\n \"* **Measurement-based implementation (with dynamic circuits)**: This method uses measurement and real-time feedforward of information during the quantum computation.\\n\",\n \"\\n\",\n \"As a starting point for this extrachallenge, this notebook will demonstrate the implementation of a Bell state between the qubit pairs **[0,1]**, **[0,3]**, and **[0,15]** of an Eagle processor in the two given ways and compare the results.\\n\",\n \"\\n\",\n \"### Import Necessary Libraries\\n\",\n \"Before we begin, we need to import the main functions from Qiskit.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"9d02f8a1-3069-4507-91ed-73b7f6610f39\",\n \"metadata\": {\n \"hidden\": true,\n \"id\": \"9d02f8a1-3069-4507-91ed-73b7f6610f39\"\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"from numpy import pi\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"\\n\",\n \"# Importing standard Qiskit libraries\\n\",\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile\\n\",\n \"from qiskit.primitives import BitArray\\n\",\n \"from qiskit.visualization import *\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"from qiskit.circuit import Gate\\n\",\n \"from qiskit.circuit.library import XGate\\n\",\n \"\\n\",\n \"from qiskit.providers.backend import BackendV2 as Backend\\n\",\n \"from qiskit.transpiler import CouplingMap, InstructionDurations\\n\",\n \"from qiskit.transpiler.passmanager import PassManager\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, Options\\n\",\n \"from qiskit_ibm_runtime import Session, Batch, SamplerV2 as Sampler\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime.transpiler.passes.scheduling import (\\n\",\n \" DynamicCircuitInstructionDurations,\\n\",\n \" ALAPScheduleAnalysis,\\n\",\n \" PadDynamicalDecoupling,\\n\",\n \")\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f79adf3-0bee-49cb-a166-e5cd4cc04bc3\",\n \"metadata\": {\n \"id\": \"4f79adf3-0bee-49cb-a166-e5cd4cc04bc3\"\n },\n \"source\": [\n \"### Set Up and Prepare the IBM Quantum Backend\\n\",\n \"\\n\",\n \"To run circuits on a real quantum computer, we first connect to the IBM Quantum service using `QiskitRuntimeService`. Then, from the available backends, we select the specific hardware we will use for this experiment.\\n\",\n \"\\n\",\n \"Here, we will select least busy 127-qubit Eagle processor, as our backend.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"3fcZL3i-akFF\",\n \"metadata\": {\n \"id\": \"3fcZL3i-akFF\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Save your API token to track your progress and have access to the quantum computers\\n\",\n \"\\n\",\n \"#your_token=\\\"your_token\\\"\\n\",\n \"#your_instace=\\\"your_crn\\\"\\n\",\n \"\\n\",\n \"QiskitRuntimeService.save_account(\\n\",\n \" channel=\\\"ibm_cloud\\\",\\n\",\n \" token=\\\"your_token\\\",\\n\",\n \" instance=\\\"your_crn\\\",\\n\",\n \" set_as_default=True,\\n\",\n \" overwrite=True,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"1c307e21-7300-4e4e-84b5-59b1d47d7a3b\",\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"1c307e21-7300-4e4e-84b5-59b1d47d7a3b\",\n \"outputId\": \"4c29119e-0451-4e74-ef45-888a2b62c194\"\n },\n \"outputs\": [],\n \"source\": [\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from typing import Any, List, Dict, Union, Optional, Callable, Tuple\\n\",\n \"from qiskit.circuit import IfElseOp\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"11e35681-d7d0-4d5d-8764-5517181d91d0\",\n \"metadata\": {\n \"id\": \"11e35681-d7d0-4d5d-8764-5517181d91d0\"\n },\n \"source\": [\n \"### **Set Primary Parameters**\\n\",\n \"\\n\",\n \"In this section, we define some common parameters that will be used throughout the experiment. These parameters need to be configured for a particular backend. For instance, the **`COUPLING_MAP`** defines the physical connectivity between qubits, which determines where two-qubit gates like CNOT can be applied.\\n\",\n \"\\n\",\n \"Here, we will filter the full `COUPLING_MAP` to create a linear map, `COUPLING_MAP_1D`, which only includes the connections along our specified `QUBIT_LINE`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"4fc13eba-bc50-4607-88f9-8ada04b69794\",\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"4fc13eba-bc50-4607-88f9-8ada04b69794\",\n \"outputId\": \"cb4518a7-1dfe-41c1-f396-3220bd2e3879\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Machine is set to: ibm_torino\\n\",\n \"Maximum number of qubits between CNOT for ibm_torino is 9 with the given qubit line.\\n\"\n ]\n }\n ],\n \"source\": [\n \"def coupling_map_from_qubit_line(coupling_map:List[List[int]],\\n\",\n \" qubit_line: List[List[int]]) -> List[List[int]]:\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Modify the full coupling map to force linearity in the qubit layout\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" new_coupling_map = []\\n\",\n \" line_edge_list = []\\n\",\n \" for i in range(len(qubit_line)-1):\\n\",\n \" line_edge_list.append([qubit_line[i],qubit_line[i+1]])\\n\",\n \"\\n\",\n \" for edge in coupling_map:\\n\",\n \" u,v = edge\\n\",\n \" edge_rev = [v,u]\\n\",\n \" if (edge in line_edge_list) or (edge_rev in line_edge_list):\\n\",\n \" new_coupling_map.append(edge)\\n\",\n \" return new_coupling_map\\n\",\n \"\\n\",\n \"\\n\",\n \"# Set which quantum computer to use\\n\",\n \"backend = service.least_busy(operational=True)\\n\",\n \"backend.target.add_instruction(IfElseOp, name=\\\"if_else\\\")\\n\",\n \"\\n\",\n \"MACHINE_NAME = backend.name\\n\",\n \"\\n\",\n \"#update qubit line according to your backend connectivity, here I used ibm_torino\\n\",\n \"qubit_lines = {backend.name:[0,1,2,3,4,16,23, 24, 25, 35, 44]}\\n\",\n \"\\n\",\n \"# Set qubit line and coupling map\\n\",\n \"QUBIT_LINE = qubit_lines[MACHINE_NAME]\\n\",\n \"COUPLING_MAP_FULL = [list(edge) for edge in list(QiskitRuntimeService().backend(MACHINE_NAME).coupling_map)]\\n\",\n \"COUPLING_MAP_1D = coupling_map_from_qubit_line(COUPLING_MAP_FULL, QUBIT_LINE)\\n\",\n \"MAX_POSSIBLE_QUBITS_BTW_CNOT = len(QUBIT_LINE) - 2\\n\",\n \"\\n\",\n \"\\n\",\n \"print(f\\\"Machine is set to: {MACHINE_NAME}\\\")\\n\",\n \"print(f\\\"Maximum number of qubits between CNOT for {MACHINE_NAME} is {MAX_POSSIBLE_QUBITS_BTW_CNOT} with the given qubit line.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"c7718980-3efe-4810-afb0-29f8d252a2ed\",\n \"metadata\": {\n \"id\": \"c7718980-3efe-4810-afb0-29f8d252a2ed\"\n },\n \"outputs\": [],\n \"source\": [\n \"OPTIMIZATION_LEVEL = 3\\n\",\n \"# Set to True to use dynamical decoupling\\n\",\n \"SHOTS = 1024\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"sampler.options.experimental = {\\\"execution_path\\\" : \\\"gen3-experimental\\\"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ab818e7a-14d7-441f-add8-fa49ebc00551\",\n \"metadata\": {\n \"id\": \"ab818e7a-14d7-441f-add8-fa49ebc00551\"\n },\n \"source\": [\n \"### Prepare for Dynamic Decoupling\\n\",\n \"\\n\",\n \"During a quantum computation, qubits in an idle state (waiting for their next operation) are susceptible to losing their quantum state due to environmental interactions (decoherence). **Dynamic Decoupling** is a technique that reduces the impact of this noise by applying a specific sequence of pulses (here, two X-gates) to the idle qubits, improving overall fidelity.\\n\",\n \"\\n\",\n \"We will prepare to apply this using the `PadDynamicalDecoupling` pass.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"be0b6bef-8f13-4a78-a2a3-d525215adaa9\",\n \"metadata\": {\n \"id\": \"be0b6bef-8f13-4a78-a2a3-d525215adaa9\"\n },\n \"outputs\": [],\n \"source\": [\n \"from qiskit_ibm_runtime.transpiler.passes.scheduling import (\\n\",\n \" ASAPScheduleAnalysis,\\n\",\n \" PadDynamicalDecoupling,\\n\",\n \")\\n\",\n \"from qiskit.circuit.library import XGate\\n\",\n \"durations = backend.target.durations()\\n\",\n \"\\n\",\n \"# This is the sequence we'll apply to idling qubits\\n\",\n \"dd_sequence = [XGate(), XGate()]\\n\",\n \"\\n\",\n \"# Run scheduling and dynamic decoupling passes on circuit\\n\",\n \"pm_dd = PassManager([ASAPScheduleAnalysis(durations), PadDynamicalDecoupling(durations, dd_sequence)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1439e5e6-af49-4b7a-95ba-ac7cc75a31e7\",\n \"metadata\": {\n \"heading_collapsed\": true,\n \"id\": \"1439e5e6-af49-4b7a-95ba-ac7cc75a31e7\"\n },\n \"source\": [\n \"#### Unitary-based implementation swapping the qubits to the middle\\n\",\n \"\\n\",\n \"First, examine the case where a long-range CNOT gate is implemented using nearest-neighbor connections and unitary gates. In the following figure, on the left is a circuit for a long-range CNOT gate spanning a 1D chain of n-qubits subject to nearest-neighbor connections only. On the right is an equivalent unitary decomposition implementable with local CNOT gates, circuit depth O(n).\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"The circuit on the right can be implemented as follows:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"a25f7b2f-18c3-4bc4-8a31-06f8aac5e45c\",\n \"metadata\": {\n \"id\": \"a25f7b2f-18c3-4bc4-8a31-06f8aac5e45c\"\n },\n \"outputs\": [],\n \"source\": [\n \"def CNOT_unitary(qc: QuantumCircuit, control_qubit: int, target_qubit: int) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"Generate a CNOT gate bewteen data qubit control_qubit and data qubit target_qubit using local CNOTs\\n\",\n \"\\n\",\n \" Assumes that the long-range CNOT gate will be spanning a 1D chain of n-qubits subject to nearest-neighbor\\n\",\n \" connections only with the chain starting at the control qubit and finishing at the target qubit.\\n\",\n \"\\n\",\n \" Assumes that control_qubit < target_qubit (as integers) and that the provided circuit qc has |0> set\\n\",\n \" qubits control_qubit+1, ..., target_qubit-1\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" qc (QuantumCicruit) : A Quantum Circuit to add the long range localized unitary CNOT\\n\",\n \" control_qubit (int) : The qubit used as the control.\\n\",\n \" target_qubi (int) : The qubit targeted by the gate.\\n\",\n \"\\n\",\n \" Example:\\n\",\n \"\\n\",\n \" qc = QuantumCircuit(8,2)\\n\",\n \" qc = CNOT_unitary(qc, 0, 7)\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" QuantumCircuit\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" assert target_qubit > control_qubit\\n\",\n \" n = target_qubit - control_qubit - 1\\n\",\n \" k = int(n/2)\\n\",\n \" qc.barrier()\\n\",\n \" for i in range(control_qubit, control_qubit + k):\\n\",\n \" qc.cx(i,i+1)\\n\",\n \" qc.cx(i+1,i)\\n\",\n \" qc.cx(-i-1,-i-2)\\n\",\n \" qc.cx(-i-2,-i-1)\\n\",\n \" if n%2==1:\\n\",\n \" qc.cx(k+2,k+1)\\n\",\n \" qc.cx(k+1,k+2)\\n\",\n \" qc.barrier()\\n\",\n \" qc.cx(k,k+1)\\n\",\n \" for i in range(control_qubit, control_qubit + k):\\n\",\n \" qc.cx(k-i,k-1-i)\\n\",\n \" qc.cx(k-1-i,k-i)\\n\",\n \" qc.cx(k+i+1,k+i+2)\\n\",\n \" qc.cx(k+i+2,k+i+1)\\n\",\n \" if n%2==1:\\n\",\n \" qc.cx(-2,-1)\\n\",\n \" qc.cx(-1,-2)\\n\",\n \" qc.barrier()\\n\",\n \" return qc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1f566ac4-b8d2-4565-977f-43717f3e40ab\",\n \"metadata\": {\n \"id\": \"1f566ac4-b8d2-4565-977f-43717f3e40ab\"\n },\n \"source\": [\n \"#### Long-range measurement-based CNOT with feedforward\\n\",\n \"\\n\",\n \"Finally, examine the case where a long-range CNOT gate is implemented using measurement-based CNOT with feedforward (dynamic circuits). In the following figure, on the left is a circuit for a long-range CNOT gate spanning a 1D chain of n-qubits subject to nearest-neighbor connections only. On the right is an equivalent implementable with local CNOT gates, measurement-based CNOT with feedforward (dynamic circuits).\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"The circuit on the right can be implemented as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"5c864fcf-0b77-44df-9750-0cd510640305\",\n \"metadata\": {\n \"hidden\": true,\n \"id\": \"5c864fcf-0b77-44df-9750-0cd510640305\"\n },\n \"outputs\": [],\n \"source\": [\n \"from qiskit.circuit.classical import expr\\n\",\n \"\\n\",\n \"def CNOT_dyn(qc: QuantumCircuit,\\n\",\n \" control_qubit: int,\\n\",\n \" target_qubit: int,\\n\",\n \" c1: Optional[ClassicalRegister]=None,\\n\",\n \" c2: Optional[ClassicalRegister]=None,\\n\",\n \" add_barriers: Optional[bool]=True) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"Generate a CNOT gate bewteen data qubit control_qubit and data qubit target_qubit using Bell Pairs.\\n\",\n \"\\n\",\n \" Post processing is used to enable the CNOT gate via the provided classicial registers c1 and c2\\n\",\n \"\\n\",\n \" Assumes that the long-range CNOT gate will be spanning a 1D chain of n-qubits subject to nearest-neighbor\\n\",\n \" connections only with the chain starting at the control qubit and finishing at the target qubit.\\n\",\n \"\\n\",\n \" Assumes that control_qubit < target_qubit (as integers) and that the provided circuit qc has |0> set\\n\",\n \" qubits control_qubit+1, ..., target_qubit-1\\n\",\n \"\\n\",\n \" n = target_qubit - control_qubit - 1 : Number of qubits between the target and control qubits\\n\",\n \" k = int(n/2) : Number of Bell pairs created\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" qc (QuantumCicruit) : A Quantum Circuit to add the long range localized unitary CNOT\\n\",\n \" control_qubit (int) : The qubit used as the control.\\n\",\n \" target_qubi (int) : The qubit targeted by the gate.\\n\",\n \"\\n\",\n \" Optional Args:\\n\",\n \" c1 (ClassicialRegister) : Default = None. Required if n > 1. Register requires k bits\\n\",\n \" c2 (ClassicalRegister) : Default = None. Required if n > 0. Register requires n - k bits\\n\",\n \" add_barriers (bool) : Default = True. Include barriers before and after long range CNOT\\n\",\n \"\\n\",\n \" Note: This approached uses two if_test statements. A better (more performant) approach is\\n\",\n \" to have the parity values combined into a single classicial register and then use a switch\\n\",\n \" statement. This was done in the associated paper my modifying the qasm file directly. The ability\\n\",\n \" to use a switch statement via Qiakit in this way is a future release capability.\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" QuantumCircuit\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" assert target_qubit > control_qubit\\n\",\n \" n = target_qubit - control_qubit - 1\\n\",\n \" t = int(n/2)\\n\",\n \"\\n\",\n \" if add_barriers is True:\\n\",\n \" qc.barrier()\\n\",\n \"\\n\",\n \" # Deteremine where to start the bell pairs and\\n\",\n \" # add an extra CNOT when n is odd\\n\",\n \" if n%2 == 0:\\n\",\n \" x0 = 1\\n\",\n \" else:\\n\",\n \" x0 = 2\\n\",\n \" qc.cx(0,1)\\n\",\n \"\\n\",\n \" # Create t Bell pairs\\n\",\n \" for i in range(t):\\n\",\n \" qc.h(x0+2*i)\\n\",\n \" qc.cx(x0+2*i,x0+2*i+1)\\n\",\n \"\\n\",\n \" # Entangle Bell pairs and data qubits and measure\\n\",\n \" for i in range(t+1):\\n\",\n \" qc.cx(x0-1+2*i,x0+2*i)\\n\",\n \"\\n\",\n \" for i in range(1,t+x0):\\n\",\n \" if (i==1):\\n\",\n \" qc.h(2*i+1-x0)\\n\",\n \" qc.measure(2*i+1-x0, c2[i-1])\\n\",\n \" parity_control = expr.lift(c2[i-1])\\n\",\n \" else:\\n\",\n \" qc.h(2*i+1-x0)\\n\",\n \" qc.measure(2*i+1-x0, c2[i-1])\\n\",\n \" parity_control = expr.bit_xor(c2[i-1], parity_control)\\n\",\n \"\\n\",\n \" for i in range(t):\\n\",\n \" if (i==0):\\n\",\n \" qc.measure(2*i+x0, c1[i])\\n\",\n \" parity_target = expr.lift(c1[i])\\n\",\n \" else:\\n\",\n \" qc.measure(2*i+x0, c1[i])\\n\",\n \" parity_target = expr.bit_xor(c1[i], parity_target)\\n\",\n \"\\n\",\n \" if (n>0):\\n\",\n \" with qc.if_test(parity_control):\\n\",\n \" qc.z(0)\\n\",\n \"\\n\",\n \" if (n>1):\\n\",\n \" with qc.if_test(parity_target):\\n\",\n \" qc.x(-1)\\n\",\n \"\\n\",\n \" if add_barriers is True:\\n\",\n \" qc.barrier()\\n\",\n \" return qc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"56e24063-c151-4388-8287-aecdac27796d\",\n \"metadata\": {\n \"id\": \"56e24063-c151-4388-8287-aecdac27796d\"\n },\n \"source\": [\n \"### **Create the Experiment Circuits**\\n\",\n \"\\n\",\n \"Now, we will create the circuits for our experiment using the three methods defined above (`CNOT_unitary`, `CNOT_dyn`) and a reference circuit with a direct CNOT. We will create circuits for the **[0,3]** and **[0,16]** qubit pairs to compare performance over different distances.\\n\",\n \"\\n\",\n \"We use `generate_preset_pass_manager` to optimize (transpile) each circuit for the backend's specific qubit layout and coupling map. Finally, we apply dynamic decoupling by running them through `pm_dd.run`.\\n\",\n \"\\n\",\n \"All circuits will be saved into `circuits`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"6487063b-811b-49c5-b734-469d0c9fa3af\",\n \"metadata\": {\n \"id\": \"6487063b-811b-49c5-b734-469d0c9fa3af\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Reference Circuit (0 & 1)\\n\",\n \"circuits = []\\n\",\n \"layout = QUBIT_LINE[:2]\\n\",\n \"pm = generate_preset_pass_manager(coupling_map = COUPLING_MAP_1D,\\n\",\n \" initial_layout = layout,\\n\",\n \" optimization_level = 3,\\n\",\n \" backend = backend)\\n\",\n \"\\n\",\n \"qr = QuantumRegister(2, name=\\\"q\\\") # Circuit with n qubits between control and target\\n\",\n \"cr = ClassicalRegister(2, name=\\\"cr\\\")\\n\",\n \"\\n\",\n \"ref_circ = QuantumCircuit(qr,cr)\\n\",\n \"ref_circ.h(0)\\n\",\n \"ref_circ.cx(0,1)\\n\",\n \"ref_circ.measure(qr,cr)\\n\",\n \"circuits.append(pm_dd.run(pm.run(ref_circ)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"f7f32034-79b9-43b3-b4f4-e3ddb372e3f5\",\n \"metadata\": {\n \"id\": \"f7f32034-79b9-43b3-b4f4-e3ddb372e3f5\"\n },\n \"outputs\": [],\n \"source\": [\n \"#[0,3]\\n\",\n \"n=2\\n\",\n \"layout = QUBIT_LINE[:n+2]\\n\",\n \"\\n\",\n \"qr = QuantumRegister(n+2, name=\\\"q\\\") # Circuit with n qubits between control and target\\n\",\n \"cr = ClassicalRegister(2, name=\\\"cr\\\") # Classicial register for measuring long range CNOT\\n\",\n \"\\n\",\n \"k = int(n/2) # Number of Bell States to be used\\n\",\n \"c1 = ClassicalRegister(k, name=\\\"c1\\\")\\n\",\n \"c2 = ClassicalRegister(n-k, name=\\\"c2\\\") # Classicial register needed for post processing\\n\",\n \"\\n\",\n \"pm = generate_preset_pass_manager(coupling_map = COUPLING_MAP_1D,\\n\",\n \" initial_layout = layout,\\n\",\n \" optimization_level = 3,\\n\",\n \" backend = backend)\\n\",\n \"\\n\",\n \"#pm.scheduling = PassManager(\\n\",\n \"# [\\n\",\n \"# ALAPScheduleAnalysis(durations),\\n\",\n \"# PadDynamicalDecoupling(durations, dd_sequence),\\n\",\n \"# ]\\n\",\n \"#)\\n\",\n \" \\n\",\n \"circuit = QuantumCircuit(qr, cr, c1,c2, name=\\\"CNOT\\\")\\n\",\n \"circuit.h(0)\\n\",\n \"circuit = CNOT_unitary(circuit,0,3,)\\n\",\n \"circuit.measure([0,3],[0,1])\\n\",\n \"circuits.append(pm_dd.run(pm.run(circuit)))\\n\",\n \"\\n\",\n \"\\n\",\n \"circuit = QuantumCircuit(qr, cr, c1,c2, name=\\\"CNOT\\\")\\n\",\n \"circuit.h(0)\\n\",\n \"circuit = CNOT_dyn(circuit,0,3,c1,c2)\\n\",\n \"circuit.measure([0,3],cr)\\n\",\n \"circuits.append(pm_dd.run(pm.run(circuit)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"05a25023-0b3f-48a6-9782-4b7eb26151c0\",\n \"metadata\": {\n \"id\": \"05a25023-0b3f-48a6-9782-4b7eb26151c0\"\n },\n \"outputs\": [],\n \"source\": [\n \"#[0,15]\\n\",\n \"\\n\",\n \"n=4\\n\",\n \"layout = QUBIT_LINE[:n+2]\\n\",\n \"\\n\",\n \"qr = QuantumRegister(n+2, name=\\\"q\\\") # Circuit with n qubits between control and target\\n\",\n \"cr = ClassicalRegister(2, name=\\\"cr\\\") # Classicial register for measuring long range CNOT\\n\",\n \"\\n\",\n \"k = int(n/2) # Number of Bell States to be used\\n\",\n \"c1 = ClassicalRegister(k, name=\\\"c1\\\")\\n\",\n \"c2 = ClassicalRegister(n-k, name=\\\"c2\\\") # Classicial register needed for post processing\\n\",\n \"\\n\",\n \"pm = generate_preset_pass_manager(coupling_map = COUPLING_MAP_1D,\\n\",\n \" initial_layout = layout,\\n\",\n \" optimization_level = 3,\\n\",\n \" backend = backend)\\n\",\n \"circuit = QuantumCircuit(qr, cr, c1,c2, name=\\\"CNOT\\\")\\n\",\n \"circuit.h(0)\\n\",\n \"circuit = CNOT_unitary(circuit,0,n+1)\\n\",\n \"circuit.measure([0,n+1],cr)\\n\",\n \"circuits.append(pm_dd.run(pm.run(circuit)))\\n\",\n \"\\n\",\n \"\\n\",\n \"circuit = QuantumCircuit(qr, cr, c1,c2, name=\\\"CNOT\\\")\\n\",\n \"circuit.h(0)\\n\",\n \"circuit = CNOT_dyn(circuit,0,n+1,c1,c2)\\n\",\n \"circuit.measure([0,n+1],cr)\\n\",\n \"circuits.append(pm_dd.run(pm.run(circuit)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"210a251f-a4b0-43f1-b081-67da215719bb\",\n \"metadata\": {\n \"id\": \"210a251f-a4b0-43f1-b081-67da215719bb\"\n },\n \"source\": [\n \"### Execute using Qiskit Primitives\\n\",\n \"\\n\",\n \"All the prepared circuits are now executed on the backend using the `Sampler` primitive. The `Sampler` is responsible for running circuits and returning the measurement outcomes as a probability distribution.\\n\",\n \"\\n\",\n \"Using a `Session` context manager allows us to efficiently group and manage multiple jobs. Each circuit will be run for **1024 shots** to gather reliable statistics.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"9b960de5\",\n \"metadata\": {\n \"id\": \"9b960de5\"\n },\n \"outputs\": [],\n \"source\": [\n \"job_ids = []\\n\",\n \"\\n\",\n \"\\n\",\n \"job = sampler.run(circuits, shots=1024)\\n\",\n \"\\n\",\n \"job_id = job.job_id()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"sp8m1s7Abfmi\",\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"id\": \"sp8m1s7Abfmi\",\n \"outputId\": \"a4763046-8fed-4a7f-965c-df505f275132\"\n },\n \"outputs\": [],\n \"source\": [\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"13158a17-2d4c-4610-89eb-2a535fb41763\",\n \"metadata\": {\n \"id\": \"13158a17-2d4c-4610-89eb-2a535fb41763\"\n },\n \"outputs\": [],\n \"source\": [\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"job = service.job(job_id)\\n\",\n \"job_result = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f0779516-c215-4e58-ba0f-fd6b604f8c44\",\n \"metadata\": {\n \"id\": \"f0779516-c215-4e58-ba0f-fd6b604f8c44\"\n },\n \"source\": [\n \"### Analyze and Visualize the Results\\n\",\n \"\\n\",\n \"Once the job is complete, we retrieve the measurement `counts` from the results of each circuit.\\n\",\n \"\\n\",\n \"Using `plot_histogram`, we can visualize and compare the results for each method (Unitary, Dynamic) and distance ([0,1], [0,3], [0,15]) in a single histogram. For an ideal Bell state, we expect to measure the `00` and `11` states with roughly 50% probability each. The histogram allows us to see how close each method came to this ideal outcome in a noisy environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"9037aa42-7d3f-45ed-8d8e-1147879172dc\",\n \"metadata\": {\n \"id\": \"9037aa42-7d3f-45ed-8d8e-1147879172dc\"\n },\n \"outputs\": [],\n \"source\": [\n \"counts = []\\n\",\n \"for result in job_result:\\n\",\n \" counts.append(result.data.cr.get_counts())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"3779952e-6035-4f5c-b8d3-b543b05f3202\",\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 487\n },\n \"id\": \"3779952e-6035-4f5c-b8d3-b543b05f3202\",\n \"outputId\": \"adbfcda8-84d2-459c-af02-65ac64c75e5e\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from qiskit.visualization import plot_histogram\\n\",\n \"\\n\",\n \"plot_histogram(counts, legend=['0&1', '0&3 Uni', '0&3 Dyn', '0&15 Uni', '0&15 Dyn'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f3809ec3-742c-4388-b0c1-7fec552c248b\",\n \"metadata\": {\n \"id\": \"f3809ec3-742c-4388-b0c1-7fec552c248b\"\n },\n \"source\": [\n \"## References\\n\",\n \"\\n\",\n \"[1] Efficient Long-Range Entanglement using Dynamic Circuits, by\\n\",\n \"*Elisa Bäumer, Vinay Tripathi, Derek S. Wang, Patrick Rall, Edward H. Chen, Swarnadeep Majumder, Alireza Seif, Zlatko K. Minev*. IBM Quantum, (2023).\\n\",\n \"https://arxiv.org/abs/2308.13065\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ea9ea4f2-0d9f-4c42-85c7-305a37a21eb9\",\n \"metadata\": {\n \"id\": \"ea9ea4f2-0d9f-4c42-85c7-305a37a21eb9\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.11\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-1/lab1-ko.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4b5d9b98-ac5b-4121-915a-893bd64960f3\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 1: Recreating famous experiments at home!\\n\",\n \"\\n\",\n \"# 0. 셋업: 라이브러리 불러오기\\n\",\n \"\\n\",\n \"모든 좋은 실험에는 장비를 준비하는 과정이 필요합니다. 이 경우에 그런 장비는 파이썬 라이브러리, 그 중에서도 우리의 양자 컴퓨팅 툴킷 Qiskit 라이브러리가 되겠습니다. 여러분이 lab 0를 완료하셨다면 모든 설정이 완료되어 있을 것입니다. 만약 lab 0를 완료하지 않으셨다면 아래 셀의 주석 표시를 지우고 그레이더와 Qiskit 2.x 등의 여름 학교에서 사용할 필수 라이브러리를 설치할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"519fb94e-18f2-4ade-a40f-58c1078424e5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0e8bb120\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3fee413b\",\n \"metadata\": {},\n \"source\": [\n \"Qiskit 버전 `>=2.0.0` 그레이더 버전 `>=0.22.9` 이상이 설치되어 있어야 합니다. 만약 버전이 다르게 표시된다면 커널을 재시작해보시고, 그래도 안 된다면 그레이더를 다시 설치해야 할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"53e15069\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 계정이 잘 저장되어 있는지 확인합니다\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"51102304\",\n \"metadata\": {},\n \"source\": [\n \"## 불러오기\\n\",\n \"\\n\",\n \"아래의 셀을 실행해 필수 라이브러리를 불러와보세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8ae038b9-2181-4c10-8018-833b52ff17a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 필수 라이브러리\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import ipywidgets as widgets\\n\",\n \"from IPython.display import display\\n\",\n \"from PIL import Image\\n\",\n \"import io\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.circuit import Parameter\\n\",\n \"from qiskit.visualization import plot_histogram, plot_distribution\\n\",\n \"from qiskit_ibm_runtime import Options, Session, SamplerV2 as Sampler\\n\",\n \"from qiskit.result import marginal_distribution\\n\",\n \"\\n\",\n \"from qiskit.transpiler import generate_preset_pass_manager\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab1_ex1_1, \\n\",\n \" grade_lab1_ex1_2, \\n\",\n \" grade_lab1_ex1_3, \\n\",\n \" grade_lab1_ex1_4, \\n\",\n \" grade_lab1_ex2, \\n\",\n \" grade_lab1_ex3,\\n\",\n \" grade_lab1_ex4,\\n\",\n \" grade_lab1_ex5,\\n\",\n \" grade_lab1_ex6\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"39f36676-bdd1-4ba2-8486-2b4796766eea\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# 목차\\n\",\n \"\\n\",\n \"1. 중첩, 간섭, 그리고 측정\\n\",\n \" 1. 이중 슬릿 실험 - 중첩과 간섭\\n\",\n \" 2. 슈뢰딩거의 고양이와 이중 슬릿 실험\\n\",\n \"2. 얽힘\\n\",\n \" 1. 앨리스와 밥의 이길 수 없는 게임 - CHSH 게임\\n\",\n \" 2. 양자 텔레포트 - 마법같은 작용을 이용한 비밀 전송\\n\",\n \" 1. 시뮬레이터에서의 텔레포트\\n\",\n \"3. (보너스 챌린지) 실제 하드웨어에서의 텔레포트\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d36937e1-9cc0-4c1b-8240-f87504825196\",\n \"metadata\": {},\n \"source\": [\n \"2025년 세계 양자의 해 QGSS의 첫 번째 랩에 오신 것을 환영합니다! 이 첫 번째 랩에서는 양자 세계에서 가장 중요한 4가지 개념 - 중첩, 간섭, 측정, 얽힘 - 을 Qiskit을 이용한 실험을 통해 공부해보도록 하겠습니다.\\n\",\n \"\\n\",\n \"# 챕터 1: 중첩, 간섭, 그리고 측정\\n\",\n \"\\n\",\n \"여러분의 여정을 시작하는 최고의 출발점으로, 그 유명한 이중 슬릿 실험을 통해 양자 중첩, 간섭, 그리고 측정에 대해 알아봅시다. **중첩**이란 전자나 광자와 같은 양자 입자가 여러 상태에 동시에 존재하는 것을 뜻합니다. 두 위치에 동시에 존재한다거나, 두 가지 다른 에너지를 동시에 갖는다거나 하는 것 말이죠. 이것은 동전을 튕겼을 때 앞면인 상태와 뒷면인 상태가 동시에 존재하는 것과도 비슷합니다... 여러분이 그것을 확인하기 전까지는요! 한편, **간섭**이란 이러한 양자적인 가능성이 파동처럼 작동해서 서로 겹쳐지는 현상을 설명합니다. 만약 이러한 가능성이 같은 위상을 갖는다면 서로 보강하게 되고(보강 간섭), 서로의 위상이 달라지면 가능성을 약화하거나 상쇄하게 됩니다(상쇄 간섭). 이러한 간섭 현상은 바로 이중 슬릿 실험의 결과를 설명하는 주요 개념으로 등장합니다.\\n\",\n \"\\n\",\n \"이러한 중첩과 간섭 현상을 잘 보여주는 **이중 슬릿 실험**은 1801년 토마스 영이 처음 보고한 이후로 전자와 같은 여러 입자를 이용하여 반복적으로 검증이 이루어졌습니다. 특히 재밌는 것은, 실험에서 입자가 어느 슬릿을 통과하는지 아무도 확인하지 않는 경우에는 파도가 겹쳐지듯이 아름다운 간섭 무늬가 나타나게 되지만, 누군가 입자가 통과하는 슬릿을 **측정**하는 순간, 간섭 무늬가 사라진다는 것입니다. 마치 입자가 누군가 보고 있는 것을 알기라도 하는 것처럼이요!\\n\",\n \"\\n\",\n \"이런 이상한 아이디어는 1935년 에르빈 슈뢰딩거에 의해 **슈뢰딩거의 고양이**라는 유명한 사고 실험으로 발전하게 됩니다. 박스를 열기 전까지는 산 고양이와 죽은 고양이가 동시에 존재한다는 바로 그 사고 실험이죠. 양자역학에서의 측정은 단순히 현실을 관측하는 것에 그치지 않습니다. 그것은 현실을 빚어냅니다.\\n\",\n \"\\n\",\n \"양자역학의 이상한 세계를 경험해보실 준비가 되셨나요? Qiskit을 이용하면 측정이 있는 이중 슬릿 실험과 없는 실험을 모두 구현하고, 중첩과 간섭이 어떻게 아름다운 간섭 무늬를 만들어내는지, 그리고 측정이 어떻게 이러한 양자적 시스템에 영향을 주는지 확인하실 수 있습니다. 양자 세계의 미스테리를 탐험하는 재밌고 놀라운 실험실로 여러분을 초대합니다!\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"309c6569-6875-447e-a514-cc049e7a37f0\",\n \"metadata\": {},\n \"source\": [\n \"## 슬릿을 통과해: 양자의 신비가 시작되는 곳\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \" image from wikipedia\\n\",\n \"
\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3d9deeff-2450-4ead-b78a-5533addb84a7\",\n \"metadata\": {},\n \"source\": [\n \"1803년 논문 \\\"Experiments and calculations relative to physical optics\\\"에서 토마스 영은 그의 실험을 다음과 같이 묘사하였습니다:\\n\",\n \"> 나는 창문 셔터에 작은 구멍을 뚫고, 그 위를 두꺼운 종이로 덮은 뒤, 가는 바늘로 다시 구멍을 뚫었다. 관찰의 편의를 위해 셔터가 없는 작은 거울을 설치하여 태양광을 반대쪽 벽으로 수평에 가깝게 반사하면서, 반사되어 발산하는 빛의 원뿔이 테이블 위를 지나가도록 조정한다. 테이블 위에는 여러 장의 작은 카드 용지로 스크린을 만들어 두었다. [1](https://royalsocietypublishing.org/doi/pdf/10.1098/rstl.1804.0001)\\n\",\n \"\\n\",\n \"이 실험에서 그는 아름다운 간섭무늬를 관측하였고, 이것이 두 슬릿을 통과한 광선의 경로차로 인해 발생하는 현상이라고 설명하였습니다. 조지 패짓 톰슨은 전자를 이용해 비슷한 실험을 하였고 (전자 회절 실험) 전자와 같은 입자도 파동과 같은 성질을 가질 수 있다는 것을 1927년 논문으로 발표하였습니다. [2](https://royalsocietypublishing.org/doi/10.1098/rspa.1927.0130) 이후 1965년에 파인만은 그의 저서 \\\"Lectures on Physics, Vol. 3, Chapter 1\\\"에 이 이중 슬릿 실험을 싣고, 이 모든 현상이 양자역학의 중첩과 간섭에 의해 나타난다고 설명하였습니다.\\n\",\n \"\\n\",\n \"사실 이제 여러분은 복잡한 물리 실험 장비 없이도 이 흥미로운 양자 현상을 Qiskit을 이용해서 재현할 수 있습니다. 중첩과 간섭 현상이 양자 회로에서는 어떻게 구현되는지 같이 한 번 살펴볼까요?\\n\",\n \"\\n\",\n \"첫 번째 단계는 바로 물리적인 이중 슬릿 실험을 양자 회로로 옮기는 것입니다. 여러분이 직접 한 번 해보시겠어요?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f79b538f-82c1-4297-a550-4360e3ab0082\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"연습문제 1: 이중 슬릿 실험을 위한 양자 회로를 만들어봅시다\\n\",\n \"\\n\",\n \"- 연습문제 1-1: 슬릿 만들기\\n\",\n \"\\n\",\n \"**목표:** 양쪽의 슬릿을 통과하는 입자(큐빗)을 묘사하는 양자회로를 만들어보세요. 이는 $|0\\\\rangle$ (아래쪽 슬릿) 과 $|1\\\\rangle$ (위쪽 슬릿) 상태의 중첩으로 나타날 것입니다.\\n\",\n \"\\n\",\n \"위쪽 슬릿을 통과한 입자를 큐빗의 $|1\\\\rangle$ 상태로, 아래쪽 슬릿을 통과한 입자를 큐빗의 $|0\\\\rangle$ 상태로 정의합니다. 처음 $|0\\\\rangle$ 상태에 있는 큐빗이 슬릿을 통과하면서 $|0\\\\rangle$ 과 $|1\\\\rangle$ 의 중첩 상태가 되는 과정을 [Hadamard 게이트](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.HGate)를 이용한 양자 회로로 묘사해보세요.\\n\",\n \"\\n\",\n \"**힌트**: 각 레지스터에 이름을 붙이는 것은 나중에 측정 데이터를 명확히 확인하는 데에 도움이 됩니다. `QuantumRegister` (e.g., `name='q'`) 또는 `ClassicalRegister` (e.g., `name='c_screen'`).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"884333a6-3489-4520-912c-99d337eb00d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\n\",\n \"\\n\",\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit = QuantumCircuit(qr, cr)\\n\",\n \"# 아래에 코드를 작성해주세요\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# 코드 작성이 완료되었습니다\\n\",\n \"double_slit.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f5d3c41c-05fc-46ff-a41e-f7de68e96275\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab1_ex1_1(double_slit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e92915b5-7396-4d04-b0da-05b84dd7e8c3\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"연습문제 1: 이중 슬릿 실험을 위한 양자 회로를 만들어봅시다\\n\",\n \"\\n\",\n \"- 연습문제 1-2: 스크린 만들기\\n\",\n \"\\n\",\n \"**목표:** 앞의 회로에 이어서 간섭이 일어나는 스크린을 묘사하여 회로로 나타내봅시다. 먼저, 위상차가 발생하지 않는 스크린 중앙의 상황을 구현하고 큐빗을 측정해보세요.\\n\",\n \"\\n\",\n \"이제 회로에 게이트를 추가해 두 중첩된 양자 상태가 간섭 무늬를 만드는 스크린의 상황을 구현해봅시다. 첫 번째로, 양쪽 슬릿을 통과한 광선이 위상차 없이 합쳐지는 (그래서 $|0\\\\rangle$ 상태가 되는) 스크린 중앙의 상황을 만들어보세요. 그리고 `qr` 큐빗을 측정하여 결과를 `c_screen` 레지스터에 저장하세요.\\n\",\n \"\\n\",\n \"**힌트**: Hadamard 게이트를 다시 사용해보세요!\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1828c3c0-7bb6-451d-891a-89f56835c6bc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래에 코드를 작성해주세요\\n\",\n \"\\n\",\n \"# 코드 작성이 완료되었습니다\\n\",\n \"double_slit.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1fcf565a-b547-43fb-8492-c327a9ff7a10\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab1_ex1_2(double_slit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88cc5027-9435-4eaf-a3c3-0e391cc997d3\",\n \"metadata\": {},\n \"source\": [\n \"실행 결과를 확인해봅시다. 스크린이 $|0\\\\rangle$ 상태의 큐빗만을 관측한다고 가정하고, 다음의 코드를 시뮬레이터로 실행해봅시다. 확률 1은 100% 확률을 의미합니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"SamplerV2에서 측정 결과를 불러오는 방법에 관한 참고 사항 \\n\",\n \"\\n\",\n \"Qiskit에서 *SamplerV2*를 사용할 때에는 측정 결과를 불러오는 방법이 고전 레지스터와 측정을 설정한 방법에 따라 달라질 수 있습니다:\\n\",\n \"\\n\",\n \"1. *QuantumCircuit*에 이름을 붙인 고전 레지스터를 넣은 경우: \\n\",\n \" 회로를 정의할 때에 이름을 붙인 고전 레지스터가 들어가는 경우, 예를 들어 *cr = ClassicalRegister(1, name='my_results')* // *qc = QuantumCircuit(qr, cr)* // *qc.measure(qr[0], cr[0])* 순서로 설정한 경우, 측정 결과는 해당 레지스터의 이름으로 액세스합니다:\\n\",\n \"\\n\",\n \" \\n\",\n \" ```python\\n\",\n \" # result = job.result()\\n\",\n \" # counts = result[0].data.my_results.get_counts()\\n\",\n \" ```\\n\",\n \" \\n\",\n \" 우리의 이중 슬릿 실험에서는 고전 레지스터를 *cr = ClassicalRegister(1, name='c_screen')*과 같이 정의했으므로, *result[0].data.c_screen.get_counts()*로 결과를 확인하도록 하겠습니다.\\n\",\n \"\\n\",\n \"3. measure_all(): \\n\",\n \" 만약 *qc.measure_all()* 방법을 사용해 측정을 하는 경우에는, Qiskit이 자동으로 `meas`라는 이름의 고전 레지스터와 출력 필드를 추가해줍니다:\\n\",\n \"\\n\",\n \" \\n\",\n \" ```python\\n\",\n \" # qc.measure_all()\\n\",\n \" # ...\\n\",\n \" # counts = result[0].data.meas.get_counts()\\n\",\n \" ```\\n\",\n \"\\n\",\n \"5. 자동 설정된 고전 레지스터 (*QuantumCircuit*에서 이름을 따로 설정하지 않은 경우): \\n\",\n \" 만약 *qc = QuantumCircuit(1, 1)* (1 큐빗, 1 고전 비트) 또는 *qc = QuantumCircuit(QuantumRegister(1), ClassicalRegister(1))* 와 같은 방법으로 고전 레지스터의 이름을 설정하지 않고 *qc.measure(0, 0)*로 측정하였다면, *SamplerV2*가 결괏값을 저장하는 레지스터의 이름은 기본값으로 설정되어 있을 것입니다. (보통 *c*이거나, 비트 색인에 따라 *c0* 등의 이름일 수 있습니다). 예를 들어 *qc = QuantumCircuit(1,1)* // *qc.measure(0,0)* 순서로 설정한 경우, 측정 결과는 다음과 같이 확인할 수 있습니다:\\n\",\n \"\\n\",\n \" ```python \\n\",\n \" # counts = result[0].data.c0.get_counts() # 하나의 비트가 c0로 이름지어졌다면 색인 번호는 바뀔 수 있습니다. 실제 색인 번호는 회로를 그려서 확인할 수 있습니다.\\n\",\n \" ```\\n\",\n \"
\\n\",\n \"연습문제 1: 이중 슬릿 실험을 위한 양자 회로를 만들어봅시다\\n\",\n \"\\n\",\n \"- 연습문제 1-3: 경로차 만들기\\n\",\n \"\\n\",\n \"**목표:** 이중 슬릿 회로를 조작하여 두 경로 (슬릿) 사이의 위상차를 만들고, 스크린에 나타나는 측정 결과에 어떤 영향을 미치는지 확인해보세요.\\n\",\n \"\\n\",\n \"자, 이제 스크린의 다른 부분의 상황을 구현해봅시다. 각각의 슬릿을 통과한 두 광선의 경로차는 $|0\\\\rangle$ 과 $|1\\\\rangle$ 상태 사이의 위상차로 구현할 수 있습니다. 중첩된 상태 중 $|1\\\\rangle$ 상태에만 $\\\\pi/2$ 만큼의 위상을 주고 측정 결과를 확인해보세요. (힌트: $|1\\\\rangle$ 상태에 위상을 주는 게이트를 사용해보세요. 대표적인 예로 [P 게이트](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.PhaseGate) 와 [S 게이트](https://quantum.cloud.ibm.com/docs//api/qiskit/qiskit.circuit.library.SGate) 가 있습니다.)\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"50a651fe-236a-4c64-ad11-f00200c1cedf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit_with_difference = QuantumCircuit(qr, cr)\\n\",\n \"double_slit_with_difference.h(0)\\n\",\n \"\\n\",\n \"# 아래에 코드를 작성해주세요\\n\",\n \"\\n\",\n \"\\n\",\n \"# 코드 작성이 완료되었습니다\\n\",\n \"\\n\",\n \"double_slit_with_difference.h(0)\\n\",\n \"double_slit_with_difference.measure(qr, cr)\\n\",\n \"double_slit_with_difference.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"810339b8-ab70-4aa8-b89c-220e8a7521f5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab1_ex1_3(double_slit_with_difference)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c4e217cf-065f-45ee-8d7c-3e8c94f7fede\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qc_isa = pm.run(double_slit_with_difference)\\n\",\n \"\\n\",\n \"# 회로를 실행하고 결과를 가져옵니다\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots=10000).result()[0].data.c_screen.get_counts()\\n\",\n \"plot_distribution(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4a08020a-6caa-4415-bec0-b5e4f53471ad\",\n \"metadata\": {},\n \"source\": [\n \"보시다시피, $|0\\\\rangle$ 상태를 관측할 확률이 약 0.5 (50%) 정도로 감소한 것을 보실 수 있습니다. 이는 스크린의 이 부분에서 밝기가 반으로 감소했다는 것을 의미합니다.\\n\",\n \"\\n\",\n \"계속해서 진행하기 전에, 이 중첩과 간섭, 각 상태를 관측할 확률에 대한 과정을 수학적으로 계산해봅시다. 만약 이 개념이 이미 익숙하시다면 이 부분은 건너뛰셔도 괜찮습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"
중첩과 간섭, 그리고 측정 확률
\\n\",\n \"\\n\",\n \"이중 슬릿 실험을 구현한 양자 회로에서 우리는 Hadamard (H) 게이트와 phase (P) 게이트를 사용했습니다. 각 게이트가 변화시키는 양자 상태가 수학적으로 어떻게 나타나는지 보고 중첩과 간섭, 그리고 측정 확률에 대해 분석해봅시다.\\n\",\n \"\\n\",\n \"**1. 초기화:**\\n\",\n \"\\n\",\n \"회로를 시작할 때 큐비트는 $|0\\\\rangle$ 상태로 초기화됩니다. 이것은 막 발생한 전자가 슬릿을 통과하기 전의 상태를 묘사합니다:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_0\\\\rangle = |0\\\\rangle = \\\\begin{pmatrix} 1 \\\\\\\\ 0 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"**2. 중첩 (첫 번째 Hadamard 게이트):**\\n\",\n \"\\n\",\n \"첫 번째 Hadamard (H) 게이트가 큐빗에 가해졌습니다. 이 게이트는 $|0\\\\rangle$ 과 $|1\\\\rangle$ 상태의 중첩을 만들어냅니다. 이 때 $|0\\\\rangle$ 상태와 $|1\\\\rangle$ 상태는 각각 아래쪽/위쪽 슬릿을 통과한 전자의 상태를 묘사합니다. 따라서 $|0\\\\rangle$ 과 $|1\\\\rangle$ 상태의 중첩은 양쪽 슬릿을 통과한 전자의 상태가 동시에 존재하는 것이죠:\\n\",\n \"\\n\",\n \"$$ H = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"$|\\\\psi_0\\\\rangle$ 에 H 게이트를 가하면:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_1\\\\rangle = H |\\\\psi_0\\\\rangle = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} \\\\begin{pmatrix} 1 \\\\\\\\ 0 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ 1 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} (|0\\\\rangle + |1\\\\rangle) $$\\n\",\n \"\\n\",\n \"$|\\\\psi_1\\\\rangle$ 상태는 큐빗이 $|0\\\\rangle$ 상태 (아래쪽 슬릿 통과) 그리고 $|1\\\\rangle$ 상태 (위쪽 슬릿 통과) 둘에 동등하게 중첩되어 있는 것을 나타냅니다.\\n\",\n \"\\n\",\n \"**3. 위상 변화 (P 게이트):**\\n\",\n \"\\n\",\n \"Phase (P) 게이트는 중첩된 $|0\\\\rangle$ 과 $|1\\\\rangle$ 상태 사이에 상대적인 위상 $\\\\phi$ 를 만들어줍니다. 이 위상차는 실제 실험에서 양쪽 슬릿을 통과한 전자가 스크린에 도달하기까지의 경로차에 해당합니다. P 게이트는 다음과 같이 정의됩니다:\\n\",\n \"\\n\",\n \"$$ P(\\\\phi) = \\\\begin{pmatrix} 1 & 0 \\\\\\\\ 0 & e^{i\\\\phi} \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"$|\\\\psi_1\\\\rangle$ 에 P($\\\\phi$) 를 가하면:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_2\\\\rangle = P(\\\\phi) |\\\\psi_1\\\\rangle = \\\\begin{pmatrix} 1 & 0 \\\\\\\\ 0 & e^{i\\\\phi} \\\\end{pmatrix} \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ 1 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ e^{i\\\\phi} \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} (|0\\\\rangle + e^{i\\\\phi}|1\\\\rangle) $$\\n\",\n \"\\n\",\n \"$|\\\\psi_2\\\\rangle$ 상태는 이제 상대적 위상이라는 정보를 가지며, 이것이 다음의 간섭 현상에 중요한 역할을 합니다.\\n\",\n \"\\n\",\n \"**4. 간섭 (두 번째 Hadamard 게이트):**\\n\",\n \"\\n\",\n \"두 번째 Hadamard 게이트를 가함으로써 중첩된 상태가 다시 하나로 모이고, 간섭이 일어나게 됩니다:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_3\\\\rangle = H |\\\\psi_2\\\\rangle = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ e^{i\\\\phi} \\\\end{pmatrix} = \\\\frac{1}{2} \\\\begin{pmatrix} 1 + e^{i\\\\phi} \\\\\\\\ 1 - e^{i\\\\phi} \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"오일러 등식 $e^{i\\\\phi} = \\\\cos(\\\\phi) + i\\\\sin(\\\\phi)$ 을 적용하면:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_3\\\\rangle = \\\\frac{1}{2} \\\\begin{pmatrix} 1 + \\\\cos(\\\\phi) + i\\\\sin(\\\\phi) \\\\\\\\ 1 - (\\\\cos(\\\\phi) + i\\\\sin(\\\\phi)) \\\\end{pmatrix} = \\\\frac{1}{2} \\\\begin{pmatrix} (1 + \\\\cos(\\\\phi)) + i\\\\sin(\\\\phi) \\\\\\\\ (1 - \\\\cos(\\\\phi)) - i\\\\sin(\\\\phi) \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"이 관측 직전의 최종 상태에 간섭의 효과가 나타나는 것을 볼 수 있습니다. $|0\\\\rangle$ 또는 $|1\\\\rangle$ 상태를 측정할 확률이 위상차 $\\\\phi$ 에 따라 달라지기 때문입니다.\\n\",\n \"\\n\",\n \"**5. 측정 확률:**\\n\",\n \"\\n\",\n \"스크린의 어느 지점에서 $|0\\\\rangle$ 상태의 큐빗을 관측할 확률은 $|\\\\psi_3\\\\rangle$ 상태에서 $|0\\\\rangle$ 상태에 해당하는 진폭값의 제곱으로 주어집니다.\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\left| \\\\frac{1}{2} (1 + e^{i\\\\phi}) \\\\right|^2 = \\\\left| \\\\frac{1}{2} (1 + \\\\cos(\\\\phi) + i\\\\sin(\\\\phi)) \\\\right|^2 $$\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\frac{1}{4} [(1 + \\\\cos(\\\\phi))^2 + (\\\\sin(\\\\phi))^2] = \\\\frac{1}{4} [1 + 2\\\\cos(\\\\phi) + \\\\cos^2(\\\\phi) + \\\\sin^2(\\\\phi)] $$\\n\",\n \"\\n\",\n \"등식 $\\\\cos^2(\\\\phi) + \\\\sin^2(\\\\phi) = 1$ 을 적용하면:\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\frac{1}{4} [1 + 2\\\\cos(\\\\phi) + 1] = \\\\frac{1}{4} [2 + 2\\\\cos(\\\\phi)] = \\\\frac{1}{2} (1 + \\\\cos(\\\\phi)) $$\\n\",\n \"\\n\",\n \"$\\\\phi = \\\\pi/2$ 인 경우, $P(|0\\\\rangle) = \\\\frac{1}{2} (1 + \\\\cos(\\\\pi/2)) = 0.5$ 이므로 앞에서 얻은 결과와 같은 값이 나오는 것을 알 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6476c1c7-c119-4e60-a0f6-821321401081\",\n \"metadata\": {},\n \"source\": [\n \"마지막으로 위상차 $\\\\phi$ 의 값을 연속적으로 변화시켜 회절 무늬를 만드는 것으로 연습문제 1을 마치겠습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5d40a6e6-fa86-47af-b556-2849906d5396\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"연습문제 1: 이중 슬릿 실험을 위한 양자 회로를 만들어봅시다\\n\",\n \"\\n\",\n \"- 연습문제 1-4: 아름다운 회절\\n\",\n \"\\n\",\n \"**목표:** 양자 회로가 위상차 $\\\\phi$ 를 변수로 갖도록 하여 전체 회절 간섭 무늬를 시뮬레이션 해봅시다.\\n\",\n \"\\n\",\n \"Qiskit에서는 [변수를 포함한 양자 회로](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.Parameter)도 만들 수 있습니다. 변수를 포함한 양자 회로를 만들고 `Sampler`로 관측하여 간섭 무늬를 확인해봅시다.\\n\",\n \"\\n\",\n \"변수 $\\\\phi$ 를 갖는 이중 슬릿 실험 양자 회로를 정의하세요. 일반적인 구조는 H 게이트, $P(\\\\phi)$ 게이트, H 게이트 순서입니다. `q` 큐빗을 관측하여 `c_screen` 레지스터에 저장하세요.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4a90de8b-f5fb-4adf-a6d3-466a46f325b8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ = Parameter('φ')\\n\",\n \"\\n\",\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit_fringe = QuantumCircuit(qr, cr)\\n\",\n \"\\n\",\n \"# 아래에 코드를 작성해주세요\\n\",\n \"\\n\",\n \"\\n\",\n \"# 코드 작성이 완료되었습니다\\n\",\n \"\\n\",\n \"double_slit_fringe.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d2d987c4-d66b-4e1a-84f4-d5915ddde852\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab1_ex1_4(double_slit_fringe)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7a2c005f-dd42-4ee8-8073-b6b483045d76\",\n \"metadata\": {},\n \"source\": [\n \"훌륭해요! 이제 이 회로를 실행해 회절 무늬를 얻고 matplotlib의 heatmap 도표로 나타내봅시다. 아래의 코드는 100개의 위상 값을 넣고, `Sampler`로 회로를 실행해 (각 1000회 측정) $|0\\\\rangle$ 상태를 얻을 확률을 저장하고, heatmap 도표를 그리는 코드입니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"리소스 제한\\n\",\n \"\\n\",\n \"아래 코드를 실제 백엔드에서 실행할 경우, 변수값의 개수를 늘리거나 측정 횟수(shot)를 늘리게 되면 양자 컴퓨터를 사용하는 시간이 그만큼 많아지게 됩니다. 현재 세팅으로 (변수값 100개 + 1000회 측정) 양자 컴퓨터에서 회로를 실행하는 경우 예상되는 사용 시간은 1분 내외입니다. 실제 실험을 해보고 싶으신 경우, 가능하면 현재 세팅을 유지하시길 추천드립니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2d447625-a7c9-46e5-9a8d-690419747ef1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ_lst = np.linspace(-3*np.pi, 3*np.pi, 100)\\n\",\n \"qc_isa = pm.run(double_slit_fringe)\\n\",\n \"\\n\",\n \"φ_hit = []\\n\",\n \"dist = sampler.run([(qc_isa, φ_lst)], shots=1000).result()[0].data.c_screen\\n\",\n \"\\n\",\n \"for i in range(len(φ_lst)):\\n\",\n \" result = dist[i].get_counts()\\n\",\n \" if '0' in result:\\n\",\n \" φ_hit.append(result['0']/1000)\\n\",\n \" else:\\n\",\n \" φ_hit.append(0)\\n\",\n \"\\n\",\n \"# heatmap 도표를 그립니다\\n\",\n \"φ_hit_2d = np.array(φ_hit).reshape(1, -1)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 2))\\n\",\n \"plt.imshow(φ_hit_2d, cmap='gray', aspect='auto', extent=[-3*np.pi, 3*np.pi, 0, 0.1])\\n\",\n \"\\n\",\n \"plt.xlabel('φ (Phase difference)', fontsize=14)\\n\",\n \"plt.colorbar(label='prob')\\n\",\n \"plt.xticks(ticks=[-3*np.pi, -2*np.pi, -np.pi, 0, np.pi, 2*np.pi, 3*np.pi],\\n\",\n \" labels=['-3π', '-2π', '-π', '0', 'π', '2π', '3π'])\\n\",\n \"plt.yticks([]) \\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0a6bc2c3-7c4a-46a2-9984-bc8a94666ead\",\n \"metadata\": {},\n \"source\": [\n \"Fantastic! You've reproduced the double-slit experiment's fringes using qubits. Next, we'll explore quantum measurement with Schrödinger's cat and then see how observation affects the double-slit outcome.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed7715c1-2169-4fef-9b7d-3ed61c4b2f41\",\n \"metadata\": {},\n \"source\": [\n \"## 슈뢰딩거의 고양이: 양자 중첩과 관측의 작용\\n\",\n \"\\n\",\n \"에르빈 슈뢰딩거가 제안한 이 사고실험은 (1935, [\\\"Die gegenwärtige Situation in der Quantenmechanik\\\"](https://link.springer.com/article/10.1007/BF01491891)) 양자 중첩의 이상한 성질과 관측이 어떻게 여러 확률적인 가능성을 하나의 결과로 붕괴시키는지를 잘 보여줍니다. 고양이 한 마리가 방사성 원자가 든 박스에 봉인되어 있습니다. 방사성 원자는 확률적으로 붕괴하거나 붕괴하지 않을 수 있으며, 방사성 원자가 붕괴하면 고양이는 죽고, 붕괴하지 않으면 고양이는 살아 있습니다. 방사성 원자가 붕괴하는 것과 붕괴하지 않는 것이 양자적인 중첩 상태에 있을 수 있다면, 그와 얽혀 있는 고양이도, 관측되기 전까지 죽은 상태와 살아있는 상태에 중첩되어 있어야 할 것입니다.\\n\",\n \"\\n\",\n \"이번 연습문제에서는 회전 게이트를 이용해 이러한 상황을 묘사해보겠습니다. 큐빗이 $|1\\\\rangle$ 상태로 관측되는 것을 싫어하는 고양이를 한 번 상상해봅시다. \\\"박스를 열었을 때\\\" (큐빗을 관측했을 때) 고양이가 기쁜 상태인지 화가 난 상태인지를 확인해볼 것입니다. 조심하세요! 화가 난 양자 고양이가 여러분을 할퀼 수도 있으니까요.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5559255f-f8c1-4a49-bcf1-738d9730f92f\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"연습문제 2: 고양이가 기쁜가요 짜증이 났나요?\\n\",\n \"\\n\",\n \"**목표:** $R_X(\\\\theta)$ 게이트를 사용해 양자 회로의 큐빗을 중첩 상태로 만들고, 큐빗을 관측해 \\\"고양이\\\"가 (큐빗의 상태로 표현됨) \\\"기쁜지\\\" ($|0\\\\rangle$) \\\"짜증이 났는지\\\" ($|1\\\\rangle$) 확인해보세요.\\n\",\n \"\\n\",\n \"[Rotational X gate](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.RXGate) 와 변수 $\\\\theta \\\\in [0, 2\\\\pi]$ 를 이용해 큐빗을 여러 가지의 중첩 상태로 만드는 코드를 완성해주세요. 그리고 큐빗을 측정하여 고양이가 기쁜지 ($|0\\\\rangle$) 짜증이 났는지 ($|1\\\\rangle$) 확인해보세요. $\\\\theta$ 값은 슬라이더로 조정할 수 있습니다.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6aa42755-b319-4aee-a6bc-cf6b34e00222\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def schrodingers_cat_experiment_theta(theta):\\n\",\n \" \\n\",\n \" qc = QuantumCircuit(1)\\n\",\n \"\\n\",\n \" # 아래에 코드를 작성해주세요\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" # 코드 작성이 완료되었습니다\\n\",\n \"\\n\",\n \" qc.measure_all()\\n\",\n \" \\n\",\n \" backend = AerSimulator()\\n\",\n \" pm = generate_preset_pass_manager( backend = backend, optimization_level=3)\\n\",\n \" qc_isa = pm.run(qc)\\n\",\n \"\\n\",\n \" # 회로를 컴파일해 1회만 측정합니다\\n\",\n \" sampler = Sampler(mode=backend)\\n\",\n \" counts = sampler.run([qc_isa], shots = 1).result()[0].data.meas.get_counts()\\n\",\n \"\\n\",\n \" measured_state = list(counts.keys())[0] if counts else '0' # 측정 결과를 가져옵니다\\n\",\n \"\\n\",\n \" if measured_state == '0':\\n\",\n \" cat_happy = True\\n\",\n \" else:\\n\",\n \" cat_happy = False\\n\",\n \"\\n\",\n \" return cat_happy, qc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"49747cda-70bf-44d7-816c-607dd601a95f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab1_ex2(schrodingers_cat_experiment_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"18096b47-adb4-47dc-a41f-2273db2aacab\",\n \"metadata\": {},\n \"source\": [\n \"이제 아래의 위젯을 사용해 고양이의 기분을 확인해보세요. `happy.png` 파일과 `grumpy.png` 파일이 이 노트북과 같은 폴더에 있어야 합니다. (Image credit: James Weaver)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4aed0937-fa98-416e-a2c8-e571df1ed907\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"happy_img = Image.open('happy.png')\\n\",\n \"grumpy_img = Image.open('grumpy.png')\\n\",\n \"\\n\",\n \"out = widgets.Output()\\n\",\n \"\\n\",\n \"slider = widgets.FloatSlider(\\n\",\n \" value=0,\\n\",\n \" min=0,\\n\",\n \" max=2*np.pi,\\n\",\n \" step=0.01,\\n\",\n \" description='θ',\\n\",\n \" continuous_update=True\\n\",\n \")\\n\",\n \"\\n\",\n \"button = widgets.Button(\\n\",\n \" description='Open the Box',\\n\",\n \" button_style='success'\\n\",\n \")\\n\",\n \" \\n\",\n \"def on_button_click(b):\\n\",\n \" with out:\\n\",\n \" out.clear_output(wait=True) # 출력창 초기화\\n\",\n \"\\n\",\n \" result = schrodingers_cat_experiment_theta(slider.value)[0]\\n\",\n \"\\n\",\n \" if result==True:\\n\",\n \" img = happy_img\\n\",\n \" txt = \\\"happy\\\"\\n\",\n \" else:\\n\",\n \" img = grumpy_img\\n\",\n \" txt = \\\"grumpy\\\"\\n\",\n \"\\n\",\n \" new_size = (400, 400)\\n\",\n \" resized_img = img.resize(new_size)\\n\",\n \" \\n\",\n \" buf = io.BytesIO()\\n\",\n \" resized_img.save(buf, format='PNG')\\n\",\n \" buf.seek(0)\\n\",\n \" probability = int(np.cos(slider.value/2)**2 * 100)\\n\",\n \"\\n\",\n \" display(f\\\"The probability of cat is happy: {probability}%\\\")\\n\",\n \" display(f\\\"The observed cat is : {txt}\\\")\\n\",\n \" display(widgets.Image(value=buf.read(), format='png'))\\n\",\n \"\\n\",\n \"button.on_click(on_button_click)\\n\",\n \"\\n\",\n \"display(slider, button, out)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7d8e5717-b4d3-4e0c-84d9-279210916258\",\n \"metadata\": {},\n \"source\": [\n \"이중 슬릿 실험으로 돌아가기 전에, 큐빗을 관측하거나 \\\"측정\\\" 하려 할 때에 정확히 어떤 일이 일어나는지 알아봅시다 - 특히 $R_x(\\\\theta)$ 같은 게이트를 가한 후에요. 만약 양자적인 측정의 개념을 잘 알고 계신다면 이 부분은 건너뛰셔도 괜찮습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"
양자 측정: 큐비트의 관측
\\n\",\n \"\\n\",\n \"**1. 측정이 이루어지기 전 큐비트의 상태:** \\n\",\n \"$|0\\\\rangle$ 상태에 $R_x(\\\\theta)$ 게이트가 가해지면 이 큐빗은 중첩 상태가 됩니다:\\n\",\n \"$$|\\\\psi\\\\rangle = R_x(\\\\theta)|0\\\\rangle = \\\\cos\\\\left(\\\\frac{\\\\theta}{2}\\\\right)|0\\\\rangle - i \\\\sin\\\\left(\\\\frac{\\\\theta}{2}\\\\right)|1\\\\rangle = \\\\alpha|0\\\\rangle + \\\\beta|1\\\\rangle$$\\n\",\n \"이 때, $|\\\\alpha|^2 + |\\\\beta|^2 = 1$.\\n\",\n \"\\n\",\n \"**2. 측정의 작용:** \\n\",\n \"계산 기저 ($\\\\{|0\\\\rangle, |1\\\\rangle\\\\}$) 에서의 측정은 큐비트를 둘 중 하나의 상태를 갖도록 강제합니다. 중첩 상태를 직접적으로 관측할 수는 없습니다.\\n\",\n \"\\n\",\n \"**3. 확률적인 결과:** \\n\",\n \"$|0\\\\rangle$ 상태를 관측할 확률: $P(0) = |\\\\alpha|^2 = \\\\cos^2\\\\left(\\\\frac{\\\\theta}{2}\\\\right)$ \\n\",\n \"$|1\\\\rangle$ 상태를 관측할 확률: $P(1) = |\\\\beta|^2 = \\\\sin^2\\\\left(\\\\frac{\\\\theta}{2}\\\\right)$\\n\",\n \"\\n\",\n \"**4. 상태의 붕괴:**\\n\",\n \"측정이 이루어진 후에 양자 상태는 붕괴합니다:\\n\",\n \"* 만약 '0' 이 관측되었다면 $\\\\rightarrow$ 큐빗의 상태는 $|0\\\\rangle$ 이 됩니다.\\n\",\n \"* 만약 '1' 이 관측되었다면 $\\\\rightarrow$ 큐빗의 상태는 $|1\\\\rangle$ 이 됩니다.\\n\",\n \"이 \\\"붕괴\\\"는 중첩 상태를 파괴합니다.\\n\",\n \"\\n\",\n \"**이중 슬릿 실험과의 연관성:**\\n\",\n \"입자가 어느 쪽 슬릿을 통과하는지 관측하는 것도 측정에 해당합니다. 이러한 측정은 입자의 파동함수를 한 쪽 경로로 붕괴시키고 중첩 상태를 파괴해 간섭 무늬를 만들 수 없게 합니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"자 이제 이중 슬릿 실험으로 돌아가봅시다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a759bf4a-f9ce-4d23-bd90-c72d15b0c87b\",\n \"metadata\": {},\n \"source\": [\n \"## 측정 장비가 있는 이중 슬릿\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 3: 입자 감지기가 있는 이중 슬릿\\n\",\n \"\\n\",\n \"**목표:** 이중 슬릿 양자 회로에 \\\"입자 감지기\\\"와 같이 작용하는 중간 측정을 추가해보세요. 이 실험을 통해 입자의 경로를 관측하는 것이 어떻게 간섭 무늬를 바꾸는지 볼 수 있을 것입니다.\\n\",\n \"\\n\",\n \"이중 슬릿 회로에 \\\"입자 감지기\\\"에 해당하는 측정을 추가해보세요. (첫 번째 H 게이트 직후 측정)\\n\",\n \"\\n\",\n \"설정:\\n\",\n \"* `qr`: 1 큐빗 (`'q'`).\\n\",\n \"* `cr1`: 1 비트 (`'c_detector'`) 입자가 통과하는 슬릿을 감지.\\n\",\n \"* `cr2`: 1 비트 (`'c_screen'`) 스크린에서의 최종 측정.\\n\",\n \"* `φ`: Phase 게이트의 `Parameter`.\\n\",\n \"\\n\",\n \"**힌트**: 회로를 두 번 측정하게 됩니다.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ba9f4861-356b-4b66-99d5-e06561e5f340\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr1 = ClassicalRegister(1, name='c_detector')\\n\",\n \"cr2 = ClassicalRegister(1, name='c_screen')\\n\",\n \"double_slit_with_detector = QuantumCircuit(qr, cr1, cr2)\\n\",\n \"\\n\",\n \"φ = Parameter('φ')\\n\",\n \"\\n\",\n \"# 아래에 코드를 작성해주세요\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# 코드 작성이 완료되었습니다\\n\",\n \"\\n\",\n \"double_slit_with_detector.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c6533c86-e7e6-4b0f-92ef-5179d75b7702\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab1_ex3(double_slit_with_detector)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"48ffc450-3cf9-423c-be89-808a03dd246a\",\n \"metadata\": {},\n \"source\": [\n \"입자 감지기가 있을 때 나타나는 간섭 무늬를 똑같이 heatmap 도표로 나타내보세요.\\n\",\n \"\\n\",\n \"**주의**: 몇 분 정도 시간이 걸릴 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1dbb4f19-9a18-4c2e-82e4-3365a2a87880\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"리소스 제한\\n\",\n \"\\n\",\n \"아래 코드를 실제 백엔드에서 실행할 경우, 변수값의 개수를 늘리거나 측정 횟수(shot)를 늘리게 되면 양자 컴퓨터를 사용하는 시간이 그만큼 많아지게 됩니다. 현재 세팅으로 (변수값 100개 + 1000회 측정) 양자 컴퓨터에서 회로를 실행하는 경우 예상되는 사용 시간은 1분 내외입니다. 실제 실험을 해보고 싶으신 경우, 가능하면 현재 세팅을 유지하시길 추천드립니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"346bf587-9e3a-41c9-a598-7856404befa9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ_lst = np.linspace(-3 * np.pi, 3 * np.pi, 100)\\n\",\n \"qc_isa = pm.run(double_slit_with_detector)\\n\",\n \"\\n\",\n \"φ_hit = []\\n\",\n \"dist = sampler.run([(qc_isa, φ_lst)], shots=1000).result()[0].data.c_screen\\n\",\n \"\\n\",\n \"for i in range(len(φ_lst)):\\n\",\n \" result = dist[i].get_counts()\\n\",\n \" if '0' in result:\\n\",\n \" φ_hit.append(result['0']/1000)\\n\",\n \" else:\\n\",\n \" φ_hit.append(0)\\n\",\n \"\\n\",\n \"φ_hit_2d = np.array(φ_hit).reshape(1, -1)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 2))\\n\",\n \"plt.imshow(φ_hit_2d, cmap='gray', aspect='auto', extent=[-3*np.pi, 3*np.pi, 0, 0.1], vmin=0, vmax=1)\\n\",\n \"\\n\",\n \"plt.xlabel('φ (Phase difference)', fontsize=14)\\n\",\n \"plt.colorbar(label='prob')\\n\",\n \"plt.xticks(ticks=[-3 * np.pi, -2 * np.pi, -np.pi, 0, np.pi, 2 * np.pi, 3 * np.pi],\\n\",\n \" labels=['-3π', '-2π', '-π', '0', 'π', '2π', '3π'])\\n\",\n \"plt.yticks([]) \\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5544b924-8d52-4826-a34a-4abbeb1fdcf4\",\n \"metadata\": {},\n \"source\": [\n \"실험을 잘 수행했다면 도표가 거의 회색으로 나타날 것입니다. 이는 위상차 $\\\\phi$ 에 상관 없이 $|0\\\\rangle$ 상태를 관측할 확률이 약 50%로 일정하기 때문입니다. 간섭 회절 무늬는 사라졌네요!\\n\",\n \"\\n\",\n \" \\n\",\n \"
감지기의 영향에 대해 알아보기
\\n\",\n \"\\n\",\n \"1. **초기 상태**: $|\\\\psi_0\\\\rangle = |0\\\\rangle$\\n\",\n \"2. **첫 번째 H 게이트**: $|\\\\psi_1\\\\rangle = \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle + |1\\\\rangle)$ (입자가 양쪽 슬릿 모두를 통과할 수 있음)\\n\",\n \"3. **첫 번째 측정 (감지기)**: 중첩 상태를 붕괴시킴.\\n\",\n \" * 0을 측정 (확률 1/2) $\\\\rightarrow |\\\\psi_{2a}\\\\rangle = |0\\\\rangle$\\n\",\n \" * 1을 측정 (확률 1/2) $\\\\rightarrow |\\\\psi_{2b}\\\\rangle = |1\\\\rangle$\\n\",\n \" 큐비트가 이제 하나의 상태로 정해지게 됩니다.\\n\",\n \"4. **P 게이트**:\\n\",\n \" * 0을 측정한 경우: $|\\\\psi_{3a}\\\\rangle = P(\\\\phi)|0\\\\rangle = |0\\\\rangle$\\n\",\n \" * 1을 측정한 경우: $|\\\\psi_{3b}\\\\rangle = P(\\\\phi)|1\\\\rangle = e^{i\\\\phi}|1\\\\rangle$\\n\",\n \" 중첩 상태가 붕괴하였기 때문에 위상 $\\\\phi$ 가 간섭에 필요한 *상대적* 위상으로 작용하지 못하게 되었습니다.\\n\",\n \"5. **두 번째 H 게이트**:\\n\",\n \" * 0을 측정한 경우: $|\\\\psi_{4a}\\\\rangle = H|0\\\\rangle = \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle + |1\\\\rangle)$\\n\",\n \" * 1을 측정한 경우: $|\\\\psi_{4b}\\\\rangle = H(e^{i\\\\phi}|1\\\\rangle) = e^{i\\\\phi} \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle - |1\\\\rangle)$\\n\",\n \"6. **두 번째 측정 (스크린)**:\\n\",\n \" * 처음에 0을 측정한 경우: Prob(final '0') = 1/2, Prob(final '1') = 1/2.\\n\",\n \" * 처음에 1을 측정한 경우: Prob(final '0') = 1/2, Prob(final '1') = 1/2.\\n\",\n \"\\n\",\n \"**결론**: 감지기의 결과에 상관 없이 스크린의 측정 결과는 언제나 $|0\\\\rangle$ 과 $|1\\\\rangle$ 상태 50/50 입니다. 중간에 들어간 측정이 중첩을 파괴함으로써 간섭도 일어나지 않게 되었습니다. 이것은 입자의 경로에 관한 정보가 어떻게 간섭을 파괴하는지를 보여줍니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"지금까지 중첩, 간섭, 그리고 측정에 대해 알아보았습니다. 이제 또다른 심오한 양자 현상인 **얽힘**으로 넘어가볼까요?\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8fdb70d8-229c-4e3b-abdc-ce9f60230e1a\",\n \"metadata\": {},\n \"source\": [\n \"# 챕터 2: 얽힘\\n\",\n \"\\n\",\n \"**양자 얽힘**이란 여러 입자가 거리와 상관 없이 서로 연결된 속성을 갖도록 양자 상태를 공유하는 현상을 말합니다. 여러 입자가 얽혀 있을 때에는 하나의 입자의 상태를 측정하는 순간 다른 입자들의 상태에 관한 정보를 바로 얻을 수 있죠. 아인슈타인을 이것을 “spooky action at a distance” (으스스한 원격 작용 등으로 번역됨) 이라고 불렀습니다.\\n\",\n \"\\n\",\n \"1964년 존 벨은 이렇게 얽힌 입자들이 고전적인 성질을 유지하는지 테스트하기 위해 벨 부등식을 제시하였습니다. 이것을 실제 실험으로 만든 것이 바로 CHSH 실험입니다. 양자역학은 이 실험에서 고전적인 예측값을 뒤엎었습니다 [3]. 1997년에는 얽힘을 이용해 입자를 옮기지 않고 양자 상태를 옮기는 양자 텔레포트를 실제 실험으로 보였습니다 [4].\\n\",\n \"\\n\",\n \"자 이제 Qiskit으로 CHSH 실험과 양자 텔레포트 실험을 같이 해볼까요?\\n\",\n \"\\n\",\n \"## 2.1. 앨리스와 밥의 이길 수 없는 게임 - CHSH 게임\\n\",\n \"\\n\",\n \"앨리스와 밥은 서로 떨어져서 소통을 할 수 없습니다. 심판은 둘에게 다음과 같은 문제를 줍니다:\\n\",\n \"1. 심판이 2개의 비트 (`x`, `y`) 를 골라 `x` 는 앨리스에게, `y` 는 밥에게 줍니다.\\n\",\n \"2. 앨리스와 밥은 이것을 보고 자신의 비트 (`a`, `b`) 를 선택합니다.\\n\",\n \"3. **승리 조건**: `a XOR b == x AND y` 이면 승리합니다.\\n\",\n \"\\n\",\n \"앨리스와 밥은 승률을 최대로 높이기 위한 전략을 구상하고 있습니다.\\n\",\n \"\\n\",\n \"**목표**: Qiskit을 이용해 CHSH 게임을 이기기 위한 *양자* 전략을 구현하고 시뮬레이션으로 승률을 확인하세요. 이 승률이 고전적인 승률과 어떻게 달라질 수 있는지 확인하여 양자 얽힘이 어떤 이점을 가져다 줄 수 있는지 이해해보세요.\\n\",\n \"\\n\",\n \"
\\n\",\n \"더 깊이 있는 학습을 위해\\n\",\n \"\\n\",\n \"CHSH 게임을 포함해 얽힘의 작용에 대해 더 공부해보고 싶으시다면 Qiskit learning의 자료를 공부해보실 수 있습니다: [Entanglement in Action](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/introduction).\\n\",\n \"
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91b4ee20-4f7a-4cdc-ae82-b8626a55f5fd\",\n \"metadata\": {},\n \"source\": [\n \"### 고전적인 한계: 75%를 넘을 수 없는 이유\\n\",\n \"\\n\",\n \"우선은 고전적인 상황을 먼저 생각해봅시다. 앨리스와 밥이 `x`와 `y`를 확인한 뒤로는 서로 의사소통을 할 수 없으므로, 앨리스의 선택 `a`는 정보 `x`에만, 밥의 선택 `b`는 정보 `y`에만 기반할 수 있습니다. 그들은 어떤 전략을 갖고 (아무거나 고르는 것도 포함해서) 게임에 참여할 수 있지만, 결국은 각자 독립적인 선택을 할 수밖에 없습니다.\\n\",\n \"\\n\",\n \"승리하는 상황은 다음과 같습니다:\\n\",\n \"* `x` `y` 가 (0,0), (0,1), (1,0) 인 경우: `a == b` 일 때 승리합니다.\\n\",\n \"* `x` `y` 가 (1,1) 인 경우: `a != b` 일 때 승리합니다.\\n\",\n \"\\n\",\n \"4가지 경우 중 3가지 경우에서 승리하는 전략을 찾으실 수 있으신가요? e.g. `a=b=0`, `a=x, b=y`... 한 번 찾아보세요! 어떤 경우에라도 승률은 **75%**에서 막히는 것을 찾게 되실 것입니다. 이것이 바로 국소적 실재성(local realism)의 한계입니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74227c42-4fd9-4acb-a55a-564b836bdc38\",\n \"metadata\": {},\n \"source\": [\n \"### 양자적인 전략: 얽힘이 우리를 구해줄 거에요!\\n\",\n \"\\n\",\n \"그러면 앨리스와 밥이 *게임을 시작하기 전*에 무엇을 준비할 수 있을까요? 만약 그들이 **얽힌 큐비트 쌍**을 나눠 갖는다면 어떻게 될까요? 얽힘을 마치 두 \\\"마술\\\" 동전이 신비한 힘으로 연결되어 있는 것으로 상상해보세요. 서로 몇 킬로미터씩 떨어져있더라도, 하나를 측정하는 순간 서로 간의 *관계*가 작동하여 다른 하나에 영향을 주는 것이죠. 이것으로 메세지와 같은 정보를 빛보다 빠르게 전달할 수는 없습니다. 하지만 그들의 운명은 얽혀들게 되죠.\\n\",\n \"\\n\",\n \"구체적인 예시로, 그들이 [벨 상태](https://en.wikipedia.org/wiki/Bell_state): $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt(2)$ 를 나눠 갖는다고 해봅시다. 만약 두 사람이 각자의 큐비트를 *같은* 방법으로 측정한다면, 그들은 언제나 같은 결과를 얻게 됩니다 (둘 다 0이거나 둘 다 1으로요).\\n\",\n \"\\n\",\n \"양자적인 전략: 큐비트를 같은 방법으로 측정하는 대신, 앨리스와 밥은 게임에서 받은 정보 (`x` 와 `y`) 에 따라 큐비트를 어떻게 측정할지 결정하기로 합니다.\\n\",\n \"1. 공유하는 리소스: 얽힌 큐빗 쌍 중에서 앨리스는 큐빗 0을, 밥은 큐빗 1을 갖습니다.\\n\",\n \"2. 측정 방향의 선택 (트릭이 들어가는 부분):\\n\",\n \" * 앨리스 (입력 `x`): `x=0` 이면 원래의 방향으로 측정하고 (표준 z축), `x=1` 이면 π/2 만큼 각도를 돌려 (x축) 측정합니다.\\n\",\n \" * 밥 (입력 `y`): `y=0` 이면 π/4 각도로 측정하고, `y=1`이면 -π/4 각도로 측정합니다.\\n\",\n \"3. 결과: 측정한 결과 그대로 `a` 와 `b` 를 선택합니다.\\n\",\n \"\\n\",\n \"이 각도가 매우 중요합니다! 그들은 벨 상태의 특별한 관계를 활용해, 모든 가능성에 대해 `a XOR b == x AND y` 조건을 충족시키게 하였습니다.\\n\",\n \"\\n\",\n \"이 방법을 양자 회로로 한 번 구현해볼까요?\\n\",\n \"\\n\",\n \"앨리스와 밥은 벨 상태의 얽힌 큐비트 쌍을 나눠가집니다: $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$. 만약 이 두 큐빗을 똑같은 방향으로 측정한다면 언제나 서로 같은 결과가 나올 것입니다.\\n\",\n \"\\n\",\n \"**참고**\\n\",\n \"\\n\",\n \"이 양자 회로의 속성이나 작동 흐름, 기반 원리에 대해 더 자세히 알고 싶으시다면 IBM Quantum Learning - Basics of Quantum Information 코스의 [이 강의](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/chsh-game) 를 참고해주세요.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"aefc0aa2-ad6f-4a61-ae6f-6f92b558a489\",\n \"metadata\": {},\n \"source\": [\n \"### Qiskit으로 구현하기: 양자 회로 만들기\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 4: CHSH 게임의 양자 회로\\n\",\n \"\\n\",\n \"**목표:** CHSH 게임에서 앨리스와 밥이 채용할 양자 전략을 구현하는 회로를 함수 `create_chsh_circuit(x, y)`으로 만들어보세요. 이것은 벨 상태를 준비하고, 주어진 input에 따라 측정 방향을 회전시키는 작업으로 이루어집니다.\\n\",\n \"\\n\",\n \"**과제:**\\n\",\n \"* **과제 1:** 벨 상태 $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$ 을 만드세요.\\n\",\n \"* **과제 2:** 밥에게 주어진 입력 `y`값에 따라 회전 게이트를 가하세요.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a174b414-36d6-40f0-a1ac-df3350b5f80e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def create_chsh_circuit(x, y):\\n\",\n \" \\\"\\\"\\\"Builds Qiskit circuit for Alice & Bob's quantum strategy.\\\"\\\"\\\"\\n\",\n \" qc = QuantumCircuit(2, 2, name=f'CHSH_{x}{y}') # 2 큐빗, 2 고전 비트\\n\",\n \"\\n\",\n \" # --- 과제 1 ---\\n\",\n \" # 벨 상태 |Φ+> = (|00> + |11>)/sqrt(2) 를 만드는 게이트를 구현하세요.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" # --- 과제 1 종료 ---\\n\",\n \" qc.barrier()\\n\",\n \" # Step 2a: 앨리스의 측정 방향 (x=0 일 때 Z 방향, x=1 일 때 X 방향)\\n\",\n \" if x == 1:\\n\",\n \" qc.h(0) # H 게이트를 가해 X 방향으로 회전\\n\",\n \"\\n\",\n \" ## --- 과제 2 ---\\n\",\n \" # Step 2b: 밥의 측정 방향\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" # --- 과제 2 종료 ---\\n\",\n \" qc.barrier()\\n\",\n \" \\n\",\n \" # Step 3: 측정\\n\",\n \" qc.measure([0, 1], [0, 1]) # q0 -> c0 (앨리스), q1 -> c1 (밥) // 'ba' 순서로 표시됨\\n\",\n \"\\n\",\n \" return qc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"27caf6cf-9497-4c02-b4ca-0f6d55a464dc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab1_ex4(create_chsh_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28c48bd5-eaeb-4360-981d-959a4ac913b6\",\n \"metadata\": {},\n \"source\": [\n \"### 모든 입력값에 대한 회로 만들기\\n\",\n \"\\n\",\n \"이제 앞에서 만든 함수를 사용해서 4개의 모든 (x,y) 입력값에 대한 회로를 만들어보겠습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7a440c15-4467-42d6-b0a1-80fd9bdd5dd5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"circuits = []\\n\",\n \"input_pairs = []\\n\",\n \"for x_in in [0, 1]:\\n\",\n \" for y_in in [0, 1]:\\n\",\n \" input_pairs.append((x_in, y_in))\\n\",\n \" circuits.append(create_chsh_circuit(x_in, y_in))\\n\",\n \"\\n\",\n \"print(\\\"Quantum circuit for inputs x=1, y=1 (Check your Exercises 1 & 2 implementation):\\\")\\n\",\n \"if len(circuits) == 4:\\n\",\n \" display(circuits[3].draw('mpl')) # (x,y) = (1,1)\\n\",\n \"else:\\n\",\n \" print(\\\"Circuits not generated. Run previous cell after completing Exercises 1 & 2.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b85480d-6172-457c-805c-6f168a86930f\",\n \"metadata\": {},\n \"source\": [\n \"### 시뮬레이션: 게임을 반복적으로 진행해보기\\n\",\n \"\\n\",\n \"실제 양자 컴퓨터는 노이즈가 있고 액세스하기 다소 어려울 수 있습니다. 다행히도, 이런 작은 회로에서는 시뮬레이션으로도 충분히 좋은 결과를 얻을 수 있습니다. Qiskit의 `AerSimulator`를 사용해 네 개의 회로를 반복적으로 실행해보고 (실행 횟수, \\\"shots\\\") 앨리스와 밥의 선택(`a`와 `b`)을 확인해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b2052339-c0ab-4051-8d22-1a77df74d56f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# AerSimulator (앞에서 정의하지 않은 경우)\\n\",\n \"# backend = AerSimulator()\\n\",\n \"# Pass manager (앞에서 정의하지 않은 경우)\\n\",\n \"# pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\\n\",\n \"\\n\",\n \"SHOTS = 1024\\n\",\n \"\\n\",\n \"print(\\\"Preparing circuits for the simulator...\\\")\\n\",\n \"isa_qc_chsh = pm.run(circuits)\\n\",\n \"\\n\",\n \"sampler_chsh = Sampler(mode=backend) # SamplerV2\\n\",\n \"job_chsh = sampler_chsh.run(isa_qc_chsh, shots=SHOTS)\\n\",\n \"results_chsh = job_chsh.result()\\n\",\n \"\\n\",\n \"# SamplerV2: 고전 레지스터의 이름 기본값이 'c'일 때 results_chsh[i].data.c.get_counts()\\n\",\n \"counts_list = [results_chsh[i].data.c.get_counts() for i in range(len(circuits))]\\n\",\n \"\\n\",\n \"print(\\\"\\\\n--- Simulation Results (Counts) ---\\\")\\n\",\n \"for i, (x, y) in enumerate(input_pairs):\\n\",\n \" print(f\\\"Inputs (x={x}, y={y}):\\\")\\n\",\n \" sorted_counts = dict(sorted(counts_list[i].items()))\\n\",\n \" print(f\\\" Outcomes (ba): {sorted_counts}\\\")\\n\",\n \"\\n\",\n \"print(\\\"\\\\nPlotting results...\\\")\\n\",\n \"display(plot_histogram(counts_list,\\n\",\n \" legend=[f'(x={x}, y={y})' for x, y in input_pairs],\\n\",\n \" title='CHSH Game Outcomes (ba format)'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3b84b66e-9503-4dbe-9023-0d08bbd584fd\",\n \"metadata\": {},\n \"source\": [\n \"### 분석: 고전적인 한계를 넘어섰나요?\\n\",\n \"\\n\",\n \"이제 진실을 확인할 시간입니다! 시뮬레이션 결과(`counts_list`)를 분석하여 승률을 계산해보세요.\\n\",\n \"\\n\",\n \"**복습:**\\n\",\n \"* **승리 조건:** `a XOR b == x AND y`\\n\",\n \"* **출력 형식:** 카운트는 `'ba'` 순서의 string으로 출력됩니다.\\n\",\n \" * 측정 결과가 `'00'` (`b=0, a=0`) 또는 `'11'` (`b=1, a=1`) 일 때 `a XOR b = 0` 입니다.\\n\",\n \" * 측정 결과가 `'01'` (`b=0, a=1`) 또는 `'10'` (`b=1, a=0`) 일 때 `a XOR b = 1` 입니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 5: CHSH 게임의 회로 분석\\n\",\n \"\\n\",\n \"**목표:** 시뮬레이션 결과에서 각 입력값(`x`, `y`)에 대한 앨리스와 밥의 승률을 계산해보세요.\\n\",\n \"\\n\",\n \"**과제:**\\n\",\n \"* **과제 1:** `x`와 `y`가 주어졌을 때 목표로 하는 `a XOR b` 값을 구하세요. (변수 `target_xor_result`)\\n\",\n \"* **과제 2:** 주어진 `x`, `y`에서의 승리 조건을 만족하는 측정 결과를 카운트하세요. (변수 `wins_for_this_case`)\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"28f7b7ef-612a-4762-a522-d32d539b37f9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"win_probabilities = {}\\n\",\n \"print(\\\"--- Calculating Win Probabilities ---\\\")\\n\",\n \"\\n\",\n \"for i, (x, y) in enumerate(input_pairs):\\n\",\n \" counts = counts_list[i]\\n\",\n \"\\n\",\n \" # --- 과제 1 ---\\n\",\n \" # 승리를 위해 목표로 하는 (a XOR b) 값을 구해 변수 `target_xor_result`에 할당하세요.\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" # --- 과제 1 종료 ---\\n\",\n \"\\n\",\n \" wins_for_this_case = 0\\n\",\n \"\\n\",\n \" # --- 과제 2 ---\\n\",\n \" # 위에서 구한 승리 조건을 만족하는 측정 결과의 개수를 변수 `wins_for_this_case`로 카운트하세요.\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" # --- 과제 2 종료 ---\\n\",\n \"\\n\",\n \" prob = wins_for_this_case / SHOTS if SHOTS > 0 else 0\\n\",\n \" win_probabilities[(x, y)] = prob\\n\",\n \" print(f\\\"Inputs (x={x}, y={y}): Target (a XOR b) = {target_xor_result}. Win Probability = {prob:.4f}\\\")\\n\",\n \"\\n\",\n \"avg_win_prob = sum(win_probabilities.values()) / 4.0\\n\",\n \"P_win_quantum_theory = np.cos(np.pi / 8)**2 # ~0.8536\\n\",\n \"P_win_classical_limit = 0.75\\n\",\n \"\\n\",\n \"print(\\\"\\\\n--- Overall Performance ---\\\")\\n\",\n \"print(f\\\"Experimental Average Win Probability: {avg_win_prob:.4f}\\\")\\n\",\n \"print(f\\\"Theoretical Quantum Win Probability: {P_win_quantum_theory:.4f}\\\")\\n\",\n \"print(f\\\"Classical Limit Win Probability: {P_win_classical_limit:.4f}\\\")\\n\",\n \"\\n\",\n \"if avg_win_prob > P_win_classical_limit + 0.01: # 약간의 시뮬레이션 편차를 허용합니다\\n\",\n \" print(f\\\"\\\\nSuccess! Your result ({avg_win_prob:.4f}) clearly beats the classical 75% limit!\\\")\\n\",\n \" print(f\\\"It's likely close to the theoretical quantum prediction of {P_win_quantum_theory:.4f}.\\\")\\n\",\n \"elif avg_win_prob > P_win_classical_limit - 0.02 : # 노이즈나 가벼운 실수 때문일 수 있습니다\\n\",\n \" print(f\\\"\\\\nClose, but no cigar? Your result ({avg_win_prob:.4f}) is around the classical limit ({P_win_classical_limit:.4f}).\\\")\\n\",\n \" print(\\\"Check your solutions for Exercises 1-4 carefully, especially the win counting logic in Ex 4.\\\")\\n\",\n \"else:\\n\",\n \" print(f\\\"\\\\nHmm, the result ({avg_win_prob:.4f}) is unexpectedly low, even below the classical limit.\\\")\\n\",\n \" print(\\\"There might be an error in Exercises 1-4. Please review your circuit and analysis code.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a8842d57-eea6-4791-b5a9-0d4b37219c15\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab1_ex5(counts_list, avg_win_prob)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5ced3f21-6210-46fc-b9c8-e539e88f854e\",\n \"metadata\": {},\n \"source\": [\n \"### 결론: 양자의 능력\\n\",\n \"\\n\",\n \"그래서, 우리가 무엇을 찾은 건가요? 여러분이 연습문제를 잘 푸셨다면 시뮬레이션에서 앨리스와 밥이 얽힌 큐비트 쌍과 측정 트릭을 통해 고전적인 한계인 75%보다 **현저히** 높은 승률을 기록하는 것을 확인하셨을 것입니다. 아마 이론적인 예측값 **85.4%** 전후의 값이 되지 않았을까 싶네요.\\n\",\n \"\\n\",\n \"이것은 단순한 수학적 속임수가 아닙니다; 이것은 가장 근본적으로 자연이 어떻게 작동하는지를 보여주는 것입니다. 얽힘은 고전적인 이론에서 설명하는 것보다 훨씬 강력한 방식으로 떨어져 있는 입자들 사이의 관계성을 부여합니다. 이 \\\"spooky action at a distance\\\"는 빛보다 빠른 통신을 허용하지는 않습니다. 하지만 다양한 양자 기술의 기반이 될 수는 있죠.\\n\",\n \"\\n\",\n \"**논의할 것:** 앞에서 얻은 결과를 볼 때, CHSH 게임이 보여주는 현실이 고전적인 직관으로 관찰되는 현실과 어떻게 다른가요? 이런 고전역학보다 강력한 관계성을 활용할 수 있는 응용분야가 있을까요?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"100cdba6-f257-4b0a-b905-380c1876839a\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2. 양자 텔레포트 - 마법같은 작용을 이용한 비밀 전송\\n\",\n \"\\n\",\n \"자 이번에는 앨리스가 어떤 섬세한 양자 상태(`|ψ⟩`)에 있는 큐빗(`q0`)을 가지고 있다고 해봅시다. 그녀는 정확히 이 양자 상태를 멀리 떨어져 있는 밥에게 보내고 싶어 합니다. 그러나 이것을 단순히 물리적으로 보내는 것은 어렵습니다. (큐빗이 매우 불안정해서 그럴 수도 있고요, 그냥 큐빗 자체는 갖고 있어야 할 수도 있겠죠.) 이런 경우 정확히 *양자 상태만* 보내는 방법이 존재할까요?\\n\",\n \"\\n\",\n \"고전적인 관점에서는 단순히 양자 상태를 측정해서 그 정보만 보내면 될 수도 있겠습니다. 하지만 앞에서 배웠다시피, 양자역학의 세계에서는 앨리스가 이 큐비트의 정보를 확인하기 위해 측정을 하는 순간, 측정으로 인해서 중첩 상태로 존재하던 원래의 상태가 붕괴하게 될 것입니다!\\n\",\n \"\\n\",\n \"그렇다면 이제 **양자 텔레포트**를 만나보세요! 이것은 **양자 얽힘**과 **고전적인 통신**만을 사용해 밝혀지지 않은 양자 상태를 한 큐비트에서 다른 큐비트로 옮겨내는 기념비적인 프로토콜입니다.\\n\",\n \"\\n\",\n \"**주의:** 여기서 텔레포트 되는 것은 물리적인 입자가 아닌 *상태*입니다! 아직은 어떤 사람도 텔레포트하고 있지 않습니다.\\n\",\n \"\\n\",\n \"**목표:** 이번 랩에서는 Qiskit을 이용해 양자 텔레포트 프로토콜을 구현해볼 것입니다. 코드를 완성하여 필요한 양자 리소스를 준비하고, 텔레포트 과정을 수행하고, 밥이 앨리스의 양자 상태를 잘 받았는지 확인해보세요.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f1dbf7d4-97aa-4fdd-90ed-51f55c74007b\",\n \"metadata\": {},\n \"source\": [\n \"### 텔레포트 프로토콜: 차근차근 알아보기\\n\",\n \"\\n\",\n \"이 프로토콜에는 세 개의 큐비트와 두 개의 비트가 필요합니다:\\n\",\n \"* **`q0`:** 앨리스가 텔레포트 시키고자 하는 `|ψ⟩` 상태의 큐비트\\n\",\n \"* **`q1`:** 앨리스가 갖고 있는 얽힌 상태의 큐비트\\n\",\n \"* **`q2`:** 밥이 갖고 있는 얽힌 상태의 큐비트\\n\",\n \"* **`c0`, `c1`:** 앨리스의 측정 결과를 저장하는 비트\\n\",\n \"\\n\",\n \"계획은 다음과 같습니다:\\n\",\n \"1. **(선택) 상태 `|ψ⟩`를 `q0`에 준비시키기.** (여기서는 간편한 확인을 위해 `|+>` 상태와 같은 알려진 상태를 사용합니다).\\n\",\n \"2. **얽힌 상태 만들기:** 벨 큐빗 `q1`과 `q2`를 만듭니다.\\n\",\n \"3. **앨리스의 작업:** 앨리스는 그녀가 갖고 있는 두 큐비트(`q0`와 `q1`)에 \\\"벨 측정\\\"을 하고 결과를 `c0`와 `c1`에 저장합니다.\\n\",\n \"4. **고전적인 통신:** 앨리스의 비트 2개를 (`c0`, `c1`) 밥에게 보냅니다.\\n\",\n \"5. **밥의 정정 작업:** 밥은 받은 `c0`와 `c1`에 따라 그가 갖고 있는 큐비트(`q2`)에 양자 게이트(X 또는 Z)를 가합니다.\\n\",\n \"\\n\",\n \"모든 프로토콜이 제대로 수행되었다면, 밥이 갖고 있는 큐비트 `q2`는 앨리스의 `q0` 큐비트에 있었던 바로 그 `|ψ⟩` 상태가 됩니다!\\n\",\n \"\\n\",\n \"
\\n\",\n \"더 깊이 있는 학습을 위해\\n\",\n \"\\n\",\n \"양자 텔레포트에 관해 더 공부해보고 싶으시다면 Qiskit learning의 자료를 공부해보실 수 있습니다: [Quantum Teleportation](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/quantum-teleportation) 그리고 [Entanglement in Action](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/quantum-teleportation) by John Watrous. 이것은 그의 [Basics of Quantum Information](https://learning.quantum.ibm.com/course/basics-of-quantum-information) 코스의 일부이기도 합니다.\\n\",\n \"
\\n\",\n \"\\n\",\n \"### 도구: Qiskit 동적 회로\\n\",\n \"\\n\",\n \"앨리스의 측정 결과에 따른 밥의 작업은 동적 회로로 구현할 수 있습니다. 이것은 회로 중간에서 측정을 통해 얻은 고전적인 정보를 이후의 양자 회로에서 활용할 수 있게 합니다. 이러한 기능은 양자 텔레포트와 같은 프로토콜에 필요할 뿐만 아니라, 최근 연구에서 확인된 바와 같이 (e.g., Bäumer et al., PRX QUANTUM 5, 030339 (2024)) 양자 하드웨어 상에서 효과적으로 장거리 얽힘을 구현하는 데에도 중요하게 활용됩니다.\\n\",\n \"\\n\",\n \"**참고 자료:**\\n\",\n \"* [동적 회로와 조건부 게이트에 대한 Qiskit 문서](https://quantum.cloud.ibm.com/docs/guides/classical-feedforward-and-control-flow)\\n\",\n \"* [Efficient Long-Range Entanglement Using Dynamic Circuits - 동적 회로를 사용한 효과적인 장거리 얽힘 구현](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.030339)\\n\",\n \"\\n\",\n \"**텔레포트에서 활용되는 주요 개념:**\\n\",\n \"\\n\",\n \"1. 회로 중간의 측정:\\n\",\n \" * Qiskit에서는 회로의 끝에서 뿐만 아니라 어느 부분에서든지 큐비트를 측정하고 결과를 저장할 수 있습니다. 이것은 앨리스가 갖고 있는 두 큐비트(`q0`와 `q1`)를 측정하고 비트 `c0`와 `c1`을 얻는데에 필요합니다.\\n\",\n \" * `QuantumCircuit.measure(qubit, classical_bit)` 명령어가 사용됩니다.\\n\",\n \"\\n\",\n \"2. 조건부 게이트 (if_test):\\n\",\n \" * 앨리스의 측정 이후, 밥은 그가 받은 비트(`c0`, `c1`)에 따라 그의 큐비트(`q2`)에 양자 게이트를 가해야 합니다.\\n\",\n \" * Qiskit에서는 비트의 값에 따라 조건부로 게이트를 가할 수 있습니다.\\n\",\n \" * `QuantumCircuit.if_test((classical_bit, value), true_circuit, false_circuit=None)` 매소드를 사용하여 비트가 특정 값(0 또는 1)을 가질 때에만 게이트를 가하도록 할 수 있습니다.\\n\",\n \" * 텔레포트에서 밥은 다음의 조건부 게이트를 사용할 것입니다:\\n\",\n \" * `c1`이 1 일 경우, `q2`에 X 게이트를 가합니다.\\n\",\n \" * `c0`이 1 일 경우, `q2`에 Z 게이트를 가합니다.\\n\",\n \"\\n\",\n \"이러한 기능은 텔레포트 프로토콜의 고전적인 통신 부분도 하나의 양자 회로 내에서 구현할 수 있도록 해줍니다.\\n\",\n \"\\n\",\n \"참고: 조건부 게이트의 순서를 기억해주세요! 양자 컴퓨팅에서 $AB != BA$ 이기 때문에 순서가 중요합니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a7505da2-a554-4e2e-9844-70c947059e78\",\n \"metadata\": {},\n \"source\": [\n \"### Qiskit으로 텔레포트 회로 만들기\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 6: 양자 텔레포트\\n\",\n \"\\n\",\n \"**목표:** 앨리스의 큐빗에 있는 미상의 양자 상태를 밥의 큐빗으로 텔레포트 시키는 양자 회로를 만들어보세요. 벨 큐빗과 회로 중간의 측정, 조건부 게이트 등을 활용하세요.\\n\",\n \"\\n\",\n \"**과제:**\\n\",\n \"* **과제 1:** `q1`과 `q2`로 벨 큐빗 $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$ 을 만듭니다.\\n\",\n \"* **과제 2:** 앨리스가 벨 측정을 할 수 있도록 게이트를 가합니다 (실제 측정 전).\\n\",\n \"* **과제 3:** 앨리스의 측정 결과에 따라 밥이 조건부 게이트를 가합니다.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"af207afd-1a82-43c8-8254-6e0b422f613d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 양자 레지스터와 고전 레지스터를 정의합니다\\n\",\n \"qr_tele = QuantumRegister(3, name='q')\\n\",\n \"cr_alice_tele = ClassicalRegister(2, name='c_alice') # 앨리스의 측정에 사용될 레지스터\\n\",\n \"\\n\",\n \"# 마지막의 상태 벡터를 직접 확인하기 위해 이 회로에서 밥의 큐비트는 측정하지 않습니다.\\n\",\n \"# 만약 실제 하드웨어에서 실행하여 카운트를 확인해야 하는 경우에는 밥의 측정을 위한 비트도 추가해야 할 것입니다.\\n\",\n \"teleport_qc = QuantumCircuit(qr_tele, cr_alice_tele, name='Teleportation')\\n\",\n \"\\n\",\n \"# q0에 앨리스의 메세지 상태 |ψ> = |+> 를 준비합니다\\n\",\n \"teleport_qc.h(qr_tele[0])\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# --- 과제 1 ---\\n\",\n \"# Step 1: q1(앨리스)과 q2(밥)로 벨 큐빗을 만듭니다\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 과제 1 종료 ---\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# --- 과제 2 ---\\n\",\n \"# Step 2: 앨리스가 벨 측정을 수행합니다 (양자 게이트 부분만 구현해주세요)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 과제 2 종료 ---\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# 앨리스가 q0와 q1 큐빗을 측정합니다\\n\",\n \"teleport_qc.measure(qr_tele[0], cr_alice_tele[0]) # q0 -> c0\\n\",\n \"teleport_qc.measure(qr_tele[1], cr_alice_tele[1]) # q1 -> c1\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# --- 과제 3 ---\\n\",\n \"# Step 3: 밥이 q2에 조건부 게이트를 가합니다\\n\",\n \"# 중요: 이전 Qiskit 버전에서처럼 XGate()에 .c_if()를 추가하는 방법은 더 이상 작동하지 않습니다.\\n\",\n \"# Qiskit 1.0 이후 버전에서 권장하는 방법은 새로운 `if_test` 컨택스트 매니저를 사용하는 것입니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 과제 3 종료 ---\\n\",\n \"\\n\",\n \"print(\\\"Full Teleportation Circuit (Check your Exercises 1, 2, 3):\\\")\\n\",\n \"display(teleport_qc.draw('mpl'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4aa33305-02cd-424e-bd3d-4788e4cfcc71\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab1_ex6(teleport_qc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"57c81d2f-1067-42c7-a476-375d2faf4ed4\",\n \"metadata\": {},\n \"source\": [\n \"### 시뮬레이션과 검증\\n\",\n \"\\n\",\n \"텔레포트가 제대로 이루어졌는지는 어떻게 검증할까요? 프로토콜 이후 밥의 큐비트를 직접 '보는' 것은 불가능합니다. 하지만 지금은 우리가 앨리스의 초기 상태 `|ψ⟩`를 알기 때문에, (`|+>` 상태가 되도록 하였습니다) 밥의 큐비트 `q2`가 정말 이 상태가 되었는지 확인하는 것으로 검증을 할 수 있습니다.\\n\",\n \"\\n\",\n \"여기서는 `AerSimulator`의 `save_statevector` 기능을 활용해 밥의 큐비트 `q2`가 앨리스가 갖고 있던 원래 상태(`|+>`)와 같아진 것이 맞는지 확인해보겠습니다. 이 시뮬레이터는 마지막의 양자 상태 벡터를 직접 계산해줍니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 7 - 번외: 양자 텔레포트의 결과 분석하기\\n\",\n \"\\n\",\n \"**목표:** 앨리스의 양자 상태가 밥의 큐비트로 잘 텔레포트 되었는지 회로를 시뮬레이션하고 큐비트의 상태를 출력하여 검증해보세요.\\n\",\n \"\\n\",\n \"**과제:**\\n\",\n \"* 마지막 상태벡터를 추출하고 `plot_bloch_multivector` 함수를 사용해 밥의 큐비트 (`q2`)의 상태를 출력해보세요. 앨리스가 갖고 있던 원래 상태 (`q0`)와도 비교해보세요.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"85f73394-d361-4c0a-afd3-28dc2005c07a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"# Statevector 시뮬레이터를 사용합니다\\n\",\n \"print(\\\"Using statevector simulator...\\\")\\n\",\n \"sv_simulator = AerSimulator(method='statevector') # 명확한 구분을 위해 method를 지정하였습니다\\n\",\n \"teleport_qc_sv = teleport_qc.copy() # 상태벡터 시뮬레이션을 위해 회로를 복사합니다\\n\",\n \"teleport_qc_sv.save_statevector() # 마지막의 상태벡터를 저장합니다\\n\",\n \"\\n\",\n \"print(\\\"Running statevector simulation...\\\")\\n\",\n \"job_sv = sv_simulator.run(teleport_qc_sv) # 상태벡터 시뮬레이션에서는 기본 실행 횟수가 1회 입니다\\n\",\n \"result_sv = job_sv.result()\\n\",\n \"\\n\",\n \"if result_sv.success:\\n\",\n \" print(\\\"Simulation successful.\\\")\\n\",\n \" final_statevector = result_sv.get_statevector()\\n\",\n \" print(\\\"Statevector retrieved successfully.\\\")\\n\",\n \" print(\\\"\\\\nVisualizing final qubit states (q2 should match initial q0 state |+>):\\\")\\n\",\n \" # q0는 |+> 상태에 있었습니다 (+X 방향 벡터). 텔레포트 이후, q2도 |+> 상태에 있어야 합니다.\\n\",\n \" # q0 와 q1 의 상태는 앨리스가 관측하였으므로 붕괴된 상태일 것입니다.\\n\",\n \" \\n\",\n \" display(\\\"TODO\\\") # \\\"TODO\\\" 부분을 지우고 plot_bloch_multivector 함수로 final_statevector를 출력하세요\\n\",\n \" \\n\",\n \"else:\\n\",\n \" print(f\\\"Statevector simulation failed! Status: {result_sv.status}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"be79ba6b-c8d7-44e3-b7fa-c2043ea7cff0\",\n \"metadata\": {},\n \"source\": [\n \"### 결과 분석\\n\",\n \"\\n\",\n \"연습문제 6번을 잘 수행했다면 다음과 같은 결과를 얻게 될 것입니다:\\n\",\n \"* `q0` (앨리스의 원래 큐빗): 무작위의 상태 (측정으로 붕괴함).\\n\",\n \"* `q1` (앨리스의 얽힌 큐빗): 무작위의 상태 (측정으로 붕괴함).\\n\",\n \"* `q2` (밥의 얽힌 큐빗): 블로흐 구면의 벡터가 **+X 방향**을 가리킴. 이것이 `|+>` 상태이며, 앨리스의 원래 큐빗 `q0`의 상태와 일치합니다!\\n\",\n \"\\n\",\n \"**성공이예요!** 상태 `|ψ⟩ = |+⟩` 가 텔레포트 되었습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2c91c647-3843-4574-ad18-c283e16f8d16\",\n \"metadata\": {},\n \"source\": [\n \"### 결론: 얽힘과 정보의 마법\\n\",\n \"\\n\",\n \"우리가 양자 텔레포트 프로토콜을 구현해냈어요!\\n\",\n \"\\n\",\n \"주요 시사점:\\n\",\n \"* 텔레포트는 미상의 양자 *상태*를 얽힘과 고전적인 통신을 통해 전송합니다.\\n\",\n \"* 이것은 물리적인 입자 그 자체를 전송하지는 않습니다.\\n\",\n \"* 앨리스가 갖고 있던 원래 상태는 파괴됩니다 (복제 불가능성 정리에 합치합니다).\\n\",\n \"* 고전적인 통신을 요구합니다 (앨리스의 측정 결과를 밥에게 알려줘야 하므로), 따라서 정보가 빛보다 빠르게 전송되는 것이 아닙니다.\\n\",\n \"\\n\",\n \"양자 텔레포트는 양자 통신과 컴퓨팅의 기반이 됩니다.\\n\",\n \"\\n\",\n \"**논의할 것**: 앨리스가 단순히 `q0`를 측정해서 (예를 들어 Z축과 X축으로) 그 결과를 밥에게 보내면 왜 안 될까요? 이것이 텔레포트와 어떻게 다를까요?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f72ca7c2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 현재의 진도 상황을 확인해보세요\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4a1fb2a1\",\n \"metadata\": {},\n \"source\": [\n \"# (보너스 챌린지) 실제 하드웨어에서의 텔레포트\\n\",\n \"\\n\",\n \"지금까지 텔레포트가 잘 작동하는 것을 시뮬레이션으로 확인하였습니다. 이제, **실제 IBM Quantum 백엔드**에서 이 실험을 해보는 건 어떨까요? 이것이 바로 진정한 챌린지이며, 양자 컴퓨팅의 즐거움을 느끼는 방법입니다. 모든 실험을 진짜 양자 컴퓨터로 해볼 수 있으니까요!\\n\",\n \"\\n\",\n \"
\\n\",\n \"동적 회로가 업그레이드 됩니다!\\n\",\n \"\\n\",\n \"현재 실제 하드웨어에서의 동적 회로가 업데이트되어, 여러분이 이 새로운 버전을 처음 시도하는 개척자가 되셨습니다! 다만, 이 얼리 액세스 버전에서는 몇 가지 사항을 주의해주세요:\\n\",\n \"* 1. 동적 회로의 얼리 액세스 버전을 활성화하기 위해 몇 가지 추가 명령어를 설정해야 합니다 (아래 코드를 확인하세요)\\n\",\n \"* 2. 현재 얼리 액세스 버전의 코드가 with문을 사용한 Session이나 Batch와 호환되지 않습니다 (e.g. with Batch(...)). 그러나 primitive에 인수로 직접 입력하여 사용하는 것은 가능합니다 (e.g. Sampler(mode=batch)).\\n\",\n \"* 3. if_test 조건에 32비트를 초과하는 ClassicalRegister를 사용할 수 없습니다. 하지만 각각이 32비트 이하(<=)인 여러 개의 ClassicalRegister는 사용할 수 있습니다.\\n\",\n \"* 4. 중첩된 조건문은 만들 수 없습니다.\\n\",\n \"* 5. 조건부 블록 내에서 측정이나 리셋을 수행할 수는 없습니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"실제 하드웨어에서 새로운 동적 회로를 사용하기 위해서는 두 가지 작업이 필요합니다:\\n\",\n \"\\n\",\n \"Step 1. 사용하는 하드웨어에 새로운 기능을 추가해주세요:\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" # (1) Patch the backend target with IfElseOp\\n\",\n \" from qiskit.circuit import IfElseOp\\n\",\n \" backend.target.add_instruction(IfElseOp, name=\\\"if_else\\\")\\n\",\n \" \\n\",\n \"```\\n\",\n \"\\n\",\n \"Step 2. 작업을 보낼 때에 experimental 옵션을 활성화해주세요:\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" # (2) Submit the job with the experimental option\\n\",\n \" sampler.options.experimental = {\\\"execution_path\\\" : \\\"gen3-experimental\\\"}\\n\",\n \" \\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f65c02e-5def-46aa-9546-ad9026669b4b\",\n \"metadata\": {},\n \"source\": [\n \"### 동적 회로: 실제 하드웨어에서 중요한 것\\n\",\n \"\\n\",\n \"양자 텔레포트 프로토콜은 본질적인 측면에서 **동적 회로**를 요구합니다: 앨리스가 그녀의 큐빗을 측정하고, 밥은 *고전적인 통신*으로 이 결과를 받아 그의 큐빗에 어떤 게이트를 가할지 결정해야 합니다. Qiskit은 이러한 \\\"회로 중간의 측정\\\"과 \\\"고전적인 피드포워드\\\" 작업을 가능하게 합니다.\\n\",\n \"\\n\",\n \"최근 Bäumer et al. 의 연구 \\\"Efficient Long-Range Entanglement Using Dynamic Circuits\\\" (PRX QUANTUM 5, 030339 (2024)) 등에서는 이 동적 회로의 힘을 확인할 수 있습니다. 이 연구에서는 CNOT 게이트의 텔레포트나 원거리 GHZ 상태 구현 등의 작업에서 동적 회로를 활용하면 거리에 상관 없이 양자 회로의 깊이를 일정하게 유지할 수 있으며, 이를 통해 유니터리 게이트만을 이용하는 방법보다 더 나은 결과를 얻을 수 있음을 보여줍니다.\\n\",\n \"\\n\",\n \"이 양자 텔레포트 프로토콜에서, 동적 회로 기능은 양자 프로세서 상에서 앨리스의 측정 결과가 밥의 게이트를 실시간으로 조작할 수 있도록 해줍니다. **IBM Cloud**를 통해 IBM Quantum 백엔드에서 이것을 직접 실험해보세요. Quantum 서비스가 활성화된 IBM Cloud 계정이 필요하실 겁니다.\\n\",\n \"\\n\",\n \"### 챌린지: - 얼마나 멀리 보낼 수 있나요?\\n\",\n \"\\n\",\n \"**목표:** 실제 IBM Quantum 백엔드에서 한 양자 상태(e.g., $|+\\\\rangle$)를 0번 큐빗(앨리스)에서 다른 큐빗(밥)으로 텔레포트 시켜보세요. 특히 서로 직접 연결되어 있지 않은 큐빗을 골라 동적 회로로 이것을 구현해보세요. 텔레포트의 충실도를 측정하여 보고해주세요. 그리고 가능하다면 더 높은 충실도를 얻도록 해보세요!\\n\",\n \"\\n\",\n \"**실제 백엔드에서 회로 실행을 위한 기본 절차:**\\n\",\n \"\\n\",\n \"자세한 설명은 lab0에서 찾으실 수 있습니다만 여기서도 간단하게 소개를 드리겠습니다.\\n\",\n \"\\n\",\n \"1. IBM Quantum 액세스를 설정합니다 (IBM Cloud를 통해):\\n\",\n \" * IBM Cloud 계정이 없다면 계정을 생성하고 IBM Quantum 서비스를 활성화하세요.\\n\",\n \" * IBM Cloud의 IBM Quantum 대시보드에서 API 토큰을 생성하세요.\\n\",\n \" * `QiskitRuntimeService`를 통해 계정을 저장하고 사용 가능한 백엔드를 확인하세요.\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \" QiskitRuntimeService.save_account(channel=\\\"ibm_quantum_platform\\\", token=\\\"이문장을지우고API키를붙여넣으세요\\\", instance=\\\"이문장을지우고CRN을붙여넣으세요\\\", overwrite=True)\\n\",\n \" service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \" print(service.backends())\\n\",\n \" ```\\n\",\n \"\\n\",\n \"2. 백엔드를 선택합니다:\\n\",\n \" * 리스트에서 사용 가능한 백엔드를 선택하세요. [least_busy](https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit-runtime-service#least_busy) 디바이스를 고르는 것도 좋은 선택입니다.\\n\",\n \" * 선택한 디바이스의 coupling map을 보고 적당한 큐비트를 찾으세요. 이 챌린지에서는 앨리스의 메세지 큐빗(`q_A_msg`)와 밥의 큐빗(`q_B_ent`)이 서로 \\\"멀리 떨어져\\\" 있거나 직접적으로 연결되지 않은 것을 골라보세요. 앨리스의 얽힌 큐빗(`q_A_ent`)은 `q_A_msg` 큐빗과 (벨 측정을 위해) `q_B_ent` 큐빗에 (벨 큐빗을 나눠갖기 위해) 연결되어 있는 편이 좋을 것입니다.\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" # backend_name = \\\"ibm_your_chosen_backend\\\" # e.g., \\\"ibm_brisbane\\\" 또는 테스트를 위한 \\\"ibmq_qasm_simulator\\\" 와 같은 시뮬레이터\\n\",\n \" backend = service.least_busy()\\n\",\n \" # print(f\\\"Coupling map: {backend.configuration().coupling_map}\\\")\\n\",\n \" # print(f\\\"Number of qubits: {backend.configuration().n_qubits}\\\")\\n\",\n \" # from qiskit.visualization import plot_gate_map # if needed\\n\",\n \" # display(plot_gate_map(backend)) # Visualizes connectivity\\n\",\n \" \\n\",\n \" ```\\n\",\n \"대부분의 코드는 원래 사용한 것을 그대로 사용하셔도 됩니다. 시뮬레이터 대신 실제 양자 프로세서(QPU)를 사용할 때에는 `Sampler`에 선택한 백엔드를 넣어주기만 하면 됩니다. 양자 회로를 `PassManager`로 트랜스파일 할 때도 선택한 백엔드를 넣어주는 걸 잊지 마세요.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"팁\\n\",\n \"\\n\",\n \"* 장거리 얽힘에 관한 기술적인 참고 자료: 큐비트를 고르거나 회로를 최적화하는 방법을 포함한 IBM 하드웨어에서 장거리 얽힘 구현에 대한 더 자세한 기술적인 설명은 이 IBM 튜토리얼을 참고하세요: [Efficiently generating long-range entanglement](https://quantum.cloud.ibm.com/docs/tutorials/long-range-entanglement).\\n\",\n \" \\n\",\n \"* 큰 회로의 시뮬레이션 (20 큐빗 이상):\\n\",\n \" * 앨리스의 메세지 큐빗, 그녀의 얽힌 큐빗, 밥의 큐빗, 그리고 텔레포트 경로나 더 발전된 텔레포트 방법에 사용되는 모든 중간 큐빗을 포함해 20 큐빗 이상의 회로를 만들고 실험하시는 경우에는 시뮬레이션으로 상태벡터를 구하는 것이 매우 어렵습니다.\\n\",\n \" * 이러한 큰 회로에서, 만약 당신의 텔레포트 회로 (그리고 벨 큐빗 생성과 벨 측정 등 그 구성 요소) 모두가 **Clifford** 게이트 (H, S, X, Y, Z, CNOT, SWAP, 또는 그들의 조합) 만으로 구현될 수 있다면 좀 더 효율적인 시뮬레이션 방법이 있습니다.\\n\",\n \" * 추천: `AerSimulator(method=\\\"stabilizer\\\")`를 사용하세요. Stabilizer 시뮬레이터는 Clifford 회로의 시뮬레이션에 최적화되어 있으며 훨씬 많은 수의 큐빗을 효과적으로 다룰 수 있습니다.\\n\",\n \"
\\n\",\n \"\\n\",\n \"양자 회로 전송의 한계를 개척하는 당신의 여정에 행운이 있기를 바랍니다! 여러분이 시도한 내용과 결과를 다른 참가자들과 공유하고 어떻게 \\\"더 멀리\\\" 큐비트를 보낼 수 있을지 토론해보세요.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eee76bb9-11ea-45d3-8cd5-853b140e7e9f\",\n \"metadata\": {},\n \"source\": [\n \"## References\\n\",\n \"\\n\",\n \"1. Young, Thomas (1804) I. The Bakerian Lecture. Experiments and calculations relative to physical optics. Phil. Trans. R. Soc. 941–16.\\n\",\n \"2. Fage, Arthur and Johansen, F. C. (1927). On the flow of air behind an inclined flat plate of infinite span. Proc. R. Soc. Lond. A116 170–197.\\n\",\n \"3. Clauser, J. F., Horne, M. A., Shimony, A., & Holt, R. A. (1969). Proposed Experiment to Test Local Hidden-Variable Theories. Phys. Rev. Lett., 23(15), 880–884.\\n\",\n \"4. Bouwmeester, D., Pan, J.-W., Mattle, K., Eibl, M., Weinfurter, H., & Zeilinger, A. (1997). Experimental quantum teleportation. Nature, 390(6660), 575–579.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae55945d-d627-42bd-8ce6-6f39b6c6d973\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Sophy Shin, James Weaver\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Jessie Yu, Junye Huang, Borja Peropadre \\n\",\n \"\\n\",\n \"**Reviewed by:** Meltem Tolunay, Abby Cross\\n\",\n \"\\n\",\n \"**Translated by:** Boseong Kim\\n\",\n \"\\n\",\n \"**Version:** 1.1.1\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.11\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-1/lab1.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4b5d9b98-ac5b-4121-915a-893bd64960f3\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 1: Recreating famous experiments at home!\\n\",\n \"\\n\",\n \"# 0. Setup: Gathering Our Tools\\n\",\n \"\\n\",\n \"Just like any good experiment, we first need to prepare our equipment. In our case, we need to prepare the Python libraries we'll use, especially Qiskit, our quantum computing toolkit. You should be all set if you finished lab 0. If not, you can uncomment the follow cell to install the grader, which will install Qiskit v2.x and the necessary libraries necessary for the Summer School.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"519fb94e-18f2-4ade-a40f-58c1078424e5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0e8bb120\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3fee413b\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.9`. If you see a lower version, you need to restart your kernel and reinstall the grader.\\n\",\n \"Also make sure you have set up everything according to lab 0 and test it with the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"53e15069\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"51102304\",\n \"metadata\": {},\n \"source\": [\n \"## Imports\\n\",\n \"\\n\",\n \"The cell below imports these necessary libraries and prints your Qiskit version.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8ae038b9-2181-4c10-8018-833b52ff17a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Essential libraries\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import ipywidgets as widgets\\n\",\n \"from IPython.display import display\\n\",\n \"from PIL import Image\\n\",\n \"import io\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.circuit import Parameter\\n\",\n \"from qiskit.visualization import plot_histogram, plot_distribution\\n\",\n \"from qiskit_ibm_runtime import Options, Session, SamplerV2 as Sampler\\n\",\n \"from qiskit.result import marginal_distribution\\n\",\n \"\\n\",\n \"from qiskit.transpiler import generate_preset_pass_manager\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab1_ex1_1, \\n\",\n \" grade_lab1_ex1_2, \\n\",\n \" grade_lab1_ex1_3, \\n\",\n \" grade_lab1_ex1_4, \\n\",\n \" grade_lab1_ex2, \\n\",\n \" grade_lab1_ex3,\\n\",\n \" grade_lab1_ex4,\\n\",\n \" grade_lab1_ex5,\\n\",\n \" grade_lab1_ex6\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"39f36676-bdd1-4ba2-8486-2b4796766eea\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Table of Contents\\n\",\n \"\\n\",\n \"1. Superposition, Interference and Measurement\\n\",\n \" 1. Double-slit Experiment - Superposition, Interference\\n\",\n \" 2. Schrödinger’s Cat and Double-slit Revisit\\n\",\n \"2. Entanglement\\n\",\n \" 1. The Curious Case of Alice, Bob, and the Unbeatable Game - CHSH Game\\n\",\n \" 2. Quantum Teleportation - Sending Secrets with Spooky Action\\n\",\n \" 1. Teleportation on a Simulator\\n\",\n \"3. (Bonus Challenge) Teleportation on Real Quantum Hardware\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d36937e1-9cc0-4c1b-8240-f87504825196\",\n \"metadata\": {},\n \"source\": [\n \"Welcome to the very first QGSS Lab of 2025, the International Year of Quantum! In the first lab, we’ll explore four of the most important concepts of the quantum world: superposition, interference, measurement, and entanglement - through hands-on experiments using Qiskit.\\n\",\n \"\\n\",\n \"# Chapter 1: Superposition, Interference and Measurement\\n\",\n \"\\n\",\n \"To kick off this exciting journey, we’ll begin by diving into superposition, interference, and quantum measurement through the famous double-slit experiment. **Superposition** means a quantum particle - for example, an electron or a photon — can exist in multiple states at once, like being in two places or having two energies at the same time. It's like flipping a coin with the result of heads and tails at the same time... until you look! **Interference** describes what happens when these quantum possibilities, behaving like waves, overlap. If they are in phase, they reinforce each other (constructive interference), and if out of phase, they can weaken or cancel each other out (destructive interference).\\n\",\n \"\\n\",\n \"One of the most famous experiments demonstrating these concepts is the **double-slit experiment**, first done by Thomas Young in 1801 and later repeated with individual particles like electrons. When no one watches which slit the particle goes through, the result is a beautiful interference pattern, like waves overlapping. But the moment you **measure** which slit it goes through, the pattern disappears. It’s as if the particle knows it’s being watched!\\n\",\n \"\\n\",\n \"This strange idea was taken to the next level by Erwin Schrödinger in 1935 with the famous thought experiment known as **Schrödinger's Cat**, where a cat can be both alive and dead until someone opens the box to check. Measurement in quantum mechanics doesn’t just observe reality, it shapes it.\\n\",\n \"\\n\",\n \"Ready to experience quantum weirdness yourself? With Qiskit, you can do the double-slit experiment - with and without measurement - and see for yourself the beautiful patterns of superposition and interference, as well as the effects measurement has on quantum systems. It’s a fun and eye-opening way to explore the mysteries of the quantum world!\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"309c6569-6875-447e-a514-cc049e7a37f0\",\n \"metadata\": {},\n \"source\": [\n \"## Through the Slits: Where Quantum Weirdness Begins\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \" image from wikipedia\\n\",\n \"
\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3d9deeff-2450-4ead-b78a-5533addb84a7\",\n \"metadata\": {},\n \"source\": [\n \"In his paper \\\"Experiments and calculations relative to physical optics\\\" 1803, Thomas Young explains about his experiments in this quotation:\\n\",\n \"> I made a small hole in a window-shutter, and covered it with a piece of thick paper, which I perforated with a fine needle. For greater convenience of observation, I placed a small looking glass without the window-shutter, in such a positioned as to reflect the sun's light, in a direction nearly horizontal, upon the opposite wall, and to cause the cone of diverging light to pass over a table, on which were several little screens of card-paper. [1](https://royalsocietypublishing.org/doi/pdf/10.1098/rstl.1804.0001)\\n\",\n \"\\n\",\n \"With this experiment, he could observe beautiful fringe patterns by light and explained this comes from the differences in the lengths of the paths of two rays. George Paget Thomson later reproduced a similar experiment using electrons (electron diffraction experiment), and showed that even particles like electrons exhibit wave-like behavior, as demonstrated in his 1927 paper. [2](https://royalsocietypublishing.org/doi/10.1098/rspa.1927.0130) Later in 1965, Feynman explained the double-slit experiment in his \\\"Lectures on Physics, Vol. 3, Chapter 1\\\", and explained this comes from the superposition and interference concepts of quantum mechanics.\\n\",\n \"\\n\",\n \"Now we can reproduce this fascinating quantum phenomenon using Qiskit, without complex physical equipment. Let's see how superposition and interference are implemented in quantum circuits.\\n\",\n \"\\n\",\n \"The first step is mapping the physical double-slit experiment to a quantum circuit, which is your first exercise.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f79b538f-82c1-4297-a550-4360e3ab0082\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 1: Build a quantum circuit for the double-slit experiment\\n\",\n \"\\n\",\n \"- Exercise 1-1: Make a slit\\n\",\n \"\\n\",\n \"**Your Goal:** Create a quantum circuit that models a particle (qubit) passing through two slits, achieving a superposition of being in the $|0\\\\rangle$ (lower slit) and $|1\\\\rangle$ (upper slit) states.\\n\",\n \"\\n\",\n \"Let's define the upper slit as the \\n\",\n \"$|1\\\\rangle$ state of a qubit and the lower slit as the $|0\\\\rangle$ state. Implement a quantum circuit where a qubit, initially in $|0\\\\rangle$, passes through the slits in a superposition of $|0\\\\rangle$ and $|1\\\\rangle$ using the [Hadamard gate](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.HGate). Draw your circuit.\\n\",\n \"\\n\",\n \"**Hint**: It can be beneficial for clarity to assign specific names to `QuantumRegister` (e.g., `name='q'`) and `ClassicalRegister` (e.g., `name='c_screen'`) when interpreting measurement data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"884333a6-3489-4520-912c-99d337eb00d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\n\",\n \"\\n\",\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit = QuantumCircuit(qr, cr)\\n\",\n \"# your code here\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# end of your code\\n\",\n \"double_slit.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f5d3c41c-05fc-46ff-a41e-f7de68e96275\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab1_ex1_1(double_slit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e92915b5-7396-4d04-b0da-05b84dd7e8c3\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 1: Build a quantum circuit for the double-slit experiment\\n\",\n \"\\n\",\n \"- Exercise 1-2: Make a screen\\n\",\n \"\\n\",\n \"**Your Goal:** Extend the previous circuit to model the screen where interference occurs. Specifically, implement the center of the screen where no phase difference is introduced, and then measure the qubit.\\n\",\n \"\\n\",\n \"Now, implement the screen where the two superposed quantum states create an interference pattern by adding a gate. First, let's implement the center of the screen, where the two beams combine with no phase difference (resulting in the $|0\\\\rangle$ state). Then measure the qubit from `qr` and store the result in `c_screen`.\\n\",\n \"\\n\",\n \"**Hint**: You can use the Hadamard gate again.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1828c3c0-7bb6-451d-891a-89f56835c6bc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# your code here\\n\",\n \"\\n\",\n \"# end of your code\\n\",\n \"double_slit.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1fcf565a-b547-43fb-8492-c327a9ff7a10\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab1_ex1_2(double_slit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88cc5027-9435-4eaf-a3c3-0e391cc997d3\",\n \"metadata\": {},\n \"source\": [\n \"Let's check the execution result. Assuming the screen detects the qubit in the $|0\\\\rangle$ state, the following code runs the circuit on an ideal simulator. A probability of 1 means 100%.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Note on Retrieving Measurement Results with SamplerV2 \\n\",\n \"\\n\",\n \"When using *SamplerV2* in Qiskit, how you retrieve measurement counts depends on how you've set up your classical registers and measurements:\\n\",\n \"\\n\",\n \"1. Named Classical Register in *QuantumCircuit*:\\n\",\n \" If you define your circuit with a named classical register, e.g., *cr = ClassicalRegister(1, name='my_results')* and *qc = QuantumCircuit(qr, cr)*, and then measure to it *qc.measure(qr[0], cr[0])*, you access the counts using that name:\\n\",\n \"\\n\",\n \" \\n\",\n \" ```python\\n\",\n \" # result = job.result()\\n\",\n \" # counts = result[0].data.my_results.get_counts()\\n\",\n \" ```\\n\",\n \" \\n\",\n \" In our double-slit circuit, we used *cr = ClassicalRegister(1, name='c_screen')*, so we will use *result[0].data.c_screen.get_counts()*.\\n\",\n \"\\n\",\n \"3. measure_all():\\n\",\n \" If you use *qc.measure_all()*, Qiskit automatically adds classical bits and names the output data field `meas`:\\n\",\n \"\\n\",\n \" \\n\",\n \" ```python\\n\",\n \" # qc.measure_all()\\n\",\n \" # ...\\n\",\n \" # counts = result[0].data.meas.get_counts()\\n\",\n \" ```\\n\",\n \"\\n\",\n \"5. Implicit Classical Bits (No Named Register in *QuantumCircuit*):\\n\",\n \" If you create a circuit like *qc = QuantumCircuit(1, 1)* (1 qubit, 1 classical bit) or *qc = QuantumCircuit(QuantumRegister(1), ClassicalRegister(1))* without naming the classical register in its constructor, and then use *qc.measure(0, 0)*, the *SamplerV2* might store results under a default name (often *c*, or based on the classical bit indices like *c0*).\\n\",\n \" For *qc = QuantumCircuit(1,1)* and *qc.measure(0,0)*, the output might be accessed as follows:\\n\",\n \"\\n\",\n \" ```python \\n\",\n \" # counts = result[0].data.c0.get_counts() # If single bit named c0, the indice can vary. You can see the actual indices when you plot your circuit.\\n\",\n \" ```\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d9d6d49e-19bc-482c-a3e4-049a986323f1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# use simulator\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"# make quantum circuit compatible to the backend\\n\",\n \"pm = generate_preset_pass_manager(backend = backend, optimization_level=3)\\n\",\n \"qc_isa = pm.run(double_slit)\\n\",\n \"\\n\",\n \"# run and get counts\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots = 1000).result()[0].data.c_screen.get_counts()\\n\",\n \"\\n\",\n \"plot_distribution(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9d9d8b9d-6db1-47b0-a7b3-46d78a0c51cb\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 1: Build a quantum circuit for the double-slit experiment\\n\",\n \"\\n\",\n \"- Exercise 1-3: Make a difference\\n\",\n \"\\n\",\n \"**Your Goal:** Modify the double-slit circuit to introduce a phase difference between the two paths (slits) and observe its effect on the measurement outcome at the screen.\\n\",\n \"\\n\",\n \"Now, let's implement another part of the screen. The path difference between the two beams is implemented as a phase difference between $|0\\\\rangle$ and $|1\\\\rangle$. Apply a phase of $\\\\pi/2$ only to the $|1\\\\rangle$ state of the superposed quantum state and observe the measurement results. (Hint: Use a gate that applies a phase to the $|1\\\\rangle$ state. The [P gate](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.PhaseGate) and the [S gate](https://quantum.cloud.ibm.com/docs//api/qiskit/qiskit.circuit.library.SGate) are representative examples.)\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"50a651fe-236a-4c64-ad11-f00200c1cedf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit_with_difference = QuantumCircuit(qr, cr)\\n\",\n \"double_slit_with_difference.h(0)\\n\",\n \"\\n\",\n \"#your code here\\n\",\n \"\\n\",\n \"\\n\",\n \"#end of your code\\n\",\n \"\\n\",\n \"double_slit_with_difference.h(0)\\n\",\n \"double_slit_with_difference.measure(qr, cr)\\n\",\n \"double_slit_with_difference.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"810339b8-ab70-4aa8-b89c-220e8a7521f5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab1_ex1_3(double_slit_with_difference)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c4e217cf-065f-45ee-8d7c-3e8c94f7fede\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qc_isa = pm.run(double_slit_with_difference)\\n\",\n \"\\n\",\n \"#run and get counts\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots=10000).result()[0].data.c_screen.get_counts()\\n\",\n \"plot_distribution(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4a08020a-6caa-4415-bec0-b5e4f53471ad\",\n \"metadata\": {},\n \"source\": [\n \"As you can see, the probability of measuring $|0\\\\rangle$ decreased to approximately 0.5 (50%), signifying reduced brightness at this point on the screen.\\n\",\n \" \\n\",\n \"\\n\",\n \"Before we go further let's do a simple mathematics demonstration of this process in regards to superposition, interference, and the probability of measuring a quantum state. You can skip this part if you are already familiar with this concept.\\n\",\n \"\\n\",\n \"\\n\",\n \"
Superposition, Interference and the Probability of Measurement
\\n\",\n \"\\n\",\n \"\\n\",\n \"In our quantum circuit implementation of the double-slit experiment, we used a sequence of Hadamard (H) gates and a phase gate (P). Let's break down the mathematical representation of the quantum state at each step and analyze the superposition, interference, and measurement probability.\\n\",\n \"\\n\",\n \"**1. Initialization:**\\n\",\n \"\\n\",\n \"We start with a single qubit initialized in the $|0\\\\rangle$ state, representing the electron starting at a source before the slits:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_0\\\\rangle = |0\\\\rangle = \\\\begin{pmatrix} 1 \\\\\\\\ 0 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"Here we will assume $ |\\\\psi_1\\\\rangle$ is another quantum state that can pass the upper slit - this will be:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_1\\\\rangle = |1\\\\rangle = \\\\begin{pmatrix} 0 \\\\\\\\ 1 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"**2. Superposition (First Hadamard Gate):**\\n\",\n \"\\n\",\n \"The first Hadamard gate (H) is applied to the qubit. This operation creates a superposition of the $|0\\\\rangle$ and $|1\\\\rangle$ states, representing the electron passing through both slits simultaneously:\\n\",\n \"\\n\",\n \"$$ H = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"Applying H to $|\\\\psi_0\\\\rangle$:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_1\\\\rangle = H |\\\\psi_0\\\\rangle = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} \\\\begin{pmatrix} 1 \\\\\\\\ 0 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ 1 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} (|0\\\\rangle + |1\\\\rangle) $$\\n\",\n \"\\n\",\n \"This state $|\\\\psi_1\\\\rangle$ shows the qubit is in an equal superposition of the $|0\\\\rangle$ state (representing one slit) and the $|1\\\\rangle$ state (representing the other slit).\\n\",\n \"\\n\",\n \"**3. Phase Shift (P Gate):**\\n\",\n \"\\n\",\n \"The phase gate (P) introduces a relative phase $\\\\phi$ between the $|0\\\\rangle$ and $|1\\\\rangle$ components of the superposition. This phase difference corresponds to the path difference experienced by the electron waves passing through the two slits in the physical experiment. The P gate is defined as:\\n\",\n \"\\n\",\n \"$$ P(\\\\phi) = \\\\begin{pmatrix} 1 & 0 \\\\\\\\ 0 & e^{i\\\\phi} \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"Applying P($\\\\phi$) to $|\\\\psi_1\\\\rangle$:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_2\\\\rangle = P(\\\\phi) |\\\\psi_1\\\\rangle = \\\\begin{pmatrix} 1 & 0 \\\\\\\\ 0 & e^{i\\\\phi} \\\\end{pmatrix} \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ 1 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ e^{i\\\\phi} \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} (|0\\\\rangle + e^{i\\\\phi}|1\\\\rangle) $$\\n\",\n \"\\n\",\n \"The state $|\\\\psi_2\\\\rangle$ now contains the relative phase information, which is crucial for interference.\\n\",\n \"\\n\",\n \"**4. Interference (Second Hadamard Gate):**\\n\",\n \"\\n\",\n \"The second Hadamard gate is applied to bring the superposed states back together, allowing them to interfere:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_3\\\\rangle = H |\\\\psi_2\\\\rangle = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ e^{i\\\\phi} \\\\end{pmatrix} = \\\\frac{1}{2} \\\\begin{pmatrix} 1 + e^{i\\\\phi} \\\\\\\\ 1 - e^{i\\\\phi} \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"Expanding $e^{i\\\\phi} = \\\\cos(\\\\phi) + i\\\\sin(\\\\phi)$:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_3\\\\rangle = \\\\frac{1}{2} \\\\begin{pmatrix} 1 + \\\\cos(\\\\phi) + i\\\\sin(\\\\phi) \\\\\\\\ 1 - (\\\\cos(\\\\phi) + i\\\\sin(\\\\phi)) \\\\end{pmatrix} = \\\\frac{1}{2} \\\\begin{pmatrix} (1 + \\\\cos(\\\\phi)) + i\\\\sin(\\\\phi) \\\\\\\\ (1 - \\\\cos(\\\\phi)) - i\\\\sin(\\\\phi) \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"This final state before measurement embodies the interference effect, where the probabilities of measuring $|0\\\\rangle$ or $|1\\\\rangle$ depend on the phase difference $\\\\phi$.\\n\",\n \"\\n\",\n \"**5. Probability of Measurement:**\\n\",\n \"\\n\",\n \"The probability of measuring the qubit in the $|0\\\\rangle$ state (corresponding to one part of the screen) is given by the squared magnitude of the amplitude of $|0\\\\rangle$ in $|\\\\psi_3\\\\rangle$:\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\left| \\\\frac{1}{2} (1 + e^{i\\\\phi}) \\\\right|^2 = \\\\left| \\\\frac{1}{2} (1 + \\\\cos(\\\\phi) + i\\\\sin(\\\\phi)) \\\\right|^2 $$\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\frac{1}{4} [(1 + \\\\cos(\\\\phi))^2 + (\\\\sin(\\\\phi))^2] = \\\\frac{1}{4} [1 + 2\\\\cos(\\\\phi) + \\\\cos^2(\\\\phi) + \\\\sin^2(\\\\phi)] $$\\n\",\n \"\\n\",\n \"Using the identity $\\\\cos^2(\\\\phi) + \\\\sin^2(\\\\phi) = 1$:\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\frac{1}{4} [1 + 2\\\\cos(\\\\phi) + 1] = \\\\frac{1}{4} [2 + 2\\\\cos(\\\\phi)] = \\\\frac{1}{2} (1 + \\\\cos(\\\\phi)) $$\\n\",\n \"\\n\",\n \"In case of $\\\\phi = \\\\pi/2$, $P(|0\\\\rangle) = \\\\frac{1}{2} (1 + \\\\cos(\\\\pi/2)) = 0.5$, as we checked already.\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6476c1c7-c119-4e60-a0f6-821321401081\",\n \"metadata\": {},\n \"source\": [\n \"Let's conclude Exercise 1 by generating the fringe pattern using a range of $\\\\phi$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5d40a6e6-fa86-47af-b556-2849906d5396\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 1: Build a quantum circuit for the double-slit experiment\\n\",\n \"\\n\",\n \"- Exercise 1-4: Beautiful Fringes\\n\",\n \"\\n\",\n \"**Your Goal:** Create a parameterized quantum circuit where the phase difference $\\\\phi$ can be varied, allowing you to simulate and visualize the complete interference fringe pattern.\\n\",\n \"\\n\",\n \"Qiskit allows [using `Parameters` in quantum circuits](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.Parameter). We'll use this with the `Sampler` to observe interference fringes.\\n\",\n \"\\n\",\n \"Define a parameterized quantum circuit for the double-slit experiment with a variable parameter $\\\\phi$. A common structure is H-gate, then $P(\\\\phi)$, then another H-gate, before measurement. Measure `q` and save to `c_screen`.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4a90de8b-f5fb-4adf-a6d3-466a46f325b8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ = Parameter('φ')\\n\",\n \"\\n\",\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit_fringe = QuantumCircuit(qr, cr)\\n\",\n \"\\n\",\n \"#your code here\\n\",\n \"\\n\",\n \"\\n\",\n \"#end of your code\\n\",\n \"\\n\",\n \"double_slit_fringe.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d2d987c4-d66b-4e1a-84f4-d5915ddde852\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab1_ex1_4(double_slit_fringe)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7a2c005f-dd42-4ee8-8073-b6b483045d76\",\n \"metadata\": {},\n \"source\": [\n \"Excellent! Now, let's use this parameterized circuit to plot the fringe pattern using matplotlib's heat map. The code below generates 100 phase values, runs the circuits (1000 shots each) via `Sampler`, stores the probability of measuring $|0\\\\rangle$, and plots the heat map.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Resource limit\\n\",\n \"\\n\",\n \"When running the code below on an actual backend, increasing the number of parameters or shots consumes more QPU time than might be expected. The current configuration (100 parameterized circuits + 1000 shots) uses less than 1 minute of QPU time, so please try to maintain these settings if possible.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2d447625-a7c9-46e5-9a8d-690419747ef1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ_lst = np.linspace(-3*np.pi, 3*np.pi, 100)\\n\",\n \"qc_isa = pm.run(double_slit_fringe)\\n\",\n \"\\n\",\n \"φ_hit = []\\n\",\n \"dist = sampler.run([(qc_isa, φ_lst)], shots=1000).result()[0].data.c_screen\\n\",\n \"\\n\",\n \"for i in range(len(φ_lst)):\\n\",\n \" result = dist[i].get_counts()\\n\",\n \" if '0' in result:\\n\",\n \" φ_hit.append(result['0']/1000)\\n\",\n \" else:\\n\",\n \" φ_hit.append(0)\\n\",\n \"\\n\",\n \"#plot heat map\\n\",\n \"φ_hit_2d = np.array(φ_hit).reshape(1, -1)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 2))\\n\",\n \"plt.imshow(φ_hit_2d, cmap='gray', aspect='auto', extent=[-3*np.pi, 3*np.pi, 0, 0.1])\\n\",\n \"\\n\",\n \"plt.xlabel('φ (Phase difference)', fontsize=14)\\n\",\n \"plt.colorbar(label='prob')\\n\",\n \"plt.xticks(ticks=[-3*np.pi, -2*np.pi, -np.pi, 0, np.pi, 2*np.pi, 3*np.pi],\\n\",\n \" labels=['-3π', '-2π', '-π', '0', 'π', '2π', '3π'])\\n\",\n \"plt.yticks([]) \\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0a6bc2c3-7c4a-46a2-9984-bc8a94666ead\",\n \"metadata\": {},\n \"source\": [\n \"Fantastic! You've reproduced the double-slit experiment's fringes using qubits. Next, we'll explore quantum measurement with Schrödinger's cat and then see how observation affects the double-slit outcome.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed7715c1-2169-4fef-9b7d-3ed61c4b2f41\",\n \"metadata\": {},\n \"source\": [\n \"## Schrödinger's Cat: Exploring Quantum Superposition and the Act of Observation\\n\",\n \"\\n\",\n \"This thought experiment by Erwin Schrödinger (1935, [\\\"Die gegenwärtige Situation in der Quantenmechanik\\\"](https://link.springer.com/article/10.1007/BF01491891)) illustrates quantum superposition and measurement's role in collapsing possibilities into a single outcome. A cat is sealed in a box with a radioactive atom. If the atom is in a superposition of decayed/not decayed, the cat, entangled with it, should be in a superposition of alive/dead until observed.\\n\",\n \"\\n\",\n \"Here, we'll use a rotational gate. Imagine a cat disliking the $|1\\\\rangle$ state. Let's see if the cat is happy or upset when we \\\"open the box\\\" (measure the qubit). Be careful! An angry quantum cat might scratch you.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5559255f-f8c1-4a49-bcf1-738d9730f92f\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 2: Is the cat happy or grumpy?\\n\",\n \"\\n\",\n \"**Your Goal:** Create a quantum circuit that prepares a qubit in a superposition state using an $R_X(\\\\theta)$ gate, then simulate a single measurement to determine if the \\\"cat\\\" (represented by the qubit state) is \\\"happy\\\" ($|0\\\\rangle$) or \\\"grumpy\\\" ($|1\\\\rangle$).\\n\",\n \"\\n\",\n \"Complete the Python code below using the [Rotational X gate](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.RXGate) with $\\\\theta \\\\in [0, 2\\\\pi]$ to prepare a qubit in various superposition states. Then, measure once to see if the cat is happy ($|0\\\\rangle$) or grumpy ($|1\\\\rangle$). $\\\\theta$ will be given to you by the slider.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6aa42755-b319-4aee-a6bc-cf6b34e00222\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def schrodingers_cat_experiment_theta(theta):\\n\",\n \" \\n\",\n \" qc = QuantumCircuit(1)\\n\",\n \"\\n\",\n \" #your code start here\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" #end of your code\\n\",\n \"\\n\",\n \" qc.measure_all()\\n\",\n \" \\n\",\n \" backend = AerSimulator()\\n\",\n \" pm = generate_preset_pass_manager( backend = backend, optimization_level=3)\\n\",\n \" qc_isa = pm.run(qc)\\n\",\n \"\\n\",\n \" # Circuit compile and run, shot = 1 \\n\",\n \" sampler = Sampler(mode=backend)\\n\",\n \" counts = sampler.run([qc_isa], shots = 1).result()[0].data.meas.get_counts()\\n\",\n \"\\n\",\n \" measured_state = list(counts.keys())[0] if counts else '0' # bring measured result\\n\",\n \"\\n\",\n \" if measured_state == '0':\\n\",\n \" cat_happy = True\\n\",\n \" else:\\n\",\n \" cat_happy = False\\n\",\n \"\\n\",\n \" return cat_happy, qc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"49747cda-70bf-44d7-816c-607dd601a95f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab1_ex2(schrodingers_cat_experiment_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"18096b47-adb4-47dc-a41f-2273db2aacab\",\n \"metadata\": {},\n \"source\": [\n \"Now, use the widget below to check the cat's mood. Make sure `happy.png` and `grumpy.png` are in the same folder. (Image credit: James Weaver)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4aed0937-fa98-416e-a2c8-e571df1ed907\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"happy_img = Image.open('happy.png')\\n\",\n \"grumpy_img = Image.open('grumpy.png')\\n\",\n \"\\n\",\n \"out = widgets.Output()\\n\",\n \"\\n\",\n \"slider = widgets.FloatSlider(\\n\",\n \" value=0,\\n\",\n \" min=0,\\n\",\n \" max=2*np.pi,\\n\",\n \" step=0.01,\\n\",\n \" description='θ',\\n\",\n \" continuous_update=True\\n\",\n \")\\n\",\n \"\\n\",\n \"button = widgets.Button(\\n\",\n \" description='Open the Box',\\n\",\n \" button_style='success'\\n\",\n \")\\n\",\n \" \\n\",\n \"def on_button_click(b):\\n\",\n \" with out:\\n\",\n \" out.clear_output(wait=True) # clean output\\n\",\n \"\\n\",\n \" result = schrodingers_cat_experiment_theta(slider.value)[0]\\n\",\n \"\\n\",\n \" if result==True:\\n\",\n \" img = happy_img\\n\",\n \" txt = \\\"happy\\\"\\n\",\n \" else:\\n\",\n \" img = grumpy_img\\n\",\n \" txt = \\\"grumpy\\\"\\n\",\n \"\\n\",\n \" new_size = (400, 400)\\n\",\n \" resized_img = img.resize(new_size)\\n\",\n \" \\n\",\n \" buf = io.BytesIO()\\n\",\n \" resized_img.save(buf, format='PNG')\\n\",\n \" buf.seek(0)\\n\",\n \" probability = int(np.cos(slider.value/2)**2 * 100)\\n\",\n \"\\n\",\n \" display(f\\\"The probability of cat is happy: {probability}%\\\")\\n\",\n \" display(f\\\"The observed cat is : {txt}\\\")\\n\",\n \" display(widgets.Image(value=buf.read(), format='png'))\\n\",\n \"\\n\",\n \"button.on_click(on_button_click)\\n\",\n \"\\n\",\n \"display(slider, button, out)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7d8e5717-b4d3-4e0c-84d9-279210916258\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"Before we revisit the double-slit experiment, let's clarify what happens when we try to observe or \\\"measure\\\" a qubit, especially after applying a gate like $R_x(\\\\theta)$. If you are familiar with quantum measurement, you can skip this part.\\n\",\n \"\\n\",\n \"\\n\",\n \"
Quantum Measurement details: Observing the Qubit
\\n\",\n \"\\n\",\n \"**1. Qubit State Before Measurement:**\\n\",\n \"After $R_x(\\\\theta)$ on $|0\\\\rangle$, the state is a superposition:\\n\",\n \"$$|\\\\psi\\\\rangle = R_x(\\\\theta)|0\\\\rangle = \\\\cos\\\\left(\\\\frac{\\\\theta}{2}\\\\right)|0\\\\rangle - i \\\\sin\\\\left(\\\\frac{\\\\theta}{2}\\\\right)|1\\\\rangle = \\\\alpha|0\\\\rangle + \\\\beta|1\\\\rangle$$\\n\",\n \"where $|\\\\alpha|^2 + |\\\\beta|^2 = 1$.\\n\",\n \"\\n\",\n \"**2. Act of Measurement:**\\n\",\n \"Measurement in the computational basis ($\\\\{|0\\\\rangle, |1\\\\rangle\\\\}$) forces the qubit to choose one state. The superposition is not directly observable.\\n\",\n \"\\n\",\n \"**3. Probabilistic Outcomes:**\\n\",\n \"Probability of measuring $|0\\\\rangle$: $P(0) = |\\\\alpha|^2 = \\\\cos^2\\\\left(\\\\frac{\\\\theta}{2}\\\\right)$\\n\",\n \"Probability of measuring $|1\\\\rangle$: $P(1) = |\\\\beta|^2 = \\\\sin^2\\\\left(\\\\frac{\\\\theta}{2}\\\\right)$\\n\",\n \"\\n\",\n \"**4. State Collapse:**\\n\",\n \"Post-measurement, the state collapses:\\n\",\n \"* If '0' is measured $\\\\rightarrow$ state becomes $|0\\\\rangle$.\\n\",\n \"* If '1' is measured $\\\\rightarrow$ state becomes $|1\\\\rangle$.\\n\",\n \"This \\\"collapse\\\" destroys the superposition.\\n\",\n \"\\n\",\n \"**Connection to Double-Slit:**\\n\",\n \"Detecting which slit a particle uses is a measurement. It collapses the particle's wave function to a definite path, destroying the superposition needed for interference fringes.\\n\",\n \"\\n\",\n \"\\n\",\n \"Let's revisit the double-slit experiment.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a759bf4a-f9ce-4d23-bd90-c72d15b0c87b\",\n \"metadata\": {},\n \"source\": [\n \"## Double-slit with Measurement\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 3: Double-slit with a path detector\\n\",\n \"\\n\",\n \"**Your Goal:** Construct a double-slit circuit that includes an intermediate measurement acting as a \\\"which-path\\\" detector. This will allow you to observe how acquiring information about the particle's path affects the final interference pattern.\\n\",\n \"\\n\",\n \"Modify the double-slit circuit to include a \\\"which-path\\\" detector (a measurement after the first H-gate).\\n\",\n \"\\n\",\n \"Setup:\\n\",\n \"* `qr`: 1 qubit (`'q'`).\\n\",\n \"* `cr1`: 1 classical bit (`'c_detector'`) for path detection.\\n\",\n \"* `cr2`: 1 classical bit (`'c_screen'`) for final measurement.\\n\",\n \"* `φ`: A `Parameter` for the phase gate.\\n\",\n \"\\n\",\n \"**Hint**: Your circuit will have two measurements.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ba9f4861-356b-4b66-99d5-e06561e5f340\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr1 = ClassicalRegister(1, name='c_detector')\\n\",\n \"cr2 = ClassicalRegister(1, name='c_screen')\\n\",\n \"double_slit_with_detector = QuantumCircuit(qr, cr1, cr2)\\n\",\n \"\\n\",\n \"φ = Parameter('φ')\\n\",\n \"\\n\",\n \"#your code here\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"#end of your code\\n\",\n \"\\n\",\n \"double_slit_with_detector.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c6533c86-e7e6-4b0f-92ef-5179d75b7702\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab1_ex3(double_slit_with_detector)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"48ffc450-3cf9-423c-be89-808a03dd246a\",\n \"metadata\": {},\n \"source\": [\n \"Now, repeat the heat map generation to see the new pattern with the detector.\\n\",\n \"\\n\",\n \"**Warning**: This can take minutes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1dbb4f19-9a18-4c2e-82e4-3365a2a87880\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Resource limit\\n\",\n \"\\n\",\n \"When running the code below on an actual backend, increasing the number of parameters or shots consumes more QPU time than might be expected. The current configuration (100 parameterized circuits $\\\\times$ 1000 shots) uses less than 1 minute of QPU time, so please try to maintain these settings.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"1. **Initial State**: $|\\\\psi_0\\\\rangle = |0\\\\rangle$\\n\",\n \"2. **First H-gate**: $|\\\\psi_1\\\\rangle = \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle + |1\\\\rangle)$ (particle can pass through both slits)\\n\",\n \"3. **First Measurement (Detector)**: Collapses the superposition.\\n\",\n \" * Outcome 0 (prob 1/2) $\\\\rightarrow |\\\\psi_{2a}\\\\rangle = |0\\\\rangle$\\n\",\n \" * Outcome 1 (prob 1/2) $\\\\rightarrow |\\\\psi_{2b}\\\\rangle = |1\\\\rangle$\\n\",\n \" The qubit is now in a definite state.\\n\",\n \"4. **P-gate**:\\n\",\n \" * If outcome 0: $|\\\\psi_{3a}\\\\rangle = P(\\\\phi)|0\\\\rangle = |0\\\\rangle$\\n\",\n \" * If outcome 1: $|\\\\psi_{3b}\\\\rangle = P(\\\\phi)|1\\\\rangle = e^{i\\\\phi}|1\\\\rangle$\\n\",\n \" The phase $\\\\phi$ cannot act as a *relative* phase for interference due to the collapse.\\n\",\n \"5. **Second H-gate**:\\n\",\n \" * If outcome 0: $|\\\\psi_{4a}\\\\rangle = H|0\\\\rangle = \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle + |1\\\\rangle)$\\n\",\n \" * If outcome 1: $|\\\\psi_{4b}\\\\rangle = H(e^{i\\\\phi}|1\\\\rangle) = e^{i\\\\phi} \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle - |1\\\\rangle)$\\n\",\n \"6. **Second Measurement (Screen)**:\\n\",\n \" * If first measurement was 0: Prob(final '0') = 1/2, Prob(final '1') = 1/2.\\n\",\n \" * If first measurement was 1: Prob(final '0') = 1/2, Prob(final '1') = 1/2.\\n\",\n \"\\n\",\n \"**Conclusion**: Regardless of the detector's outcome, the final measurement is always 50/50 for $|0\\\\rangle$ or $|1\\\\rangle$. The intermediate measurement destroyed the superposition, and thus the interference. This demonstrates that \\\"which-path\\\" information destroys interference.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"So far, we've explored superposition, interference, and measurement. Now, let's dive into another profound quantum phenomenon: **entanglement**.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8fdb70d8-229c-4e3b-abdc-ce9f60230e1a\",\n \"metadata\": {},\n \"source\": [\n \"# Chapter 2: Entanglement\\n\",\n \"\\n\",\n \"**Quantum entanglement** is a phenomenon where particles become interconnected, sharing a quantum state that links their properties, no matter how far apart they are. Measuring one particle's state instantly reveals information about the state of the other. Einstein called this “spooky action at a distance”.\\n\",\n \"\\n\",\n \"In 1964, John Bell proposed Bell’s inequality to test if entangled particles behave classically. The CHSH experiment provided a concrete test. Quantum mechanics passed, defying classical expectations [3]. In 1997, quantum teleportation — transferring a quantum state without moving the particle — was achieved using entanglement [4].\\n\",\n \"\\n\",\n \"With Qiskit, let's try the CHSH experiment and quantum teleportation.\\n\",\n \"\\n\",\n \"\\n\",\n \"## 2.1. The Curious Case of Alice, Bob, and the Unbeatable Game - CHSH Game\\n\",\n \"\\n\",\n \"Alice and Bob are far apart and cannot communicate. A Referee gives them a challenge:\\n\",\n \"1. Referee picks two bits (`x`, `y`), sends `x` to Alice, `y` to Bob.\\n\",\n \"2. Alice and Bob instantly reply with their bits (`a`, `b`).\\n\",\n \"3. **Win Condition**: `a XOR b == x AND y`.\\n\",\n \"\\n\",\n \"They want a pre-agreed strategy to maximize their average win rate.\\n\",\n \"\\n\",\n \"**Your Goal**: Implement the *quantum* strategy for the CHSH game using Qiskit, simulate its performance, and compare it to the classical limit to understand the advantage provided by quantum entanglement. \\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"For your in-depth study \\n\",\n \"\\n\",\n \"For a more in-depth explanation and exploration of entanglement in action, including the CHSH game, you can refer to the Qiskit learning resource: [Entanglement in Action](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/introduction).\\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91b4ee20-4f7a-4cdc-ae82-b8626a55f5fd\",\n \"metadata\": {},\n \"source\": [\n \"### The Classical Limit: Why 75% is the Barrier\\n\",\n \"\\n\",\n \"Let's first think classically. Since Alice and Bob can't communicate after receiving `x` and `y`, Alice's output `a` can only depend on `x`, and Bob's `b` only on `y`. They could pre-agree on a strategy, even a random one, but it boils down to local choices.\\n\",\n \"\\n\",\n \"Winning condition means:\\n\",\n \"* Inputs (0,0), (0,1), or (1,0): Win if `a == b`.\\n\",\n \"* Input (1,1): Win if `a != b`.\\n\",\n \"\\n\",\n \"Can you find a fixed strategy (like `a=0, b=0` or `a=x, b=y`) that wins more than 3 out of the 4 possible input cases? Try it! You'll find the maximum average win rate is stubbornly stuck at **75%**, a fundamental limit of local realism.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74227c42-4fd9-4acb-a55a-564b836bdc38\",\n \"metadata\": {},\n \"source\": [\n \"### The Quantum Strategy: Entanglement to the Rescue!\\n\",\n \"\\n\",\n \"What if Alice and Bob prepared something special *before* the game? What if they shared a pair of **entangled qubits**? Imagine entanglement like two \\\"magic\\\" coins that are mysteriously linked. Even miles apart, measuring one instantly influences the *correlations* you'll find when measuring the other. They don't send messages faster than light, but their fates are intertwined.\\n\",\n \"\\n\",\n \"Specifically, they share a [Bell state](https://en.wikipedia.org/wiki/Bell_state): $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt(2)$. If they both measure their qubit in the *same* way, they always get the same result (both 0 or both 1).\\n\",\n \"\\n\",\n \"The Quantum Strategy: Instead of outputting fixed bits, Alice and Bob use their input (`x` or `y`) to decide *how* to measure their shared qubit.\\n\",\n \"1. Shared Resource: Alice holds qubit 0, Bob holds qubit 1 of their entangled pair.\\n\",\n \"2. Measurement Choice (The Clever Part):\\n\",\n \" * Alice (input `x`): Measures along angle 0 (standard Z basis) if `x=0`, or angle π/2 (X basis) if `x=1`.\\n\",\n \" * Bob (input `y`): Measures along angle -π/4 if `y=0`, or angle π/4 if `y=1`, by using `Ry` gate.\\n\",\n \"3. Output: They output the result of their respective measurements as `a` and `b`.\\n\",\n \"\\n\",\n \"These specific angles are crucial! They exploit the unique correlations of the Bell state to maximize their chances of satisfying `a XOR b == x AND y` across all input pairs.\\n\",\n \"\\n\",\n \"Let's build the quantum circuit for this strategy.\\n\",\n \"\\n\",\n \"Alice and Bob share an entangled qubit pair in the Bell state: $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$. If measured the same way, they always get the same result.\\n\",\n \"\\n\",\n \"**Note**\\n\",\n \"\\n\",\n \"For detailed information on the quantum circuit's configuration, its operational flow, or the underlying principles, please refer to [this lesson](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/chsh-game) in the IBM Quantum Learning course, Basics of Quantum Information.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"aefc0aa2-ad6f-4a61-ae6f-6f92b558a489\",\n \"metadata\": {},\n \"source\": [\n \"### Qiskit Implementation: Building the Quantum Circuit\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 4: Quantum Circuit for CHSH Game\\n\",\n \"\\n\",\n \"**Your Goal:** Define a function `create_chsh_circuit(x, y)` by constructing the quantum circuit that Alice and Bob will use for their quantum strategy in the CHSH game. This involves preparing an entangled Bell state and then applying measurement basis rotations based on their respective inputs.\\n\",\n \"\\n\",\n \"**Tasks:**\\n\",\n \"* **Task 1:** Create the Bell state $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$.\\n\",\n \"* **Task 2:** Implement Bob's rotation based on his input `y`.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a174b414-36d6-40f0-a1ac-df3350b5f80e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def create_chsh_circuit(x, y):\\n\",\n \" \\\"\\\"\\\"Builds Qiskit circuit for Alice & Bob's quantum strategy.\\\"\\\"\\\"\\n\",\n \" qc = QuantumCircuit(2, 2, name=f'CHSH_{x}{y}') # 2 qubits, 2 classical bits\\n\",\n \"\\n\",\n \" # ---- TODO : Task 1 ---\\n\",\n \" # Implement the gates to create the Bell state |Φ+> = (|00> + |11>)/sqrt(2).\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \" qc.barrier()\\n\",\n \" # Step 2a: Alice's measurement basis (X if x=1, Z if x=0)\\n\",\n \" if x == 1:\\n\",\n \" qc.h(0) # H for X-basis measurement\\n\",\n \"\\n\",\n \" ## --- TODO: Task 2 ----\\n\",\n \" # Step 2b: Bob's measurement basis\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" # --- End of TODO ---\\n\",\n \" qc.barrier()\\n\",\n \" \\n\",\n \" # Step 3: Measure\\n\",\n \" qc.measure([0, 1], [0, 1]) # q0 to c0 (Alice), q1 to c1 (Bob) -> 'ba' format\\n\",\n \"\\n\",\n \" return qc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"27caf6cf-9497-4c02-b4ca-0f6d55a464dc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab1_ex4(create_chsh_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28c48bd5-eaeb-4360-981d-959a4ac913b6\",\n \"metadata\": {},\n \"source\": [\n \"### Create Circuits for All Input Pairs\\n\",\n \"\\n\",\n \"Now we use the function you created to generate circuits for all four (x,y) scenarios.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7a440c15-4467-42d6-b0a1-80fd9bdd5dd5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"circuits = []\\n\",\n \"input_pairs = []\\n\",\n \"for x_in in [0, 1]:\\n\",\n \" for y_in in [0, 1]:\\n\",\n \" input_pairs.append((x_in, y_in))\\n\",\n \" circuits.append(create_chsh_circuit(x_in, y_in))\\n\",\n \"\\n\",\n \"print(\\\"Quantum circuit for inputs x=1, y=1 (Check your Exercises 1 & 2 implementation):\\\")\\n\",\n \"if len(circuits) == 4:\\n\",\n \" display(circuits[3].draw('mpl')) # (x,y) = (1,1)\\n\",\n \"else:\\n\",\n \" print(\\\"Circuits not generated. Run previous cell after completing Exercises 1 & 2.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b85480d-6172-457c-805c-6f168a86930f\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"### Simulation: Playing the Game Many Times\\n\",\n \"\\n\",\n \"Real quantum computers are noisy and sometimes hard to access. Thankfully, we can simulate their behavior quite accurately for small systems like this one. We'll use Qiskit's `AerSimulator` to run our four circuits many times (\\\"shots\\\") and collect statistics on Alice and Bob's outputs (`a` and `b`).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b2052339-c0ab-4051-8d22-1a77df74d56f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# AerSimulator (if not already defined)\\n\",\n \"# backend = AerSimulator()\\n\",\n \"# Pass manager (if not already defined)\\n\",\n \"# pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\\n\",\n \"\\n\",\n \"SHOTS = 1024\\n\",\n \"\\n\",\n \"print(\\\"Preparing circuits for the simulator...\\\")\\n\",\n \"isa_qc_chsh = pm.run(circuits)\\n\",\n \"\\n\",\n \"sampler_chsh = Sampler(mode=backend) # SamplerV2\\n\",\n \"job_chsh = sampler_chsh.run(isa_qc_chsh, shots=SHOTS)\\n\",\n \"results_chsh = job_chsh.result()\\n\",\n \"\\n\",\n \"# SamplerV2: results_chsh[i].data.c.get_counts() where 'c' is the default name of classical register\\n\",\n \"counts_list = [results_chsh[i].data.c.get_counts() for i in range(len(circuits))]\\n\",\n \"\\n\",\n \"print(\\\"\\\\n--- Simulation Results (Counts) ---\\\")\\n\",\n \"for i, (x, y) in enumerate(input_pairs):\\n\",\n \" print(f\\\"Inputs (x={x}, y={y}):\\\")\\n\",\n \" sorted_counts = dict(sorted(counts_list[i].items()))\\n\",\n \" print(f\\\" Outcomes (ba): {sorted_counts}\\\")\\n\",\n \"\\n\",\n \"print(\\\"\\\\nPlotting results...\\\")\\n\",\n \"display(plot_histogram(counts_list,\\n\",\n \" legend=[f'(x={x}, y={y})' for x, y in input_pairs],\\n\",\n \" title='CHSH Game Outcomes (ba format)'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3b84b66e-9503-4dbe-9023-0d08bbd584fd\",\n \"metadata\": {},\n \"source\": [\n \"### Analysis: Did They Beat the Classical Limit?\\n\",\n \"\\n\",\n \"Now, the moment of truth! We need to analyze the simulation results (`counts_list`) to calculate the average win probability.\\n\",\n \"\\n\",\n \"**Recap:**\\n\",\n \"* **Win Condition:** `a XOR b == x AND y`\\n\",\n \"* **Output Format:** Counts are for `'ba'` strings.\\n\",\n \" * `a XOR b = 0` for outcomes `'00'` (`b=0, a=0`) and `'11'` (`b=1, a=1`).\\n\",\n \" * `a XOR b = 1` for outcomes `'01'` (`b=0, a=1`) and `'10'` (`b=1, a=0`).\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 5: Analyze Circuit for CHSH Game\\n\",\n \"\\n\",\n \"**Your Goal:** Calculate the win probability for Alice and Bob for each input case (`x`, `y`) based on the simulation results, and then determine their overall average win probability using the quantum strategy.\\n\",\n \"\\n\",\n \"**Tasks:**\\n\",\n \"* **Task 1:** Determine the target `a XOR b` value for a win, given `x` and `y`.\\n\",\n \"* **Task 2:** Count shots (`wins_for_this_case`) satisfying the win condition for the current (`x`, `y`).\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"28f7b7ef-612a-4762-a522-d32d539b37f9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"win_probabilities = {}\\n\",\n \"print(\\\"--- Calculating Win Probabilities ---\\\")\\n\",\n \"\\n\",\n \"for i, (x, y) in enumerate(input_pairs):\\n\",\n \" counts = counts_list[i]\\n\",\n \"\\n\",\n \" # ---- TODO : Task 1 ---\\n\",\n \" # Target (a XOR b) value for winning\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" # --- End of TODO --\\n\",\n \"\\n\",\n \" wins_for_this_case = 0\\n\",\n \"\\n\",\n \" # ---- TODO : Task 2 ---\\n\",\n \" # Calculate the total number of shots that satisfy the winning condition determined above. Check the 'target_xor_result'\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" # --- End of TODO --\\n\",\n \"\\n\",\n \" prob = wins_for_this_case / SHOTS if SHOTS > 0 else 0\\n\",\n \" win_probabilities[(x, y)] = prob\\n\",\n \" print(f\\\"Inputs (x={x}, y={y}): Target (a XOR b) = {target_xor_result}. Win Probability = {prob:.4f}\\\")\\n\",\n \"\\n\",\n \"avg_win_prob = sum(win_probabilities.values()) / 4.0\\n\",\n \"P_win_quantum_theory = np.cos(np.pi / 8)**2 # ~0.8536\\n\",\n \"P_win_classical_limit = 0.75\\n\",\n \"\\n\",\n \"print(\\\"\\\\n--- Overall Performance ---\\\")\\n\",\n \"print(f\\\"Experimental Average Win Probability: {avg_win_prob:.4f}\\\")\\n\",\n \"print(f\\\"Theoretical Quantum Win Probability: {P_win_quantum_theory:.4f}\\\")\\n\",\n \"print(f\\\"Classical Limit Win Probability: {P_win_classical_limit:.4f}\\\")\\n\",\n \"\\n\",\n \"if avg_win_prob > P_win_classical_limit + 0.01: # Allow for small simulation variance\\n\",\n \" print(f\\\"\\\\nSuccess! Your result ({avg_win_prob:.4f}) clearly beats the classical 75% limit!\\\")\\n\",\n \" print(f\\\"It's likely close to the theoretical quantum prediction of {P_win_quantum_theory:.4f}.\\\")\\n\",\n \"elif avg_win_prob > P_win_classical_limit - 0.02 : # Could be noise or minor error\\n\",\n \" print(f\\\"\\\\nClose, but no cigar? Your result ({avg_win_prob:.4f}) is around the classical limit ({P_win_classical_limit:.4f}).\\\")\\n\",\n \" print(\\\"Check your solutions for Exercises 1-4 carefully, especially the win counting logic in Ex 4.\\\")\\n\",\n \"else:\\n\",\n \" print(f\\\"\\\\nHmm, the result ({avg_win_prob:.4f}) is unexpectedly low, even below the classical limit.\\\")\\n\",\n \" print(\\\"There might be an error in Exercises 1-4. Please review your circuit and analysis code.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a8842d57-eea6-4791-b5a9-0d4b37219c15\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab1_ex5(counts_list, avg_win_prob)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5ced3f21-6210-46fc-b9c8-e539e88f854e\",\n \"metadata\": {},\n \"source\": [\n \"### Conclusion: The Quantum Edge\\n\",\n \"\\n\",\n \"So, what did we find? If you successfully completed the exercises, your simulation should clearly show that Alice and Bob, using their shared entangled qubits and clever measurements, achieved an average win rate **significantly** higher than the 75% classical limit, likely approaching the theoretical quantum maximum of around **85.4%**.\\n\",\n \"\\n\",\n \"This isn't just a mathematical trick; it reflects how nature works at its most fundamental level. Entanglement allows for correlations between distant particles that are stronger than any classical theory can explain. This \\\"spooky action at a distance\\\" doesn't allow faster-than-light communication, but it underpins many quantum technologies.\\n\",\n \"\\n\",\n \"**Discussion Point:** Based on these results, what does the CHSH game demonstrate about the nature of reality compared to classical intuition? Can you think of any potential applications where these stronger-than-classical correlations might be useful?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"100cdba6-f257-4b0a-b905-380c1876839a\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2. Quantum Teleportation - Sending Secrets with Spooky Action\\n\",\n \"\\n\",\n \"Imagine Alice has a qubit (`q0`) in a specific, delicate quantum state (`|ψ⟩`). She wants to send this exact state to Bob, who is far away. She can't simply physically send the qubit (maybe it's too fragile, or she needs the original particle). Can she somehow transfer *only the state* itself?\\n\",\n \"\\n\",\n \"Classically, this seems impossible. If Alice measures her qubit to see what state it's in, the measurement itself will likely collapse the superposition and destroy the original state.\\n\",\n \"\\n\",\n \"Enter **Quantum Teleportation**! It's a remarkable protocol that uses **quantum entanglement** and **classical communication** to transfer an unknown quantum state from one qubit to another.\\n\",\n \"\\n\",\n \"**Disclaimer:** We're teleporting the *state*, not the physical particle! No people are being beamed up here.\\n\",\n \"\\n\",\n \"**Your Goal:** In this lab, you will implement the quantum teleportation protocol using Qiskit. You'll complete the code to prepare the necessary quantum resources, perform the key steps, and verify that Bob successfully receives Alice's quantum state.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f1dbf7d4-97aa-4fdd-90ed-51f55c74007b\",\n \"metadata\": {},\n \"source\": [\n \"### The Teleportation Protocol: Step-by-Step\\n\",\n \"\\n\",\n \"The protocol requires three qubits and two classical bits:\\n\",\n \"* **`q0`:** Alice's qubit, initially in the state `|ψ⟩` we want to teleport.\\n\",\n \"* **`q1`:** Alice's half of a shared entangled pair.\\n\",\n \"* **`q2`:** Bob's half of the shared entangled pair.\\n\",\n \"* **`c0`, `c1`:** Classical bits to store Alice's measurement results.\\n\",\n \"\\n\",\n \"Here's the plan:\\n\",\n \"1. **(Optional) Prepare Alice's state `|ψ⟩` on `q0`.** (We'll create a specific state like `|+>` for verification).\\n\",\n \"2. **Create entanglement:** Generate a Bell pair between `q1` and `q2`.\\n\",\n \"3. **Alice's operations:** Alice performs a \\\"Bell measurement\\\" on her two qubits (`q0` and `q1`) and stores the classical results in `c0` and `c1`.\\n\",\n \"4. **Classical communication:** Alice sends her two classical bits (`c0`, `c1`) to Bob.\\n\",\n \"5. **Bob's corrections:** Bob applies specific quantum gates (X and/or Z) to his qubit (`q2`), conditioned on the values of `c0` and `c1` he received.\\n\",\n \"\\n\",\n \"If everything is done correctly, Bob's qubit `q2` will end up in the state `|ψ⟩`, the original state of Alice's `q0`!\\n\",\n \"\\n\",\n \"
\\n\",\n \"For your in-depth study \\n\",\n \"\\n\",\n \"For a more in-depth explanation and exploration of quantum teleportation, you can refer to the IBM Quantum Learning resources: [Quantum Teleportation](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/quantum-teleportation) and [Entanglement in Action](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/quantum-teleportation) by John Watrous. These are part of his [Basics of Quantum Information](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information) course.\\n\",\n \"
\\n\",\n \"\\n\",\n \"### Tool: Qiskit Dynamic Circuits\\n\",\n \"\\n\",\n \"For Bob's actions depending on Alice's measurements, we use dynamic circuits. This allows classical information from mid-circuit measurements to influence later quantum operations. This capability is crucial for protocols like teleportation and for creating efficient long-range entanglement on quantum hardware, as explored in recent research (e.g., Bäumer et al., PRX QUANTUM 5, 030339 (2024)).\\n\",\n \"\\n\",\n \"\\n\",\n \"**Reference:**\\n\",\n \"* [Qiskit documentation on dynamic circuits and conditional operations](https://quantum.cloud.ibm.com/docs/guides/classical-feedforward-and-control-flow)\\n\",\n \"* [Efficient Long-Range Entanglement Using Dynamic Circuits](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.030339)\\n\",\n \"\\n\",\n \"**Key Concepts for Teleportation:**\\n\",\n \"\\n\",\n \"1. Mid-Circuit Measurement:\\n\",\n \" * In Qiskit, you can measure a qubit and store the result in a classical bit at any point in the circuit, not just at the end. This is crucial for Alice to measure her qubits (`q0` and `q1`) and obtain classical bits `c0` and `c1`.\\n\",\n \" * The `QuantumCircuit.measure(qubit, classical_bit)` instruction is used.\\n\",\n \"\\n\",\n \"2. Conditional Operations (if_test):\\n\",\n \" * After Alice's measurements, Bob needs to apply corrective gates to his qubit (`q2`) based on the classical bits (`c0`, `c1`) he receives.\\n\",\n \" * Qiskit allows gates to be applied conditionally based on the state of classical bits.\\n\",\n \" * The `QuantumCircuit.if_test((classical_bit, value), true_circuit, false_circuit=None)` method can be used to apply gates only if a certain classical bit has a specific value (0 or 1).\\n\",\n \" * For teleportation, Bob will use these conditional operations:\\n\",\n \" * If `c1` (here `cr_alice_tele[1]`) is 1, apply an X gate to `q2`.\\n\",\n \" * If `c0` (here `cr_alice_tele[0]`) is 1, apply a Z gate to `q2`.\\n\",\n \"\\n\",\n \"These features enable the classical communication aspect of the teleportation protocol to be directly implemented and simulated within a single quantum circuit description.\\n\",\n \"\\n\",\n \"Note: Please remember the sequence of conditional gates! In Quantum Computing $AB != BA$ so the order is matter.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a7505da2-a554-4e2e-9844-70c947059e78\",\n \"metadata\": {},\n \"source\": [\n \"### Building the Teleportation Circuit in Qiskit\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 6: Quantum Teleportation\\n\",\n \"\\n\",\n \"**Your Goal:** Construct the complete quantum circuit for teleporting an unknown quantum state from Alice's message qubit to Bob's qubit, utilizing an entangled Bell pair and mid-circuit measurements with conditional operation.\\n\",\n \"\\n\",\n \"**Tasks:**\\n\",\n \"* **Step 1:** Create the shared entangled Bell pair $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$ between `q1` and `q2`.\\n\",\n \"* **Step 2:** Implement Alice's Bell measurement gates (before actual measurement).\\n\",\n \"* **Step 3:** Implement Bob's conditional correction gates based on Alice's measurement results.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"af207afd-1a82-43c8-8254-6e0b422f613d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define quantum and classical registers\\n\",\n \"qr_tele = QuantumRegister(3, name='q')\\n\",\n \"cr_alice_tele = ClassicalRegister(2, name='c_alice') # For Alice's measurements\\n\",\n \"\\n\",\n \"# For verification with statevector, we don't measure Bob's final qubit in this circuit.\\n\",\n \"# If we were to run on hardware and verify by counts, we'd add a classical bit for Bob.\\n\",\n \"teleport_qc = QuantumCircuit(qr_tele, cr_alice_tele, name='Teleportation')\\n\",\n \"\\n\",\n \"# Prepare Alice's message state |ψ> = |+> on q0\\n\",\n \"teleport_qc.h(qr_tele[0])\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# ---- TODO : Task 1 ---\\n\",\n \"# Step 1: Create Bell pair between q1 (Alice) and q2 (Bob)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- End of TODO --\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# ---- TODO : Task 2 ---\\n\",\n \"# Step 2: Alice's Bell Measurement (gates part))\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- End of TODO --\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# Alice measures her qubits q0 and q1\\n\",\n \"teleport_qc.measure(qr_tele[0], cr_alice_tele[0]) # q0 -> c0\\n\",\n \"teleport_qc.measure(qr_tele[1], cr_alice_tele[1]) # q1 -> c1\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# ---- TODO : Task 3 ---\\n\",\n \"# Step 3: Bob's Conditional Corrections on q2\\n\",\n \"# IMPORTANT: .c_if() on gates like XGate() no longer works directly as in older Qiskit versions.\\n\",\n \"# The recommended method in Qiskit 1.0+ is to use the new `if_test` context manager.\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- End of TODO --\\n\",\n \"\\n\",\n \"print(\\\"Full Teleportation Circuit (Check your Exercises 1, 2, 3):\\\")\\n\",\n \"display(teleport_qc.draw('mpl'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4aa33305-02cd-424e-bd3d-4788e4cfcc71\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab1_ex6(teleport_qc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"57c81d2f-1067-42c7-a476-375d2faf4ed4\",\n \"metadata\": {},\n \"source\": [\n \"### Simulation and Verification\\n\",\n \"\\n\",\n \"How do we verify that the teleportation worked? We can't directly 'see' the state of Bob's qubit after the protocol. However, since we *prepared* Alice's initial state `|ψ⟩` (we chose `|+>`), we can use a special type of simulation to check if Bob's qubit `q2` ended up in that same state.\\n\",\n \"\\n\",\n \"We'll use `AerSimulator` with `save_statevector` to check if Bob's qubit `q2` ends up in Alice's original state (`|+>`). This simulator calculates the final quantum state vector.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 7 - No Grading: Analyze result of Quantum Teleportation\\n\",\n \"\\n\",\n \"**Your Goal:** Verify the successful teleportation of Alice's quantum state to Bob's qubit by simulating the circuit and visualizing the final state of Bob's qubit.\\n\",\n \"\\n\",\n \"**Task:**\\n\",\n \"* Extract the final statevector and use `plot_bloch_multivector` to visualize Bob's qubit (`q2`). Compare to Alice's initial state (`q0`).\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"85f73394-d361-4c0a-afd3-28dc2005c07a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"# Use Statevector Simulator\\n\",\n \"print(\\\"Using statevector simulator...\\\")\\n\",\n \"sv_simulator = AerSimulator(method='statevector') # Explicitly set method for clarity\\n\",\n \"teleport_qc_sv = teleport_qc.copy() # Work with a copy for statevector simulation\\n\",\n \"teleport_qc_sv.save_statevector() # Save statevector at the end\\n\",\n \"\\n\",\n \"print(\\\"Running statevector simulation...\\\")\\n\",\n \"job_sv = sv_simulator.run(teleport_qc_sv) # shots=1 is default for statevector\\n\",\n \"result_sv = job_sv.result()\\n\",\n \"\\n\",\n \"if result_sv.success:\\n\",\n \" print(\\\"Simulation successful.\\\")\\n\",\n \" final_statevector = result_sv.get_statevector()\\n\",\n \" print(\\\"Statevector retrieved successfully.\\\")\\n\",\n \" print(\\\"\\\\nVisualizing final qubit states (q2 should match initial q0 state |+>):\\\")\\n\",\n \" # q0 was |+> (points along +X). After teleportation, q2 should be |+>.\\n\",\n \" # q0 and q1 states are after Alice's measurement, so they'll be collapsed.\\n\",\n \" \\n\",\n \" display(\\\"TODO\\\") #TODO, use plot_bloch_multivector to plot final_state\\n\",\n \" \\n\",\n \"else:\\n\",\n \" print(f\\\"Statevector simulation failed! Status: {result_sv.status}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"be79ba6b-c8d7-44e3-b7fa-c2043ea7cff0\",\n \"metadata\": {},\n \"source\": [\n \"### Interpreting the Results\\n\",\n \"\\n\",\n \"If Exercise 6 was correct, you should see:\\n\",\n \"* `q0` (Alice's original): Random state (collapsed by measurement).\\n\",\n \"* `q1` (Alice's entangled): Random state (collapsed by measurement).\\n\",\n \"* `q2` (Bob's entangled): Bloch sphere vector pointing along the **positive X-axis**. This is the `|+>` state, matching Alice's initial `q0`!\\n\",\n \"\\n\",\n \"**Success!** The state `|ψ⟩ = |+⟩` was teleported.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2c91c647-3843-4574-ad18-c283e16f8d16\",\n \"metadata\": {},\n \"source\": [\n \"### Conclusion: The Magic of Entanglement and Information\\n\",\n \"\\n\",\n \"You have successfully implemented the quantum teleportation protocol!\\n\",\n \"\\n\",\n \"Key Takeaways:\\n\",\n \"* Teleportation transfers an unknown quantum *state* using shared entanglement and classical communication.\\n\",\n \"* It doesn't transfer the physical particle itself.\\n\",\n \"* The original state on Alice's qubit is destroyed (consistent with the No-Cloning Theorem).\\n\",\n \"* Requires classical communication (Alice's measurement results to Bob), so no faster-than-light information transfer.\\n\",\n \"\\n\",\n \"Quantum teleportation is foundational for quantum communication and computation.\\n\",\n \"\\n\",\n \"**Discussion Point**: Why can't Alice just measure `q0` (e.g., in the Z basis then the X basis) and send results to Bob for reconstruction? What's different about teleportation?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f72ca7c2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Check your submission status with the code belowf\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4a1fb2a1\",\n \"metadata\": {},\n \"source\": [\n \"# (Bonus Challenge) Teleportation on Real Quantum Hardware\\n\",\n \"\\n\",\n \"We've seen teleportation work perfectly on a simulator. Now, let's try it on a **real IBM Quantum backend**! This is where the true challenges and excitement of quantum computing lie. Real devices have noise and limited qubit connectivity.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Dynamic Circuits are being upgraded!\\n\",\n \"\\n\",\n \"There is currently an update going on for dynamic circuits on the real hardware, and you are one of the first people to be able to test the new version! However, this early access comes with some caveats: \\n\",\n \"* 1. We need to set some additional lines of codes for them to activate the early access (see below)\\n\",\n \"* 2. This early access doesn't work when using Session or Batch with a with... (e.g. with Batch(...)). You can, however, pass one directly to the primitive (e.g. Sampler(mode=batch)).\\n\",\n \"* 3. You cannot use a ClassicalRegister of more than 32 bits in an if_test condition. You can, however, use multiple ClassicalRegisters, each of <=32 bits.\\n\",\n \"* 4. No nested conditional.\\n\",\n \"* 5. Cannot have measure or reset inside a conditional block.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Two steps are needed to use dynamic circuits on the real hardware:\\n\",\n \"\\n\",\n \"\\n\",\n \"Step 1 you need to add the instruction to the hardware you use:\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" # (1) Patch the backend target with IfElseOp\\n\",\n \" from qiskit.circuit import IfElseOp\\n\",\n \" backend.target.add_instruction(IfElseOp, name=\\\"if_else\\\")\\n\",\n \" \\n\",\n \"```\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Step 2 when submitting the jobs you need to enable the experimental obtions:\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" #(2) Submit the job with the experimental option\\n\",\n \" sampler.options.experimental = {\\\"execution_path\\\" : \\\"gen3-experimental\\\"}\\n\",\n \" \\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f65c02e-5def-46aa-9546-ad9026669b4b\",\n \"metadata\": {},\n \"source\": [\n \"### Dynamic Circuits: A Key for Real Hardware\\n\",\n \"\\n\",\n \"The teleportation protocol inherently requires **dynamic circuits**: Alice measures her qubits, and Bob *classically communicates* these results to decide which correction gates to apply to his qubit. Qiskit enables these \\\"mid-circuit measurements\\\" and \\\"classical feed-forward\\\" operations.\\n\",\n \"\\n\",\n \"Recent research, such as the work by Bäumer et al. in \\\"Efficient Long-Range Entanglement Using Dynamic Circuits\\\" (PRX QUANTUM 5, 030339 (2024)), demonstrates the power of dynamic circuits. They show that for tasks like CNOT gate teleportation and preparing GHZ states over long distances, dynamic circuits can outperform their purely unitary counterparts on large-scale devices by keeping the quantum circuit depth constant regardless of the distance.\\n\",\n \"\\n\",\n \"For our teleportation protocol, dynamic circuits allow Alice's measurement outcomes to directly control Bob's gate applications in real time on the quantum processor. Accessing IBM Quantum backends is done through the **IBM Cloud**. You'll need an IBM Cloud account with Quantum services enabled.\\n\",\n \"\\n\",\n \"### The Challenge: – How Far Can You Send It?\\n\",\n \"\\n\",\n \"**Your Goal:** Successfully teleport a quantum state (e.g., $|+\\\\rangle$) from 0th qubit (Alice's message) to another qubit (Bob's) on a real IBM Quantum backend, ideally choosing qubits that are not directly connected on the chip by using Qiskit Dynamic Circuits. Observe and report the fidelity of the teleportation. Try to improve it!\\n\",\n \"\\n\",\n \"**Basic steps for a real backend run:**\\n\",\n \"\\n\",\n \"You can find more detailed explanations in lab0, but here we give you an abstracted intro.\\n\",\n \"\\n\",\n \"1. Set up IBM Quantum Access (via IBM Cloud):\\n\",\n \" * If you don't have one, create an IBM Cloud account and enable access to IBM Quantum services.\\n\",\n \" * Get your API token from the IBM Quantum dashboard on IBM Cloud.\\n\",\n \" * Use `QiskitRuntimeService` to save your account and list available backends.\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \" QiskitRuntimeService.save_account(channel=\\\"ibm_quantum_platform\\\", token=\\\"YOUR_IBM_CLOUD_API_TOKEN\\\", instance=\\\"YOUR_CRN_INSTANCE_ID_OR_CLOUD_INSTANCE_NAME\\\", overwrite=True)\\n\",\n \" service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \" print(service.backends())\\n\",\n \" ```\\n\",\n \" \\n\",\n \"2. Choose a Backend:\\n\",\n \" * Select an available backend from the list. Selecting the [least_busy](https://quantum.cloud.ibm.com/docs/api/qiskit-ibm-runtime/qiskit-runtime-service) QPU is also a good choice.\\n\",\n \" * Examine its coupling map to find suitable qubits. For this challenge, try to pick qubits for Alice's message (`q_A_msg`) and Bob's final qubit (`q_B_ent`) that are \\\"far apart\\\" or not directly connected. Alice's entangled qubit (`q_A_ent`) should ideally be connectable to both `q_A_msg` (for her Bell measurement) and `q_B_ent` (for creating the initial Bell pair).\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" #backend_name = \\\"ibm_your_chosen_backend\\\" # e.g., \\\"ibm_brisbane\\\" or a simulator like \\\"ibmq_qasm_simulator\\\" to test flow\\n\",\n \" backend = service.least_busy()\\n\",\n \" # print(f\\\"Coupling map: {backend.configuration().coupling_map}\\\")\\n\",\n \" # print(f\\\"Number of qubits: {backend.configuration().n_qubits}\\\")\\n\",\n \" # from qiskit.visualization import plot_gate_map # if needed\\n\",\n \" # display(plot_gate_map(backend)) # Visualizes connectivity\\n\",\n \" \\n\",\n \" ```\\n\",\n \"You can keep most of the code the same. To use a real quantum processing unit (QPU) instead of a simulator, simply pass the real backend object to the `Sampler`. However, it's crucial to transpile your circuit using a `PassManager`, especially when targeting actual quantum hardware.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Tips\\n\",\n \"\\n\",\n \"* Technical Reference for Long-Range Entanglement: For a deeper technical dive into creating entanglement over distances on IBM hardware, including techniques that might inspire your qubit selection or circuit optimization, refer to the tutorial [Efficiently generating long-range entanglement](https://quantum.cloud.ibm.com/docs/tutorials/long-range-entanglement).\\n\",\n \" \\n\",\n \"* Simulating Larger Circuits (20+ Qubits):\\n\",\n \" * If you're designing or testing teleportation schemes that scale to 20 qubits or more for the total circuit size (including Alice's message, her ancilla, Bob's qubit, and any intermediary qubits for routing or advanced teleportation schemes), simulating the full statevector can become computationally intensive.\\n\",\n \" * For such larger circuits, if your teleportation circuit (or components of it, like Bell state creation and Bell measurements) can be constructed using only **Clifford** gates (H, S, X, Y, Z, CNOT, SWAP, and their combinations), you can use a more efficient simulator.\\n\",\n \" * Recommendation: Use `AerSimulator(method=\\\"stabilizer\\\")`. The stabilizer simulator is optimized for Clifford circuits and can handle much larger qubit counts efficiently.\\n\",\n \"
\\n\",\n \"\\n\",\n \"Good luck pushing the limits of quantum state transfer! Share your try and result with participants and discuss how you can send qubits \\\"farther\\\".\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eee76bb9-11ea-45d3-8cd5-853b140e7e9f\",\n \"metadata\": {},\n \"source\": [\n \"## References\\n\",\n \"\\n\",\n \"1. Young, Thomas (1804) I. The Bakerian Lecture. Experiments and calculations relative to physical optics. Phil. Trans. R. Soc. 941–16.\\n\",\n \"2. Fage, Arthur and Johansen, F. C. (1927). On the flow of air behind an inclined flat plate of infinite span. Proc. R. Soc. Lond. A116 170–197.\\n\",\n \"3. Clauser, J. F., Horne, M. A., Shimony, A., & Holt, R. A. (1969). Proposed Experiment to Test Local Hidden-Variable Theories. Phys. Rev. Lett., 23(15), 880–884.\\n\",\n \"4. Bouwmeester, D., Pan, J.-W., Mattle, K., Eibl, M., Weinfurter, H., & Zeilinger, A. (1997). Experimental quantum teleportation. Nature, 390(6660), 575–579.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae55945d-d627-42bd-8ce6-6f39b6c6d973\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Sophy Shin, James Weaver\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Jessie Yu, Junye Huang, Borja Peropadre \\n\",\n \"\\n\",\n \"**Reviewed by:** Meltem Tolunay, Abby Cross\\n\",\n \"\\n\",\n \"**Version:** 1.1.1\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.11\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-1/lab1_hindi.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4b5d9b98-ac5b-4121-915a-893bd64960f3\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 1: मशहूर प्रयोगों को घर पर दोहराना!\\n\",\n \"\\n\",\n \"# 0. सेटअप: अपने उपकरण एकत्र करना\\n\",\n \"\\n\",\n \"जैसे किसी भी अच्छे प्रयोग की शुरुआत उपकरणों की तैयारी से होती है, वैसे ही हमें भी पहले अपने टूल्स तैयार करने होंगे।\\n\",\n \"हमारे केस में, इसका मतलब है — Python लाइब्रेरीज़, विशेष रूप से Qiskit, जो कि हमारा क्वांटम कंप्यूटिंग टूलकिट है।\\n\",\n \"\\n\",\n \"अगर आपने Lab 0 पूरा कर लिया है, तो आप पहले से ही तैयार हैं। यदि नहीं, तो आप नीचे दिए गए सेल को अनकमेंट कर सकते हैं — यह Grader को इंस्टॉल करेगा, जो कि Qiskit v2.x और समर स्कूल के लिए आवश्यक सभी लाइब्रेरीज़ को इंस्टॉल कर देगा।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"519fb94e-18f2-4ade-a40f-58c1078424e5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0e8bb120\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3fee413b\",\n \"metadata\": {},\n \"source\": [\n \"आपके पास Qiskit का संस्करण `>=2.0.0` और Grader का संस्करण `>=0.22.9` होना चाहिए। यदि आपको इससे कम संस्करण दिखाई देता है, \\n\",\n \"तो आपको अपना कर्नेल (kernel) रीस्टार्ट करना होगा और Grader को फिर से इंस्टॉल करना होगा। साथ ही यह सुनिश्चित करें कि आपने Lab 0 के अनुसार सभी सेटअप ठीक से किए हैं, और नीचे दिए गए सेल से उसे टेस्ट करें।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"53e15069\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"51102304\",\n \"metadata\": {},\n \"source\": [\n \"## इम्पोर्ट्स (Imports)\\n\",\n \"\\n\",\n \"नीचे दिया गया सेल आवश्यक लाइब्रेरीज़ को इम्पोर्ट करता है और आपकी Qiskit का संस्करण (version) प्रिंट करता है।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8ae038b9-2181-4c10-8018-833b52ff17a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Essential libraries\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import ipywidgets as widgets\\n\",\n \"from IPython.display import display\\n\",\n \"from PIL import Image\\n\",\n \"import io\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.circuit import Parameter\\n\",\n \"from qiskit.visualization import plot_histogram, plot_distribution\\n\",\n \"from qiskit_ibm_runtime import Options, Session, SamplerV2 as Sampler\\n\",\n \"from qiskit.result import marginal_distribution\\n\",\n \"\\n\",\n \"from qiskit.transpiler import generate_preset_pass_manager\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab1_ex1_1, \\n\",\n \" grade_lab1_ex1_2, \\n\",\n \" grade_lab1_ex1_3, \\n\",\n \" grade_lab1_ex1_4, \\n\",\n \" grade_lab1_ex2, \\n\",\n \" grade_lab1_ex3,\\n\",\n \" grade_lab1_ex4,\\n\",\n \" grade_lab1_ex5,\\n\",\n \" grade_lab1_ex6\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"39f36676-bdd1-4ba2-8486-2b4796766eea\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# विषय-सूची (Table of Contents)\\n\",\n \"\\n\",\n \"1. सुपरपोज़िशन, इंटरफेरेंस और मापन (Measurement)\\n\",\n \"\\n\",\n \" A. डबल-स्लिट प्रयोग – सुपरपोज़िशन और इंटरफेरेंस \\n\",\n \" \\n\",\n \" B. श्रॉडिंगर की बिल्ली और डबल-स्लिट पर दोबारा नज़र\\n\",\n \"\\n\",\n \"2. एन्टैंगलमेंट (Entanglement)\\n\",\n \"\\n\",\n \" A. ऐलिस, बॉब और एक अद्भुत खेल का रहस्य – CHSH गेम \\n\",\n \" \\n\",\n \" B. क्वांटम टेलीपोर्टेशन – रहस्यों को भेजना डरावनी क्रिया के साथ \\n\",\n \" a. सिम्युलेटर पर टेलीपोर्टेशन
\\n\",\n \"3. (बोनस चुनौती) वास्तविक क्वांटम हार्डवेयर पर टेलीपोर्टेशन\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d36937e1-9cc0-4c1b-8240-f87504825196\",\n \"metadata\": {},\n \"source\": [\n \"QGSS 2025 की पहली लैब में आपका स्वागत है, जो कि क्वांटम का अंतर्राष्ट्रीय वर्ष है! इस पहली लैब में, हम क्वांटम दुनिया की चार सबसे महत्वपूर्ण अवधारणाओं को समझेंगे — सुपरपोज़िशन (Superposition), इंटरफेरेंस (Interference), मापन (Measurement) और एन्टैंगलमेंट (Entanglement) — वो भी Qiskit के ज़रिए हाथों-हाथ प्रयोग करके।\\n\",\n \"\\n\",\n \"# अध्याय 1: सुपरपोज़िशन, इंटरफेरेंस और मापन\\n\",\n \"\\n\",\n \"इस रोमांचक यात्रा की शुरुआत हम करेंगे सुपरपोज़िशन, इंटरफेरेंस, और क्वांटम मापन को समझकर —\\n\",\n \"वह भी प्रसिद्ध डबल-स्लिट प्रयोग के ज़रिए।\\n\",\n \"\\n\",\n \"**सुपरपोज़िशन** का मतलब है कि एक क्वांटम कण — जैसे कि इलेक्ट्रॉन या फोटॉन — एक साथ कई अवस्थाओं में रह सकता है।\\n\",\n \"जैसे कि वह एक ही समय में दो जगह पर हो, या उसके पास दो अलग-अलग उर्जाएँ हों।\\n\",\n \"इसे ऐसे समझिए जैसे आप एक सिक्का उछालें और वह सिर **और पूँछ दोनों** एक साथ हो… जब तक कि आप देख न लें!\\n\",\n \"\\n\",\n \"**इंटरफेरेंस** दर्शाता है कि जब ये संभावित क्वांटम अवस्थाएँ, जो तरंगों की तरह बर्ताव करती हैं, एक-दूसरे से टकराती हैं तो क्या होता है।\\n\",\n \"अगर ये तरंगें एक ही फेज़ में हों, तो वे एक-दूसरे को मज़बूत करती हैं (constructive interference)।\\n\",\n \"और अगर वे फेज़ से बाहर हों, तो वे एक-दूसरे को कमजोर या रद्द भी कर सकती हैं (destructive interference)।\\n\",\n \"\\n\",\n \"इन विचारों को दर्शाने वाला सबसे प्रसिद्ध प्रयोग है — डबल-स्लिट प्रयोग,\\n\",\n \"जिसे पहली बार थॉमस यंग ने 1801 में किया था और बाद में इसे इलेक्ट्रॉनों जैसे व्यक्तिगत कणों के साथ दोहराया गया।\\n\",\n \"\\n\",\n \"जब कोई नहीं देख रहा होता कि कण किस स्लिट से गया,\\n\",\n \"तब परिणाम होता है एक खूबसूरत इंटरफेरेंस पैटर्न, जैसे पानी की तरंगें एक-दूसरे से टकरा रही हों।\\n\",\n \"लेकिन जैसे ही आप यह मापते हैं कि कण किस स्लिट से गया —\\n\",\n \"वह पैटर्न गायब हो जाता है।\\n\",\n \"जैसे कण को पता चल गया हो कि कोई उसे देख रहा है!\\n\",\n \"\\n\",\n \"इस अजीब विचार को और आगे बढ़ाया एर्विन श्रॉडिंगर ने 1935 में, अपने प्रसिद्ध सोच-प्रयोग\\n\",\n \"**श्रॉडिंगर की बिल्ली** (Schrödinger’s Cat) के ज़रिए।\\n\",\n \"इसमें बिल्ली एक साथ जीवित और मृत मानी जाती है — जब तक कि कोई डिब्बा खोल कर देख न ले।\\n\",\n \"क्वांटम मापन केवल देखता नहीं है, बल्कि वास्तविकता को आकार देता है।\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"309c6569-6875-447e-a514-cc049e7a37f0\",\n \"metadata\": {},\n \"source\": [\n \"## स्लिट्स के पार: जहाँ से क्वांटम की अजीब दुनिया शुरू होती है\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \" विकिपीडिया से चित्र\\n\",\n \"
\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3d9deeff-2450-4ead-b78a-5533addb84a7\",\n \"metadata\": {},\n \"source\": [\n \"अपने शोध पत्र \\\"Experiments and calculations relative to physical optics\\\" (1803) में, थॉमस यंग ने इस उद्धरण में अपने प्रयोगों के बारे में बताया है:\\n\",\n \"> मैंने खिड़की के शटर में एक छोटा सा छेद किया, और उसे मोटे कागज़ के एक टुकड़े से ढक दिया, जिसमें मैंने एक बारीक सुई से छेद किया। अवलोकन में अधिक सुविधा के लिए, मैंने खिड़की के बाहर एक छोटा दर्पण इस तरह से रखा कि वह सूर्य के प्रकाश को लगभग क्षैतिज दिशा में सामने की दीवार पर परावर्तित करे, और विकीर्ण प्रकाश की शंकु को एक मेज़ के ऊपर से गुजार सके, जिस पर कार्ड पेपर की कई छोटी स्क्रीनें रखी थीं। [1](https://royalsocietypublishing.org/doi/pdf/10.1098/rstl.1804.0001)\\n\",\n \"\\n\",\n \"इस प्रयोग के माध्यम से, यंग ने प्रकाश द्वारा सुंदर फ्रिंज पैटर्न देखे और बताया कि यह दो किरणों के पथ की लंबाई में अंतर के कारण होता है।\\n\",\n \"\\n\",\n \"बाद में जॉर्ज पैगेट थॉमसन ने इसी तरह का प्रयोग इलेक्ट्रॉनों के साथ दोहराया (इलेक्ट्रॉन विवर्तन प्रयोग) और यह दिखाया कि इलेक्ट्रॉन जैसे कण भी तरंगों जैसा व्यवहार करते हैं, जैसा कि उन्होंने अपने 1927 के शोध पत्र में दर्शाया। [2](https://royalsocietypublishing.org/doi/10.1098/rspa.1927.0130) बाद में 1965 में, रिचर्ड फेनमैन ने अपने प्रसिद्ध \\\"Lectures on Physics, Vol. 3, Chapter 1\\\" में डबल-स्लिट प्रयोग को समझाया, और बताया कि यह क्वांटम यांत्रिकी के सुपरपोजीशन और इंटरफेरेंस जैसे सिद्धांतों से समझा जा सकता है।\\n\",\n \"\\n\",\n \"अब हम इस अद्भुत क्वांटम घटना को Qiskit की मदद से दोहरा सकते हैं, वह भी बिना किसी जटिल भौतिक उपकरण के। आइए देखें कि सुपरपोजीशन और इंटरफेरेंस को क्वांटम सर्किट में कैसे लागू किया जाता है।\\n\",\n \"\\n\",\n \"पहला कदम है भौतिक डबल-स्लिट प्रयोग को एक क्वांटम सर्किट में मैप करना, और यही आपका पहला अभ्यास है।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f79b538f-82c1-4297-a550-4360e3ab0082\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"अभ्यास 1: डबल-स्लिट प्रयोग के लिए एक क्वांटम सर्किट बनाएं\\n\",\n \"\\n\",\n \"- अभ्यास 1-1: एक स्लिट बनाएं\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** एक ऐसा क्वांटम सर्किट बनाना है जो एक कण (क्यूबिट) के दो स्लिटों से गुजरने की प्रक्रिया को मॉडल करे, जिसमें क्यूबिट $|0\\\\rangle$ (निचला स्लिट) और $|1\\\\rangle$ (ऊपरी स्लिट) की सुपरपोजीशन में हो।\\n\",\n \"\\n\",\n \"\\n\",\n \"मान लेते हैं कि ऊपरी स्लिट को क्यूबिट की \\n\",\n \"$|1\\\\rangle$ स्थिति और निचले स्लिट को $|0\\\\rangle$ स्थिति के रूप में परिभाषित किया गया है। ऐसा क्वांटम सर्किट लागू करें जहाँ एक क्यूबिट, जो शुरुआत में $|0\\\\rangle$ स्थिति में है, स्लिटों से गुजरते हुए $|0\\\\rangle$ और $|1\\\\rangle$ की सुपरपोजीशन में आ जाए। इसके लिए [Hadamard gate](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.HGate) का उपयोग करें। अपने सर्किट को ड्रॉ करें।\\n\",\n \"\\n\",\n \"**सुझाव**: माप परिणामों की व्याख्या करते समय स्पष्टता के लिए `QuantumRegister` (e.g., `name='q'`) और `ClassicalRegister` (e.g., `name='c_screen'`) को विशिष्ट नाम देना उपयोगी हो सकता है।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"884333a6-3489-4520-912c-99d337eb00d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\n\",\n \"\\n\",\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit = QuantumCircuit(qr, cr)\\n\",\n \"# यहां अपना कोड लिखें\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# यहां कोड समाप्त होता है\\n\",\n \"double_slit.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f5d3c41c-05fc-46ff-a41e-f7de68e96275\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# निम्नलिखित कोड का उपयोग करके अपना उत्तर सबमिट करें\\n\",\n \"grade_lab1_ex1_1(double_slit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e92915b5-7396-4d04-b0da-05b84dd7e8c3\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"अभ्यास 1: डबल-स्लिट प्रयोग के लिए एक क्वांटम सर्किट बनाएं\\n\",\n \"\\n\",\n \"- अभ्यास 1-2: एक स्क्रीन बनाएं\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** पिछले सर्किट को इस प्रकार विस्तारित करना है कि यह उस स्क्रीन का मॉडल तैयार करे जहाँ इंटरफेरेंस होता है। विशेष रूप से, उस स्क्रीन के केंद्र को लागू करें जहाँ कोई फेज़ अंतर नहीं होता है, और फिर क्यूबिट को मापें।\\n\",\n \"\\n\",\n \"अब स्क्रीन को लागू करें जहाँ दो सुपरपोज़्ड क्वांटम अवस्थाएँ इंटरफेरेंस पैटर्न बनाती हैं, इसके लिए एक गेट जोड़ें। सबसे पहले, स्क्रीन के केंद्र को लागू करें, जहाँ दोनों किरणें बिना किसी फेज़ अंतर के मिलती हैं (जिससे $|0\\\\rangle$ अवस्था प्राप्त होती है)। फिर `qr` से क्यूबिट को मापें और परिणाम को `c_screen` में संग्रहित करें।\\n\",\n \"\\n\",\n \"**संकेत**: आप फिर से Hadamard गेट का उपयोग कर सकते हैं।\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1828c3c0-7bb6-451d-891a-89f56835c6bc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# यहां अपना कोड लिखें\\n\",\n \"\\n\",\n \"# यहां कोड समाप्त होता है\\n\",\n \"double_slit.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1fcf565a-b547-43fb-8492-c327a9ff7a10\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# निम्नलिखित कोड का उपयोग करके अपना उत्तर सबमिट करें\\n\",\n \"grade_lab1_ex1_2(double_slit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88cc5027-9435-4eaf-a3c3-0e391cc997d3\",\n \"metadata\": {},\n \"source\": [\n \"आइए निष्पादन परिणाम की जाँच करें। मान लेते हैं कि स्क्रीन $|0\\\\rangle$ स्थिति में क्यूबिट का पता लगाती है, निम्नलिखित कोड सर्किट को एक आदर्श सिम्युलेटर पर चलाता है। 1 की संभावना का अर्थ है 100%।\\n\",\n \"\\n\",\n \"
\\n\",\n \"SamplerV2 के साथ माप परिणाम प्राप्त करने पर एक टिप्पणी \\n\",\n \"\\n\",\n \"Qiskit में *SamplerV2* का उपयोग करते समय, आप माप के परिणाम (measurement counts) कैसे प्राप्त करेंगे यह इस बात पर निर्भर करता है कि आपने अपने classical registers और माप को कैसे सेट किया है:\\n\",\n \"\\n\",\n \"1. *QuantumCircuit* में नामित Classical Register:\\n\",\n \" यदि आप अपने सर्किट को एक नामित classical register के साथ परिभाषित करते हैं, e.g., *cr = ClassicalRegister(1, name='my_results')* और *qc = QuantumCircuit(qr, cr)*, और फिर इसे मापते हैं जैसे *qc.measure(qr[0], cr[0])*, तो आप counts इस नाम का उपयोग करके प्राप्त करेंगे:\\n\",\n \"\\n\",\n \" \\n\",\n \" ```python\\n\",\n \" # result = job.result()\\n\",\n \" # counts = result[0].data.my_results.get_counts()\\n\",\n \" ```\\n\",\n \" \\n\",\n \" हमारे डबल-स्लिट सर्किट में, हमने *cr = ClassicalRegister(1, name='c_screen')* का उपयोग किया है, इसलिए हम *result[0].data.c_screen.get_counts()* का उपयोग करेंगे।\\n\",\n \"\\n\",\n \"3. measure_all():\\n\",\n \" यदि आप *qc.measure_all()* का उपयोग करते हैं, तो Qiskit स्वचालित रूप से classical बिट्स जोड़ता है और आउटपुट डेटा फ़ील्ड का नाम `meas` रखता है:\\n\",\n \"\\n\",\n \" \\n\",\n \" ```python\\n\",\n \" # qc.measure_all()\\n\",\n \" # ...\\n\",\n \" # counts = result[0].data.meas.get_counts()\\n\",\n \" ```\\n\",\n \"\\n\",\n \"5. अप्रत्यक्ष Classical बिट्स (*QuantumCircuit* में नामित register नहीं):\\n\",\n \" यदि आप ऐसा सर्किट बनाते हैं जैसे *qc = QuantumCircuit(1, 1)* (1 qubit, 1 classical bit) or *qc = QuantumCircuit(QuantumRegister(1), ClassicalRegister(1))* बिना classical register को कोई नाम दिए, और फिर *qc.measure(0, 0)*, का उपयोग करते हैं, तो *SamplerV2* परिणामों को एक डिफ़ॉल्ट नाम (अक्सर *c*, या classical बिट इंडेक्स के आधार पर जैसे *c0*) के अंतर्गत संग्रहित कर सकता है।\\n\",\n \" उदाहरण के लिए, *qc = QuantumCircuit(1,1)* और *qc.measure(0,0)* के लिए, आउटपुट इस तरह से एक्सेस किया जा सकता है:\\n\",\n \"\\n\",\n \" ```python \\n\",\n \" # counts = result[0].data.c0.get_counts()\\n\",\n \" ``` \\n\",\n \" यदि सिंगल बिट का नाम c0 है, तो इंडेक्स भिन्न हो सकता है। आप अपने सर्किट को प्लॉट करते समय वास्तविक इंडेक्स देख सकते हैं।\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d9d6d49e-19bc-482c-a3e4-049a986323f1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# use simulator\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"# make quantum circuit compatible to the backend\\n\",\n \"pm = generate_preset_pass_manager(backend = backend, optimization_level=3)\\n\",\n \"qc_isa = pm.run(double_slit)\\n\",\n \"\\n\",\n \"# run and get counts\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots = 1000).result()[0].data.c_screen.get_counts()\\n\",\n \"\\n\",\n \"plot_distribution(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9d9d8b9d-6db1-47b0-a7b3-46d78a0c51cb\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"अभ्यास 1: डबल-स्लिट प्रयोग के लिए एक क्वांटम सर्किट बनाएं\\n\",\n \"\\n\",\n \"- अभ्यास 1-3: एक अंतर बनाएं\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** डबल-स्लिट सर्किट को संशोधित करें ताकि दोनों मार्गों (स्लिट्स) के बीच एक फेज़ अंतर (phase difference) उत्पन्न किया जा सके और स्क्रीन पर माप के परिणाम पर इसका प्रभाव देखा जा सके।\\n\",\n \"\\n\",\n \"अब आइए स्क्रीन का एक और भाग लागू करें। दो बीमों के बीच पथ का अंतर एक क्वांटम स्थिति $|0\\\\rangle$ और $|1\\\\rangle$ के बीच फेज़ अंतर के रूप में लागू किया जाता है। सुपरपोज़्ड क्वांटम स्थिति की केवल $|1\\\\rangle$ स्थिति पर $\\\\pi/2$ का फेज़ लागू करें और माप के परिणामों को देखें। (संकेत: एक ऐसा गेट उपयोग करें जो केवल $|1\\\\rangle$ स्थिति पर फेज़ लागू करता है। [P gate](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.PhaseGate) और [S gate](https://quantum.cloud.ibm.com/docs//api/qiskit/qiskit.circuit.library.SGate) इसके प्रतिनिधि उदाहरण हैं।)\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"50a651fe-236a-4c64-ad11-f00200c1cedf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit_with_difference = QuantumCircuit(qr, cr)\\n\",\n \"double_slit_with_difference.h(0)\\n\",\n \"\\n\",\n \"# यहां अपना कोड लिखें\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# यहां कोड समाप्त होता है\\n\",\n \"\\n\",\n \"double_slit_with_difference.h(0)\\n\",\n \"double_slit_with_difference.measure(qr, cr)\\n\",\n \"double_slit_with_difference.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"810339b8-ab70-4aa8-b89c-220e8a7521f5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# निम्नलिखित कोड का उपयोग करके अपना उत्तर सबमिट करें\\n\",\n \"grade_lab1_ex1_3(double_slit_with_difference)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c4e217cf-065f-45ee-8d7c-3e8c94f7fede\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qc_isa = pm.run(double_slit_with_difference)\\n\",\n \"\\n\",\n \"#run and get counts\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots=10000).result()[0].data.c_screen.get_counts()\\n\",\n \"plot_distribution(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4a08020a-6caa-4415-bec0-b5e4f53471ad\",\n \"metadata\": {},\n \"source\": [\n \"जैसा कि आप देख सकते हैं, $|0\\\\rangle$ को मापने की संभावना लगभग 0.5 (50%) तक घट गई है, जो स्क्रीन पर इस बिंदु पर चमक में कमी को दर्शाती है।\\n\",\n \" \\n\",\n \"\\n\",\n \"आगे बढ़ने से पहले, आइए इस प्रक्रिया का एक सरल गणितीय प्रदर्शन करें, जिसमें सुपरपोज़िशन, इंटरफेरेंस और एक क्वांटम अवस्था को मापने की संभावना शामिल है। यदि आप इस अवधारणा से पहले से ही परिचित हैं, तो आप इस भाग को छोड़ सकते हैं।\\n\",\n \"\\n\",\n \"\\n\",\n \"
सुपरपोज़िशन, इंटरफेरेंस और मापन की संभावना
\\n\",\n \"\\n\",\n \"\\n\",\n \"हमारे क्वांटम सर्किट के डबल-स्लिट प्रयोग में, हमने हेडामार्ड (H) गेट्स और एक फेज़ गेट (P) का अनुक्रम इस्तेमाल किया। आइए प्रत्येक चरण पर क्वांटम स्थिति का गणितीय प्रतिनिधित्व देखें और सुपरपोज़िशन, इंटरफेरेंस और मापन की संभावना का विश्लेषण करें।\\n\",\n \"\\n\",\n \"**1. प्रारंभिक स्थिति (Initialization):**\\n\",\n \"\\n\",\n \"हम एक सिंगल क्यूबिट के साथ शुरू करते हैं जो $|0\\\\rangle$ स्थिति में होता है, जो यह दर्शाता है कि इलेक्ट्रॉन स्लिट्स से पहले स्रोत पर है:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_0\\\\rangle = |0\\\\rangle = \\\\begin{pmatrix} 1 \\\\\\\\ 0 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"यहाँ हम मानते हैं कि $ |\\\\psi_1\\\\rangle$ एक और क्वांटम स्थिति है जो ऊपरी स्लिट से गुजर सकती है:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_1\\\\rangle = |1\\\\rangle = \\\\begin{pmatrix} 0 \\\\\\\\ 1 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"**2. सुपरपोज़िशन (पहला हेडामार्ड गेट):**\\n\",\n \"\\n\",\n \"पहला हेडामार्ड गेट (H) क्यूबिट पर लागू किया जाता है। यह $|0\\\\rangle$ और $|1\\\\rangle$ की सुपरपोज़िशन बनाता है:\\n\",\n \"\\n\",\n \"$$ H = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"H को $|\\\\psi_0\\\\rangle$ पर लागू करें:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_1\\\\rangle = H |\\\\psi_0\\\\rangle = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} \\\\begin{pmatrix} 1 \\\\\\\\ 0 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ 1 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} (|0\\\\rangle + |1\\\\rangle) $$\\n\",\n \"\\n\",\n \"यह अवस्था $|\\\\psi_1\\\\rangle$ दर्शाती है कि क्यूबिट $|0\\\\rangle$ अवस्था (जो एक स्लिट को दर्शाती है) और $|1\\\\rangle$ अवस्था (जो दूसरे स्लिट को दर्शाती है) की समान सुपरपोज़िशन में है।\\n\",\n \"\\n\",\n \"**3. फेज़ शिफ्ट (P Gate):**\\n\",\n \"\\n\",\n \"फेज गेट (P) सुपरपोज़िशन की $|0\\\\rangle$ और $|1\\\\rangle$ अवस्थाओं के बीच एक सापेक्ष फेज $\\\\phi$ जोड़ता है। यह फेज अंतर उस पथ अंतर के अनुरूप होता है जिसे दो स्लिट्स से गुजरती इलेक्ट्रॉन तरंगें भौतिक प्रयोग में अनुभव करती हैं। P गेट को इस प्रकार परिभाषित किया गया है:\\n\",\n \"\\n\",\n \"$$ P(\\\\phi) = \\\\begin{pmatrix} 1 & 0 \\\\\\\\ 0 & e^{i\\\\phi} \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"अब P($\\\\phi$) को $|\\\\psi_1\\\\rangle$ पर लागू करते हैं:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_2\\\\rangle = P(\\\\phi) |\\\\psi_1\\\\rangle = \\\\begin{pmatrix} 1 & 0 \\\\\\\\ 0 & e^{i\\\\phi} \\\\end{pmatrix} \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ 1 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ e^{i\\\\phi} \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} (|0\\\\rangle + e^{i\\\\phi}|1\\\\rangle) $$\\n\",\n \"\\n\",\n \"स्थिति $|\\\\psi_2\\\\rangle$ अब सापेक्ष फेज की जानकारी को समाहित करती है, जो कि इंटरफेरेंस के लिए आवश्यक है।\\n\",\n \"\\n\",\n \"**4. इंटरफेरेंस (दूसरा हैडामार्ड गेट):**\\n\",\n \"\\n\",\n \"दूसरा Hadamard गेट सुपरपोज़िशन वाली अवस्थाओं को एक साथ लाने के लिए लगाया जाता है ताकि वे एक-दूसरे के साथ इंटरफेर करें:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_3\\\\rangle = H |\\\\psi_2\\\\rangle = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ e^{i\\\\phi} \\\\end{pmatrix} = \\\\frac{1}{2} \\\\begin{pmatrix} 1 + e^{i\\\\phi} \\\\\\\\ 1 - e^{i\\\\phi} \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"यहाँ $e^{i\\\\phi} = \\\\cos(\\\\phi) + i\\\\sin(\\\\phi)$ को विस्तारित करते हैं:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_3\\\\rangle = \\\\frac{1}{2} \\\\begin{pmatrix} 1 + \\\\cos(\\\\phi) + i\\\\sin(\\\\phi) \\\\\\\\ 1 - (\\\\cos(\\\\phi) + i\\\\sin(\\\\phi)) \\\\end{pmatrix} = \\\\frac{1}{2} \\\\begin{pmatrix} (1 + \\\\cos(\\\\phi)) + i\\\\sin(\\\\phi) \\\\\\\\ (1 - \\\\cos(\\\\phi)) - i\\\\sin(\\\\phi) \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"मापन से पहले की यह अंतिम स्थिति इंटरफेरेंस प्रभाव को दर्शाती है, जहाँ $|0\\\\rangle$ या $|1\\\\rangle$ के मापन की संभावनाएँ फेज अंतर $\\\\phi$ पर निर्भर करती हैं।\\n\",\n \"\\n\",\n \"**5. मापन की संभावना:**\\n\",\n \"\\n\",\n \"क्यूबिट को $|0\\\\rangle$ अवस्था में मापे जाने की संभावना (जो स्क्रीन का एक भाग दर्शाती है), $|\\\\psi_3\\\\rangle$ में $|0\\\\rangle$ के गुणांक के वर्ग के परिमाण से दी जाती है:\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\left| \\\\frac{1}{2} (1 + e^{i\\\\phi}) \\\\right|^2 = \\\\left| \\\\frac{1}{2} (1 + \\\\cos(\\\\phi) + i\\\\sin(\\\\phi)) \\\\right|^2 $$\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\frac{1}{4} [(1 + \\\\cos(\\\\phi))^2 + (\\\\sin(\\\\phi))^2] = \\\\frac{1}{4} [1 + 2\\\\cos(\\\\phi) + \\\\cos^2(\\\\phi) + \\\\sin^2(\\\\phi)] $$\\n\",\n \"\\n\",\n \"यहाँ पर $\\\\cos^2(\\\\phi) + \\\\sin^2(\\\\phi) = 1$ पहचान का उपयोग करते हुए:\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\frac{1}{4} [1 + 2\\\\cos(\\\\phi) + 1] = \\\\frac{1}{4} [2 + 2\\\\cos(\\\\phi)] = \\\\frac{1}{2} (1 + \\\\cos(\\\\phi)) $$\\n\",\n \"\\n\",\n \"यदि $\\\\phi = \\\\pi/2$, तो $P(|0\\\\rangle) = \\\\frac{1}{2} (1 + \\\\cos(\\\\pi/2)) = 0.5$ जैसा कि हमने पहले जाँचा था।\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6476c1c7-c119-4e60-a0f6-821321401081\",\n \"metadata\": {},\n \"source\": [\n \"आइए अब Exercise 1 को समाप्त करें और विभिन्न $\\\\phi$ मानों के लिए फ्रिंज पैटर्न (fringe pattern) उत्पन्न करके इसका निष्कर्ष निकालें।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5d40a6e6-fa86-47af-b556-2849906d5396\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"एक्सरसाइज़ 1: डबल-स्लिट प्रयोग के लिए एक क्वांटम सर्किट बनाएँ\\n\",\n \"\\n\",\n \"- एक्सरसाइज़ 1-4: सुंदर फ्रिंज पैटर्न\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** एक पैरामीटरयुक्त क्वांटम सर्किट बनाना जिसमें चरण अंतर $\\\\phi$ को बदला जा सके, जिससे आप संपूर्ण इंटरफेरेंस फ्रिंज पैटर्न का अनुकरण और कल्पना कर सकें।\\n\",\n \"\\n\",\n \"Qiskit आपको [ क्वांटम सर्किट में `Parameters` का उपयोग करने ](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.Parameter) की अनुमति देता है। हम इसका उपयोग `Sampler` के साथ करेंगे ताकि इंटरफेरेंस फ्रिंज को देखा जा सके।\\n\",\n \"\\n\",\n \"डबल-स्लिट प्रयोग के लिए एक पैरामीटरयुक्त क्वांटम सर्किट परिभाषित करें जिसमें एक परिवर्तनीय पैरामीटर $\\\\phi$ हो। एक सामान्य संरचना यह होगी: पहले H-gate, फिर $P(\\\\phi)$, फिर एक और H-gate, उसके बाद मापन। क्यूबिट `q` को मापें और परिणाम `c_screen` में सहेजें।\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4a90de8b-f5fb-4adf-a6d3-466a46f325b8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ = Parameter('φ')\\n\",\n \"\\n\",\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit_fringe = QuantumCircuit(qr, cr)\\n\",\n \"\\n\",\n \"# यहां अपना कोड लिखें\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# यहां कोड समाप्त होता है\\n\",\n \"\\n\",\n \"double_slit_fringe.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d2d987c4-d66b-4e1a-84f4-d5915ddde852\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# निम्नलिखित कोड का उपयोग करके अपना उत्तर सबमिट करें\\n\",\n \"grade_lab1_ex1_4(double_slit_fringe)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7a2c005f-dd42-4ee8-8073-b6b483045d76\",\n \"metadata\": {},\n \"source\": [\n \"बहुत बढ़िया! अब आइए इस पैरामीटरयुक्त सर्किट का उपयोग करके matplotlib की हीट मैप के माध्यम से फ्रिंज पैटर्न को प्लॉट करें। नीचे दिया गया कोड 100 अलग-अलग फ़ेज़ वैल्यूज तैयार करता है, प्रत्येक सर्किट को (1000 शॉट्स के साथ) `Sampler` के माध्यम से चलाता है, $|0\\\\rangle$ मापने की संभावना को स्टोर करता है, और फिर उसका हीट मैप बनाता है।\\n\",\n \"\\n\",\n \"
\\n\",\n \"रिसोर्स सीमा\\n\",\n \"\\n\",\n \"जब आप नीचे दिए गए कोड को किसी वास्तविक बैकएंड पर चलाते हैं, तो पैरामीटर की संख्या या शॉट्स को बढ़ाने से अपेक्षा से अधिक QPU समय खर्च हो सकता है। वर्तमान कॉन्फ़िगरेशन (100 पैरामीटरयुक्त सर्किट + 1000 शॉट्स) 1 मिनट से कम QPU समय का उपयोग करता है, इसलिए कृपया यदि संभव हो तो इन्हीं सेटिंग्स को बनाए रखें।\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2d447625-a7c9-46e5-9a8d-690419747ef1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ_lst = np.linspace(-3*np.pi, 3*np.pi, 100)\\n\",\n \"qc_isa = pm.run(double_slit_fringe)\\n\",\n \"\\n\",\n \"φ_hit = []\\n\",\n \"dist = sampler.run([(qc_isa, φ_lst)], shots=1000).result()[0].data.c_screen\\n\",\n \"\\n\",\n \"for i in range(len(φ_lst)):\\n\",\n \" result = dist[i].get_counts()\\n\",\n \" if '0' in result:\\n\",\n \" φ_hit.append(result['0']/1000)\\n\",\n \" else:\\n\",\n \" φ_hit.append(0)\\n\",\n \"\\n\",\n \"#plot heat map\\n\",\n \"φ_hit_2d = np.array(φ_hit).reshape(1, -1)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 2))\\n\",\n \"plt.imshow(φ_hit_2d, cmap='gray', aspect='auto', extent=[-3*np.pi, 3*np.pi, 0, 0.1])\\n\",\n \"\\n\",\n \"plt.xlabel('φ (Phase difference)', fontsize=14)\\n\",\n \"plt.colorbar(label='prob')\\n\",\n \"plt.xticks(ticks=[-3*np.pi, -2*np.pi, -np.pi, 0, np.pi, 2*np.pi, 3*np.pi],\\n\",\n \" labels=['-3π', '-2π', '-π', '0', 'π', '2π', '3π'])\\n\",\n \"plt.yticks([]) \\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0a6bc2c3-7c4a-46a2-9984-bc8a94666ead\",\n \"metadata\": {},\n \"source\": [\n \"शानदार! आपने क्विबिट्स का उपयोग करके डबल-स्लिट प्रयोग के फ्रिंज पैटर्न को पुनः उत्पन्न कर लिया है।\\n\",\n \"अब अगला कदम है क्वांटम मापन (quantum measurement) को समझना — श्रॉडिंगर की बिल्ली (Schrödinger's Cat) के उदाहरण के साथ। फिर हम देखेंगे कि किसी क्वांटम सिस्टम का निरीक्षण (observation) डबल-स्लिट प्रयोग के परिणाम को कैसे प्रभावित करता है।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed7715c1-2169-4fef-9b7d-3ed61c4b2f41\",\n \"metadata\": {},\n \"source\": [\n \"## Schrödinger's Cat: क्वांटम सुपरपोजिशन और निरीक्षण की क्रिया की खोज\\n\",\n \"\\n\",\n \"यह विचार प्रयोग एर्विन श्रॉडिंगर द्वारा 1935 में प्रस्तुत किया गया था, [\\\"Die gegenwärtige Situation in der Quantenmechanik\\\"](https://link.springer.com/article/10.1007/BF01491891) यह प्रयोग क्वांटम सुपरपोजिशन और मापन (measurement) की उस भूमिका को दर्शाता है जिससे संभावनाएँ एक निश्चित परिणाम में बदल जाती हैं।\\n\",\n \"\\n\",\n \"कल्पना कीजिए, एक बिल्ली को एक बंद डिब्बे में रखा गया है, जिसमें एक रेडियोएक्टिव परमाणु भी रखा गया है। यदि वह परमाणु सड़ चुका है या नहीं सड़ा है, इन दोनों अवस्थाओं में सुपरपोजिशन में है, तो बिल्ली भी इस परमाणु के साथ उलझी हुई (entangled) होगी — और जब तक डिब्बा नहीं खोला जाता, वह ज़िंदा और मरी हुई दोनों अवस्थाओं में simultaneously हो सकती है।\\n\",\n \"\\n\",\n \"यहाँ हम एक rotation gate का उपयोग करेंगे। मान लीजिए बिल्ली को $|1\\\\rangle$ स्थिति पसंद नहीं है। चलो देखते हैं कि जब हम \\\"डिब्बा खोलते हैं\\\" (qubit को मापते हैं) तो बिल्ली खुश है या गुस्से में।\\n\",\n \"\\n\",\n \"सावधान रहें! गुस्साई हुई क्वांटम बिल्ली आपको खरोंच भी सकती है।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5559255f-f8c1-4a49-bcf1-738d9730f92f\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"व्यायाम 2: क्या बिल्ली खुश है या चिड़चिड़ी?\\n\",\n \"\\n\",\n \"**आपका उद्देश्य:** एक क्वांटम सर्किट बनाना है जो एक qubit को $R_X(\\\\theta)$ गेट का उपयोग करके सुपरपोजिशन स्थिति में तैयार करे, फिर एक बार मापन करके निर्धारित करें कि \\\"cat\\\" (जिसे qubit की स्थिति द्वारा दर्शाया गया है) \\\"खुश\\\" है ($|0\\\\rangle$) या \\\"चिड़चिड़ी\\\" है ($|1\\\\rangle$)।\\n\",\n \"\\n\",\n \"नीचे दिए गए Python कोड को पूरा करें, जिसमें [Rotational X gate](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.library.RXGate) का उपयोग किया गया है, जहाँ $\\\\theta \\\\in [0, 2\\\\pi]$ होगा। यह विभिन्न सुपरपोजिशन अवस्थाओं में qubit को तैयार करेगा। फिर एक बार मापन करें और देखें कि बिल्ली खुश (happy) ($|0\\\\rangle$) है या चिड़चिड़ी (gummy) ($|1\\\\rangle$)। आपको $\\\\theta$ का मान स्लाइडर के माध्यम से दिया जाएगा।\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6aa42755-b319-4aee-a6bc-cf6b34e00222\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def schrodingers_cat_experiment_theta(theta):\\n\",\n \" \\n\",\n \" qc = QuantumCircuit(1)\\n\",\n \"\\n\",\n \" # यहां अपना कोड लिखें\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" # यहां कोड समाप्त होता है\\n\",\n \"\\n\",\n \" qc.measure_all()\\n\",\n \" \\n\",\n \" backend = AerSimulator()\\n\",\n \" pm = generate_preset_pass_manager( backend = backend, optimization_level=3)\\n\",\n \" qc_isa = pm.run(qc)\\n\",\n \"\\n\",\n \" # Circuit compile and run, shot = 1 \\n\",\n \" sampler = Sampler(mode=backend)\\n\",\n \" counts = sampler.run([qc_isa], shots = 1).result()[0].data.meas.get_counts()\\n\",\n \"\\n\",\n \" measured_state = list(counts.keys())[0] if counts else '0' # bring measured result\\n\",\n \"\\n\",\n \" if measured_state == '0':\\n\",\n \" cat_happy = True\\n\",\n \" else:\\n\",\n \" cat_happy = False\\n\",\n \"\\n\",\n \" return cat_happy, qc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"49747cda-70bf-44d7-816c-607dd601a95f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# निम्नलिखित कोड का उपयोग करके अपना उत्तर सबमिट करें\\n\",\n \"grade_lab1_ex2(schrodingers_cat_experiment_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"18096b47-adb4-47dc-a41f-2273db2aacab\",\n \"metadata\": {},\n \"source\": [\n \"अब, नीचे दिए गए विजेट का उपयोग करके बिल्ली का मूड जांचें। सुनिश्चित करें कि happy.png और grumpy.png एक ही फ़ोल्डर में हों। (छवि स्रोत: James Weaver)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4aed0937-fa98-416e-a2c8-e571df1ed907\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"happy_img = Image.open('happy.png')\\n\",\n \"grumpy_img = Image.open('grumpy.png')\\n\",\n \"\\n\",\n \"out = widgets.Output()\\n\",\n \"\\n\",\n \"slider = widgets.FloatSlider(\\n\",\n \" value=0,\\n\",\n \" min=0,\\n\",\n \" max=2*np.pi,\\n\",\n \" step=0.01,\\n\",\n \" description='θ',\\n\",\n \" continuous_update=True\\n\",\n \")\\n\",\n \"\\n\",\n \"button = widgets.Button(\\n\",\n \" description='Open the Box',\\n\",\n \" button_style='success'\\n\",\n \")\\n\",\n \" \\n\",\n \"def on_button_click(b):\\n\",\n \" with out:\\n\",\n \" out.clear_output(wait=True) # clean output\\n\",\n \"\\n\",\n \" result = schrodingers_cat_experiment_theta(slider.value)[0]\\n\",\n \"\\n\",\n \" if result==True:\\n\",\n \" img = happy_img\\n\",\n \" txt = \\\"happy\\\"\\n\",\n \" else:\\n\",\n \" img = grumpy_img\\n\",\n \" txt = \\\"grumpy\\\"\\n\",\n \"\\n\",\n \" new_size = (400, 400)\\n\",\n \" resized_img = img.resize(new_size)\\n\",\n \" \\n\",\n \" buf = io.BytesIO()\\n\",\n \" resized_img.save(buf, format='PNG')\\n\",\n \" buf.seek(0)\\n\",\n \" probability = int(np.cos(slider.value/2)**2 * 100)\\n\",\n \"\\n\",\n \" display(f\\\"The probability of cat is happy: {probability}%\\\")\\n\",\n \" display(f\\\"The observed cat is : {txt}\\\")\\n\",\n \" display(widgets.Image(value=buf.read(), format='png'))\\n\",\n \"\\n\",\n \"button.on_click(on_button_click)\\n\",\n \"\\n\",\n \"display(slider, button, out)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7d8e5717-b4d3-4e0c-84d9-279210916258\",\n \"metadata\": {},\n \"source\": [\n \"इससे पहले कि हम डबल-स्लिट प्रयोग को दोबारा देखें, आइए स्पष्ट करें कि जब हम किसी क्विबिट को देखने या \\\"मापने\\\" की कोशिश करते हैं, विशेष रूप से जब उस पर कोई गेट जैसे कि $R_x(\\\\theta)$ लगाया गया हो, तब क्या होता है। यदि आप क्वांटम मापन से परिचित हैं, तो आप इस भाग को छोड़ सकते हैं।\\n\",\n \"\\n\",\n \"\\n\",\n \"
क्वांटम मापन विवरण: क्विबिट को देखना
\\n\",\n \"\\n\",\n \"**1. मापन से पहले क्विबिट की स्थिति:**\\n\",\n \"जब $R_x(\\\\theta)$ को $|0\\\\rangle$ पर लागू किया जाता है, तो स्थिति सुपरपोज़िशन बन जाती है:\\n\",\n \"$$|\\\\psi\\\\rangle = R_x(\\\\theta)|0\\\\rangle = \\\\cos\\\\left(\\\\frac{\\\\theta}{2}\\\\right)|0\\\\rangle - i \\\\sin\\\\left(\\\\frac{\\\\theta}{2}\\\\right)|1\\\\rangle = \\\\alpha|0\\\\rangle + \\\\beta|1\\\\rangle$$\\n\",\n \"जहाँ $|\\\\alpha|^2 + |\\\\beta|^2 = 1$ होता है।\\n\",\n \"\\n\",\n \"**2. मापन की क्रिया:**\\n\",\n \"गणनात्मक आधार (computational basis) ($\\\\{|0\\\\rangle, |1\\\\rangle\\\\}$) में मापन क्विबिट को एक निश्चित स्थिति चुनने के लिए मजबूर करता है। सुपरपोज़िशन को सीधे नहीं देखा जा सकता।\\n\",\n \"\\n\",\n \"**3. संभावनात्मक परिणाम:** \\n\",\n \"मापने की संभावना -> $|0\\\\rangle$: $P(0) = |\\\\alpha|^2 = \\\\cos^2\\\\left(\\\\frac{\\\\theta}{2}\\\\right)$ \\n\",\n \"मापने की संभावना -> $|1\\\\rangle$: $P(1) = |\\\\beta|^2 = \\\\sin^2\\\\left(\\\\frac{\\\\theta}{2}\\\\right)$\\n\",\n \"\\n\",\n \"**4. स्थिति का पतन (State Collapse):**\\n\",\n \"मापन के बाद स्थिति गिर जाती है (collapse होती है):\\n\",\n \"* If '0' is measured $\\\\rightarrow$ state becomes $|0\\\\rangle$.\\n\",\n \"* If '1' is measured $\\\\rightarrow$ state becomes $|1\\\\rangle$.\\n\",\n \"यह \\\"गिरावट\\\" सुपरपोज़िशन को नष्ट कर देती है।\\n\",\n \"\\n\",\n \"**डबल-स्लिट से संबंध:**\\n\",\n \"यह पता लगाना कि कण किस स्लिट से गया, एक मापन है। यह कण की वेव फंक्शन को एक निश्चित पथ पर गिरा देता है, और इंटरफेरेंस फ्रिंज के लिए आवश्यक सुपरपोज़िशन नष्ट हो जाती है।\\n\",\n \"\\n\",\n \"\\n\",\n \"अब आइए डबल-स्लिट प्रयोग पर वापस लौटते हैं।\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a759bf4a-f9ce-4d23-bd90-c72d15b0c87b\",\n \"metadata\": {},\n \"source\": [\n \"## डबल-स्लिट मापन के साथ\\n\",\n \"\\n\",\n \"
\\n\",\n \"अभ्यास 3: एक पथ डिटेक्टर के साथ डबल-स्लिट\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** एक ऐसा डबल-स्लिट क्वांटम सर्किट बनाना जिसमें एक मध्यवर्ती मापन (measurement) शामिल हो, जो \\\"किस रास्ते से गया\\\" (which-path) डिटेक्टर के रूप में कार्य करे। इससे आप देख सकेंगे कि कण के रास्ते की जानकारी प्राप्त करने से अंतिम इंटरफेरेंस पैटर्न पर क्या प्रभाव पड़ता है।\\n\",\n \"\\n\",\n \"डबल-स्लिट सर्किट को संशोधित करें ताकि उसमें एक \\\"किस रास्ते से गया\\\" डिटेक्टर शामिल हो (पहले H-गेट के बाद एक मापन जोड़ा जाए)।\\n\",\n \"\\n\",\n \"सेटअप:\\n\",\n \"* `qr`: 1 क्विबिट (`'q'`).\\n\",\n \"* `cr1`: 1 क्लासिकल बिट (`'c_detector'`) पथ का पता लगाने के लिए\\n\",\n \"* `cr2`: 1 क्लासिकल बिट (`'c_screen'`) अंतिम मापन के लिए\\n\",\n \"* `φ`: फेज गेट के लिए एक `Parameter`\\n\",\n \"\\n\",\n \"**संकेत**: आपके सर्किट में दो मापन होंगे।\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ba9f4861-356b-4b66-99d5-e06561e5f340\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr1 = ClassicalRegister(1, name='c_detector')\\n\",\n \"cr2 = ClassicalRegister(1, name='c_screen')\\n\",\n \"double_slit_with_detector = QuantumCircuit(qr, cr1, cr2)\\n\",\n \"\\n\",\n \"φ = Parameter('φ')\\n\",\n \"\\n\",\n \"# यहां अपना कोड लिखें\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# यहां कोड समाप्त होता है\\n\",\n \"\\n\",\n \"double_slit_with_detector.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c6533c86-e7e6-4b0f-92ef-5179d75b7702\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# निम्नलिखित कोड का उपयोग करके अपना उत्तर सबमिट करें\\n\",\n \"grade_lab1_ex3(double_slit_with_detector)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"48ffc450-3cf9-423c-be89-808a03dd246a\",\n \"metadata\": {},\n \"source\": [\n \"अब, नए डिटेक्टर के साथ पैटर्न देखने के लिए हीट मैप जनरेशन को दोहराएं।\\n\",\n \"\\n\",\n \"**चेतावनी**: इसमें कुछ मिनट लग सकते हैं।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1dbb4f19-9a18-4c2e-82e4-3365a2a87880\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"संसाधन सीमा\\n\",\n \"\\n\",\n \"जब आप नीचे दिया गया कोड वास्तविक बैकएंड पर चलाते हैं, तो पैरामीटरों या शॉट्स की संख्या बढ़ाने से अपेक्षा से अधिक QPU समय खपत हो सकता है। वर्तमान कॉन्फ़िगरेशन (100 पैरामीटरयुक्त सर्किट + 1000 शॉट्स) 1 मिनट से कम QPU समय का उपयोग करता है, इसलिए कृपया यदि संभव हो तो इन्हीं सेटिंग्स को बनाए रखने की कोशिश करें।\\n\",\n \"\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"346bf587-9e3a-41c9-a598-7856404befa9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ_lst = np.linspace(-3 * np.pi, 3 * np.pi, 100)\\n\",\n \"qc_isa = pm.run(double_slit_with_detector)\\n\",\n \"\\n\",\n \"φ_hit = []\\n\",\n \"dist = sampler.run([(qc_isa, φ_lst)], shots=1000).result()[0].data.c_screen\\n\",\n \"\\n\",\n \"for i in range(len(φ_lst)):\\n\",\n \" result = dist[i].get_counts()\\n\",\n \" if '0' in result:\\n\",\n \" φ_hit.append(result['0']/1000)\\n\",\n \" else:\\n\",\n \" φ_hit.append(0)\\n\",\n \"\\n\",\n \"φ_hit_2d = np.array(φ_hit).reshape(1, -1)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 2))\\n\",\n \"plt.imshow(φ_hit_2d, cmap='gray', aspect='auto', extent=[-3*np.pi, 3*np.pi, 0, 0.1], vmin=0, vmax=1)\\n\",\n \"\\n\",\n \"plt.xlabel('φ (Phase difference)', fontsize=14)\\n\",\n \"plt.colorbar(label='prob')\\n\",\n \"plt.xticks(ticks=[-3 * np.pi, -2 * np.pi, -np.pi, 0, np.pi, 2 * np.pi, 3 * np.pi],\\n\",\n \" labels=['-3π', '-2π', '-π', '0', 'π', '2π', '3π'])\\n\",\n \"plt.yticks([]) \\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5544b924-8d52-4826-a34a-4abbeb1fdcf4\",\n \"metadata\": {},\n \"source\": [\n \"आपको अधिकांशतः एक धूसर (gray) पैटर्न दिखाई देना चाहिए, जो दर्शाता है कि $\\\\phi$ के मान की परवाह किए बिना $|0\\\\rangle$ को मापने की संभावना लगभग 50% है। हस्तक्षेप (interference) की धारियाँ गायब हो गई हैं!\\n\",\n \"\\n\",\n \" \\n\",\n \"
देखें कि डिटेक्टर कैसे काम करता हैs
\\n\",\n \"\\n\",\n \"1. **प्रारंभिक अवस्था**: $|\\\\psi_0\\\\rangle = |0\\\\rangle$\\n\",\n \"2. **पहला H-Gate**: $|\\\\psi_1\\\\rangle = \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle + |1\\\\rangle)$ (कण दोनों स्लिट से गुजर सकता है)\\n\",\n \"3. **पहला मापन (डिटेक्टर)**: सुपरपोजिशन को कोलैप्स करता है।\\n\",\n \" * परिणाम 0 (prob 1/2) $\\\\rightarrow |\\\\psi_{2a}\\\\rangle = |0\\\\rangle$\\n\",\n \" * परिणाम 1 (prob 1/2) $\\\\rightarrow |\\\\psi_{2b}\\\\rangle = |1\\\\rangle$\\n\",\n \" अब क्यूबिट एक निश्चित अवस्था में है।\\n\",\n \"4. **P-गेट**:\\n\",\n \" * यदि परिणाम 0: $|\\\\psi_{3a}\\\\rangle = P(\\\\phi)|0\\\\rangle = |0\\\\rangle$\\n\",\n \" * यदि परिणाम 1: $|\\\\psi_{3b}\\\\rangle = P(\\\\phi)|1\\\\rangle = e^{i\\\\phi}|1\\\\rangle$\\n\",\n \" चरण $\\\\phi$ हस्तक्षेप के लिए *सापेक्ष चरण* के रूप में कार्य नहीं कर सकता क्योंकि सुपरपोजिशन पहले ही समाप्त हो चुकी है।\\n\",\n \"5. **दूसरा H-गेटe**:\\n\",\n \" * यदि परिणाम 0: $|\\\\psi_{4a}\\\\rangle = H|0\\\\rangle = \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle + |1\\\\rangle)$\\n\",\n \" * यदि परिणाम 1: $|\\\\psi_{4b}\\\\rangle = H(e^{i\\\\phi}|1\\\\rangle) = e^{i\\\\phi} \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle - |1\\\\rangle)$\\n\",\n \"6. **दूसरा मापन (स्क्रीन पर)**:\\n\",\n \" * यदि पहला मापन 0 था: अंतिम '0' की संभावना = 1/2, अंतिम ‘1’ की संभावना = 1/2\\n\",\n \" * यदि पहला मापन 1 था: अंतिम '0' की संभावना = 1/2, अंतिम ‘1’ की संभावना = 1/2\\n\",\n \"\\n\",\n \"**निष्कर्ष**: डिटेक्टर के परिणाम की परवाह किए बिना, अंतिम मापन हमेशा $|0\\\\rangle$ या $|1\\\\rangle$ के लिए 50/50 होता है। मध्यवर्ती मापन ने सुपरपोजिशन को समाप्त कर दिया, और इसीलिए हस्तक्षेप नष्ट हो गया। यह दर्शाता है कि \\\"कौन से रास्ते से\\\" (which-path) की जानकारी प्राप्त करना हस्तक्षेप को समाप्त कर देता है।\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"अब तक, हमने सुपरपोजिशन, इंटरफेरेंस और मापन की खोज की है। अब चलिए एक और गहरे क्वांटम घटना की ओर बढ़ते हैं: **एंटैंगलमेंट (entanglement)**.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8fdb70d8-229c-4e3b-abdc-ce9f60230e1a\",\n \"metadata\": {},\n \"source\": [\n \"# अध्याय 2: एंटैंगलमेंट (Entanglement)\\n\",\n \"\\n\",\n \"**क्वांटम एंटैंगलमेंट** एक ऐसा अद्भुत घटना है जहाँ दो कण आपस में इतने गहरे जुड़ जाते हैं कि वे एक साझा क्वांटम अवस्था में होते हैं — भले ही वे एक-दूसरे से कितनी भी दूर हों। जैसे ही आप एक कण की अवस्था को मापते हैं, दूसरे कण की अवस्था के बारे में तुरंत जानकारी मिल जाती है। आइंस्टीन ने इसे \\\"दूरी पर रहस्यमयी क्रिया\\\" (spooky action at a distance) कहा था।\\n\",\n \"\\n\",\n \"1964 में, जॉन बेल ने यह जांचने के लिए बेल की असमानता (Bell’s inequality) प्रस्तावित की कि क्या एंटैंगल्ड कण पारंपरिक (classical) तरीके से व्यवहार करते हैं। CHSH प्रयोग ने इसे व्यवहार में परखा। क्वांटम यांत्रिकी (Quantum mechanics) ने इसमें जीत हासिल की, और पारंपरिक अपेक्षाओं को नकार दिया [3]। 1997 में, क्वांटम टेलीपोर्टेशन — जिसमें एक क्वांटम अवस्था को बिना कण को भेजे स्थानांतरित किया गया — एंटैंगलमेंट के उपयोग से संभव हुआ [4]।\\n\",\n \"\\n\",\n \"अब Qiskit की मदद से, आइए हम CHSH प्रयोग और क्वांटम टेलीपोर्टेशन करके देखें।\\n\",\n \"\\n\",\n \"\\n\",\n \"## 2.1. एलिस, बॉब और एक अजेय खेल का दिलचस्प मामला — CHSH गेम\\n\",\n \"\\n\",\n \"एलिस और बॉब एक-दूसरे से दूर हैं और आपस में संपर्क नहीं कर सकते। एक रेफ़री उन्हें एक चुनौती देता है:\\n\",\n \"1. रेफ़री दो बिट (`x`, `y`) चुनता है और `x` एलिस को, `y` बॉब को भेजता है।\\n\",\n \"2. एलिस और बॉब तुरंत अपनी प्रतिक्रिया में एक-एक बिट (`a`, `b`) भेजते हैं।\\n\",\n \"3. **जीत की शर्त**: `a XOR b == x AND y` होनी चाहिए।\\n\",\n \"\\n\",\n \"वे चाहते हैं कि वे पहले से एक ऐसी रणनीति तय करें जो उनकी औसत जीत की दर को अधिकतम करे।\\n\",\n \"\\n\",\n \"**आपका लक्ष्य**: CHSH गेम के लिए *quantum* रणनीति Qiskit के माध्यम से लागू करें, उसके प्रदर्शन का सिमुलेशन करें, और पारंपरिक सीमा (classical limit) से उसकी तुलना करें ताकि यह समझ सकें कि क्वांटम एंटैंगलमेंट किस तरह लाभ देता है।\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"गहराई से अध्ययन के लिए\\n\",\n \"\\n\",\n \"एंटैंगलमेंट के व्यवहार और CHSH गेम सहित इसके व्यावहारिक प्रयोगों की अधिक गहराई से जानकारी के लिए, आप Qiskit की यह लर्निंग सामग्री देख सकते हैं: [Entanglement in Action](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/introduction).\\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91b4ee20-4f7a-4cdc-ae82-b8626a55f5fd\",\n \"metadata\": {},\n \"source\": [\n \"### क्लासिकल सीमा: क्यों 75% ही एक रुकावट है\\n\",\n \"\\n\",\n \"चलो पहले इसे पारंपरिक (क्लासिकल) तरीके से सोचते हैं। चूँकि एलिस और बॉब `x` और `y` प्राप्त करने के बाद आपस में बात नहीं कर सकते, एलिस का आउटपुट `a` केवल `x` पर निर्भर कर सकता है, और बॉब का आउटपुट `b` केवल `y` पर। वे पहले से किसी रणनीति पर सहमत हो सकते हैं, चाहे वो यादृच्छिक (random) ही क्यों न हो, लेकिन अंततः यह उनके स्थानीय निर्णयों तक ही सीमित रहता है।\\n\",\n \"\\n\",\n \"विजय की शर्त:\\n\",\n \"* इनपुट्स (0,0), (0,1), या (1,0): जीत तभी जब `a == b` हो।\\n\",\n \"* इनपुट (1,1): जीत तभी जब `a != b` हो।\\n\",\n \"\\n\",\n \"क्या आप कोई तय रणनीति खोज सकते हैं (जैसे `a=0, b=0` या `a=x, b=y`) जो इन चार में से तीन से ज्यादा मामलों में जीत हासिल कर सके? ज़रा कोशिश कीजिए! आप पाएँगे कि अधिकतम औसत जीत की दर ज़िद्दी रूप से **75%** पर ही अटकी रहती है — यह स्थानीय यथार्थवाद (local realism) की एक मौलिक सीमा है।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74227c42-4fd9-4acb-a55a-564b836bdc38\",\n \"metadata\": {},\n \"source\": [\n \"### क्वांटम रणनीति: उलझाव (Entanglement) ने बचा लिया!\\n\",\n \"\\n\",\n \"क्या हो अगर एलिस और बॉब खेल शुरू होने से *before* कुछ खास तैयार कर लें? क्या हो अगर उनके पास एक जोड़ी **उलझे हुए क्यूबिट्स (entangled qubits)** हो? सोचिए, जैसे दो \\\"जादुई\\\" सिक्के जो रहस्यमय रूप से आपस में जुड़े हुए हों। चाहे वे मीलों दूर हों, एक को मापने से दूसरे को मापने पर मिलने वाला *संबंध (correlations)* तुरंत प्रभावित होता है। ये प्रकाश की गति से तेज़ संदेश नहीं भेजते, लेकिन उनका नतीजा आपस में गहराई से जुड़ा होता है।\\n\",\n \"\\n\",\n \"विशेष रूप से, वे एक साझा [बेल अवस्था (Bell state)](https://en.wikipedia.org/wiki/Bell_state) साझा करते हैं: $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt(2)$ अगर वे दोनों अपने क्यूबिट को *एक ही तरीके* से मापते हैं, तो उन्हें हमेशा एक जैसे परिणाम मिलते हैं — या तो दोनों 0, या दोनों 1।\\n\",\n \"\\n\",\n \"क्वांटम रणनीति: एलिस और बॉब तय बिट्स आउटपुट करने की बजाय, अपने इनपुट(`x` या `y`) का उपयोग करते हैं ताकि वे तय कर सकें कि अपने साझा क्यूबिट को *कैसे* मापना है।\\n\",\n \"1. साझा संसाधन: एलिस के पास उलझी जोड़ी का क्यूबिट 0 होता है, और बॉब के पास क्यूबिट 1।\\n\",\n \"2. मापन का चयन (सबसे चतुर हिस्सा):\\n\",\n \" * एलिस (`x` के आधार पर): अगर `x=0`, तो वह कोण 0 (Z-बेसिस) पर मापती है| अगर `x=1`, तो वह कोण π/2 (X-बेसिस) पर मापती है।\\n\",\n \" * बॉब (`y` के आधार पर): अगर `y=0`, तो वह कोण −π/4 पर मापता है। अगर `y=1` , तो वह कोण +π/4 पर मापता है।\\n\",\n \"3. आउटपुट: वे अपने-अपने मापन का परिणाम `a` और `b` के रूप में आउटपुट करते हैं।\\n\",\n \"\\n\",\n \"ये विशेष कोण बहुत महत्वपूर्ण हैं! ये बेल अवस्था की अनूठी सहसंबद्धताओं (correlations) का पूरा लाभ उठाते हैं ताकि `a XOR b == x AND y` की शर्त को हर इनपुट जोड़ी पर अधिकतम सफलता के साथ पूरा किया जा सके।\\n\",\n \"\\n\",\n \"अब आइए इस रणनीति के लिए क्वांटम सर्किट बनाते हैं।\\n\",\n \"\\n\",\n \"एलिस और बॉब एक उलझे हुए क्यूबिट जोड़े को बेल अवस्था में साझा करते हैं: $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$ यदि दोनों एक ही तरीके से मापें, तो उन्हें हमेशा एक जैसे परिणाम मिलते हैं।\\n\",\n \"\\n\",\n \"**नोट**\\n\",\n \"\\n\",\n \"यदि आप क्वांटम सर्किट की संरचना, इसके काम करने की प्रक्रिया या सिद्धांतों के बारे में विस्तार से जानना चाहते हैं, तो कृपया IBM Quantum Learning पाठ्यक्रम की इस [ पाठ्य सामग्री](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/chsh-game) को देखें – Basics of Quantum Information।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"aefc0aa2-ad6f-4a61-ae6f-6f92b558a489\",\n \"metadata\": {},\n \"source\": [\n \"### Qiskit Implementation: Building the Quantum Circuit\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 4: Quantum Circuit for CHSH Game\\n\",\n \"\\n\",\n \"**Your Goal:** Define a function `create_chsh_circuit(x, y)` by constructing the quantum circuit that Alice and Bob will use for their quantum strategy in the CHSH game. This involves preparing an entangled Bell state and then applying measurement basis rotations based on their respective inputs.\\n\",\n \"\\n\",\n \"**Tasks:**\\n\",\n \"* **Task 1:** Create the Bell state $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$.\\n\",\n \"* **Task 2:** Implement Bob's rotation based on his input `y`.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a174b414-36d6-40f0-a1ac-df3350b5f80e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def create_chsh_circuit(x, y):\\n\",\n \" \\\"\\\"\\\"Builds Qiskit circuit for Alice & Bob's quantum strategy.\\\"\\\"\\\"\\n\",\n \" qc = QuantumCircuit(2, 2, name=f'CHSH_{x}{y}') # 2 qubits, 2 classical bits\\n\",\n \"\\n\",\n \" ## ---- TODO : कार्य 1 ---\\n\",\n \" # Implement the gates to create the Bell state |Φ+> = (|00> + |11>)/sqrt(2).\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" # --- TODO समाप्त ---\\n\",\n \" qc.barrier()\\n\",\n \" # Step 2a: Alice's measurement basis (X if x=1, Z if x=0)\\n\",\n \" if x == 1:\\n\",\n \" qc.h(0) # H for X-basis measurement\\n\",\n \"\\n\",\n \" ## ---- TODO : कार्य 2 ---\\n\",\n \" # Step 2b: Bob's measurement basis\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" # --- TODO समाप्त ---\\n\",\n \" qc.barrier()\\n\",\n \" \\n\",\n \" # Step 3: Measure\\n\",\n \" qc.measure([0, 1], [0, 1]) # q0 to c0 (Alice), q1 to c1 (Bob) -> 'ba' format\\n\",\n \"\\n\",\n \" return qc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"27caf6cf-9497-4c02-b4ca-0f6d55a464dc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# निम्नलिखित कोड का उपयोग करके अपना उत्तर सबमिट करें\\n\",\n \"grade_lab1_ex4(create_chsh_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28c48bd5-eaeb-4360-981d-959a4ac913b6\",\n \"metadata\": {},\n \"source\": [\n \"### सभी इनपुट जोड़ों के लिए सर्किट बनाएँ\\n\",\n \"\\n\",\n \"अब हम आपके द्वारा बनाई गई फ़ंक्शन का उपयोग करके चारों (x,y) स्थितियों के लिए क्वांटम सर्किट बनाएंगे।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7a440c15-4467-42d6-b0a1-80fd9bdd5dd5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"circuits = []\\n\",\n \"input_pairs = []\\n\",\n \"for x_in in [0, 1]:\\n\",\n \" for y_in in [0, 1]:\\n\",\n \" input_pairs.append((x_in, y_in))\\n\",\n \" circuits.append(create_chsh_circuit(x_in, y_in))\\n\",\n \"\\n\",\n \"print(\\\"Quantum circuit for inputs x=1, y=1 (Check your Exercises 1 & 2 implementation):\\\")\\n\",\n \"if len(circuits) == 4:\\n\",\n \" display(circuits[3].draw('mpl')) # (x,y) = (1,1)\\n\",\n \"else:\\n\",\n \" print(\\\"Circuits not generated. Run previous cell after completing Exercises 1 & 2.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b85480d-6172-457c-805c-6f168a86930f\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"### सिमुलेशन: गेम को कई बार खेलना\\n\",\n \"\\n\",\n \"असल क्वांटम कंप्यूटर शोरयुक्त (noisy) होते हैं और कई बार इन तक पहुँचना मुश्किल हो सकता है। सौभाग्य से, हम छोटे सिस्टम जैसे इस गेम के लिए इनके व्यवहार को काफ़ी सटीकता से सिमुलेट कर सकते हैं। हम Qiskit के `AerSimulator` का उपयोग करेंगे ताकि चारों सर्किट को कई बार (\\\"shots\\\") चलाया जा सके और Alice और Bob के आउटपुट (`a` और `b`) पर आँकड़े एकत्र किए जा सकें।\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b2052339-c0ab-4051-8d22-1a77df74d56f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# AerSimulator (if not already defined)\\n\",\n \"# backend = AerSimulator()\\n\",\n \"# Pass manager (if not already defined)\\n\",\n \"# pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\\n\",\n \"\\n\",\n \"SHOTS = 1024\\n\",\n \"\\n\",\n \"print(\\\"Preparing circuits for the simulator...\\\")\\n\",\n \"isa_qc_chsh = pm.run(circuits)\\n\",\n \"\\n\",\n \"sampler_chsh = Sampler(mode=backend) # SamplerV2\\n\",\n \"job_chsh = sampler_chsh.run(isa_qc_chsh, shots=SHOTS)\\n\",\n \"results_chsh = job_chsh.result()\\n\",\n \"\\n\",\n \"# SamplerV2: results_chsh[i].data.c.get_counts() where 'c' is the default name of classical register\\n\",\n \"counts_list = [results_chsh[i].data.c.get_counts() for i in range(len(circuits))]\\n\",\n \"\\n\",\n \"print(\\\"\\\\n--- Simulation Results (Counts) ---\\\")\\n\",\n \"for i, (x, y) in enumerate(input_pairs):\\n\",\n \" print(f\\\"Inputs (x={x}, y={y}):\\\")\\n\",\n \" sorted_counts = dict(sorted(counts_list[i].items()))\\n\",\n \" print(f\\\" Outcomes (ba): {sorted_counts}\\\")\\n\",\n \"\\n\",\n \"print(\\\"\\\\nPlotting results...\\\")\\n\",\n \"display(plot_histogram(counts_list,\\n\",\n \" legend=[f'(x={x}, y={y})' for x, y in input_pairs],\\n\",\n \" title='CHSH Game Outcomes (ba format)'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3b84b66e-9503-4dbe-9023-0d08bbd584fd\",\n \"metadata\": {},\n \"source\": [\n \"### विश्लेषण: क्या उन्होंने क्लासिकल लिमिट को पार किया?\\n\",\n \"\\n\",\n \"अब वह निर्णायक क्षण आ गया है! हमें सिमुलेशन परिणामों (`counts_list`) का विश्लेषण करना है ताकि औसत जीत की संभावना (average win probability) की गणना की जा सके।\\n\",\n \"\\n\",\n \"**सारांश:**\\n\",\n \"* **जीत की शर्त:** `a XOR b == x AND y`\\n\",\n \"* **आउटपुट प्रारूप:** काउंट `'ba'` स्ट्रिंग्स के लिए होते हैं।\\n\",\n \" * `a XOR b = 0` होता है आउटपुट `'00'` (`b=0, a=0`) और `'11'` (`b=1, a=1`).\\n\",\n \" * `a XOR b = 1` होता है आउटपुट `'01'` (`b=0, a=1`) और `'10'` (`b=1, a=0`).\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"व्यायाम 5: CHSH गेम के लिए सर्किट का विश्लेषण करें\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** प्रत्येक इनपुट केस (`x`, `y`) के लिए सिमुलेशन परिणामों के आधार पर Alice और Bob की जीत की संभावना की गणना करें, और फिर क्वांटम रणनीति का उपयोग करके उनकी कुल औसत जीत की संभावना निकालें।\\n\",\n \"\\n\",\n \"**कार्य:**\\n\",\n \"* **कार्य 1:** दिए गए `a XOR b` के आधार पर जीत के लिए लक्षित `x` and `y` मान निर्धारित करें।\\n\",\n \"* **कार्य 2:** उस (`x`, `y`) केस के लिए जीत की शर्त को पूरा करने वाले शॉट्स (`wins_for_this_case`) की गिनती करें।\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"28f7b7ef-612a-4762-a522-d32d539b37f9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"win_probabilities = {}\\n\",\n \"print(\\\"--- Calculating Win Probabilities ---\\\")\\n\",\n \"\\n\",\n \"for i, (x, y) in enumerate(input_pairs):\\n\",\n \" counts = counts_list[i]\\n\",\n \"\\n\",\n \" # ---- TODO : कार्य 1 ---\\n\",\n \" # जीतने के लिए लक्ष्य (a XOR b) मान निर्धारित करें\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" # --- TODO समाप्त ---\\n\",\n \"\\n\",\n \" wins_for_this_case = 0\\n\",\n \"\\n\",\n \" # ---- TODO : कार्य 2 ---\\n\",\n \" # ऊपर निर्धारित जीत की शर्त को पूरा करने वाले कुल शॉट्स की संख्या की गणना करें। 'target_xor_result' को जांचें\\n\",\n \"\\n\",\n \"\\n\",\n \" # --- TODO समाप्त --\\n\",\n \"\\n\",\n \" prob = wins_for_this_case / SHOTS if SHOTS > 0 else 0\\n\",\n \" win_probabilities[(x, y)] = prob\\n\",\n \" print(f\\\"Inputs (x={x}, y={y}): Target (a XOR b) = {target_xor_result}. Win Probability = {prob:.4f}\\\")\\n\",\n \"\\n\",\n \"avg_win_prob = sum(win_probabilities.values()) / 4.0\\n\",\n \"P_win_quantum_theory = np.cos(np.pi / 8)**2 # ~0.8536\\n\",\n \"P_win_classical_limit = 0.75\\n\",\n \"\\n\",\n \"print(\\\"\\\\n--- Overall Performance ---\\\")\\n\",\n \"print(f\\\"Experimental Average Win Probability: {avg_win_prob:.4f}\\\")\\n\",\n \"print(f\\\"Theoretical Quantum Win Probability: {P_win_quantum_theory:.4f}\\\")\\n\",\n \"print(f\\\"Classical Limit Win Probability: {P_win_classical_limit:.4f}\\\")\\n\",\n \"\\n\",\n \"if avg_win_prob > P_win_classical_limit + 0.01: # Allow for small simulation variance\\n\",\n \" print(f\\\"\\\\nSuccess! Your result ({avg_win_prob:.4f}) clearly beats the classical 75% limit!\\\")\\n\",\n \" print(f\\\"It's likely close to the theoretical quantum prediction of {P_win_quantum_theory:.4f}.\\\")\\n\",\n \"elif avg_win_prob > P_win_classical_limit - 0.02 : # Could be noise or minor error\\n\",\n \" print(f\\\"\\\\nClose, but no cigar? Your result ({avg_win_prob:.4f}) is around the classical limit ({P_win_classical_limit:.4f}).\\\")\\n\",\n \" print(\\\"Check your solutions for Exercises 1-4 carefully, especially the win counting logic in Ex 4.\\\")\\n\",\n \"else:\\n\",\n \" print(f\\\"\\\\nHmm, the result ({avg_win_prob:.4f}) is unexpectedly low, even below the classical limit.\\\")\\n\",\n \" print(\\\"There might be an error in Exercises 1-4. Please review your circuit and analysis code.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a8842d57-eea6-4791-b5a9-0d4b37219c15\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# निम्नलिखित कोड का उपयोग करके अपना उत्तर सबमिट करें\\n\",\n \"grade_lab1_ex5(counts_list, avg_win_prob)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5ced3f21-6210-46fc-b9c8-e539e88f854e\",\n \"metadata\": {},\n \"source\": [\n \"### Conclusion: The Quantum Edge\\n\",\n \"\\n\",\n \"तो, हमने क्या पाया? यदि आपने अभ्यासों को सफलतापूर्वक पूरा किया है, तो आपका सिमुलेशन स्पष्ट रूप से दिखाएगा कि एलिस और बॉब ने अपने साझा उलझे हुए क्यूबिट्स (entangled qubits) और चतुर मापन विधियों का उपयोग करके औसतन जीत की दर **क्लासिकल सीमा (significantly)** 75% से काफी अधिक हासिल की — जो सैद्धांतिक रूप से क्वांटम अधिकतम लगभग **85.4%** के करीब हो सकती है।\\n\",\n \"\\n\",\n \"यह केवल एक गणितीय चाल नहीं है; यह इस बात को दर्शाता है कि प्रकृति सबसे मूलभूत स्तर पर कैसे कार्य करती है। एंटैंगलमेंट (उलझाव) दूर-दराज के कणों के बीच ऐसे सहसंबंधों (correlations) की अनुमति देता है जो किसी भी क्लासिकल सिद्धांत से अधिक मजबूत होते हैं। यह \\\"दूरी पर डरावना प्रभाव\\\" (spooky action at a distance) प्रकाश की गति से तेज संचार की अनुमति नहीं देता, लेकिन यह कई क्वांटम प्रौद्योगिकियों की नींव है।\\n\",\n \"\\n\",\n \"**चर्चा बिंदु:** इन परिणामों के आधार पर, CHSH गेम वास्तविकता की प्रकृति के बारे में क्या दर्शाता है जो पारंपरिक क्लासिकल सोच से अलग है? क्या आप ऐसे संभावित अनुप्रयोगों के बारे में सोच सकते हैं जहां ये क्लासिकल से अधिक मजबूत सहसंबंध उपयोगी हो सकते हैं?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"100cdba6-f257-4b0a-b905-380c1876839a\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2. क्वांटम टेलीपोर्टेशन - डरावने प्रभाव के साथ रहस्य भेजना\\n\",\n \"\\n\",\n \"कल्पना कीजिए कि एलिस के पास एक क्यूबिट (`q0`) है जो एक विशेष, नाज़ुक क्वांटम अवस्था (`|ψ⟩`) में है। वह इस सटीक अवस्था को बॉब तक भेजना चाहती है, जो बहुत दूर है। वह केवल क्यूबिट को शारीरिक रूप से नहीं भेज सकती (शायद वह बहुत नाज़ुक है, या उसे मूल कण की ज़रूरत है)। क्या वह किसी तरह *केवल अवस्था* को ही स्थानांतरित कर सकती है?\\n\",\n \"\\n\",\n \"क्लासिकल रूप से, यह असंभव लगता है। अगर एलिस अपने क्यूबिट को मापने की कोशिश करती है यह जानने के लिए कि वह किस अवस्था में है, तो मापन स्वयं ही सुपरपोजीशन को ढहाकर मूल अवस्था को नष्ट कर सकता है।\\n\",\n \"\\n\",\n \"यहाँ आता है **क्वांटम टेलीपोर्टेशन!**! यह एक अद्भुत प्रोटोकॉल है जो **क्वांटम उलझाव (quantum entanglement)** and **क्लासिकल संचार** का उपयोग करके एक अज्ञात क्वांटम अवस्था को एक क्यूबिट से दूसरे में स्थानांतरित करता है।\\n\",\n \"\\n\",\n \"**स्पष्टीकरण:** हम कण को नहीं, बल्कि उसकी *अवस्था* को टेलीपोर्ट कर रहे हैं! यहाँ किसी इंसान को बीम नहीं किया जा रहा।\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** इस प्रयोगशाला में, आप Qiskit का उपयोग करके क्वांटम टेलीपोर्टेशन प्रोटोकॉल को लागू करेंगे। आप आवश्यक क्वांटम संसाधनों को तैयार करने, प्रमुख चरणों को पूरा करने, और यह सत्यापित करने के लिए कोड पूरा करेंगे कि बॉब को एलिस की क्वांटम अवस्था सफलतापूर्वक प्राप्त हो गई है।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f1dbf7d4-97aa-4fdd-90ed-51f55c74007b\",\n \"metadata\": {},\n \"source\": [\n \"### टेलीपोर्टेशन प्रोटोकॉल: चरण-दर-चरण\\n\",\n \"\\n\",\n \"इस प्रोटोकॉल के लिए तीन क्यूबिट्स और दो क्लासिकल बिट्स की आवश्यकता होती है:\\n\",\n \"* **`q0`:** ऐलिस का क्यूबिट, जो प्रारंभ में उस अवस्था `|ψ⟩` में होता है जिसे हम टेलीपोर्ट करना चाहते हैं।\\n\",\n \"* **`q1`:** ऐलिस की साझा उलझी हुई जोड़ी का आधा हिस्सा।\\n\",\n \"* **`q2`:** बॉब की साझा उलझी हुई जोड़ी का आधा हिस्सा।\\n\",\n \"* **`c0`, `c1`:** ऐलिस के मापन परिणामों को संग्रहीत करने के लिए क्लासिकल बिट्स।\\n\",\n \"\\n\",\n \"यहाँ योजना है:\\n\",\n \"1. **(वैकल्पिक) ऐलिस की अवस्था `|ψ⟩` को `q0` पर तैयार करें।** (हम सत्यापन के लिए एक विशेष अवस्था जैसे `|+>` बनाएंगे)।\\n\",\n \"2. **उलझाव बनाएँ:** `q1` और `q2` के बीच एक बेल जोड़ी (Bell pair) तैयार करें।\\n\",\n \"3. **ऐलिस के संचालन:** ऐलिस अपने दो क्यूबिट्स (`q0` और `q1`) पर \\\"बेल मापन\\\" (Bell measurement) करती है और परिणाम `c0` और `c1` में स्टोर करती है।\\n\",\n \"4. **क्लासिकल संचार:** ऐलिस अपने दो क्लासिकल बिट्स (`c0`, `c1`) बॉब को भेजती है।\\n\",\n \"5. **बॉब का सुधार:** बॉब अपने क्यूबिट (`q2`) पर विशिष्ट क्वांटम गेट्स (X और/या Z) लागू करता है, जो उसे प्राप्त `c0` और `c1` के मानों पर निर्भर होते हैं।\\n\",\n \"\\n\",\n \"यदि सब कुछ सही ढंग से किया गया, तो बॉब का क्यूबिट `q2` ऐलिस के मूल `q0` की अवस्था `|ψ⟩` में परिवर्तित हो जाएगा!\\n\",\n \"\\n\",\n \"
\\n\",\n \"आपके गहन अध्ययन के लिए \\n\",\n \"\\n\",\n \"क्वांटम टेलीपोर्टेशन की अधिक गहन व्याख्या और खोज के लिए, आप IBM Quantum Learning संसाधनों को देख सकते हैं: [Quantum Teleportation](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/quantum-teleportation) और [Entanglement in Action](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information/entanglement-in-action/quantum-teleportation) जो John Watrous द्वारा तैयार की गई हैं। ये उनके [Basics of Quantum Information](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information) कोर्स का हिस्सा हैं।\\n\",\n \"
\\n\",\n \"\\n\",\n \"### टूल: Qiskit डायनामिक सर्किट्स\\n\",\n \"\\n\",\n \"ऐलिस के मापन पर आधारित बॉब की क्रियाओं के लिए, हम डायनामिक सर्किट्स का उपयोग करते हैं। यह सुविधा सर्किट के मध्य में किए गए मापन से प्राप्त क्लासिकल जानकारी को बाद की क्वांटम क्रियाओं को प्रभावित करने की अनुमति देती है। यह क्षमता टेलीपोर्टेशन जैसे प्रोटोकॉल और क्वांटम हार्डवेयर पर प्रभावी दीर्घ दूरी के उलझाव (long-range entanglement) के निर्माण के लिए आवश्यक है, जैसा कि हाल के शोध (उदाहरण: Bäumer et al., PRX QUANTUM 5, 030339 (2024)) में खोजा गया है।\\n\",\n \"\\n\",\n \"\\n\",\n \"**Reference:**\\n\",\n \"* [Qiskit documentation on dynamic circuits and conditional operations](https://quantum.cloud.ibm.com/docs/guides/classical-feedforward-and-control-flow)\\n\",\n \"* [Efficient Long-Range Entanglement Using Dynamic Circuits](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.030339)\\n\",\n \"\\n\",\n \"**टेलीपोर्टेशन के लिए प्रमुख अवधारणाएँ:**\\n\",\n \"\\n\",\n \"1. मध्य-सर्किट मापन (Mid-Circuit Measurement):\\n\",\n \" * Qiskit में, आप सर्किट के किसी भी बिंदु पर क्यूबिट को माप सकते हैं और परिणाम को एक क्लासिकल बिट में स्टोर कर सकते हैं — केवल अंत में नहीं। यह आवश्यक है ताकि ऐलिस अपने क्यूबिट्स (`q0` और `q1`) को माप सके और क्लासिकल बिट्स `c0` और `c1` प्राप्त कर सके।\\n\",\n \"\\n\",\n \" * इसके लिए `QuantumCircuit.measure(qubit, classical_bit)` निर्देश का उपयोग किया जाता है।\\n\",\n \"\\n\",\n \"2. शर्तीय क्रियाएं (Conditional Operations) (if_test):\\n\",\n \" * ऐलिस के मापन के बाद, बॉब को अपने क्यूबिट (`q2`) पर सुधारात्मक गेट्स लागू करने होते हैं, जो उसे प्राप्त क्लासिकल बिट्स (`c0`, `c1`) पर आधारित होते हैं।\\n\",\n \" * Qiskit गेट्स को क्लासिकल बिट्स की स्थिति पर आधारित करके शर्तीय रूप से लागू करने की अनुमति देता है।\\n\",\n \" * `QuantumCircuit.if_test((classical_bit, value), true_circuit, false_circuit=None)` विधि (Qiskit के नवीनतम संस्करणों में) या पुरानी `Instruction.c_if(classical_bit, value)` विधि का उपयोग किया जा सकता है ताकि गेट केवल तब लागू हो जब कोई क्लासिकल बिट किसी विशेष मान (0 या 1) में हो।\\n\",\n \" \\n\",\n \" * टेलीपोर्टेशन के लिए, बॉब निम्नलिखित शर्तीय क्रियाओं का उपयोग करेगा:\\n\",\n \" * यदि `c1` (यहाँ `cr_alice_tele[1]`) का मान 1 हो, तो `q2` पर X गेट लागू करें।\\n\",\n \" * यदि `c0` (यहाँ `cr_alice_tele[0]`) का मान 1 हो, तो `q2` पर Z गेट लागू करें।\\n\",\n \"\\n\",\n \"ये विशेषताएँ टेलीपोर्टेशन प्रोटोकॉल के क्लासिकल संचार पक्ष को एक ही क्वांटम सर्किट विवरण के भीतर सीधे कार्यान्वित और अनुकरण (simulate) करने की अनुमति देती हैं।\\n\",\n \"\\n\",\n \"नोट: कृपया शर्तीय गेट्स के क्रम को याद रखें! क्वांटम कंप्यूटिंग में **AB! = BA**, अर्थात् क्रियाओं का क्रम महत्वपूर्ण होता है।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a7505da2-a554-4e2e-9844-70c947059e78\",\n \"metadata\": {},\n \"source\": [\n \"### Qiskit में टेलीपोर्टेशन सर्किट बनाना\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"व्यायाम 6: क्वांटम टेलीपोर्टेशन\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** एक पूर्ण क्वांटम सर्किट बनाना जो ऐलिस के संदेश क्यूबिट से बॉब के क्यूबिट तक एक अज्ञात क्वांटम अवस्था को टेलीपोर्ट करे, जिसमें एक उलझी हुई बेल जोड़ी और सर्किट के मध्य में मापन के साथ शर्तीय संचालन शामिल हो।\\n\",\n \"\\n\",\n \"**कार्यक्षेत्र:**\\n\",\n \"* **चरण 1:** `q1` और `q2` के बीच साझा उलझी हुई बेल जोड़ी $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$ बनाएँ। \\n\",\n \"* **चरण 2:** ऐलिस के बेल मापन गेट्स लागू करें (वास्तविक मापन से पहले)।\\n\",\n \"* **चरण 3:** ऐलिस के मापन परिणामों के आधार पर बॉब के शर्तीय सुधारात्मक गेट्स लागू करें।\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"af207afd-1a82-43c8-8254-6e0b422f613d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# क्वांटम और क्लासिकल रजिस्टर परिभाषित करें\\n\",\n \"qr_tele = QuantumRegister(3, name='q')\\n\",\n \"cr_alice_tele = ClassicalRegister(2, name='c_alice') # ऐलिस के मापन के लिए\\n\",\n \"\\n\",\n \"# सत्यापन के लिए स्टेटवेक्टर के साथ, हम बॉब के अंतिम क्यूबिट को इस सर्किट में मापते नहीं हैं।\\n\",\n \"# यदि हम हार्डवेयर पर चलाते और काउंट्स द्वारा सत्यापन करते, तो हम बॉब के लिए एक क्लासिकल बिट जोड़ते।\\n\",\n \"teleport_qc = QuantumCircuit(qr_tele, cr_alice_tele, name='Teleportation')\\n\",\n \"\\n\",\n \"# ऐलिस की संदेश अवस्था तैयार करें |ψ⟩ = |+⟩ को q0 पर\\n\",\n \"teleport_qc.h(qr_tele[0])\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# ---- TODO : कार्य 1 ---\\n\",\n \"# चरण 1: q1 (ऐलिस) और q2 (बॉब) के बीच बेल जोड़ी बनाएँ\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- TODO समाप्त --\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# ---- TODO : कार्य 2 ---\\n\",\n \"# चरण 2: ऐलिस का बेल मापन (गेट्स वाला भाग)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- TODO समाप्त --\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# ऐलिस अपने क्यूबिट्स q0 और q1 को मापती है\\n\",\n \"teleport_qc.measure(qr_tele[0], cr_alice_tele[0]) # q0 -> c0\\n\",\n \"teleport_qc.measure(qr_tele[1], cr_alice_tele[1]) # q1 -> c1\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# ---- TODO : कार्य 3 ---\\n\",\n \"# चरण 3: बॉब की शर्तीय सुधार गेट्स q2 पर लागू करें\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- TODO समाप्त --\\n\",\n \"\\n\",\n \"print(\\\"Full Teleportation Circuit (Check your Exercises 1, 2, 3):\\\")\\n\",\n \"display(teleport_qc.draw('mpl'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4aa33305-02cd-424e-bd3d-4788e4cfcc71\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# निम्नलिखित कोड का उपयोग करके अपना उत्तर सबमिट करें\\n\",\n \"grade_lab1_ex6(teleport_qc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"57c81d2f-1067-42c7-a476-375d2faf4ed4\",\n \"metadata\": {},\n \"source\": [\n \"### सिमुलेशन और सत्यापन\\n\",\n \"\\n\",\n \"हम कैसे सत्यापित करें कि टेलीपोर्टेशन सफल रहा? हम सीधे बॉब के क्यूबिट की अवस्था को प्रोटोकॉल के बाद 'देख' नहीं सकते। लेकिन, *prepared* चूँकि हमने ऐलिस की प्रारंभिक अवस्था `|ψ⟩` (हमने `|+>` चुना था) तैयार की थी, हम एक विशेष प्रकार की सिमुलेशन का उपयोग करके जाँच सकते हैं कि बॉब का क्यूबिट `q2` उसी अवस्था में पहुँचा या नहीं।\\n\",\n \"\\n\",\n \"हम `AerSimulator` के साथ `save_statevector` का उपयोग करेंगे ताकि यह जांच सकें कि बॉब का क्यूबिट `q2` ऐलिस की मूल अवस्था (`|+>`) में समाप्त हुआ या नहीं। यह सिम्युलेटर अंतिम क्वांटम स्टेट वेक्टर की गणना करता है।\\n\",\n \"\\n\",\n \"
\\n\",\n \"व्यायाम 7 - कोई ग्रेडिंग नहीं: क्वांटम टेलीपोर्टेशन के परिणाम का विश्लेषण करें\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** सर्किट का सिमुलेशन करके और बॉब के क्यूबिट की अंतिम अवस्था को विज़ुअलाइज़ करके, ऐलिस की क्वांटम अवस्था के सफल टेलीपोर्टेशन को सत्यापित करें।\\n\",\n \"\\n\",\n \"**कार्य:**\\n\",\n \"* अंतिम स्टेटवेक्टर को निकालें और `plot_bloch_multivector` का उपयोग करके बॉब के क्यूबिट (`q2`) को विज़ुअलाइज़ करें। इसकी तुलना ऐलिस की प्रारंभिक अवस्था (`q0`) से करें।\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"85f73394-d361-4c0a-afd3-28dc2005c07a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"# Use Statevector Simulator\\n\",\n \"print(\\\"Using statevector simulator...\\\")\\n\",\n \"sv_simulator = AerSimulator(method='statevector') # स्पष्टता के लिए विधि को स्पष्ट रूप से सेट करें\\n\",\n \"teleport_qc_sv = teleport_qc.copy() # स्टेटवेक्टर सिमुलेशन के लिए कॉपी के साथ काम करें\\n\",\n \"teleport_qc_sv.save_statevector() # अंतिम पर स्टेटवेक्टर को सेव करें\\n\",\n \"\\n\",\n \"print(\\\"Running statevector simulation...\\\")\\n\",\n \"job_sv = sv_simulator.run(teleport_qc_sv) # shots=1 डिफ़ॉल्ट होता है स्टेटवेक्टर के लिए\\n\",\n \"result_sv = job_sv.result()\\n\",\n \"\\n\",\n \"if result_sv.success:\\n\",\n \" print(\\\"Simulation successful.\\\")\\n\",\n \" final_statevector = result_sv.get_statevector()\\n\",\n \" print(\\\"Statevector retrieved successfully.\\\")\\n\",\n \" print(\\\"\\\\nVisualizing final qubit states (q2 should match initial q0 state |+>):\\\")\\n\",\n \" # q0 था |+> (जो +X की दिशा में पॉइंट करता है)। टेलीपोर्टेशन के बाद, q2 को |+> होना चाहिए।\\n\",\n \" # q0 और q1 की अवस्थाएं ऐलिस के मापन के बाद की हैं, इसलिए वे कोलेप्स हो जाएंगी।\\n\",\n \" \\n\",\n \" display(\\\"TODO\\\") # TODO: plot_bloch_multivector का उपयोग करें final_state को प्लॉट करने के लिए\\n\",\n \" \\n\",\n \"else:\\n\",\n \" print(f\\\"Statevector simulation failed! Status: {result_sv.status}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"be79ba6b-c8d7-44e3-b7fa-c2043ea7cff0\",\n \"metadata\": {},\n \"source\": [\n \"### परिणामों की व्याख्या\\n\",\n \"\\n\",\n \"यदि व्यायाम 6 सही तरीके से किया गया है, तो आपको निम्नलिखित दिखाई देना चाहिए:\\n\",\n \"* `q0` (ऐलिस का मूल क्यूबिट): रैंडम अवस्था (मापन के कारण कोलैप्स हो गई)।\\n\",\n \"* `q1` (ऐलिस का एंटैंगल्ड क्यूबिट): रैंडम अवस्था (मापन के कारण कोलैप्स हो गई)।\\n\",\n \"* `q2` (बॉब का एंटैंगल्ड क्यूबिट): ब्लॉख स्फीयर वेक्टर **पॉज़िटिव X-अक्ष** की दिशा में इशारा कर रहा है। यह `|+⟩` अवस्था है, जो ऐलिस की प्रारंभिक `q0` अवस्था से मेल खाती है!\\n\",\n \"\\n\",\n \"**सफलता!** अवस्था `|ψ⟩ = |+⟩` को सफलतापूर्वक टेलीपोर्ट किया गया।\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2c91c647-3843-4574-ad18-c283e16f8d16\",\n \"metadata\": {},\n \"source\": [\n \"### निष्कर्ष: एंटैंगलमेंट और सूचना का जादू\\n\",\n \"\\n\",\n \"आपने सफलतापूर्वक क्वांटम टेलीपोर्टेशन प्रोटोकॉल को लागू किया!\\n\",\n \"\\n\",\n \"मुख्य बातें:\\n\",\n \"* टेलीपोर्टेशन एक अज्ञात क्वांटम *अवस्था* को साझा किए गए एंटैंगलमेंट और क्लासिकल संचार के माध्यम से स्थानांतरित करता है।\\n\",\n \"* यह भौतिक कण को स्वयं ट्रांसफर नहीं करता।\\n\",\n \"* ऐलिस के क्यूबिट पर मूल अवस्था नष्ट हो जाती है (जो नो-क्लोनिंग प्रमेय के अनुरूप है)।\\n\",\n \"* इसमें क्लासिकल संचार की आवश्यकता होती है (ऐलिस के मापन परिणाम बॉब को भेजे जाते हैं), इसलिए यह प्रकाश की गति से तेज सूचना स्थानांतरण नहीं करता।\\n\",\n \"\\n\",\n \"क्वांटम टेलीपोर्टेशन, क्वांटम संचार और गणना की नींव है।\\n\",\n \"\\n\",\n \"**चर्चा का विषय**: ऐलिस `q0` को बस माप क्यों नहीं सकती (उदाहरण के लिए पहले Z बेसिस में, फिर X बेसिस में) और परिणाम बॉब को पुनर्निर्माण के लिए भेज दे? टेलीपोर्टेशन में क्या अंतर है?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f72ca7c2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#नीचे दिए गए कोड से अपनी सबमिशन स्थिति जांचें\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4a1fb2a1\",\n \"metadata\": {},\n \"source\": [\n \"# (बोनस चैलेंज) वास्तविक क्वांटम हार्डवेयर पर टेलीपोर्टेशन\\n\",\n \"\\n\",\n \"\\n\",\n \"हमने सिम्युलेटर पर टेलीपोर्टेशन को पूरी तरह से काम करते देखा है। अब, आइए इसे एक **real IBM Quantum backend**! बैकएंड पर आज़माएँ! यही वह जगह है जहाँ क्वांटम कंप्यूटिंग की असली चुनौतियाँ और रोमांच शुरू होते हैं। वास्तविक डिवाइसेज़ में शोर (noise) और सीमित क्यूबिट कनेक्टिविटी होती है।\\n\",\n \"\\n\",\n \"
\\n\",\n \"डायनामिक सर्किट्स को अपग्रेड किया जा रहा है!\\n\",\n \"\\n\",\n \"वास्तविक हार्डवेयर पर डायनामिक सर्किट्स के लिए वर्तमान में एक अपडेट जारी है, और आप उन पहले लोगों में से एक हैं जो इस नए संस्करण का परीक्षण कर रहे हैं! हालांकि, इस शुरुआती एक्सेस के साथ कुछ शर्तें भी हैं: \\n\",\n \"* 1. इनको सक्रिय करने के लिए कुछ अतिरिक्त कोड की पंक्तियाँ जोड़नी होंगी (नीचे देखें)।\\n\",\n \"* 2. यह प्रारंभिक एक्सेस Session या Batch का उपयोग करते समय with ... ब्लॉक के अंदर काम नहीं करता है (जैसे with Batch(...))। हालाँकि, आप इसे प्राइमिटिव को सीधे पास कर सकते हैं (जैसे Sampler(mode=batch))।\\n\",\n \"* 3. आप किसी if_test कंडीशन में 32-बिट से अधिक का ClassicalRegister उपयोग नहीं कर सकते। हालाँकि, आप कई ClassicalRegisters का उपयोग कर सकते हैं, प्रत्येक ≤32 बिट्स में।\\n\",\n \"नेस्टेड कंडीशनल की अनुमति नहीं है।\\n\",\n \"* 5. किसी कंडीशनल ब्लॉक के अंदर measure या reset नहीं होना चाहिए।\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"वास्तविक हार्डवेयर पर डायनामिक सर्किट्स का उपयोग करने के लिए दो चरणों की आवश्यकता होती है:\\n\",\n \"\\n\",\n \"\\n\",\n \"चरण 1: उस हार्डवेयर बैकएंड में निर्देश जोड़ें जिसका आप उपयोग कर रहे हैं:\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" # (1) Patch the backend target with IfElseOp\\n\",\n \" from qiskit.circuit import IfElseOp\\n\",\n \" backend.target.add_instruction(IfElseOp, name=\\\"if_else\\\")\\n\",\n \" \\n\",\n \"```\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"चरण 2: जब आप जॉब सबमिट करें, तो एक्सपेरिमेंटल विकल्प सक्षम करें:\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" #(2) Submit the job with the experimental option\\n\",\n \" sampler.options.experimental = {\\\"execution_path\\\" : \\\"gen3-experimental\\\"}\\n\",\n \" \\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f65c02e-5def-46aa-9546-ad9026669b4b\",\n \"metadata\": {},\n \"source\": [\n \"### डायनामिक सर्किट्स: वास्तविक हार्डवेयर की कुंजी\\n\",\n \"\\n\",\n \"टेलीपोर्टेशन प्रोटोकॉल में मूल रूप से **डायनामिक सर्किट्स** की आवश्यकता होती है: ऐलिस अपने क्यूबिट्स को मापती है, और बॉब **क्लासिकली कम्युनिकेट** किए गए इन परिणामों के आधार पर यह तय करता है कि अपने क्यूबिट पर कौन से करेक्शन गेट्स लगाए जाएं। Qiskit इन \\\"मिड-सर्किट मेज़रमेंट्स\\\" और \\\"क्लासिकल फीड-फॉरवर्ड\\\" ऑपरेशनों को सक्षम बनाता है।\\n\",\n \"\\n\",\n \"हालिया शोध, जैसे कि Bäumer आदि द्वारा किया गया \\\"Efficient Long-Range Entanglement Using Dynamic Circuits\\\" (PRX QUANTUM 5, 030339 (2024)), डायनामिक सर्किट्स की शक्ति को दर्शाता है। उन्होंने दिखाया कि CNOT गेट टेलीपोर्टेशन और लंबे दूरी पर GHZ स्टेट्स तैयार करने जैसे कार्यों के लिए, डायनामिक सर्किट्स बड़े-स्केल डिवाइसेज़ पर अपने केवल यूनिटरी समकक्षों की तुलना में बेहतर प्रदर्शन कर सकते हैं, क्योंकि वे क्वांटम सर्किट की गहराई को दूरी की परवाह किए बिना स्थिर रखते हैं।\\n\",\n \"\\n\",\n \"हमारे टेलीपोर्टेशन प्रोटोकॉल के लिए, डायनामिक सर्किट्स ऐलिस के मापन परिणामों को सीधे बॉब के गेट अप्लिकेशनों को वास्तविक समय में नियंत्रित करने की अनुमति देते हैं। IBM Quantum बैकएंड्स तक पहुँच **IBM Cloud** के माध्यम से की जाती है। इसके लिए IBM Cloud अकाउंट और Quantum सेवाओं का एक्टिवेशन जरूरी है।\\n\",\n \"\\n\",\n \"### चुनौती: कितनी दूर तक आप इसे भेज सकते हैं?\\n\",\n \"\\n\",\n \"**आपका लक्ष्य:** एक क्वांटम स्टेट (जैसे, $|+\\\\rangle$) को ऐलिस के 0वें क्यूबिट (मैसेज) से एक अन्य क्यूबिट (बॉब) तक एक वास्तविक IBM Quantum बैकएंड पर सफलतापूर्वक टेलीपोर्ट करना, आदर्श रूप से ऐसे क्यूबिट्स को चुनकर जो चिप पर सीधे जुड़े न हों, और Qiskit डायनामिक सर्किट्स का उपयोग करके टेलीपोर्टेशन करना। टेलीपोर्टेशन की fidelity (विश्वसनीयता) को देखें और उसे बेहतर बनाने की कोशिश करें।\\n\",\n \"\\n\",\n \"**वास्तविक बैकएंड रन के लिए बुनियादी चरण:**\\n\",\n \"\\n\",\n \"आप अधिक विस्तृत विवरण lab0 में देख सकते हैं, लेकिन यहाँ हम एक सारांश दे रहे हैं।\\n\",\n \"\\n\",\n \"1. IBM Quantum एक्सेस सेटअप करें (IBM Cloud के माध्यम से):\\n\",\n \" * यदि आपके पास अकाउंट नहीं है, तो IBM Cloud पर एक अकाउंट बनाएँ और Quantum सेवाओं को सक्रिय करें।\\n\",\n \" * IBM Cloud डैशबोर्ड से अपना API टोकन प्राप्त करें।\\n\",\n \" * `QiskitRuntimeService` का उपयोग करके अपना अकाउंट सेव करें और उपलब्ध बैकएंड्स की सूची प्राप्त करें।\\n\",\n \" ```python\\n\",\n \" from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \" QiskitRuntimeService.save_account(channel=\\\"ibm_quantum_platform\\\", token=\\\"YOUR_IBM_CLOUD_API_TOKEN\\\", instance=\\\"YOUR_CRN_INSTANCE_ID_OR_CLOUD_INSTANCE_NAME\\\", overwrite=True)\\n\",\n \" service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \" print(service.backends())\\n\",\n \" ```\\n\",\n \" \\n\",\n \"2. एक बैकएंड चुनें:\\n\",\n \" * सूची से एक उपलब्ध बैकएंड चुनें। [least_busy](https://quantum.cloud.ibm.com/docs/api/qiskit-ibm-runtime/qiskit-runtime-service) QPU का चयन एक अच्छा विकल्प हो सकता है।\\n\",\n \" * उसकी coupling map की जाँच करें और उपयुक्त क्यूबिट्स चुनें। इस चैलेंज के लिए, ऐलिस के मैसेज (`q_A_msg`) और बॉब के अंतिम क्यूबिट (`q_B_ent`) के लिए ऐसे क्यूबिट्स चुनने की कोशिश करें जो “दूरी पर” हों या सीधे जुड़े न हों। ऐलिस का एंटैंगल्ड क्यूबिट (`q_A_ent`) आदर्श रूप से `q_A_msg` और `q_B_ent` दोनों से कनेक्ट हो सकना चाहिए।\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" #backend_name = \\\"ibm_your_chosen_backend\\\" # e.g., \\\"ibm_brisbane\\\" or a simulator like \\\"ibmq_qasm_simulator\\\" to test flow\\n\",\n \" backend = service.least_busy()\\n\",\n \" # print(f\\\"Coupling map: {backend.configuration().coupling_map}\\\")\\n\",\n \" # print(f\\\"Number of qubits: {backend.configuration().n_qubits}\\\")\\n\",\n \" # from qiskit.visualization import plot_gate_map # if needed\\n\",\n \" # display(plot_gate_map(backend)) # Visualizes connectivity\\n\",\n \" \\n\",\n \" ```\\n\",\n \"आप अधिकतर कोड को समान ही रख सकते हैं। सिम्युलेटर के स्थान पर वास्तविक क्वांटम प्रोसेसिंग यूनिट (QPU) का उपयोग करने के लिए, केवल वास्तविक बैकएंड ऑब्जेक्ट को `Sampler` में पास करें। हालाँकि, वास्तविक क्वांटम हार्डवेयर को टार्गेट करने पर आपके सर्किट को एक `PassManager` के साथ transpile करना बेहद आवश्यक होता है।\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"टिप्स:\\n\",\n \"\\n\",\n \"* लंबी दूरी की एंटैंगलमेंट के लिए टेक्निकल रेफरेंस: IBM हार्डवेयर पर लंबी दूरी की एंटैंगलमेंट तैयार करने की गहराई से टेक्निकल समझ के लिए, ट्यूटोरियल देखें: [Efficiently generating long-range entanglement](https://quantum.cloud.ibm.com/docs/tutorials/long-range-entanglement).\\n\",\n \" \\n\",\n \"* बड़े सर्किट्स का सिमुलेशन (20+ क्यूबिट्स):\\n\",\n \" * यदि आप टेलीपोर्टेशन स्कीम को 20 या अधिक क्यूबिट्स तक स्केल कर रहे हैं (जिसमें ऐलिस का मैसेज, उसकी ऐंसिला, बॉब का क्यूबिट, और रूटिंग या एडवांस टेलीपोर्टेशन स्कीम्स के लिए मध्यवर्ती क्यूबिट्स शामिल हैं), तो पूर्ण statevector का सिमुलेशन कम्प्यूटेशनली भारी हो सकता है।\\n\",\n \" * यदि आपका टेलीपोर्टेशन सर्किट (या इसके घटक जैसे Bell स्टेट निर्माण और Bell मापन) केवल **Clifford** गेट्स (H, S, X, Y, Z, CNOT, SWAP आदि) का उपयोग करके बनाए गए हैं, तो आप एक अधिक कुशल सिम्युलेटर का उपयोग कर सकते हैं।\\n\",\n \" * सुझाव: उपयोग करें `AerSimulator(method=\\\"stabilizer\\\")` । यह सिम्युलेटर Clifford सर्किट्स के लिए अनुकूलित है और बड़े क्यूबिट्स को प्रभावी रूप से हैंडल कर सकता है।\\n\",\n \"
\\n\",\n \"\\n\",\n \"量子状態転送の限界に挑戦してみてください！ぜひ結果を参加者同士で共有し、「より遠く」まで量子ビットを送る方法を議論しましょう。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eee76bb9-11ea-45d3-8cd5-853b140e7e9f\",\n \"metadata\": {},\n \"source\": [\n \"## 参考文献\\n\",\n \"\\n\",\n \"1. Young, Thomas (1804) I. The Bakerian Lecture. Experiments and calculations relative to physical optics. Phil. Trans. R. Soc. 941–16.\\n\",\n \"2. Fage, Arthur and Johansen, F. C. (1927). On the flow of air behind an inclined flat plate of infinite span. Proc. R. Soc. Lond. A116 170–197.\\n\",\n \"3. Clauser, J. F., Horne, M. A., Shimony, A., & Holt, R. A. (1969). Proposed Experiment to Test Local Hidden-Variable Theories. Phys. Rev. Lett., 23(15), 880–884.\\n\",\n \"4. Bouwmeester, D., Pan, J.-W., Mattle, K., Eibl, M., Weinfurter, H., & Zeilinger, A. (1997). Experimental quantum teleportation. Nature, 390(6660), 575–579.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae55945d-d627-42bd-8ce6-6f39b6c6d973\",\n \"metadata\": {},\n \"source\": [\n \"# 追加情報\\n\",\n \"\\n\",\n \"**作成：** Sophy Shin、James Weaver\\n\",\n \"\\n\",\n \"**監修：** Marcel Pfaffhauser, Jessie Yu, Junye Huang, Borja Peropadre \\n\",\n \"\\n\",\n \"**校正：** Meltem Tolunay, Abby Cross\\n\",\n \"\\n\",\n \"**翻訳：** Daiki Murata\\n\",\n \"\\n\",\n \"**バージョン：** 1.1.0\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"609f6ec0\",\n \"metadata\": {},\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-2/Lab2-ko.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 2: Cutting through the noise\\n\",\n \"\\n\",\n \"이번 랩에서는 양자 노이즈의 세계를 탐험하며 현대의 양자 컴퓨터에 발생하는 노이즈를 극복하고 원하는 데이터를 얻는 방법에 대해 알아보도록 하겠습니다. 작은 Max-cut 문제를 예시로 양자 컴퓨터에서 양자 알고리즘을 실행할 때에 노이즈를 어떻게 줄일 수 있는지 차근차근 배워봅시다. 양자 컴퓨터에 노이즈가 있더라도, 다양한 트랜스파일과 오차 완화 (Error Mitigation) 테크닉을 통해 오차를 최소화하고 올바른 결과를 얻을 수 있습니다. 랩의 마지막에는 랩에서 다룬 모든 테크닉들을 실제 양자 하드웨어에서 구현해보는 보너스 챌린지가 있습니다.\\n\",\n \"\\n\",\n \"# 목차\\n\",\n \"\\n\",\n \"0. [요구사항](#requirements)\\n\",\n \"1. [서론](#intro) \\n\",\n \" - [이번 랩의 목표](#spirit)\\n\",\n \" - [양자 노이즈란?](#quantum-noise) \\n\",\n \" - [노이즈의 원인과 지표](#sources)\\n\",\n \" * [연습문제 1: T1, T2, 게이트 충실도가 높고 측정 오류는 낮은 큐빗 찾아보기](#exercise_1)\\n\",\n \"2. [Max-cut 문제](#max-cut)\\n\",\n \" - [문제: 그래프 정의하기](#the-graph)\\n\",\n \" - [정의: 양자컴퓨터로 그래프 옮기기](#the-mapping)\\n\",\n \" * [연습문제 2: Max-cut 문제를 Ising 해밀토니안으로 대응시키기](#exercise_2)\\n\",\n \" - [QAOA 알고리즘](#qaoa-solution)\\n\",\n \" - [해 확인하기](#checking)\\n\",\n \"3. [양자 노이즈 시뮬레이터](#noisy-simulator)\\n\",\n \" - [백엔드 선택](#choosing-backend)\\n\",\n \" * [연습문제 3: 오류율 예측](#exercise_3)\\n\",\n \" - [NEAT를 이용한 오류율 예측](#neat)\\n\",\n \"4. [트랜스파일러](#transpiler)\\n\",\n \" - [이중 큐빗 게이트의 수 최소화하기](#min-two-qubit)\\n\",\n \" - [최적의 레이아웃 찾기](#opt-layout)\\n\",\n \" * [연습문제 4: 모든 적합한 레이아웃 찾기](#exercise_4)\\n\",\n \" * [연습문제 5: 트랜스파일러를 사용해 최선의 레이아웃 찾기](#exercise_5)\\n\",\n \"5. [오차 완화](#em)\\n\",\n \" - [무잡음 추정법 (ZNE)](#zne)\\n\",\n \" * [연습문제 6a: 양자 회로에서 전체적 접기 구현](#exercise_6a)\\n\",\n \" * [연습문제 6b: 양자 회로에서 국소적 접기 구현](#exercise_6b)\\n\",\n \"6. [결론](#conclusions)\\n\",\n \"7. [보너스 챌린지: 더 키워보세요!](#bonus)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 0. 요구사항\\n\",\n \"\\n\",\n \"이 랩을 실행하는데 필요한 라이브러리는 다음과 같습니다:\\n\",\n \"\\n\",\n \"* Qiskit SDK v2.0 with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"* Rustworkx (`pip install rustworkx`)\\n\",\n \"* Qiskit Aer v0.17\\n\",\n \"* Pylatexenc\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install -U qiskit \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Qiskit 버전 `>=2.0.0` 그레이더 버전 `>=0.22.10` 이상이 설치되어 있어야 합니다. 만약 버전이 다르게 표시된다면 커널을 재시작해보시고, 그래도 안 된다면 그레이더를 다시 설치해야 할 수 있습니다.\\n\",\n \"랩 0에 따라 환경설정을 완료했는지, IBM Quantum 계정이 잘 저장되어 있는지 아래의 셀을 실행해 확인해주세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 계정이 잘 저장되어 있는지 확인합니다\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 불러오기\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import rustworkx as rx\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from rustworkx.visualization import mpl_draw as draw_graph\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from scipy.optimize import minimize\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.providers.fake_provider import GenericBackendV2\\n\",\n \"from qiskit.quantum_info import SparsePauliOp, Statevector, DensityMatrix, Operator\\n\",\n \"from qiskit.circuit.library import QAOAAnsatz\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.transpiler import Layout\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import (\\n\",\n \" Session,\\n\",\n \" EstimatorV2 as Estimator,\\n\",\n \" SamplerV2 as Sampler,\\n\",\n \" EstimatorOptions,\\n\",\n \")\\n\",\n \"from qiskit_ibm_runtime.debug_tools import Neat\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from utils import zne_method, plot_zne, plot_backend_errors_and_counts\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab2_ex1,\\n\",\n \" grade_lab2_ex2,\\n\",\n \" grade_lab2_ex3,\\n\",\n \" grade_lab2_ex4,\\n\",\n \" grade_lab2_ex5,\\n\",\n \" grade_lab2_ex6a,\\n\",\n \" grade_lab2_ex6b,\\n\",\n \")\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. 서론 \\n\",\n \"\\n\",\n \"### 1.1 이번 랩의 목표 \\n\",\n \"\\n\",\n \"*이번 랩에서는 적절한 트랜스파일 및 오차 완화 기술을 통해 IBM Quantum 하드웨어의 성능을 십분 활용하여 실생활의 문제를 해결하는 방법을 알아봅니다.*\\n\",\n \"\\n\",\n \"### 1.2 양자 노이즈란? \\n\",\n \"\\n\",\n \"노이즈는 양자 컴퓨터를 만들고 사용하는 모든 과정의 중심이 되는 개념으로, 현대 양자 컴퓨터의 주요한 특성 중 하나입니다. 하지만 양자 정보에서 노이즈란 정확히 어떤 것을 의미할까요? 노이즈(에러, 오류, 오차)라 함은 주어진 양자 계산을 수행하여 측정하였을 때 예상되는 결과를 얻지 못하도록 방해하고 왜곡시키는 작용입니다. 우리는 이러한 노이즈를 크게 두 가지로 구분합니다:\\n\",\n \"\\n\",\n \"- 결맞음(coherent) 오류: 이 종류의 노이즈는 결맞음을 해치지 않으면서 양자 계산 자체에서 발생하는 오류입니다. 주로 양자 게이트가 가해질 때에 존재하는 약간의 오차가 누적되어 발생하며, 이러한 오류는 정보를 파괴하지 않으므로, 이론상 원래 상태를 찾는 것도 가능합니다. 전형적인 예시로는 원래 각도 $\\\\theta$ 만큼 큐빗을 회전시켜야 하는 게이트가 부정확한 조정 등으로 인해 실제로는 $\\\\theta+\\\\varepsilon$ 만큼 큐빗을 회전시키는 경우가 있습니다. Hadamard 게이트가 이런 식으로 잘못 조정되어 있다면 완벽한 중첩 상태를 만들지 못할 것이고, 결과에 편향성이 생길 수도 있겠죠.\\n\",\n \"- 결어긋남(incoherent) 오류: 이 종류의 노이즈는 양자적인 시스템이 주변 환경과 상호 작용을 하기 때문에 발생합니다. 이 종류의 오류 때문에, 우리는 대부분의 양자 컴퓨터를 mK 스케일의 극저온으로 냉각하여 주변 환경과의 상호 작용을 최소화하고 양자적인 결맞음 상태를 최대한 유지하려고 합니다. 이 노이즈는 큐빗의 상태를 혼합된 (순수하지 않은) 상태로 만듭니다. 이것은 큐비트에 통제되지 않은 무작위성을 부여하기 때문에 더욱 치명적입니다. 구체적인 예시로는 들뜬 상태 $|1\\\\rangle$ 에 있던 큐비트가 시간이 지나면서 바닥 상태인 $|0\\\\rangle$ 으로 떨어지는 현상을 들 수 있습니다. 이것을 이완(relaxation)이라고 하며, 양자 시스템에서 이완이 일어나는 시간을 이완 시간 $T_1$ 이라고 합니다. 만약 어떤 이유로든 이 시간 이상 측정이 지연된다면 큐빗이 이완되어 제대로 된 결과를 얻을 수 없을 것입니다.\\n\",\n \"\\n\",\n \"### 1.3 노이즈의 원인과 지표 \\n\",\n \"\\n\",\n \"앞에서 잠깐 언급했지만, 노이즈의 원인은 다양합니다. 양자 게이트를 전자기 펄스로 완벽하게 구현하지 못하는 것을 포함해 측정 (판독) 오류, 양자 시스템의 결어긋남, 그 외 많은 것들이 있을 수 있겠죠.\\n\",\n \"\\n\",\n \"이러한 노이즈에 대한 양자 컴퓨터(또는 큐비트)의 저항력을 평가할 수 있는 지표 중 몇 가지를 논의해봅시다:\\n\",\n \"\\n\",\n \"* T1: 이완 시간은 $|1\\\\rangle$ 상태에 있는 큐빗이 $|0\\\\rangle$ 상태로 떨어지기까지의 시간을 나타냅니다.\\n\",\n \"* T2: 위상 어긋남(dephasing) 시간은 $|+\\\\rangle$ 상태에 있는 큐빗이 $|+\\\\rangle$ 와 $|-\\\\rangle$ 를 구분할 수 없는 혼합 상태로 떨어지기까지의 시간을 나타냅니다.\\n\",\n \"* 단일 큐빗 게이트 충실도(fidelity)는 하드웨어가 구현하는 단일 큐빗 게이트 $\\\\mathcal{E}$ 가 이상적인 유니터리 게이트 $U$ 에 얼마나 가까운지를 나타내는 값입니다. 충실도가 1 이면 실제 게이트와 이상적인 게이트가 오차 없이 정확히 일치하는 것입니다.\\n\",\n \"* 이중 큐빗 게이트 충실도는 하드웨어가 구현하는 이중 큐빗 게이트 $\\\\mathcal{E}_2$ 가 이상적인 유니터리 게이트 $U_2$ 에 얼마나 가까운지를 나타내는 값입니다. 이중 큐빗 게이트는 단일 큐빗 게이트보다 복잡하기 때문에 충실도도 좀 더 낮아지는 경향을 보입니다. 단일 큐빗 게이트에서와 같이 충실도 1이 완벽한 상태에 해당합니다.\\n\",\n \"* 측정 판독 오류: 큐비트가 부정확하게 측정되어 실제 양자 상태와 측정하여 판독한 정보가 불일치할 확률을 뜻합니다.\\n\",\n \"\\n\",\n \"특정 IBM Quantum 하드웨어의 노이즈 정보는 [IBM Quantum Cloud Platform](https://quantum.cloud.ibm.com/computers) 에서 확인하실 수 있습니다. 원하는 디바이스를 선택하여 기기의 전반적인 속성과 각 큐빗의 자세한 특성을 확인해보세요. 아래 이미지에서 `ibm_brisbane` 디바이스의 특성 요약을 예시로 확인하실 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"플랫폼에서뿐만 아니라 이 정보를 코드로 직접 액세스하여 확인하는 것도 가능합니다.\\n\",\n \"\\n\",\n \"어떻게 할 수 있는지 함께 알아봅시다!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 1: 좋은 큐빗 찾기 \\n\",\n \"\\n\",\n \"**목표:** T1, T2, 게이트 충실도가 높고 측정 오류는 낮은 큐빗을 찾아보세요\\n\",\n \"\\n\",\n \"첫 번째 연습문제에서는 주어진 각 지표의 최댓값과, 그 값을 갖는 큐비트(또는 큐비트 쌍)를 찾아주시면 됩니다.\\n\",\n \"\\n\",\n \"- 이완 시간: T1\\n\",\n \"- 위상 어긋남 시간: T2\\n\",\n \"- 측정 오류\\n\",\n \"- 단일 큐빗 게이트 오류: 'PauliX' 오류\\n\",\n \"- 이중 큐빗 게이트 오류: 'Ecr' 오류\\n\",\n \"\\n\",\n \"함수를 다양하게 활용할 수 있도록 백엔드에 상관없이 작동하는 코드를 작성해주세요.\\n\",\n \"\\n\",\n \"참고 1: ECR 게이트는 `ibm_brisbane` 백엔드에서 사용하는 이중 큐빗 게이트이며, 다른 디바이스에서는 CZ 게이트나 CNOT 게이트를 사용할 수 있습니다. 백엔드에서 사용하는 기본 게이트 종류를 확인하려면 [backend.basis_gates](/docs/api/qiskit-ibm-runtime/ibm-backend) 속성을 확인해보세요.\\n\",\n \"\\n\",\n \"참고 2: 같은 디바이스에서도 좋은 큐비트의 번호와 성능값은 계속해서 달라질 수 있습니다. 좋은 큐비트를 찾을 때에는 파이썬의 `min()` 함수와 `max()` 함수를 사용하여 전체 큐빗 중 최적의 큐빗을 자동으로 찾을 수 있도록 코드를 작성해주세요.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"아래의 코드는 `ibm_brisbane` 백엔드에서 각각의 성능 지표를 불러와 배열로 저장하는 코드입니다. 이 코드를 참고하여 연습문제를 풀어주세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 이 셀을 실행하여 속성값의 배열을 만듭니다\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"# 정보를 확인할 백엔드를 지정합니다\\n\",\n \"brisbane_backend = service.backend(\\\"ibm_brisbane\\\")\\n\",\n \"# 시스템 속성과 큐비트의 개수, 연결도(coupling map)를 불러옵니다\\n\",\n \"properties = brisbane_backend.properties()\\n\",\n \"num_qubits = brisbane_backend.num_qubits\\n\",\n \"coupling_map = brisbane_backend.coupling_map\\n\",\n \"\\n\",\n \"# 백엔드의 모든 큐빗의 성능 지표를 읽어 배열로 만듭니다\\n\",\n \"t1, t2, gate_error_x, readout_error, gate_error_ecr = [], [], [], [], []\\n\",\n \"for i in range(num_qubits):\\n\",\n \" t1.append(properties.t1(i))\\n\",\n \" t2.append(properties.t2(i))\\n\",\n \" gate_error_x.append(properties.gate_error(gate=\\\"x\\\", qubits=i))\\n\",\n \" readout_error.append(properties.readout_error(i))\\n\",\n \"for pair in coupling_map:\\n\",\n \" gate_error_ecr.append(properties.gate_error(gate=\\\"ecr\\\", qubits=pair))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def find_best_metrics(backend: QiskitRuntimeService.backend) -> list[tuple[int or list, float]]:\\n\",\n \" \\\"\\\"\\\"다양한 하드웨어 성능 지표를 확인해 가장 좋은 큐비트와 큐빗 쌍을 찾습니다\\\"\\\"\\\"\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" # 목표: 각각의 성능 지표에서 최선의 값과 해당하는 큐비트의 번호 얻기:\\n\",\n \"\\n\",\n \" # T1 시간이 가장 긴 큐비트와 (index_t1_max) 그 값을 (max_t1) 찾으세요\\n\",\n \" index_t1_max = \\n\",\n \" max_t1 = \\n\",\n \"\\n\",\n \" # T2 시간이 가장 긴 큐비트와 (index_t2_max) 그 값을 (max_t2) 찾으세요\\n\",\n \" index_t2_max = \\n\",\n \" max_t2 = \\n\",\n \"\\n\",\n \" # X 게이트 오류가 가장 작은 큐비트와 (index_min_x_error) 그 값을 (min_x_error) 찾으세요\\n\",\n \" index_min_x_error = \\n\",\n \" min_x_error = \\n\",\n \"\\n\",\n \" # 측정 오류가 가장 작은 큐비트와 (index_min_readout) 그 값을 (min_readout) 찾으세요\\n\",\n \" index_min_readout = \\n\",\n \" min_readout = \\n\",\n \"\\n\",\n \" # ECR 게이트 오류가 가장 작은 큐빗 쌍과 (min_ecr_pair) 그 값을 (min_ecr_error) 찾으세요\\n\",\n \" \\n\",\n \" min_ecr_pair = \\n\",\n \" min_ecr_error = \\n\",\n \" \\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \" solutions = [\\n\",\n \" [int(index_t1_max), max_t1],\\n\",\n \" [int(index_t2_max), max_t2],\\n\",\n \" [int(index_min_x_error), min_x_error],\\n\",\n \" [int(index_min_readout), min_readout],\\n\",\n \" [list(min_ecr_pair), min_ecr_error],\\n\",\n \" ]\\n\",\n \" return solutions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab2_ex1(find_best_metrics)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"잘하셨습니다! 여러분은 디바이스의 속성에 액세스하고 그 중 원하는 특성을 찾는 법을 배우셨습니다. 여기까지 보셨을 때 몇 분은 왜 단순히 가장 좋은 지표 하나에 의거해 가장 좋은 큐비트를 찾지 않는지 궁금하실 수도 있을 것 같습니다. 글쎄요, 그것이 쉽지만은 않습니다.\\n\",\n \"\\n\",\n \"방금 만드신 함수로 선택된 큐비트들을 직접 확인해보시면, 이 큐비트들이 서로 같지도 않고 디바이스 어느 한 쪽에 몰려있지도 않은 것을 보게 되실 것입니다. 오히려 디바이스의 여기저기에 퍼져 있을 확률이 더 높겠네요. 이것은 양자 디바이스의 문제점 중 하나를 보여줍니다: 이완 시간이 긴 큐비트는 보통 단일 큐빗 게이트 에러가 높고, 그 반대도 마찬가지입니다. 이러한 상충 관계는 하나의 지표에 의존해 큐비트를 선택해서는 주어진 양자 알고리즘에서의 최선의 결과를 보장할 수 없다는 것을 보여줍니다. 나아가서, 연결도에서 보이는 바와 같이 모든 큐비트가 서로 연결되어 있지 않다는 것도 고려해야 합니다. 이 모든 요소를 고려하여 큐비트를 고르고 배치하는 작업은 또 하나의 복잡하지만 흥미로운 도전이 됩니다. 다행히도 여러분께는 이 문제를 풀기 위해 설계된 Qiskit 툴이 있습니다: 바로 **트랜스파일러**입니다.\\n\",\n \"\\n\",\n \"트랜스파일러에 대해서는 이후 섹션에서 더 자세히 다뤄보도록 하겠습니다. 우선은 흥미로운 예시 문제를 먼저 살펴볼까요?\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2. Max-cut 문제 \\n\",\n \"\\n\",\n \"최대 절단 ([Max-cut](https://en.wikipedia.org/wiki/Maximum_cut)) 문제는 [NP-hard](https://en.wikipedia.org/wiki/NP-hardnes) 분류에 속하는 그래프 문제입니다. 즉, 어떤 알고리즘도 이 문제를 다항시간 내에 풀지 못한다는 것이죠. Max-cut은 클러스터링, 네트워크, 통계물리, 머신러닝 등의 분야에 폭넓게 적용되는 최적화문제입니다. 이 문제의 목표는 그래프의 점들을 두 그룹으로 나눠 두 그룹 사이의 선분의 개수(그룹을 절단하는 선분의 개수)가 최대가 되도록 하는 것입니다. 예시 문제를 같이 확인해봅시다.\\n\",\n \"\\n\",\n \"아래 그림은 점 다섯 개의 그래프에서 최대 절단을 갖도록 그룹을 분리하는 예시를 보여줍니다. 이 문제를 보는 다른 방법으로 그림에서와 같이 하나의 선을 그어서 점들을 나누고, 이 기준선이 최대한 많은 선분을 자르도록 배치하는 것으로 이해할 수도 있습니다. 그래프가 커지면 다소 상상하기 어려울 수도 있지만요.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"이번 랩에서는 이 Max-cut 문제를 양자 알고리즘으로 풀어보도록 하겠습니다. 또한 양자 노이즈가 이 알고리즘에 어떤 영향을 주는지 공부하고, 노이즈의 영향을 줄여 정확한 답을 얻을 수 있도록 하는 방법을 논의해보도록 하겠습니다.\\n\",\n \"\\n\",\n \"## 2.1 문제: 그래프 정의하기 \\n\",\n \"\\n\",\n \"Max-cut 문제에 대한 감을 잡았다면 이제 이 랩에서 풀 Max-cut 문제 그래프를 정의해봅시다. 이번에는 특히 모든 점이 서로 연결되어 있는 그래프를 고르도록 하겠습니다 $^*$.\\n\",\n \"\\n\",\n \"$^*$ *문제 그래프가 완전한 대칭이 되지 않도록 그래프의 마지막 점 두 개(3, 4번)의 연결을 약간 약하게 설정하였습니다.*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 시드를 고정하여 일정한 결과를 얻을 수 있도록 합니다\\n\",\n \"seed = 43\\n\",\n \"# 그래프의 점의 개수를 정합니다\\n\",\n \"n = 5\\n\",\n \"# 그래프를 정의합니다\\n\",\n \"graph = rx.PyGraph()\\n\",\n \"graph.add_nodes_from(np.arange(0, n, 1))\\n\",\n \"generic_backend = GenericBackendV2(n, seed=seed)\\n\",\n \"weights = 1\\n\",\n \"# 마지막 선분 하나를 제외하고 모든 점을 양방향으로 연결합니다\\n\",\n \"# 여기서는 모든 큐빗이 서로 연결되어 있는 `GenericBackend`의 연결도를 참조하여 선분을 정의합니다\\n\",\n \"graph.add_edges_from([(edge[0], edge[1], weights) for edge in generic_backend.coupling_map][:-1])\\n\",\n \"draw_graph(graph, node_size=200, with_labels=True, width=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2 정의: 양자컴퓨터로 그래프 옮기기 \\n\",\n \"\\n\",\n \"양자 컴퓨터에서 이 그래프 문제를 풀기 위해서는 먼저 문제를 컴퓨터가 이해할 수 있는 방식으로 바꿔줘야 합니다. 양자 컴퓨팅에서 이 문제에 접근하는 일반적이면서 강력한 방법은 최적화문제를 **해밀토니안**으로 재정의하는 것입니다. 해밀토니안이란 양자역학에서 에너지를 정의하는 수학적인 방법입니다. 이 경우, 우리 문제의 해는 이 해밀토니안의 가장 낮은 에너지 상태(\\\"바닥 상태\\\")로 정의될 것입니다.\\n\",\n \"\\n\",\n \"이러한 문제를 수식화하는 것과 관련된 개념이 두 가지 있습니다:\\n\",\n \"\\n\",\n \"1. **QUBO (Quadratic Unconstrained Binary Optimization):** QUBO는 컴퓨터 과학과 고전적인 최적화 문제에서 자주 사용되는 개념입니다. 이것은 이진 변수 **xi ∈ {0, 1}**를 조작하여 다음의 목적 함수를 최소화하는 최적화 문제입니다:\\n\",\n \"\\n\",\n \" $$ \\\\text{minimize} \\\\sum_{i} Q_{ii}x_{i} + \\\\sum_{ii ∈ {+1, -1}**를 변수로 하여 다음의 에너지 해밀토니안을 최소화하는 상태를 찾습니다:\\n\",\n \"\\n\",\n \" $$ H = -\\\\sum_{i} h_{i}s_{i} - \\\\sum_{ii = 2xi - 1**.\\n\",\n \"\\n\",\n \"이번 랩에서는 주어진 그래프 문제를 이 **Ising 해밀토니안**으로 대응시켜 보겠습니다. 이것은 양자 컴퓨터의 언어로도 곧바로 대응되는데, Ising 모델의 스핀 변수 **si**를 양자 컴퓨터의 관측가능량 Z(Z축 방향 측정)의 고윳값 **+1**과 **-1**로 대응시킬 수 있기 때문입니다.\\n\",\n \"\\n\",\n \"### Max-cut 문제를 Ising 해밀토니안으로 대응시키기\\n\",\n \"\\n\",\n \"그래프 $G = (V, E)$ 에서 정의되는 Max-cut 문제에서, 우리는 $V$ 의 점들을 두 그룹으로 나누고자 합니다. 문제의 목표는 나눠진 두 그룹의 점들 사이를 연결하는 선분의 개수가 최대가 되도록 하는 것입니다. 이것을 스핀 변수 **si**에 대한 함수로 나타내봅시다. 만약 연결된 두 점 `i`와 `j`가 서로 다른 그룹에 속해 있다면 (si ≠ sj 이라면), $s_i * s_j$ 의 값은 (-1) 이 될 것입니다. 만약 두 점이 같은 그룹으로 분류된다면 (si = sj 이라면), 두 변수를 곱한 값은 (+1) 이 됩니다.\\n\",\n \"\\n\",\n \"따라서, 절단을 *최대화*하기 위해서는 모든 연결된 선분에서 $s_i * s_j$ 의 값의 합을 *최소화*하면 됩니다. 이것을 해밀토니안으로 표현하면 다음과 같습니다.\\n\",\n \"\\n\",\n \"$$ H_{cost} = \\\\sum_{(i,j) \\\\in E} Z_i \\\\otimes Z_j $$\\n\",\n \"\\n\",\n \"여기에서는 앞의 해밀토니안과 달리 스핀 변수 `s_i`가 양자역학적 관측가능량 **Zi**로 대체되었습니다. Zi는 `i`번째 큐비트를 관측하여 `|0⟩` 상태를 관측할 경우 고윳값 (+1)을, `|1⟩` 상태를 관측할 경우 고윳값 (-1)을 돌려줄 것입니다. 이 해밀토니안의 바닥 상태는 이 에너지의 값을 최소화하는 큐비트의 상태(`|0⟩` 과 `|1⟩`)의 배열이며, 이 바닥 상태가 바로 Max-cut 문제의 해에 해당합니다.\\n\",\n \"\\n\",\n \"더 자세한 내용을 알고 싶다면 다음의 [튜토리얼](https://quantum.cloud.ibm.com/docs/tutorials/quantum-approximate-optimization-algorithm)을 참고하세요.\\n\",\n \"\\n\",\n \"이 내용을 잘 이해했는지가 바로 다음 연습문제에서 물어보는 내용입니다!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 2: 그래프를 해밀토니안으로 대응시키기 \\n\",\n \"\\n\",\n \"**목표:** 앞에서 만든 'graph'를 Ising 해밀토니안으로 대응시켜 Max-cut 문제의 비용 함수를 정의해보세요.\\n\",\n \"\\n\",\n \"이 두 번째 연습문제에서는 주어진 그래프 문제를 항등 연산자와 파울리 연산자를 사용해 해밀토니안으로 대응시키는 방법을 찾아주시면 되겠습니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \" 힌트 💡: (클릭해서 펼쳐보세요)\\n\",\n \" 이 해밀토니안을 만드는 방법은 그래프의 점 개수 n개 만큼의 힐베르트 공간(관측가능량 연산자의 길이로 나타남)을 가정한 뒤, 그래프의 선분을 불러와 선분으로 연결된 점들은 파울리 연산자 Z로, 나머지 점은 항등 연산자 I로 대응시키는 것입니다.
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def graph_to_Pauli(graph: rx.PyGraph) -> list[tuple[str, float]]:\\n\",\n \" \\\"\\\"\\\"그래프를 파울리 배열로 변환합니다.\\\"\\\"\\\"\\n\",\n \" pauli_list = []\\n\",\n \"\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" # 목표: 그래프를 다음과 같은 리스트로 변환해주세요: [['PauliWord_1', weight_1], ['PauliWord_2', weight_2],...]\\n\",\n \" # 큐비트의 순서와 파이썬 문자열의 순서가 서로 반대라는 점에 주의하세요\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \" return pauli_list\\n\",\n \"\\n\",\n \"\\n\",\n \"max_cut_paulis = graph_to_Pauli(graph)\\n\",\n \"cost_hamiltonian = SparsePauliOp.from_list(max_cut_paulis)\\n\",\n \"print(\\\"Cost Function Hamiltonian:\\\", cost_hamiltonian)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab2_ex2(graph_to_Pauli)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2.3 QAOA 알고리즘 \\n\",\n \"\\n\",\n \"이로써 여러분은 주어진 Max-cut 문제를 Ising 해밀토니안으로 성공적으로 정의하였으며, 고전적인 그래프 문제를 양자 바닥 상태를 찾는 문제로 변환하였습니다. 원래의 최적화 문제의 해는 이 해밀토니안의 가장 낮은 에너지 (\\\"바닥 상태\\\") 상태로 대응됩니다.\\n\",\n \"\\n\",\n \"이처럼 해밀토니안의 바닥 상태를 찾는 문제는 굉장히 다양한 상황에서 정의될 수 있으며, 이 문제를 푸는 양자 알고리즘도 여러 가지가 논의되고 있습니다. 그 중에서도 현재 가장 일반적으로 사용되는 알고리즘은 Quantum Approximate Optimization Algorithm ([QAOA](https://en.wikipedia.org/wiki/Quantum_optimization_algorithms)) 입니다. QAOA는 다양한 최적화 문제에서 폭 넓은 적용성과 비교적 간단한 회로, 준수한 성능으로 잘 알려져 있습니다.\\n\",\n \"\\n\",\n \"QAOA에 대한 더 자세한 설명을 원하신다면 다음의 [튜토리얼](https://quantum.cloud.ibm.com/docs/tutorials/quantum-approximate-optimization-algorithm) 또는 Quantum computing in practice 코스의 [Utility-scale QAOA](https://quantum.cloud.ibm.com/learning/courses/quantum-computing-in-practice/utility-scale-qaoa) 강의를 읽어보시길 추천드립니다. 여기에서는 이 알고리즘의 아이디어만 간단하게 설명드리도록 하겠습니다.\\n\",\n \"\\n\",\n \"QAOA는 단열 정리(adiabatic theorem)에 기반한 변분(variational) 양자 알고리즘입니다. 단열 정리란 양자역학적 시스템의 변화가 매우 천천히 일어난다면, 처음의 바닥 상태에서 출발한 계가 변화된 바닥 상태로 넘어갈 수 있다는 정리입니다. QAOA는 이것을 모방하여, 알려진 해밀토니안(믹서)의 바닥 상태를 준비하고, 원하는 해밀토니안(목표)으로 시스템을 천천히 변화시킵니다. 이것은 두 해밀토니안을 번갈아가며 가하는 방식으로 적용되며, 각 해밀토니안을 가하는 세기를 변수로 설정하여 최적화하는 것으로 바닥 상태를 찾습니다.\\n\",\n \"\\n\",\n \"짧은 이론적인 설명을 들어두고, 이제 실제로 Qiskit의 `QAOAAnsatz`를 이용해 주어진 Max-cut 문제를 푸는 QAOA 회로를 만들어봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"layers = 2\\n\",\n \"qaoa_circuit = QAOAAnsatz(cost_operator=cost_hamiltonian, reps=layers)\\n\",\n \"qaoa_circuit.measure_all()\\n\",\n \"qaoa_circuit.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"간단하죠? QAOA 회로를 정의했다면 이것을 패스매니저로 넘겨 백엔드의 기본 게이트로 트랜스파일해야 합니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 프리셋 패스매니저를 불러와 트랜스파일을 수행합니다\\n\",\n \"\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=generic_backend, seed_transpiler=seed\\n\",\n \")\\n\",\n \"\\n\",\n \"qaoa_circuit_transpiled = pm.run(qaoa_circuit)\\n\",\n \"qaoa_circuit_transpiled.draw(\\\"mpl\\\", fold=False, idle_wires=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이제 정의한 QAOA 회로의 변수의 초기값을 설정합니다.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"init_params = np.zeros(2 * layers)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`Estimator`를 사용해 QAOA 회로에서 에너지 기댓값을 구하는 방법을 정의하고, 이것을 비용 함수로 최적화를 수행합니다. 최적화는 간단하게 `scipy` 라이브러리의 `minimize` 함수로 수행할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"objective_func_vals = []\\n\",\n \"\\n\",\n \"\\n\",\n \"def cost_func_estimator(\\n\",\n \" params: list, ansatz: QuantumCircuit, isa_hamiltonian: SparsePauliOp, estimator: Estimator\\n\",\n \") -> float:\\n\",\n \" \\\"\\\"\\\"Compute the cost function value using a parameterized ansatz and an estimator for a given Hamiltonian.\\\"\\\"\\\"\\n\",\n \" if isa_hamiltonian.num_qubits != ansatz.num_qubits:\\n\",\n \" isa_hamiltonian = isa_hamiltonian.apply_layout(ansatz.layout)\\n\",\n \" pub = (ansatz, isa_hamiltonian, params)\\n\",\n \" job = estimator.run([pub])\\n\",\n \" results = job.result()[0]\\n\",\n \" cost = results.data.evs\\n\",\n \" objective_func_vals.append(cost)\\n\",\n \" return cost\\n\",\n \"\\n\",\n \"\\n\",\n \"def train_qaoa(\\n\",\n \" params: list,\\n\",\n \" circuit: QuantumCircuit,\\n\",\n \" hamiltonian: SparsePauliOp,\\n\",\n \" backend: QiskitRuntimeService.backend,\\n\",\n \") -> tuple:\\n\",\n \" \\\"\\\"\\\"Optimize QAOA parameters using COBYLA and an estimator on a given backend.\\\"\\\"\\\"\\n\",\n \" with Session(backend=backend) as session:\\n\",\n \" options = {\\\"simulator\\\": {\\\"seed_simulator\\\": seed}}\\n\",\n \" estimator = Estimator(mode=session, options=options)\\n\",\n \" estimator.options.default_shots = 100000\\n\",\n \"\\n\",\n \" result = minimize(\\n\",\n \" cost_func_estimator,\\n\",\n \" params,\\n\",\n \" args=(circuit, hamiltonian, estimator),\\n\",\n \" method=\\\"COBYLA\\\",\\n\",\n \" options={\\\"maxiter\\\": 200, \\\"rhobeg\\\": 1, \\\"catol\\\": 1e-3, \\\"tol\\\": 0.0001},\\n\",\n \" )\\n\",\n \" print(result)\\n\",\n \" return result, objective_func_vals\\n\",\n \"\\n\",\n \"\\n\",\n \"result_qaoa, objective_func_vals = train_qaoa(\\n\",\n \" init_params, qaoa_circuit_transpiled, cost_hamiltonian, generic_backend\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"최적화가 완료되면 아래의 `matplotlib` 함수로 최적화 곡선을 출력해 확인할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(12, 6))\\n\",\n \"plt.plot(objective_func_vals)\\n\",\n \"plt.xlabel(\\\"Iteration\\\")\\n\",\n \"plt.ylabel(\\\"Cost\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"잘 하셨습니다! 보시다시피 QAOA 알고리즘이 꽤 잘 작동하여 비용 함수가 문제 해밀토니안의 최소 에너지 값으로 잘 수렴하였습니다. 하지만 이걸로 끝난 걸까요? 구해진 값이 정말 문제의 최소 에너지 값이 맞는지는 어떻게 알 수 있을까요? 그리고 그만큼 중요한 문제로, 찾아낸 최소 에너지 값에 대응되는 바닥 상태는 무엇일까요? 다른 말로, Max-cut 문제의 실제 해가 무엇인지는 어떻게 알 수 있을까요?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2.4 해 확인하기 \\n\",\n \"\\n\",\n \"Estimator를 사용해 QAOA 회로의 해밀토니안 에너지를 최적화하였다면, 그 바닥 상태는 Sampler와 최적화가 완료된 변수값을 통해 얻을 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 결과로부터 최적화된 변수 값을 가져옵니다\\n\",\n \"opt_params = result_qaoa.x\\n\",\n \"SHOTS = 10000\\n\",\n \"\\n\",\n \"\\n\",\n \"def sample_qaoa(opt_params, circuit, backend):\\n\",\n \"\\n\",\n \" # Sampler로 회로를 실행합니다\\n\",\n \" options = {\\\"simulator\\\": {\\\"seed_simulator\\\": seed}}\\n\",\n \" sampler = Sampler(mode=backend, options=options)\\n\",\n \" job = sampler.run([(circuit, opt_params)], shots=SHOTS)\\n\",\n \" results_sampler = job.result()\\n\",\n \" counts_list = results_sampler[0].data.meas.get_counts()\\n\",\n \" display(plot_histogram(counts_list, title=f\\\"Max cut with {backend.name}\\\"))\\n\",\n \"\\n\",\n \" return counts_list\\n\",\n \"\\n\",\n \"\\n\",\n \"counts_list = sample_qaoa(opt_params, qaoa_circuit_transpiled, generic_backend)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"그래프를 확인하면 8개의 상태가 다른 상태에 비해 높은 확률로 나타나는 것으로 보입니다. 이는 8개의 바닥 상태가 존재할 수 있다는 것을 의미합니다. 하지만 그것을 어떻게 검증할 수 있을까요? 다행히도, 지금 정의한 Max-cut 문제는 그리 큰 문제가 아니어서, 고전적인 방법으로 정확한 해를 찾을 수 있습니다. 우리가 찾은 답안이 좋은 답안인지 한 번 확인해봅시다!\\n\",\n \"\\n\",\n \"고전적인 방법으로 이 Max-cut 문제를 풀 때는 주어진 해밀토니안에 대각화 알고리즘을 적용하는 것으로 정확한 해를 찾을 수 있습니다. 이것은 문제가 작은 그래프에서 정의되기 때문에 가능한 방법이며, 점(큐빗)의 개수가 늘어나면 이 문제의 복잡도는 기하급수적으로 늘어나기 때문에 고전적인 방법으로는 정확한 해를 찾을 수 없게 됩니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eigenvalues, eigenvectors = np.linalg.eig(cost_hamiltonian)\\n\",\n \"ground_energy = min(eigenvalues).real\\n\",\n \"num_solutions = eigenvalues.tolist().count(ground_energy)\\n\",\n \"index_solutions = np.where(eigenvalues == ground_energy)[0].tolist()\\n\",\n \"print(f\\\"The ground energy of the Hamiltonian is {ground_energy}\\\")\\n\",\n \"print(f\\\"The number of solutions of the problem is {num_solutions}\\\")\\n\",\n \"print(f\\\"The list of the solutions based on their index is {index_solutions}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"훌륭합니다! 고전적인 방법으로 찾은 해의 개수도 8개가 나왔습니다. 이제 간단한 후처리 코드로 두 방법으로 찾은 해가 서로 일치하는지 확인해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def decimal_to_binary(decimal_list, n):\\n\",\n \" return [bin(num)[2:].zfill(n) for num in decimal_list]\\n\",\n \"\\n\",\n \"\\n\",\n \"# 해를 양자 상태로 변환합니다\\n\",\n \"states_solutions = decimal_to_binary(index_solutions, n)\\n\",\n \"# 샘플의 카운트가 높은 것부터 순서대로 딕셔너리를 정렬합니다\\n\",\n \"sorted_states = sorted(counts_list.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"# 가장 많이 관측된 해부터 순서대로 num_solutions개의 해를 가져옵니다\\n\",\n \"top_states = sorted_states[:num_solutions]\\n\",\n \"# 두 방법으로 찾은 해를 출력하여 비교합니다\\n\",\n \"qaoa_ground_states = sorted([state for state, count in top_states])\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions}\\\")\\n\",\n \"print(f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"야호! QAOA로 찾은 해가 최적해와 잘 일치하네요!\\n\",\n \"\\n\",\n \"하지만 이제, 이 결과가 시뮬레이터를 통해 얻어졌다는 것을 기억해주세요. 실제 양자 컴퓨터에서는 노이즈와 실재적인 문제로 인해 이만큼 좋은 결과를 얻지는 못할 수도 있습니다. 다음 섹션에서는 QAOA 알고리즘을 노이즈가 있는 시뮬레이터에서 실행해보고 노이즈가 양자 알고리즘에 어떤 영향을 주는지 살펴보도록 하겠습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 3. 양자 노이즈 시뮬레이터 \\n\",\n \"\\n\",\n \"이번 섹션에서는 QAOA 알고리즘을 노이즈가 있는 양자 시뮬레이터에서 실행해보겠습니다. 이 시뮬레이터는 실제 양자 프로세서에서 발생하는 노이즈의 효과를 묘사할 수 있는 유용한 도구입니다. 비싼 양자 리소스를 절약하면서 알고리즘이 실제 하드웨어에서 어떻게 동작할지 미리 테스트해볼 수 있는 것이죠. 그에 더해, 노이즈 시뮬레이터는 사용자의 컴퓨터 자원만으로도 실행할 수 있기 때문에 클라우드의 양자 컴퓨터를 사용하기 위해 대기하는 시간을 절약할 수 있습니다.\\n\",\n \"\\n\",\n \"자 그러면 먼저 노이즈 시뮬레이터의 기반이 될 실제 양자 컴퓨터를 선택해봅시다. 이 문제는 가끔 과소평가되기도 하지만 생각보다 중요한 문제입니다. 다음의 코드에서는 특정 양자 알고리즘을 실행하기에 가장 적합한 디바이스를 고르는 방법을 알아보도록 하겠습니다.\\n\",\n \"\\n\",\n \"## 3.1 백엔드 선택 \\n\",\n \"\\n\",\n \"이제 노이즈 시뮬레이터가 묘사할 실제 양자 컴퓨터를 선택해보겠습니다. 여러분이 사용 가능한 백엔드의 목록을 한 번 확인해볼까요?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"real_backends = service.backends()\\n\",\n \"print(f\\\"The quantum computers available for you are {real_backends}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"만약 Open Plan(무료)을 사용하고 계시다면 현재 `ibm_brisbane`, `ibm_sherbrooke`, 그리고 `ibm_torino`가 사용 가능한 백엔드로 표시될 것입니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이제 각각의 사용 가능한 백엔드를 묘사하는 노이즈 시뮬레이터를 생성하고, 각 백엔드에서 사용 가능한 게이트의 목록을 확인해보겠습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"사용 가능한 백엔드\\n\",\n \"\\n\",\n \"사용하고 계신 계정에 따라서 사용 가능한 백엔드의 목록은 달라질 수 있습니다. 가능하면 ibm_brisbane, ibm_sherbrooke, 그리고 ibm_torino 3개의 백엔드만을 사용해주시되, 만약 해당 백엔드를 사용할 수 없다면 임의의 백엔드 3개를 지정해주셔도 괜찮습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"real_backends = [\\n\",\n \" service.backend(\\\"ibm_brisbane\\\"),\\n\",\n \" service.backend(\\\"ibm_sherbrooke\\\"),\\n\",\n \" service.backend(\\\"ibm_torino\\\"),\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"noisy_fake_backends = []\\n\",\n \"for backend in real_backends:\\n\",\n \" noisy_fake_backends.append(AerSimulator.from_backend(backend, seed_simulator=seed))\\n\",\n \"print(f\\\"The noisy simulators are {noisy_fake_backends}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이제 QAOA 회로를 각각의 백엔드에 맞게 트랜스파일하고, 예상되는 오차의 크기를 미리 예측해봅시다.\\n\",\n \"\\n\",\n \"다음의 연습문제에서는 앞에서 지정한 세 가지 백엔드로 트랜스파일된 양자 회로에서 발생하는 오차의 누적 예측값을 계산해볼 것입니다. 이것은 [`backend.properties()`](https://quantum.cloud.ibm.com/docs/en/guides/get-qpu-information#dynamic-backend-information), [`circuit.data`](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.QuantumCircuit) 그리고 [`backend.configuration()`](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.QuantumCircuit) 에서 양자 회로를 구성하는 양자 게이트와, 각 게이트를 수행할 때 발생하는 다양한 오차에 대한 정보를 모아 수행할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 3: 오류율 예측 \\n\",\n \"\\n\",\n \"**목표:** 양자 회로를 실행할 때 발생할 오차의 크기를 예측해보세요.\\n\",\n \"\\n\",\n \"세 번째 연습문제에서 여러분은 각 백엔드에서 양자 회로를 실행할 때에 발생하는 오차의 크기를 예측하는 함수 `accululated_errors`를 작성해야 합니다. \\n\",\n \"특히 다음의 연산에서 발생하는 오류를 고려해보세요:\\n\",\n \"\\n\",\n \"- 단일 큐빗 게이트: 백엔드의 종류에 따라 'rz', 'x', 'sx' 등의 게이트가 수행되며, 각 게이트를 수행할 때에 발생하는 오차를 가져와야 합니다.\\n\",\n \"- 이중 큐빗 게이트: 백엔드의 종류에 따라 'cz', 'cx', 또는 'ecr' 게이트가 될 수 있습니다. 각 백엔드에서 어떤 게이트를 사용하는지 따로 확인이 필요합니다.\\n\",\n \"- 측정 오류: 이 오류는 회로의 마지막에 측정을 수행할 때에 누적 오차에 추가하여 계산합니다. 회로를 트랜스파일하여 선택된 큐비트를 확인하여 해당 큐비트의 측정 오류율을 가져오세요.\\n\",\n \"\\n\",\n \"각 큐비트의 게이트 오류율과 측정 오류율이 모두 다르기 때문에, 단순히 IBM Quantum 플랫폼의 리소스 요약에 나타나는 평균 오류율을 곱하는 것은 안 됩니다. \\n\",\n \"각 백엔드에서의 오차의 크기가 다소 비슷해보일 수도 있습니다. 다만 이 연습문제의 목표는 백엔드의 노이즈 정보와 각 큐비트의 오류 정보를 확인하고 비교하는 방법을 배우는 것이라는 점을 명심해주세요.\\n\",\n \"\\n\",\n \"측정 게이트의 오차는 측정 오류율과 같으므로, 측정 게이트를 단일 큐빗 게이트로 취급하여 계산하지 마세요.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 단일 큐빗 게이트, 이중 큐빗 게이트, 측정 오류의 오차를 누적하여 계산하는 함수를 정의합니다\\n\",\n \"\\n\",\n \"\\n\",\n \"def accumulated_errors(backend: QiskitRuntimeService.backend, circuit: QuantumCircuit) -> list:\\n\",\n \" \\\"\\\"\\\"주어진 백엔드에서 회로의 게이트 및 측정 오류의 누적값을 계산합니다.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # 값을 초기화합니다\\n\",\n \" acc_single_qubit_error = 0\\n\",\n \" acc_two_qubit_error = 0\\n\",\n \" single_qubit_gate_count = 0\\n\",\n \" two_qubit_gate_count = 0\\n\",\n \" acc_readout_error = 0\\n\",\n \"\\n\",\n \" # 주요 변수를 정의합니다\\n\",\n \" properties = backend.properties()\\n\",\n \" qubit_layout = list(circuit.layout.initial_layout.get_physical_bits().keys())[:n]\\n\",\n \"\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" # 목표: 다음의 값을 계산하는 방법을 정의하세요:\\n\",\n \" # acc_total_error, acc_two_qubit_error, acc_single_qubit_error, acc_readout_error, single_qubit_gate_count, two_qubit_gate_count\\n\",\n \"\\n\",\n \" # TODO `properties.readout_error`에서 측정 오류율을 불러오세요 (only for qubits in qubit_layout)\\n\",\n \" acc_readout_error=0\\n\",\n \" for q in qubit_layout:\\n\",\n \" acc_readout_error+= # TODO\\n\",\n \" # TODO `backend.configuration()`에서 백엔드의 이중 큐빗 게이트를 확인하세요\\n\",\n \" if \\\"ecr\\\" in (): # TODO\\n\",\n \" two_qubit_gate = \\\"ecr\\\"\\n\",\n \" elif \\\"cz\\\" in (): # TODO\\n\",\n \" two_qubit_gate = \\\"cz\\\"\\n\",\n \" # TODO `circuit.data`의 모든 연산에 대해 단일/이중 큐빗 게이트 개수와 오류율을 계산하세요\\n\",\n \" # 팁: 게이트가 가해지는 큐빗의 인덱스는 Qubit._index 로 액세스해야 합니다\\n\",\n \" for instruction in circuit.data:\\n\",\n \" if(): # TODO 단일 큐빗 게이트의 개수와 오류율을 더하세요\\n\",\n \"\\n\",\n \" elif(): # TODO 이중 큐빗 게이트의 개수와 오류율을 더하세요\\n\",\n \" \\n\",\n \"\\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \" acc_total_error = acc_two_qubit_error + acc_single_qubit_error + acc_readout_error\\n\",\n \" results = [\\n\",\n \" acc_total_error,\\n\",\n \" acc_two_qubit_error,\\n\",\n \" acc_single_qubit_error,\\n\",\n \" acc_readout_error,\\n\",\n \" single_qubit_gate_count,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" ]\\n\",\n \" return results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이제 아래의 코드를 실행해 `errors_and_counts_list`를 얻어보고 답안을 그레이더에 제출하세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qaoa_transpiled_list = []\\n\",\n \"errors_and_counts_list = []\\n\",\n \"for noisy_fake_backend in noisy_fake_backends:\\n\",\n \" pm = generate_preset_pass_manager(\\n\",\n \" backend=noisy_fake_backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed,\\n\",\n \" )\\n\",\n \" circuit = pm.run(qaoa_circuit)\\n\",\n \" qaoa_transpiled_list.append(circuit)\\n\",\n \"\\n\",\n \" errors_and_counts = accumulated_errors(noisy_fake_backend, circuit)\\n\",\n \" errors_and_counts_list.append(errors_and_counts)\\n\",\n \"# 계산 결과가 정상적으로 출력되는지 확인해보세요\\n\",\n \"for backend, (\\n\",\n \" acc_total_error,\\n\",\n \" acc_two_qubit_error,\\n\",\n \" acc_single_qubit_error,\\n\",\n \" acc_readout_error,\\n\",\n \" single_qubit_gate_count,\\n\",\n \" two_qubit_gate_count,\\n\",\n \") in zip(noisy_fake_backends, errors_and_counts_list):\\n\",\n \" print(f\\\"Backend {backend.name}\\\")\\n\",\n \" print(f\\\"Accumulated two-qubit error of {two_qubit_gate_count} gates: {acc_two_qubit_error:.3f}\\\")\\n\",\n \" print(\\n\",\n \" f\\\"Accumulated one-qubit error of {single_qubit_gate_count} gates: {acc_single_qubit_error:.3f}\\\"\\n\",\n \" )\\n\",\n \" print(f\\\"Accumulated readout error: {acc_readout_error:.3f}\\\")\\n\",\n \" print(f\\\"Accumulated total error: {acc_total_error:.3f}\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"결과를 그래프로 나타내봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_backend_errors_and_counts(noisy_fake_backends, errors_and_counts_list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab2_ex3(accumulated_errors)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"아마 다른 백엔드에 비해 `ibm_torino`의 누적 오차가 작은 것을 확인할 수 있을 것입니다.\\n\",\n \"\\n\",\n \"이제 정말 노이즈 시뮬레이터에서 QAOA 알고리즘을 실행하여 결과를 비교해봅시다!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"주의: 2분 가량이 소요됩니다. 이 코드는 건너 뛰지 마세요!\\n\",\n \"\\n\",\n \"아래의 코드 실행에는 약 2분 가량이 소요되며, 그동안 이 노트북의 다른 코드를 실행할 수 없을 것입니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"주의: 8분 가량이 소요됩니다.\\n\",\n \"\\n\",\n \"아래의 코드 실행에는 약 8분 가량이 소요되며, 그동안 이 노트북의 다른 코드를 실행할 수 없을 것입니다.\\n\",\n \"\\n\",\n \"아래의 셀을 실행하지 않기를 원하신다면 [Section 4](#transpiler)로 바로 진행해주세요.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for noisy_fake_backend, circuit in zip(noisy_fake_backends[1:], qaoa_transpiled_list[1:]):\\n\",\n \" result_backend, _ = train_qaoa(init_params, circuit, cost_hamiltonian, noisy_fake_backend)\\n\",\n \" opt_params = result_backend.x\\n\",\n \" opt_params_list.append(opt_params)\\n\",\n \" counts_list_backend = sample_qaoa(opt_params, circuit, noisy_fake_backend)\\n\",\n \" counts_list_backends.append(counts_list_backend)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"세 백엔드에서 모두 정확한 결과가 얻어지는 것으로 보입니다. 하지만 `ibm_torino` 백엔드에서는 노이즈가 비교적 적어 정답이 아닌 상태를 관측할 확률이 더 낮은 것도 확인하실 수 있습니다.\\n\",\n \"\\n\",\n \"여기에서 우리는 정확한 답을 관측할 확률을 백엔드의 성능을 비교하는 지표로 삼을 수 있습니다. 세 백엔드에서 모두 8개의 정답을 가장 높은 확률로 관측하였으므로, 이러한 지표가 실질적으로 가장 신뢰할 수 있는 백엔드를 선택하는 방법이 될 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for noisy_fake_backend, counts_list_backend in zip(noisy_fake_backends, counts_list_backends):\\n\",\n \" solutions_counts = [counts_list_backend[key] for key in states_solutions]\\n\",\n \" print(\\n\",\n \" f\\\"Probability of measuring a solution for {noisy_fake_backend.name} is {float(sum(solutions_counts)/SHOTS)}\\\"\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"예상한 바와 같이, `ibm_torino`에서 정답을 관측할 확률이 가장 높은 것을 확인할 수 있습니다. 이것은 또한, 앞에서 오차의 크기를 예측한 것이 주어진 알고리즘을 최소한의 오차로 실행할 수 있는 백엔드를 찾는데에 도움이 되었다는 것을 의미합니다. 노이즈의 영향에 대해 더 논의해보기 위해, 앞에서 노이즈가 없는 시뮬레이터 `GenericBackend` 에서 관측한 정답 확률도 같이 살펴봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"solutions_counts_noiseless = [counts_list[key] for key in states_solutions]\\n\",\n \"print(\\n\",\n \" f\\\"Probability of measuring a solution for {generic_backend.name} is {float(sum(solutions_counts_noiseless)/SHOTS)}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"역시 예상한 바와 같이 노이즈가 없는 `GenericBackend`에서 정답을 관측할 확률이 노이즈가 있는 백엔드에서의 확률보다 높은 것을 확인할 수 있습니다. 그러나, 그 차이가 생각보다는 크지 않기도 합니다. 이 결과는 중요한 시사점을 가집니다. 현대의 노이즈가 있는 양자 컴퓨터를 가지고도, 적절한 도구를 사용하여 주의 깊게 회로를 설계한다면, 여전히 꽤 괜찮은 정확도로 여러 문제를 풀어볼 수 있다는 것입니다!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 3.2 NEAT를 이용한 오류율 예측 \\n\",\n \"\\n\",\n \"위에서 여러분은 디바이스의 노이즈를 꽤 근사하게 예측하였습니다. 한편 Qiskit에는 노이즈를 예측하는 또 다른 도구인 Noisy Estimator Analyzer Tool (NEAT) 를 제공하고 있습니다. NEAT는 특히 Estimator를 사용하여 기댓값을 계산하는 양자 알고리즘의 성능을 예측할 수 있도록 도와주는 함수입니다. 이 함수는 주어진 양자 회로의 이상적 (노이즈가 없는) 시뮬레이션과 노이즈 시뮬레이션을 `qiskit-aer` 시뮬레이터로 수행합니다.\\n\",\n \"\\n\",\n \"NEAT의 주요 기능은 클리포드(Clifford) 회로를 이용한 효과적인 연산입니다. 일반적인 비클리포드 회로에 대해서, NEAT는 해당 회로에서 얻어지는 양자 상태를 근사하는 가장 가까운 클리포드 회로를 자동으로 찾습니다. 이것은 일반적인 시뮬레이터에서 실행 가능한 회로보다 더 큰 회로를 실행하는 경우에도 회로의 노이즈를 간단히 분석할 수 있게 해줍니다. 그러나 이러한 근사는 원래의 회로를 정확하게 묘사하는 것이 아니기 때문에, 경우에 따라 정확도가 다소 낮아질 수 있다는 한계도 있습니다.\\n\",\n \"\\n\",\n \"실용적인 관점에서, NEAT를 활용해 다양한 회로에서의 노이즈의 영향을 비교하는 방법 하나는, 노이즈가 없을 때의 기댓값이 정확히 1인 관측가능량을 측정해보는 것입니다. 이 경우, 노이즈 시뮬레이션에서 측정한 기댓값이 1에서 어느 정도 벗어나는지 확인하는 것으로 노이즈의 영향을 확인할 수 있습니다. 이러한 관측가능량은 다음과 같이 간단하게 찾을 수 있습니다.\\n\",\n \"$$\\n\",\n \"\\\\hat{O}=|\\\\psi\\\\rangle\\\\langle \\\\psi|, \\\\quad \\\\textrm{since} \\\\quad \\\\langle \\\\psi |\\\\hat{O}|\\\\psi \\\\rangle=1, \\n\",\n \"$$\\n\",\n \"여기에서 $|\\\\psi\\\\rangle$ 는 시뮬레이션하는 회로의 최종 상태입니다.\\n\",\n \"\\n\",\n \"앞에서와 같이 노이즈가 있는 백엔드에서 NEAT를 실행해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results = []\\n\",\n \"for backend, opt_params in zip(noisy_fake_backends, opt_params_list):\\n\",\n \" print(f\\\"\\\\nRunning on backend: {backend.name}\\\")\\n\",\n \"\\n\",\n \" # 최적화된 변수를 불러와 QAOA 회로를 정의합니다\\n\",\n \" qaoa_neat = QAOAAnsatz(cost_operator=cost_hamiltonian, reps=layers)\\n\",\n \" qc = qaoa_neat.assign_parameters(opt_params)\\n\",\n \"\\n\",\n \" # 회로를 트랜스파일합니다\\n\",\n \" qc_transpiled = generate_preset_pass_manager(\\n\",\n \" optimization_level=3,\\n\",\n \" basis_gates=backend.configuration().basis_gates[:n],\\n\",\n \" seed_transpiler=seed,\\n\",\n \" ).run(qc)\\n\",\n \" # 회로를 클리포드화 할 때에 기존의 해밀토니안을 관측가능량으로 고려합니다\\n\",\n \" obs = cost_hamiltonian\\n\",\n \" analyzer = Neat(backend)\\n\",\n \" clifford_pub = analyzer.to_clifford([(qc_transpiled, obs)])[0]\\n\",\n \"\\n\",\n \" # 클리포드 회로에서 새로운 관측가능량을 정의합니다\\n\",\n \" state_clifford = Statevector.from_instruction(clifford_pub.circuit)\\n\",\n \" obs_clifford = SparsePauliOp.from_operator(Operator(DensityMatrix(state_clifford).data))\\n\",\n \"\\n\",\n \" # 클리포드 회로를 다시 트랜스파일합니다\\n\",\n \" pm = generate_preset_pass_manager(backend=backend, optimization_level=1, seed_transpiler=seed)\\n\",\n \" isa_qc = pm.run(clifford_pub.circuit)\\n\",\n \" isa_obs = obs_clifford.apply_layout(isa_qc.layout)\\n\",\n \"\\n\",\n \" # 시뮬레이션을 수행합니다\\n\",\n \" pubs = [(isa_qc, isa_obs)]\\n\",\n \" result_noiseless = (\\n\",\n \" analyzer.ideal_sim(pubs, cliffordize=True, seed_simulator=seed)._pub_results[0].vals\\n\",\n \" )\\n\",\n \" noisy_results = (\\n\",\n \" analyzer.noisy_sim(pubs, cliffordize=True, seed_simulator=seed)._pub_results[0].vals\\n\",\n \" )\\n\",\n \"\\n\",\n \" # 결과를 저장하고 출력합니다\\n\",\n \" results.append({\\\"backend\\\": backend.name, \\\"noiseless\\\": result_noiseless, \\\"noisy\\\": noisy_results})\\n\",\n \" print(f\\\"Ideal results on {backend.name}:\\\\n{result_noiseless:.3f}\\\")\\n\",\n \" print(f\\\"Noisy results on {backend.name}:\\\\n{noisy_results:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"NEAT 툴을 이용하여 노이즈 시뮬레이션의 기댓값이 이론값 1에서 얼마나 벗어나는지를 확인하였습니다. 이는 [Section 3.1](#choosing-backend)에서 수행한 노이즈 분석의 결과와도 거의 일치하는 것을 확인할 수 있습니다.\\n\",\n \"\\n\",\n \"그러나 앞에서 언급한 바와 같이, 여기에서는 회로를 클리포드화 하여 분석을 수행하고 있다는 점에 유의하세요. 클리포드화 된 회로는 원래 회로와 비슷하지만 일부 게이트가 클리포드 게이트로 변환되면서 다소간의 근사가 이루어지게 됩니다. 따라서, NEAT로 주어진 백엔드에서 노이즈의 영향을 예측할 수는 있지만, 예측의 정확도까지 보장되지는 않습니다. 이 점은 꼭 명심해주세요!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 4. 트랜스파일러 \\n\",\n \"\\n\",\n \"트랜스파일러는 실제 양자 하드웨어에서 양자 회로를 실행하는데 필요한 가장 중요하고 유용한 도구 중 하나입니다. 이것은 추상적이고 이론적인 버전의 양자 회로과 실제 양자 디바이스에서의 구현되는 물리적 연산 사이를 연결해주는 기능을 합니다. 여러분이 양자 회로를 설계할 때에 사용하는 큐비트와 게이트는 사실 실제 하드웨어의 특성과는 거리가 있는 가상의 큐비트 / 이상적인 게이트입니다. 하지만 트랜스파일러를 사용하면 이런 고수준의 회로를 실제 디바이스에서 사용 가능한 게이트로 변환할 수 있습니다.\\n\",\n \"\\n\",\n \"예를 들어, 여러분의 회로에 가상의 큐비트 0과 1에 가해지는 CNOT 게이트가 있다고 해봅시다. 실제 디바이스에서, 이 두 큐비트는 직접적으로 연결되어 있지 않을 수도 있습니다. 그러한 경우, 트랜스파일러는 SWAP 게이트를 사용해 두 큐비트를 서로 연결된 큐비트로 옮겨 CNOT 연산을 수행할 수 있도록 합니다. 또는, 가상의 큐비트 0과 1이 대응되는 물리적 큐비트가 서로 연결되어 있는 좀 더 좋은 레이아웃을 찾을 수도 있죠 - 예를 들어, 서로 연결되어 있는 물리적 큐빗 3번과 4번을 선택하여 SWAP 게이트가 없이도 CNOT 게이트를 가할 수 있도록 할 수 있습니다.\\n\",\n \"\\n\",\n \"지금까지 우리는 `generate_preset_pass_manager`로 생성되는 미리 설정된 패스매니저로 트랜스파일을 수행하였습니다. 하지만 이번에는 실제로 트랜스파일러에서 일어나는 일을 좀 더 자세히 이해해보고, 효과적인 회로 설계에 대해 생각해봅시다.\\n\",\n \"\\n\",\n \"# 4.1 이중 큐빗 게이트의 수 최소화하기 \\n\",\n \"\\n\",\n \"양자 트랜스파일러가 수행하는 가장 중요한 작업 중 하나는 양자 회로를 실행할 큐비트의 레이아웃을 찾는 일입니다. 이것은 회로에 존재하는 가상의 큐비트를 디바이스의 물리적 큐비트로 대응시키는 최선의 방법을 찾는 것을 포함합니다. 그렇게 하기 위해서, 몇 가지 고려해야 할 사항들이 있습니다.\\n\",\n \"\\n\",\n \"첫 번째로, 트랜스파일러는 회로에 존재하는 모든 이중 큐빗 게이트를 확인하여 선택된 물리적 큐비트에서 해당 연산을 수행할 수 있도록 해야 하며, 이를 위해 필요한 SWAP 게이트의 수를 최소화해야 합니다. 이를 위해 디바이스의 물리적 큐비트가 어떻게 연결되어 있는지 나타내는 연결도(coupling map)를 확인할 필요가 있습니다.\\n\",\n \"\\n\",\n \"우리는 먼저 주어진 양자 회로를 양자 컴퓨터의 물리적 레이아웃에 대응시키며, 이중 큐빗 게이트의 수를 최소화하는 작업을 수행할 것입니다. 이는 이중 큐빗 게이트가 비교적 많은 오류를 발생시키기 때문입니다.\\n\",\n \"\\n\",\n \"이 작업은 `ibm_brisbane` 백엔드를 기준으로 수행해봅시다. 이 백엔드에서는 [Section 3.1](#choosing-backend)에서 보았듯 전체 누적 오류의 대부분이 이중 큐빗 게이트에서 발생합니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# `ibm_brisbane` 백엔드를 선택합니다\\n\",\n \"num_backend = 0\\n\",\n \"noisy_fake_backend = noisy_fake_backends[num_backend]\\n\",\n \"\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" backend=noisy_fake_backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed,\\n\",\n \" layout_method=\\\"sabre\\\",\\n\",\n \")\\n\",\n \"circuit_transpiled = pm.run(qaoa_circuit)\\n\",\n \"\\n\",\n \"\\n\",\n \"def two_qubit_gate_errors_per_circuit_layout(\\n\",\n \" circuit: QuantumCircuit, backend: QiskitRuntimeService.backend\\n\",\n \") -> tuple:\\n\",\n \" \\\"\\\"\\\"주어진 회로 레이아웃에서 누적 이중 큐빗 게이트 오류와 관련 지표를 계산합니다.\\\"\\\"\\\"\\n\",\n \" pair_list = []\\n\",\n \" error_pair_list = []\\n\",\n \" error_acc_pair_list = []\\n\",\n \" two_qubit_gate_count = 0\\n\",\n \" properties = backend.properties()\\n\",\n \" if \\\"ecr\\\" in (backend.configuration().basis_gates):\\n\",\n \" two_qubit_gate = \\\"ecr\\\"\\n\",\n \" elif \\\"cz\\\" in (backend.configuration().basis_gates):\\n\",\n \" two_qubit_gate = \\\"cz\\\"\\n\",\n \" for instruction in circuit.data:\\n\",\n \" if instruction.operation.num_qubits == 2:\\n\",\n \" two_qubit_gate_count += 1\\n\",\n \" pair = [instruction.qubits[0]._index, instruction.qubits[1]._index]\\n\",\n \" error_pair = properties.gate_error(gate=two_qubit_gate, qubits=pair)\\n\",\n \" if pair not in (pair_list):\\n\",\n \" pair_list.append(pair)\\n\",\n \" error_pair_list.append(error_pair)\\n\",\n \" error_acc_pair_list.append(error_pair)\\n\",\n \" else:\\n\",\n \" pos = pair_list.index(pair)\\n\",\n \" error_acc_pair_list[pos] += error_pair\\n\",\n \"\\n\",\n \" acc_two_qubit_error = sum(error_acc_pair_list)\\n\",\n \" return (\\n\",\n \" acc_two_qubit_error,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" pair_list,\\n\",\n \" error_pair_list,\\n\",\n \" error_acc_pair_list,\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \"(\\n\",\n \" acc_two_qubit_error,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" pair_list,\\n\",\n \" error_pair_list,\\n\",\n \" error_acc_pair_list,\\n\",\n \") = two_qubit_gate_errors_per_circuit_layout(circuit_transpiled, noisy_fake_backend)\\n\",\n \"two_qubit_ops_list = [int(a / b) for a, b in zip(error_acc_pair_list, error_pair_list)]\\n\",\n \"# 결과를 출력합니다\\n\",\n \"print(f\\\"The pairs of qubits that need to perform two-qubit operations are:\\\\n {pair_list}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The errors introduced by each of the two-qubit operations are:\\\\n {[round(err,3) for err in error_pair_list]}\\\"\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The accumulated errors introduced by each of the two-qubit operations are:\\\\n {[round(err,3) for err in error_acc_pair_list]}\\\"\\n\",\n \")\\n\",\n \"print(f\\\"The repetitions of each one of the two-qubit operations is:\\\\n {two_qubit_ops_list}\\\")\\n\",\n \"print(f\\\"The number of two-qubit operations in total:\\\\n {two_qubit_gate_count}\\\")\\n\",\n \"print(f\\\"The total accumulated error by two-qubit operations is:\\\\n {acc_two_qubit_error:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 4.2 최적의 레이아웃 찾기 \\n\",\n \"\\n\",\n \"다음으로, 트랜스파일러는 주어진 연결성 조건을 만족하는 \\\"최선의\\\" 물리적 큐비트들을 선택해야 합니다. 그러나 앞에서도 언급한 바와 같이, 이런 상황에서 무엇이 \\\"최선\\\"인지 정의하는 것은 다소 복잡한 문제입니다. 하드웨어마다 다른 결맞음 시간 ($T_1$, $T_2$), 단일 큐빗 게이트 오류, 측정 오류, 이중 큐빗 게이트 오류를 모두 고려해야 하기 때문입니다. 이 모든 지표를 동시에 최적화하는 것은 다소 어려운 문제이며, 종종 서로 상충하는 상황도 발생합니다.\\n\",\n \"\\n\",\n \"이 연습문제에서는 문제를 단순화하기 위해 두 가지 기준에 초점을 맞추겠습니다:\\n\",\n \"1. 주어진 연결 조건을 만족하는 큐비트를 선택한다.\\n\",\n \"2. 이중 큐빗 게이트 오류가 가장 작은 큐비트들을 선택한다.\\n\",\n \"\\n\",\n \"[Section 3.1](#choosing-backend)에서 보았듯 이중 큐빗 게이트 오류는 대부분의 양자 디바이스에서 오류의 지배적인 원인이 됩니다.\\n\",\n \"\\n\",\n \"이중 큐빗 게이트의 오류만을 고려해 다양한 큐비트 레이아웃 중 누적 오류가 가장 작은 것을 찾아보세요.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"아래의 코드를 실행하여 큐비트의 연결도를 출력해보세요. 이는 큐비트가 어떻게 연결되어 있는지를 보고 원하는 조건을 만족하는 레이아웃을 찾는데에 도움을 줄 것입니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 주어진 백엔드의 연결도와 오류율을 가중치 그래프로 만들고 출력합니다.\\n\",\n \"graph = rx.PyDiGraph()\\n\",\n \"graph.add_nodes_from(np.arange(0, noisy_fake_backend.num_qubits, 1))\\n\",\n \"two_qubit_gate = \\\"ecr\\\"\\n\",\n \"graph.add_edges_from(\\n\",\n \" [\\n\",\n \" (\\n\",\n \" edge[0],\\n\",\n \" edge[1],\\n\",\n \" noisy_fake_backend.properties().gate_error(\\n\",\n \" gate=two_qubit_gate, qubits=(edge[0], edge[1])\\n\",\n \" ),\\n\",\n \" )\\n\",\n \" for edge in noisy_fake_backend.coupling_map\\n\",\n \" ]\\n\",\n \")\\n\",\n \"draw_graph(graph, node_size=150, with_labels=True, width=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"찾아야 하는 레이아웃의 연결 조건은 앞에서 찾은 `pair_list`에서 확인할 수 있습니다. 이것은 아래에서 `logical_pair_list`로 다시 단순화하여 표현하였습니다:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def remap_nodes(original_labels: list, edge_list: list[list]) -> list[list[int]]:\\n\",\n \" \\\"\\\"\\\"큐비트의 번호를 0부터 시작하는 가상의 논리 큐비트 번호로 치환합니다\\\"\\\"\\\"\\n\",\n \" label_mapping = {label: idx for idx, label in enumerate(original_labels)}\\n\",\n \" remapped = [[label_mapping[src], label_mapping[dst]] for src, dst in edge_list]\\n\",\n \" return remapped\\n\",\n \"\\n\",\n \"\\n\",\n \"layout_list = list(circuit_transpiled.layout.initial_layout.get_physical_bits().keys())[:5]\\n\",\n \"logical_pair_list = remap_nodes(layout_list, pair_list)\\n\",\n \"print(f\\\"Physical qubit layout list:\\\\n {layout_list}\\\")\\n\",\n \"print(f\\\"\\\\nOriginal two-qubit gates list:\\\\n {pair_list}\\\")\\n\",\n \"print(f\\\"\\\\nRemapped two-qubit gates list (in logical qubits):\\\\n {logical_pair_list}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 4: 모든 적합한 레이아웃 찾기 \\n\",\n \"\\n\",\n \"**목표:** 주어진 회로를 실행하기에 적합한 큐비트 레이아웃을 모두 찾아보세요.\\n\",\n \"\\n\",\n \"네 번째 연습문제에서는 앞에서 지정된 레이아웃에서보다 낮은 이중 큐빗 게이트 오류율을 갖는 모든 큐비트 레이아웃을 찾아야 합니다. 기존의 누적 오류율은 `acc_two_qubit_error` 변수에 저장되어 있습니다. 이 문제는 위의 그래프에서 `logical_pair_list`와 같은 연결성을 가지면서 각 선분의 가중치의 누적값이 주어진 값보다 작아지도록 하는 모든 경로를 찾는 그래프 문제와 같습니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"주의: 이 문제를 완전 탐색 알고리즘으로 풀지 마세요!\\n\",\n \"\\n\",\n \"이 문제를 완전 탐색 알고리즘으로 푸는 경우, 가능한 경로의 수가 너무 많아 계산에 수시간이 넘게 걸릴 수 있습니다. 대신, `logical_pair_list`에서 요구하는 연결을 갖는 경로로 탐색 범위를 한정하세요. 이 경우의 수는 100개가 채 안 되기 때문에 1초 내로 계산을 완료할 수 있습니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \" 힌트 💡: (클릭해서 펼쳐보세요)\\n\",\n \" \\n\",\n \"이 연습문제의 워크플로우를 제안하자면:\\n\",\n \"\\n\",\n \"1. 그래프의 각 노드에서 시작하여 한 번에 한 칸씩 경로를 확장하세요.\\n\",\n \"\\n\",\n \"2. 각 단계에서 `logical_pair_list`를 참고하여 어느 노드에서 경로를 확장해야 하는지 결정하세요.\\n\",\n \"\\n\",\n \"3. 확장하는 노드가 제어(control) 큐빗인지 대상(target) 큐빗인지에 따라 (`logical_pair_list`를 참고하세요) 노드를 확장하는 방법을 달리 해야 합니다:\\n\",\n \"\\n\",\n \" 3.1 제어 큐빗에서 대상 큐빗으로 확장하는 경우, 유향(directed) 선분을 확인합니다 (`graph.neighbors`).\\n\",\n \"\\n\",\n \" 3.2 대상 큐빗에서 제어 큐빗으로 확장하는 경우, 무향(undirected) 선분을 확인합니다 (`graph.neighbors_undirected`).\\n\",\n \"\\n\",\n \"4. 각 경로를 확장하면서, 해당 선분의 가중치에 (`graph.get_edge_data(edge_1,edge_2)`) `two_qubit_ops_list`의 대응하는 값을 곱해 누적 오류율을 업데이트합니다. 무향 선분으로 확장하는 경우에는 `graph.get_edge_data(edge_2,edge_1)` 순서로 가중치를 불러와야 한다는 점을 참고하세요.\\n\",\n \"\\n\",\n \"5. 경로가 완성되어 그 길이가 `two_qubit_ops_list`의 길이와 같아지면 누적 오류율이 기준값 이하인 경로만을 저장합니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이번 연습문제에서는 다음의 함수를 유용하게 사용할 수 있을 것입니다: [rx.PyDiGraph.neighbors](https://www.rustworkx.org/dev/apiref/rustworkx.PyDiGraph.neighbors.html), [rx.PyDiGraph.neighbors_undirected](https://www.rustworkx.org/dev/apiref/rustworkx.PyDiGraph.neighbors_undirected.html), 그리고 [rx.PyDiGraph.has_edge](https://www.rustworkx.org/apiref/rustworkx.PyDiGraph.has_edge.html) 모두 아래의 코드에서 사용되고 있습니다.\\n\",\n \"\\n\",\n \"추가로, [rx.PyDiGraph.get_edge_data](https://www.rustworkx.org/apiref/rustworkx.PyDiGraph.get_edge_data.html) 함수가 특히 유용하게 사용될 수 있습니다. 이것은 그래프의 각 선분에 대응하는 이중 큐빗 게이트 오류를 얻을 수 있게 해줍니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def find_paths_with_weight_sum_below_threshold(\\n\",\n \" graph: rx.PyDiGraph,\\n\",\n \" threshold: float,\\n\",\n \" two_qubit_ops_list: list[int],\\n\",\n \" logical_pair_list: list[list[int]],\\n\",\n \") -> tuple[list[list[int]], list[float]]:\\n\",\n \" \\\"\\\"\\\"그래프에서 주어진 기준값 이하의 누적 오류율을 갖는 모든 유효한 경로를 찾습니다.\\\"\\\"\\\"\\n\",\n \" valid_paths = []\\n\",\n \" valid_weights = []\\n\",\n \"\\n\",\n \" # --- 연습문제 4 ---\\n\",\n \" # 목표: 그래프에서 주어진 기준값 이하의 누적 오류율을 갖는 모든 유효한 경로를 찾아보세요.\\n\",\n \"\\n\",\n \" # 그래프에서 설정 가능한 시작점을 모두 탐색합니다\\n\",\n \" for start_node in range(graph.num_nodes()):\\n\",\n \" # 주어진 시작점으로 경로를 초기화합니다\\n\",\n \" paths = [[start_node]]\\n\",\n \" # 경로의 누적 오류율을 초기화합니다 (0부터 시작)\\n\",\n \" weights = [0]\\n\",\n \" # 필요한 이중 큐빗 연산을 순서대로 불러옵니다\\n\",\n \" for i in range(len(two_qubit_ops_list)):\\n\",\n \" new_paths = [] \\n\",\n \" new_weights = [] \\n\",\n \" # 현재까지 설정된 경로의 노드와 가중치를 불러옵니다\\n\",\n \" for path, weight in zip(paths, weights):\\n\",\n \" # 이번 연산에 참여하는 노드가 현재 경로에 포함된 노드 중 어느 것인지 확인합니다.\\n\",\n \" # logical_pair_list를 참고하여 어느 노드에서 경로를 확장할지 결정합니다.\\n\",\n \" if logical_pair_list[i][0] < logical_pair_list[i][1]:\\n\",\n \" index_of_expanding_node = logical_pair_list[i][0] # 제어 큐빗에 해당\\n\",\n \" node_to_expand_from = path[index_of_expanding_node]\\n\",\n \" # 선택된 node_to_expand_from의 모든 neighbors를 탐색합니다\\n\",\n \" for neighbor in graph.neighbors(node_to_expand_from):\\n\",\n \" # 이미 선택한 노드나 선분은 다시 고르지 않습니다\\n\",\n \" if neighbor not in path and graph.has_edge(node_to_expand_from, neighbor):\\n\",\n \" # two_qubit_ops_list에서 추가되는 게이트의 개수를 불러와 해당 선분의 가중치를 곱해 오류율을 구합니다\\n\",\n \" \\n\",\n \" #### 아래에 코드를 작성하세요 ####\\n\",\n \" \\n\",\n \" edge_weight = \\n\",\n \" \\n\",\n \" #### 코드 작성이 완료되었습니다 ####\\n\",\n \" \\n\",\n \" # 경로를 확장하고 가중치를 업데이트합니다\\n\",\n \" new_paths.append(path + [neighbor])\\n\",\n \" new_weights.append(weight + edge_weight)\\n\",\n \" else:\\n\",\n \" index_of_expanding_node = logical_pair_list[i][1] # 대상 큐빗에 해당\\n\",\n \" node_to_expand_from = path[index_of_expanding_node]\\n\",\n \" # 선택된 node_to_expand_from의 모든 undirected_neighbors를 탐색합니다\\n\",\n \" for neighbor in graph.neighbors_undirected(node_to_expand_from):\\n\",\n \" # 이미 선택한 노드나 선분은 다시 고르지 않습니다\\n\",\n \" if neighbor not in path and graph.has_edge(neighbor, node_to_expand_from):\\n\",\n \" # two_qubit_ops_list에서 추가되는 게이트의 개수를 불러와 해당 선분의 가중치를 곱해 오류율을 구합니다\\n\",\n \" \\n\",\n \" #### 아래에 코드를 작성하세요 ####\\n\",\n \" \\n\",\n \" edge_weight = \\n\",\n \" \\n\",\n \" #### 코드 작성이 완료되었습니다 ####\\n\",\n \" \\n\",\n \" # 경로를 확장하고 가중치를 업데이트합니다\\n\",\n \" new_paths.append(path + [neighbor])\\n\",\n \" new_weights.append(weight + edge_weight)\\n\",\n \" # 다음으로 진행하기 전에 경로와 가중치를 업데이트합니다\\n\",\n \" paths = new_paths\\n\",\n \" weights = new_weights\\n\",\n \"\\n\",\n \" # 모든 가능한 경로를 탐색한 후, 누적 오류율을 기준치와 비교합니다\\n\",\n \" for path, weight in zip(paths, weights):\\n\",\n \"\\n\",\n \" ### 아래에 코드를 작성하세요 ###\\n\",\n \" # if문의 조건문을 완성하세요\\n\",\n \" if( ): \\n\",\n \" valid_paths.append(path)\\n\",\n \" valid_weights.append(weight)\\n\",\n \"\\n\",\n \" ### 코드 작성이 완료되었습니다 ###\\n\",\n \" \\n\",\n \" # --- 연습문제 4 종료 ---\\n\",\n \"\\n\",\n \" return valid_paths, valid_weights\\n\",\n \"\\n\",\n \"\\n\",\n \"threshold = acc_two_qubit_error\\n\",\n \"\\n\",\n \"valid_paths, valid_weights = find_paths_with_weight_sum_below_threshold(\\n\",\n \" graph, threshold, two_qubit_ops_list, logical_pair_list\\n\",\n \")\\n\",\n \"# 처음에 찾은 레이아웃보다 좋은 레이아웃이 없을 수도 있다는 점에 주의하세요\\n\",\n \"if valid_weights:\\n\",\n \" minimum_weight_index = valid_weights.index(min(valid_weights))\\n\",\n \" opt_layout = valid_paths[minimum_weight_index]\\n\",\n \"else:\\n\",\n \" minimum_weight_index = None\\n\",\n \" opt_layout = layout_list\\n\",\n \"print(f\\\"We found {len(valid_paths)} valid paths\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"init_layout = Layout({q: phys for q, phys in zip(qaoa_circuit.qubits, opt_layout)})\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" backend=noisy_fake_backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed,\\n\",\n \" initial_layout=init_layout,\\n\",\n \" layout_method=\\\"sabre\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"circuit_opt = pm.run(qaoa_circuit)\\n\",\n \"\\n\",\n \"(\\n\",\n \" acc_total_error_opt,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" pair_list,\\n\",\n \" error_pair_list,\\n\",\n \" error_acc_pair_list,\\n\",\n \") = two_qubit_gate_errors_per_circuit_layout(circuit_opt, noisy_fake_backend)\\n\",\n \"\\n\",\n \"print(\\n\",\n \" f\\\"The path with smaller errors in its two-qubit gates is: {opt_layout} \\\\n\\\",\\n\",\n \" f\\\"With total accumulated error of {acc_total_error_opt:.3f}\\\",\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab2_ex4(find_paths_with_weight_sum_below_threshold)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"잘 하셨습니다! 여러분은 트랜스파일에서 가장 복잡한 부분을 해내셨습니다. 이 방법으로 발견한 모든 조합은 최초의 누적 오류율보다 낮은 누적 오류율을 기록하였습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"여기까지 진행하시면서, 여러분은 큐비트의 레이아웃을 조정하는 것만으로도 이중 큐빗 게이트에서 발생하는 에러를 줄일 수 있다는 것을 보셨을 것입니다. 하지만 이것을 실제 하드웨어에서 양자 회로를 실행할 때마다 일일히 확인하는 것이 가능할까요? 그것은 놀라울 정도로 지루한 작업이 될 것입니다. 하지만 걱정하지 마세요 - 지금은 그저 트랜스파일러에서 수행되는 작업의 일부를 배워본 것 뿐이니까요. 트랜스파일러에서는 이처럼 주어진 회로의 연결성 조건을 만족하면서 이중 큐빗 게이트의 숫자를 최소화하는 레이아웃을 찾는 작업을 수행하고 있으며, `layout_methods`를 설정하여 그 알고리즘을 간편하게 변경할 수 있습니다.\\n\",\n \"\\n\",\n \"특히 다음 연습문제에서는 확률적인 방법으로 SWAP 게이트의 개수를 최소화하는 `sabre` 매소드를 사용해보겠습니다. 트랜스파일러의 성능을 최대로 활용하기 위해, 트랜스파일러에 주는 시드의 값도 다양화해보겠습니다. 이를 통해 좀 더 다양한 레이아웃을 탐색하여 이중 큐빗 게이트 오류를 최소화하는 최선의 경우를 찾아낼 수 있을 것입니다. 이 때, 각각의 경우를 비교하는 지표가 회로의 깊이인지, 이중 큐빗 게이트의 개수인지, 측정 오류인지, 또는 다른 어떤 지표인지는 확실히 정의해두어야 할 것입니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 5: 트랜스파일러를 사용해 최선의 레이아웃 찾기 \\n\",\n \"\\n\",\n \"**목표:** 트랜스파일러를 사용하여 최선의 레이아웃을 찾아보세요.\\n\",\n \"\\n\",\n \"다섯 번째 연습문제에서는 `sabre` `layout_method`를 사용하여 누적 이중 큐빗 게이트 오류율이 가장 낮은 레이아웃을 찾아보도록 하겠습니다. \\n\",\n \"\\n\",\n \"그렇게 하기 위하여, `seed_transpiler`의 값을 0부터 500까지 올리면서 `optimization_level=3` 으로 `generate_preset_pass_manager`를 설정하여 최선의 레이아웃을 찾아보세요. 누적 이중 큐빗 게이트 오류율은 앞에서 정의한 `two_qubit_gate_errors_per_circuit_layout` 함수로 구할 수 있다는 점을 기억하세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def finding_best_seed(\\n\",\n \" circuit: QuantumCircuit, backend: QiskitRuntimeService.backend\\n\",\n \") -> tuple[QuantumCircuit, int, float, int]:\\n\",\n \" \\\"\\\"\\\"주어진 회로와 백엔드에서 누적 이중 큐빗 게이트 오류율을 최소화하는 시드를 찾습니다.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # 누적 오류율을 초기화합니다\\n\",\n \" min_err_acc_seed_loop = 100\\n\",\n \" circuit_opt_best_seed = None\\n\",\n \" # 500개의 시드를 주고 회로를 트랜스파일합니다\\n\",\n \" for seed_transpiler in range(0, 500):\\n\",\n \" pm = generate_preset_pass_manager(\\n\",\n \" backend=backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed_transpiler,\\n\",\n \" layout_method=\\\"sabre\\\",\\n\",\n \" )\\n\",\n \" circuit_opt_seed = pm.run([circuit])[0]\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" # 목표: 주어진 시드로 회로를 트랜스파일하여 누적 오류율을 최소화하는 시드를 찾아주세요.\\n\",\n \" # TODO `two_qubit_gate_errors_per_circuit_layout` 함수를 사용하여 트랜스파일 된 회로의 누적 오류율을 구하세요.\\n\",\n \"\\n\",\n \" # TODO 누적 오류율이 min_err_acc_seed_loop 값보다 작은지 확인하세요. 만약 그렇다면, 새로 얻은 오류율을 저장해두세요.\\n\",\n \"\\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \" return (\\n\",\n \" circuit_opt_best_seed,\\n\",\n \" best_seed_transpiler,\\n\",\n \" min_err_acc_seed_loop,\\n\",\n \" two_qubit_gate_count_seed_loop,\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(\\n\",\n \" circuit_opt_seed_loop,\\n\",\n \" best_seed_transpiler,\\n\",\n \" min_err_acc_seed_loop,\\n\",\n \" two_qubit_gate_count_seed_loop,\\n\",\n \") = finding_best_seed(qaoa_circuit, noisy_fake_backend)\\n\",\n \"\\n\",\n \"best_layout = list(circuit_opt_seed_loop.layout.initial_layout.get_physical_bits().keys())[:n]\\n\",\n \"print(f\\\"Best transpiler seed: {best_seed_transpiler}\\\")\\n\",\n \"print(f\\\"Minimum accumulated two-qubit gate error: {min_err_acc_seed_loop:.3f}\\\")\\n\",\n \"print(f\\\"Two-qubit gate count for best seed: {two_qubit_gate_count_seed_loop}\\\")\\n\",\n \"print(f\\\"Best layout (first n logical qubits mapped to physical qubits):\\\\n {best_layout}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab2_ex5(finding_best_seed)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"다음으로 트랜스파일된 QAOA 회로를 실행하여 좋은 트랜스파일의 효과를 확인해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"주의: 5분 가량이 소요됩니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"아래의 코드 실행에는 약 5분 가량이 소요되며, 그동안 이 노트북의 다른 코드를 실행할 수 없을 것입니다. 코드 실행을 원하지 않는 경우 다음으로 넘어가셔도 괜찮습니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"counts_list_transpiled_circuits = []\\n\",\n \"circuit_transpiled_list = [circuit_transpiled, circuit_opt_seed_loop]\\n\",\n \"opt_params_list_transpiled_circuits = []\\n\",\n \"for circuit in circuit_transpiled_list:\\n\",\n \" result_backend, _ = train_qaoa(init_params, circuit, cost_hamiltonian, noisy_fake_backend)\\n\",\n \" opt_params = result_backend.x\\n\",\n \" opt_params_list_transpiled_circuits.append(opt_params)\\n\",\n \" counts_list_transpiled_circuit = sample_qaoa(opt_params, circuit, noisy_fake_backend)\\n\",\n \" counts_list_transpiled_circuits.append(counts_list_transpiled_circuits)\\n\",\n \" solutions_counts = [counts_list_transpiled_circuit[key] for key in states_solutions]\\n\",\n \" print(f\\\"Probability of measuring a solution for is {float(sum(solutions_counts)/SHOTS)}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이후에 회로를 실행할 때에도, `layout_method`를 지정하고 다양한 시드를 시도하여 노이즈 지표를 최소화하는 방법을 적용할 수 있다는 것을 기억하세요. 이러한 지표는 이중 큐빗 게이트의 개수가 될 수도 있고, 회로의 깊이나, 누적 오류율도 좋습니다.\\n\",\n \"\\n\",\n \"이번 섹션에서 여러분은 양자 회로를 실제 하드웨어에서 실행할 때에 트랜스파일이 매우 중요하다는 것을 배웠습니다. 디바이스마다 다른 큐빗 레이아웃과 기본 게이트에 맞추면서도, 회로의 깊이나 오류율을 최소화해줄 수 있는 좋은 트랜스파일러가 있어야 양자 알고리즘의 성능을 최대한으로 끌어낼 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 5. 오차 완화 (Error Mitigation, EM) \\n\",\n \"\\n\",\n \"**오차 완화**는 양자 디바이스에서 필연적으로 발생하는 노이즈를 해결하는 중요한 연구 분야입니다. 오차 완화는 현재까지 제한적으로만 사용 가능한 복잡한 오류 정정 코드나 추가적인 큐비트 없이도 노이즈의 영향을 줄일 수 있는 지능적인 테크닉입니다. 오류가 발생하는 것을 직접 정정하는 대신, 오차 완화에서는 회로의 반복, 기기 조정과 조율, 고전적인 후처리와 같은 전략을 통해 최종 결과의 품질을 높이고 양자 알고리즘의 성능 향상을 꾀합니다.\\n\",\n \"\\n\",\n \"이 접근법은 현대의 소형-중형의 노이즈가 심한 양자 디바이스에 특히 필요합니다. 완전한 결함 허용 양자 컴퓨터가 아니더라도, 현재 우리가 갖고 있는 디바이스의 성능을 최대로 활용하는 실질적인 방법을 바로 오차 완화 기술이 제시합니다. 오차 완화는 특히 양자 컴퓨팅과 고전 컴퓨팅을 오가는 하이브리드 알고리즘에 자연스럽게 통합될 수 있습니다:\\n\",\n \"\\n\",\n \"- Variational Quantum Eigensolver (VQE),\\n\",\n \"- Quantum Approximate Optimization Algorithm (QAOA), 그리고\\n\",\n \"- Quantum-enhanced machine learning models.\\n\",\n \"\\n\",\n \"주목할 만한 점은, 오차 완화가 모든 오류를 완벽히 제거해주는 것은 아니지만, 종종 충분히 유용하거나 작동 가능한 결과를 얻을 수 있도록 결과를 가공해줄 수는 있다는 것입니다. 이것은 노이즈가 낀 양자 연산의 결과와 의미 있는 결과 사이의 간극을 좁혀줌으로써, 대규모의 오류 정정 디바이스가 구현되기 전에도 실용적인 양자 어플리케이션을 가능하게 해주는 도구입니다.\\n\",\n \"\\n\",\n \"여기에는 서로 다른 종류의 노이즈와 결함을 관리하는 몇 가지 잘 알려진 오류 정정 테크닉이 있습니다.\\n\",\n \"\\n\",\n \"오류 정정에서 가장 널리 사용되는 방법 중 하나는 무잡음 추정법(Zero Noise Extrapolation, ZNE) 입니다. 이 방법에서는 노이즈를 의도적으로 증폭시키며 하나의 양자 회로를 반복적으로 실행하게 됩니다. 그리고는 수학적인 추정 기법을 사용하여 노이즈가 없을 때의 결과를 예측합니다. 이 방법은 [2017년 Temme et al.](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509)에 의해 처음 도입되었습니다.\\n\",\n \"\\n\",\n \"## 5.1 무잡음 추정법 (ZNE) \\n\",\n \"\\n\",\n \"ZNE는 추가적인 큐비트나 오류 정정을 하지 않고도 양자 회로에서의 노이즈의 영향을 감소시킬 수 있는 강력한 오차 완화 기법입니다. 이 과정은 크게 노이즈 증폭, 반복 실행, 무잡음 상태에 대한 고전적인 추정의 세 단계로 나누어집니다:\\n\",\n \"\\n\",\n \"### 노이즈 증폭\\n\",\n \"\\n\",\n \"첫 번째 단계인 노이즈 증폭은 ZNE에서 가장 중심이 되는 단계입니다. 이를 위해서는 양자 회로를 평소보다 노이즈가 심한 환경에서 실행하되, 이 과정을 일관적이고 조작 가능한 방식으로 수행할 수 있어야 합니다. 노이즈가 증가함에 따라 결괏값이 어떻게 달라지는지 비교하는 것으로, 노이즈가 전혀 없을 때의 결과를 추정하는 것이 가능해집니다. 일반적으로 노이즈를 증폭할 때에 사용할 수 있는 방법 중 하나는 회로 접기(folding) 방법이 있습니다.\\n\",\n \"\\n\",\n \"회로 접기 방법은 이론적으로 양자 회로의 결과를 바꾸지 않는 게이트를 추가적으로 수행하여 양자 컴퓨터의 노이즈를 인위적으로 증가시키는 방법입니다. 이러한 추가적인 게이트는 원래 가해야 하는 게이트의 역연산 게이트가 될 수 있습니다. 예를 들어, 유니터리 연산 $U$ 를 $ U \\\\cdot U^\\\\dagger \\\\cdot U$ 로 바꾸면 이론적으로는 $U$ 연산과 동등하지만, 실제 실행에는 더 많은 시간과 게이트가 필요해지고, 따라서 하드웨어의 노이즈에 더 많이 노출될 것이라고 예측할 수 있습니다.\\n\",\n \"\\n\",\n \"접기 방법에는 크게 두 가지가 있습니다: **전체적 접기** 방법과 **국소적 접기** 방법입니다.\\n\",\n \"\\n\",\n \"#### 전체적 접기\\n\",\n \"\\n\",\n \"전체적 접기 (global folding) 방법에서는 전체 양자 회로를 하나의 블록으로 취급하여 접기 연산을 수행합니다. 즉, 회로 전체의 유니터리 연산을 통째로 $U$ 로 정의하고, 그것의 역연산을 구해 다음과 같은 변환을 적용하는 것입니다:\\n\",\n \"\\n\",\n \"$$U \\\\rightarrow U \\\\cdot U^\\\\dagger \\\\cdot U$$\\n\",\n \"\\n\",\n \"이 변환은 $U^\\\\dagger \\\\cdot U$ 가 서로를 상쇄하기 때문에 원본 회로와 이론적으로 동일합니다. 그러나 회로가 더 길어지고 더 많은 게이트를 실행하게 되므로, 환경적인 노이즈와 하드웨어의 결함에 의한 영향을 그만큼 더 받게 됩니다.\\n\",\n \"\\n\",\n \"전체적 접기 방법은 전체 회로에서의 노이즈를 빠르고 균일하게 증폭시킬 수 있다는 장점이 있습니다. 이 방법은 쉽게 구현할 수 있으며, 회로의 내부 구조에 대한 지식을 요구하지 않습니다. 이런 장점 덕분에, 전체적 접기 방법은 정교한 조작이 필요하지 않은 일반적인 상황에서 노이즈를 증폭시킬 때에 간편하게 사용할 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 6a: 전체적 접기\\n\",\n \"\\n\",\n \"**목표:** 양자 회로에서 전체적 접기를 구현해보세요.\\n\",\n \"\\n\",\n \"연습문제 6번의 a에서는 주어진 양자 회로에 전체적 접기를 가하는 함수를 만들어볼 것입니다. 함수를 설계할 때, 회로의 이론적 결괏값을 유지하면서 주어지는 여러 가지 노이즈 증폭 인수에 맞게 노이즈를 증폭시킬 수 있도록 해보세요. 노이즈 증폭 인수는 회로 $U$ 또는 $U^\\\\dagger$ 가 가해지는 횟수에 대응되며, 인수가 1이면 회로를 접지 않는 상황에 해당합니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def fold_global_circuit(circuit: QuantumCircuit, scale_factor: int) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"주어진 노이즈 증폭 인수에 따라 전체적 접기를 적용합니다.\\\"\\\"\\\"\\n\",\n \" if scale_factor % 2 == 0 or scale_factor < 1:\\n\",\n \" raise ValueError(\\\"scale_factor must be an odd positive integer (1, 3, 5, ...)\\\")\\n\",\n \" # 회로를 \\\"접는\\\" 횟수를 정의합니다\\n\",\n \" n_repeat = (scale_factor - 1) // 2\\n\",\n \" folded_circuit = QuantumCircuit(circuit.qubits, circuit.clbits)\\n\",\n \"\\n\",\n \" def remove_all_measurements(qc: QuantumCircuit) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"회로의 모든 측정 연산을 제거합니다.\\\"\\\"\\\"\\n\",\n \" clean_qc = QuantumCircuit(qc.num_qubits)\\n\",\n \" for instr in qc.data:\\n\",\n \" if instr.operation.name != \\\"measure\\\":\\n\",\n \" clean_qc.append(instr.operation, instr.qubits)\\n\",\n \" return clean_qc\\n\",\n \"\\n\",\n \" # 측정을 제거하여 만든 이 양자 회로를 U (Unitary) 로 정의합니다\\n\",\n \" clean_circuit = remove_all_measurements(circuit)\\n\",\n \"\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" # 전체적 회로 접기를 구현해보세요. `QuantumCircuit.append`와 `QuantumCircuit.inverse` 함수를 사용하세요.\\n\",\n \" # 회로에 U^† (inverse of clean_circuit) 와 U(clean_circuit) 를 순서대로 가하세요.\\n\",\n \" \\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \" return folded_circuit\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab2_ex6a(fold_global_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 국소적 접기\\n\",\n \"\\n\",\n \"국소적 접기 (local folding) 방법은 양자 회로의 특정 부분의 - 주로 오류에 취약한 게이트나 회로 일부분의 - 노이즈를 선택적으로 증폭시킬 수 있습니다. 이 방법은 전체 회로를 블록화하여 접는 대신 개별적인 양자 게이트나 회로 일부분을 접습니다. 이것은 좀 더 유연하고 특정적인 방식으로 노이즈 증폭을 수행할 수 있습니다. 여기에서 접는 대상이 되는 연산은 양자 회로의 개별적인 양자 게이트 $G$ 입니다. 국소적 접기에서는 해당 연산에 역연산을 다음과 같이 적용하여 접기를 수행합니다:\\n\",\n \"\\n\",\n \"$$ G \\\\rightarrow G \\\\cdot G^\\\\dagger \\\\cdot G $$\\n\",\n \"\\n\",\n \"이 변환은 이론적으로 추가된 $G^\\\\dagger \\\\cdot G$ 쌍이 상쇄되므로 기존의 연산과 같은 연산이 됩니다. 그러나, 실제 연산에서는 게이트 연산이 세 배 많아지게 되므로, 게이트 연산이 가해지는 큐비트에는 국소적으로 더 많은 노이즈가 끼게 됩니다. 이것은 회로에 존재하는 게이트에 각각 개별적으로 적용이 가능하므로 전체적 접기 방법보다 더 다양한 노이즈 세기를 선택적, 체계적으로 시뮬레이션 할 수 있다는 장점이 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 6b: 국소적 접기\\n\",\n \"\\n\",\n \"**목표:** 양자 회로에서 국소적 접기를 구현해보세요.\\n\",\n \"\\n\",\n \"연습문제 6번의 b에서는 양자 회로에 국소적 접기를 가하는 함수를 만들어볼 것입니다. 전체적 접기와 달리, 이 방법은 개별 게이트를 선택적으로 접어 회로의 특정 부분에서의 노이즈를 증폭시킵니다. 함수를 설계할 때, 접고자 하는 게이트를 특정하여 (예를 들어 측정 게이트는 접으면 안 됩니다) 주어진 노이즈 증폭 세기에 따라 접기를 수행할 수 있도록 해보세요.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def fold_local_circuit(circuit: QuantumCircuit, scale_factor: int) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"주어진 노이즈 증폭 인수에 따라 국소적 접기를 적용합니다.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" if scale_factor % 2 == 0:\\n\",\n \" raise ValueError(\\\"scale must be an odd positive integer (1, 3, 5, ...)\\\")\\n\",\n \" # 각 연산을 \\\"접는\\\" 횟수를 정의합니다\\n\",\n \" n_repeat = (scale_factor - 1) // 2\\n\",\n \" qc_folded = QuantumCircuit(circuit.qubits, circuit.clbits)\\n\",\n \"\\n\",\n \" if scale_factor == 1:\\n\",\n \" return circuit\\n\",\n \" else:\\n\",\n \" for instruction in circuit.data:\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" # 국소적 회로 접기를 구현해보세요. 측정 게이트는 접지 마세요!\\n\",\n \" \\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \" return qc_folded\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab2_ex6b(fold_local_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 추정\\n\",\n \"\\n\",\n \"접기를 통해 노이즈를 증폭시켰다면, 두 번째 단계는 양자 회로를 여러 노이즈 단계에서 반복 실행하는 것입니다. 마지막으로 세 번째 단계는, 이 결과들을 모아 선형, 다항, 또는 지수 함수 등의 고전적 피팅 방법을 적용해 노이즈가 없을 때의 결과를 **추정**하는 것입니다. 이러한 작업을 통해 ZNE는 현재의 노이즈가 있는 양자 하드웨어에서 높은 정확도의 결과를 되찾을 수 있도록 해주며, 양자 컴퓨팅 응용에 단기적으로 특히 중요한 위상을 차지합니다.\\n\",\n \"\\n\",\n \"이제 QAOA 회로에 ZNE를 적용하여 노이즈가 있는 양자 하드웨어에서의 결과를 향상시켜봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def basic_zne(\\n\",\n \" circuit,\\n\",\n \" scales,\\n\",\n \" backend,\\n\",\n \" opt_params,\\n\",\n \" observable,\\n\",\n \"):\\n\",\n \" \\\"\\\"\\\"국소적 접기를 이용한 기본 무잡음 추정법 (ZNE).\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" exp_vals = []\\n\",\n \" xdata = np.array(scales)\\n\",\n \" estimator = Estimator(mode=backend)\\n\",\n \"\\n\",\n \" for scale in scales:\\n\",\n \" # 국소적 접기를 가합니다\\n\",\n \" folded = fold_local_circuit(circuit, scale)\\n\",\n \"\\n\",\n \" # 백엔드에 맞게 트랜스파일합니다\\n\",\n \" basis_gates = backend.target.operation_names\\n\",\n \" transpiled_folded = generate_preset_pass_manager(\\n\",\n \" basis_gates=basis_gates, optimization_level=0, seed_transpiler=seed\\n\",\n \" ).run(folded)\\n\",\n \" pub = (\\n\",\n \" transpiled_folded,\\n\",\n \" observable.apply_layout(circuit.layout),\\n\",\n \" opt_params,\\n\",\n \" )\\n\",\n \" # 기댓값을 구합니다\\n\",\n \" job = estimator.run([pub])\\n\",\n \" results = job.result()[0]\\n\",\n \" exp_vals.append(results.data.evs)\\n\",\n \"\\n\",\n \" return xdata, exp_vals, pub\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"scales = [1, 3, 5, 7, 9, 11, 13]\\n\",\n \"xdata, ydata, pub = basic_zne(\\n\",\n \" qaoa_circuit_transpiled,\\n\",\n \" scales,\\n\",\n \" noisy_fake_backend,\\n\",\n \" opt_params_list[num_backend],\\n\",\n \" cost_hamiltonian,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"방금 구현한 ZNE의 결과를 검증하고 분석하기 위해 세 가지 추정 방법을 사용할 수 있습니다: 선형, 다항, 지수 추정입니다. 이 추정법을 통해 노이즈를 증폭하여 얻은 데이터로부터 노이즈가 없을 때의 회로의 결과를 예측할 수 있게 됩니다. 이것이 이루어지는 예시를 아래의 코드를 실행하여 확인해보세요:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"methods = [\\\"linear\\\", \\\"quadratic\\\", \\\"exponential\\\"]\\n\",\n \"\\n\",\n \"for method in methods:\\n\",\n \" print(f\\\"\\\\n Extrapolation Method: {method.upper()}\\\")\\n\",\n \"\\n\",\n \" # 추정을 수행합니다\\n\",\n \" zero_val, fitted_vals, fit_params, fit_fn = zne_method(method=method, xdata=xdata, ydata=ydata)\\n\",\n \"\\n\",\n \" # 추정된 기댓값을 출력합니다\\n\",\n \" print(f\\\"⟨Z⟩ (ZNE Estimate): {zero_val:.3f}\\\")\\n\",\n \"\\n\",\n \" # 결과를 그래프로 출력합니다\\n\",\n \" plot_zne(xdata, fitted_vals, zero_val, fit_fn, fit_params, method)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 결론 \\n\",\n \"\\n\",\n \"Lab 2를 훌륭하게 해내셨군요!\\n\",\n \"\\n\",\n \"이번 랩에서는 양자회로에 영향을 줄 수 있는 다양한 노이즈의 원인과 (더 중요한 것은) 이 *노이즈를 극복하는* 도구를 알아보았습니다. 이 도구들을 얻음으로써, 여러분은 다양한 최신 양자 알고리즘들을 실제 양자 하드웨어에서 실행할 준비를 - 훌륭하게 - 마쳤습니다. 지금까지 논의한 워크플로우을 잘 기억하세요:\\n\",\n \"\\n\",\n \"- **적절한 하드웨어를 골라** 회로를 실행하기.\\n\",\n \"- **트랜스파일러를 사용**하여 최선의 레이아웃을 고르고 이중 큐빗 게이트의 수와 기타 지표를 최적화하기.\\n\",\n \"- **오차 완화와 억제를 적용**하여 노이즈의 영향을 줄이고 성능을 향상시키기.\\n\",\n \"\\n\",\n \"이번 랩을 해냄으로써 여러분은 앞으로의 챌린지도 훌륭하게 수행할 준비가 되었으며, 이를 자랑스럽게 여기셔도 좋습니다.\\n\",\n \"\\n\",\n \"이번 챌린지는 여기까지였습니다! 잠깐, 아직인가요?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 현재의 진도 상황을 확인해보세요\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 보너스 챌린지: 더 키워보세요! \\n\",\n \"\\n\",\n \"보너스로 Max-cut 문제를 실제 IBM Quantum 하드웨어에서 푸는 더 복잡한 버전의 챌린지를 준비하였습니다. 이것은 지금까지 배운 것들을 실제로 사용해볼 수 있는 기회입니다. 오차 완화 테크닉을 포함해서요!\\n\",\n \"\\n\",\n \"### 문제\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 점의 개수를 정의합니다\\n\",\n \"n_ext = 7\\n\",\n \"# 그래프를 정의합니다\\n\",\n \"graph_ext = rx.PyGraph()\\n\",\n \"graph_ext.add_nodes_from(np.arange(0, n_ext, 1))\\n\",\n \"generic_backend_ext = GenericBackendV2(n_ext, seed=seed)\\n\",\n \"weights = 1\\n\",\n \"# 그래프의 선분 7개를 제거하여 비대칭적인 문제로 정의합니다\\n\",\n \"graph_ext.add_edges_from(\\n\",\n \" [(edge[0], edge[1], weights) for edge in generic_backend_ext.coupling_map][:-7]\\n\",\n \")\\n\",\n \"draw_graph(graph_ext, node_size=200, with_labels=True, width=1)\\n\",\n \"max_cut_paulis_ext = graph_to_Pauli(graph_ext)\\n\",\n \"cost_hamiltonian_ext = SparsePauliOp.from_list(max_cut_paulis_ext)\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=generic_backend_ext, seed_transpiler=seed\\n\",\n \")\\n\",\n \"layers = 2\\n\",\n \"init_params = np.zeros(2 * layers)\\n\",\n \"qaoa_circuit_ext = QAOAAnsatz(cost_operator=cost_hamiltonian_ext, reps=layers)\\n\",\n \"qaoa_circuit_ext.measure_all()\\n\",\n \"qaoa_circuit_ext_transpiled = pm.run(qaoa_circuit_ext)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 백엔드 선택하기\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qaoa_circuit_transpiled_ext_list = []\\n\",\n \"acc_error_list = []\\n\",\n \"\\n\",\n \"\\n\",\n \"for backend_hw in real_backends:\\n\",\n \"\\n\",\n \" pm = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend_hw, seed_transpiler=seed\\n\",\n \" )\\n\",\n \" qaoa_circuit_transpiled_ext = pm.run([qaoa_circuit_ext])[0]\\n\",\n \" qaoa_circuit_transpiled_ext_list.append(qaoa_circuit_transpiled_ext)\\n\",\n \"\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \"\\n\",\n \" # accumulated_errors 함수에 `backend_hw`와 `qaoa_circuit_transpiled_ext`를 넣고 [0] 번째 값을 반환하세요\\n\",\n \" acc_error = \\n\",\n \"\\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \" acc_error_list.append(acc_error)\\n\",\n \"# 오류율이 가장 낮은 하드웨어을 선택합니다\\n\",\n \"best_backend_ext = real_backends[acc_error_list.index(min(acc_error_list))]\\n\",\n \"print(\\n\",\n \" f\\\"The backend {best_backend_ext.name} has the circuit with the smallest accumulated error: {min(acc_error_list):.3f}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 레이아웃을 최적화하고 누적 오류율을 최소화합니다\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"circuit_ext_opt_seed, best_seed_transpiler, min_err_acc_seed, _ = finding_best_seed(\\n\",\n \" qaoa_circuit_ext, best_backend_ext\\n\",\n \")\\n\",\n \"print(f\\\"Best transpiler seed: {best_seed_transpiler}\\\")\\n\",\n \"print(f\\\"Minimum accumulated two-qubit gate error: {min_err_acc_seed:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 시뮬레이터에서 회로를 실행합니다\\n\",\n \"\\n\",\n \"우선, 이 QAOA 문제의 변수를 시뮬레이터로 최적화해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"best_backend_sim = AerSimulator.from_backend(best_backend_ext, seed_simulator=seed)\\n\",\n \"result_qaoa_sim, objective_func_vals_sim = train_qaoa(\\n\",\n \" init_params, circuit_ext_opt_seed, cost_hamiltonian_ext, best_backend_sim\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이제 QAOA 회로에서 답안 샘플을 얻을 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"counts_list_sim = sample_qaoa(opt_params_sim, circuit_ext_opt_seed, best_backend_sim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 결과 확인\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eigenvalues_ext, eigenvectors_ext = np.linalg.eig(cost_hamiltonian_ext)\\n\",\n \"ground_energy_ext = min(eigenvalues_ext).real\\n\",\n \"num_solutions_ext = eigenvalues_ext.tolist().count(ground_energy_ext)\\n\",\n \"index_solutions_ext = np.where(eigenvalues_ext == ground_energy_ext)[0].tolist()\\n\",\n \"print(f\\\"The ground energy of the Hamiltonian is {ground_energy_ext}\\\")\\n\",\n \"print(f\\\"The number of solutions of the problem is {num_solutions_ext}\\\")\\n\",\n \"print(f\\\"The list of the solutions based on their index is {index_solutions_ext}\\\")\\n\",\n \"\\n\",\n \"states_solutions_ext = decimal_to_binary(index_solutions_ext, n_ext)\\n\",\n \"# 샘플의 카운트가 높은 것부터 순서대로 딕셔너리를 정렬합니다\\n\",\n \"sorted_states_sim = sorted(counts_list_sim.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"# 가장 많이 관측된 해부터 순서대로 num_solutions개의 해를 가져옵니다\\n\",\n \"top_states_sim = sorted_states_sim[:num_solutions_ext]\\n\",\n \"# 가져온 해를 출력합니다\\n\",\n \"qaoa_ground_states_sim = sorted([state for state, count in top_states_sim])\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions_ext}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_sim} for {best_backend_sim.name}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 하드웨어에서 실행하기\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" 리소스 제한: \\n\",\n \"\\n\",\n \"아래의 섹션에서는 위에서 정의한 회로를 실제 하드웨어에서 실행해볼 것입니다. 이는 양자 컴퓨터 사용량 약 2-3분 정도에 해당합니다. 양자 컴퓨터 사용에 동의하시는 경우에만 아래의 섹션을 진행해주세요. 또한, 사용량이 너무 커지지 않도록 주어진 설정값을 가능하면 변경하지 않도록 해주세요.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"드디어 실제 하드웨어에서 이 알고리즘을 실행하여 결과를 확인해볼 때가 되었습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Estimator\\n\",\n \"\\n\",\n \"QAOA 회로의 변수를 최적화하는 것은 실제 하드웨어에서 수행하지 않을 것입니다. 이는 특히 사용량이 제한된 Open Plan 유저분들의 양자 컴퓨터 사용시간을 아끼기 위해서입니다. 변수는 앞에서 시뮬레이터로 값을 가져오기로 하고, 대신 오류 완화 테크닉을 적용하여 결과가 어떻게 향상되는지를 확인해보도록 하겠습니다.\\n\",\n \"\\n\",\n \"먼저 오류 완화를 적용하지 않고 실제 하드웨어에서 바닥 상태 에너지를 계산해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"\\n\",\n \"# 앞의 섹션을 참고하여 회로를 실행하기에 가장 적합한 백엔드를 선택해보세요\\n\",\n \"best_backend_hw = \\n\",\n \"\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \"options = EstimatorOptions(default_shots=100000)\\n\",\n \"estimator = Estimator(mode=best_backend_hw, options=options)\\n\",\n \"ground_energy_ext_hw = cost_func_estimator(\\n\",\n \" opt_params_sim, circuit_ext_opt_seed, cost_hamiltonian_ext, estimator\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The ground energy of the QAOA circuit executed on {best_backend_hw.name} without EM is: {ground_energy_ext_hw}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이제 오차 완화와 오류 억제 테크닉을 적용해보겠습니다. 이번에는 이것을 수동으로 수행하지 않고 QiskitRuntime의 기능을 이용해 더 많은 테크닉을 적용해봅시다:\\n\",\n \"\\n\",\n \"- **Dynamical decoupling**: 이 오류 억제 테크닉은 쉬고 있는 큐비트에 게이트를 가해 주변 환경으로부터의 노이즈와 결맞음 오류를 상쇄시키는 기술입니다. 이것은 큐비트를 주변의 노이즈 원인으로부터 실질적으로 \\\"분리\\\"시킴으로써 양자 결맞음 상태를 유지하는 데에 도움을 줍니다.\\n\",\n \"- **Pauli twirling**: 이 오차 완화 테크닉은 연산 전후로 랜덤하게 파울리 게이트를 가하고 상쇄함으로써 복잡한 특성을 가질 수 있는 다양한 노이즈를 훨씬 단순한 파울리 노이즈로 바꿔주는 효과를 얻습니다. 이것은 결맞음 오류를 감소시키고 노이즈 모델링이나 정정 등 다른 테크닉을 더 효과적으로 적용하는 데에 도움을 줍니다.\\n\",\n \"- **TREX**: TREX란 Twirled Readout Error eXtinction의 약자로, 큐비트를 측정하기 전에 랜덤하게 큐비트를 뒤집어서 측정하는 방식으로 측정 오류를 완화합니다. 이것은 측정 오류 행렬을 대각화하는 효과도 있어, 측정 오류의 역연산을 통해 더 정확한 결괏값을 얻는 데에도 도움을 줍니다.\\n\",\n \"- **ZNE**: 앞에서 설명한 바와 같이, ZNE는 양자 계산이 노이즈가 없는 디바이스에서 실행됐을 때의 결과를 추정하는 오류 완화 테크닉입니다. 이것은 인위적으로 노이즈를 증가시켜, 결과를 측정하고, 거꾸로 노이즈가 없을 때의 결과를 추정하는 순서로 이루어집니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"options = EstimatorOptions(default_shots=100000)\\n\",\n \"# Dynamical Decoupling을 활성화합니다\\n\",\n \"options.dynamical_decoupling.enable = True\\n\",\n \"\\n\",\n \"# 랜덤하게 Twirling을 수행합니다\\n\",\n \"options.twirling.enable_gates = True\\n\",\n \"options.twirling.num_randomizations = 10\\n\",\n \"options.twirling.shots_per_randomization = 10000\\n\",\n \"\\n\",\n \"# TREX를 활성화합니다\\n\",\n \"options.resilience.measure_mitigation = True\\n\",\n \"options.resilience.measure_noise_learning.num_randomizations = 10\\n\",\n \"options.resilience.measure_noise_learning.shots_per_randomization = 10000\\n\",\n \"\\n\",\n \"# ZNE를 설정합니다\\n\",\n \"options.resilience.zne_mitigation = True\\n\",\n \"options.resilience.zne.amplifier = \\\"gate_folding\\\"\\n\",\n \"options.resilience.zne.extrapolator = \\\"polynomial_degree_2\\\"\\n\",\n \"options.resilience.zne.noise_factors = (1, 3, 5)\\n\",\n \"\\n\",\n \"# 하드웨어에서 실행합니다\\n\",\n \"estimator_EM = Estimator(mode=best_backend_hw, options=options)\\n\",\n \"ground_energy_ext_hw_EM = cost_func_estimator(\\n\",\n \" opt_params_sim, circuit_ext_opt_seed, cost_hamiltonian_ext, estimator_EM\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The ground energy of the QAOA circuit executed on {best_backend_hw.name} with EM is: {ground_energy_ext_hw_EM}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"같은 방법으로 실제 하드웨어에서 오류 완화를 적용하지 않고 Sampler를 실행해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"# 실제 하드웨어에서 Sampler를 실행합니다\\n\",\n \"sampler = Sampler(mode=best_backend_hw)\\n\",\n \"job = sampler.run([(circuit_ext_opt_seed, opt_params_sim)], shots=SHOTS)\\n\",\n \"results_sampler = job.result()\\n\",\n \"counts_list_hw = results_sampler[0].data.meas.get_counts()\\n\",\n \"display(plot_histogram(counts_list_hw, title=f\\\"Max cut with {best_backend_hw.name} \\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이번에는 오류 완화를 적용하여 Sampler를 실행해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"# 실제 하드웨어에서 Sampler를 실행합니다\\n\",\n \"sampler = Sampler(mode=best_backend_hw)\\n\",\n \"# Sampler의 런타임 옵션을 직접 지정해줍니다\\n\",\n \"sampler.options.dynamical_decoupling.enable = True\\n\",\n \"job = sampler.run([(circuit_ext_opt_seed, opt_params_sim)], shots=SHOTS)\\n\",\n \"results_sampler = job.result()\\n\",\n \"counts_list_hw_EM = results_sampler[0].data.meas.get_counts()\\n\",\n \"display(plot_histogram(counts_list_hw_EM, title=f\\\"Max cut with {best_backend_hw.name} with EM\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 결과 확인\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sorted_states_hw = sorted(counts_list_hw.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"top_states_hw = sorted_states_hw[:num_solutions_ext]\\n\",\n \"qaoa_ground_states_hw = sorted([state for state, count in top_states_hw])\\n\",\n \"\\n\",\n \"sorted_states_hw_EM = sorted(counts_list_hw_EM.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"top_states_hw_EM = sorted_states_hw_EM[:num_solutions_ext]\\n\",\n \"qaoa_ground_states_hw_EM = sorted([state for state, count in top_states_hw_EM])\\n\",\n \"\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions_ext}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_hw} for {best_backend_hw.name}\\\"\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_hw_EM} for {best_backend_hw.name} with EM\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"이번 랩을 끝까지 해낸 것을 축하드립니다!\\n\",\n \"만약 하드웨어에서 더 실험을 해보고 싶으시다면, 다음의 것들을 추가로 시도해보실 수 있습니다:\\n\",\n \"\\n\",\n \"- 오류 완화 테크닉 실험해보기: 다양한 오류 완화 / 억제 전략을 적용하여 회로를 실행해보세요. 특정 기능을 껐다 켜보거나 다양한 매개변수를 조정해보며 그 효과를 확인해볼 수 있습니다.\\n\",\n \"\\n\",\n \"- 다른 하드웨어에서 보너스 챌린지를 실행하기: 전체 보너스 챌린지 섹션을 다른 하드웨어에서 실행해보고 결과와 성능을 비교해보세요.\\n\",\n \"\\n\",\n \"- 더 크게 키워보기: 더 큰 그래프에서 문제를 정의해보세요. 예를 들면 10개의 큐비트를 사용해 실험을 진행해볼 수 있습니다. 다만 큐비트의 숫자가 많아질수록 시뮬레이터와 하드웨어에서 회로를 실행하는데 걸리는 시간은 길어진다는 점에 주의하세요.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# References:\\n\",\n \"\\n\",\n \"[1] Fahri et al., \\\"A Quantum Approximate Optimization Algorithm\\\" (2014). [arXiv:quant-ph/1411.4028](https://arxiv.org/abs/1411.4028)\\n\",\n \"\\n\",\n \"[2] Nation et al., \\\"Suppressing Quantum Circuit Errors Due to System Variability\\\" (2023). [PRX Quantum 4, 010327](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.4.010327)\\n\",\n \"\\n\",\n \"[3] Temme et al., \\\"Error Mitigation for Short-Depth Quantum Circuits\\\" (2017). [Phys. Rev. Lett. 119, 180509](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509)\\n\",\n \"\\n\",\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Jorge Martínez de Lejarza, Alberto Maldonado\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Paul Nation, Junye Huang, Sophy Shin\\n\",\n \"\\n\",\n \"**Translated by:** Boseong Kim\\n\",\n \"\\n\",\n \"**Version:** 1.0\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.11\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "lab-2/Lab2.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 2: Cutting through the noise\\n\",\n \"\\n\",\n \"In this lab we dive into the world of quantum noise, and explore the different techniques for navigating the challenges of noise in order to obtain good results with current quantum computers. In the context of a small-size Max-cut problem, we provide a step-by-step guide to reduce noise in order to execute a quantum algorithm on a quantum computer. We present different transpilation and error mitigation techniques to minimize the error and ensure that the results remain correct despite the noise. Finally, the lab concludes with a bonus exercise where all the discussed techniques are implemented on real quantum hardware.\\n\",\n \"\\n\",\n \"# Table of contents\\n\",\n \"\\n\",\n \"0. [Requirements](#requirements)\\n\",\n \"1. [Introduction](#intro) \\n\",\n \" - [Spirit of the Lab](#spirit)\\n\",\n \" - [What is quantum noise?](#quantum-noise) \\n\",\n \" - [Sources of noise](#sources)\\n\",\n \" * [Exercise 1: Find the qubits with longer T1, T2, higher gate fidelities, and smaller readout errors](#exercise_1)\\n\",\n \"2. [Max-cut problem](#max-cut)\\n\",\n \" - [The problem: Define the graph](#the-graph)\\n\",\n \" - [The mapping: a graph into a quantum computer](#the-mapping)\\n\",\n \" * [Exercise 2: Find the Ising Hamiltonian of the Max-cut problem](#exercise_2)\\n\",\n \" - [QAOA solution](#qaoa-solution)\\n\",\n \" - [Checking our solution](#checking)\\n\",\n \"3. [Noisy quantum simulator](#noisy-simulator)\\n\",\n \" - [Choosing backend](#choosing-backend)\\n\",\n \" * [Exercise 3: Counting errors](#exercise_3)\\n\",\n \" - [Estimating errors using NEAT](#neat)\\n\",\n \"4. [Transpiler](#transpiler)\\n\",\n \" - [Minimizing two-qubit gates](#min-two-qubit)\\n\",\n \" - [Find the optimal layout](#opt-layout)\\n\",\n \" * [Exercise 4: Find all the good mappings](#exercise_4)\\n\",\n \" * [Exercise 5: Use the transpiler to find the optimal layout](#exercise_5)\\n\",\n \"5. [Error mitigation](#em)\\n\",\n \" - [Zero Noise Extrapolation (ZNE)](#zne)\\n\",\n \" * [Exercise 6a: Implement global folding on quantum circuits](#exercise_6a)\\n\",\n \" * [Exercise 6b: Implement local folding on quantum circuits](#exercise_6b)\\n\",\n \" * [Exercise 7: Implement Local Zero Noise Extrapolation (ZNE)](#exercise_7)\\n\",\n \"6. [Conclusions](#conclusions)\\n\",\n \"7. [Bonus challenge: Scale it up!](#bonus)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 0. Requirements \\n\",\n \"\\n\",\n \"Before starting this tutorial, please make sure that you have the following libraries installed:\\n\",\n \"\\n\",\n \"* Qiskit SDK v2.0 with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"* Rustworkx (`pip install rustworkx`)\\n\",\n \"* Qiskit Aer v0.17\\n\",\n \"* Pylatexenc\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install -U qiskit \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.10`. If you see a lower version, restart your kernel and reinstall the grader.\\n\",\n \"Also make sure you have set up everything according to lab 0 and test it with the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Imports\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import rustworkx as rx\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from rustworkx.visualization import mpl_draw as draw_graph\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from scipy.optimize import minimize\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.providers.fake_provider import GenericBackendV2\\n\",\n \"from qiskit.quantum_info import SparsePauliOp, Statevector, DensityMatrix, Operator\\n\",\n \"from qiskit.circuit.library import QAOAAnsatz\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.transpiler import Layout\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import (\\n\",\n \" Session,\\n\",\n \" EstimatorV2 as Estimator,\\n\",\n \" SamplerV2 as Sampler,\\n\",\n \" EstimatorOptions,\\n\",\n \")\\n\",\n \"from qiskit_ibm_runtime.debug_tools import Neat\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from utils import zne_method, plot_zne, plot_backend_errors_and_counts\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab2_ex1,\\n\",\n \" grade_lab2_ex2,\\n\",\n \" grade_lab2_ex3,\\n\",\n \" grade_lab2_ex4,\\n\",\n \" grade_lab2_ex5,\\n\",\n \" grade_lab2_ex6a,\\n\",\n \" grade_lab2_ex6b,\\n\",\n \")\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Introduction \\n\",\n \"\\n\",\n \"### 1.1 Spirit of the lab \\n\",\n \"\\n\",\n \"\\n\",\n \"*Here we walk you through the process of tackling real-world problems by using the right transpilation and error mitigation techniques to maximize performance on IBM quantum hardware*\\n\",\n \"\\n\",\n \"### 1.2 What is quantum noise? \\n\",\n \"\\n\",\n \"Noise is a central topic in designing and using quantum computers, and it is one of the main characteristics of current devices. But what exactly does noise mean in quantum information? We usually refer to the term noise as any undesired transformation that disturbs the expected outcome of the measurement of a quantum state. We categorize errors in two groups:\\n\",\n \"\\n\",\n \"- Coherent errors: These errors are caused by small, consistent mistakes in how quantum operations (gates) are applied. These errors are unitary, meaning they don't destroy information and can, in theory, be reversed. A typical example is a gate that's supposed to rotate a qubit by an angle $\\\\theta$, but instead rotates it by $\\\\theta+\\\\varepsilon$ due to imperfect calibration. For instance, if a Hadamard gate is slightly miscalibrated, it might not create a perfect superposition, producing a biased measurement outcome.\\n\",\n \"- Incoherent errors: The origin of these errors is naturally quantum as it comes from the interaction of the quantum system with the environment. Incoherent errors are the reason why most of the current quantum computers need to be cooled down to a temperature of the order of mK, to reduce these unwanted interactions and preserve the system's coherence. These interactions create mixed states, which introduce randomness into the output and make these errors particularly problematic. A concrete example is $T_1$ relaxation time, which describes how a qubit in the excited state $|1\\\\rangle$ spontaneously decays to the ground state $∣0\\\\rangle$ over time. If a measurement is delayed, this decay can cause the qubit to flip states, leading to incorrect results.\\n\",\n \"\\n\",\n \"### 1.3 Sources of noise \\n\",\n \"\\n\",\n \"As mentioned above, some sources of noise come from different factors, including imperfect implementation of quantum gates as electromagnetic pulses, readout errors, decoherence of the quantum system, and so on.\\n\",\n \"\\n\",\n \"Among the different metrics that can be used to evaluate how good or noise-resilient a quantum computer (or a qubit) is against sources of noise, we can distinguish a few:\\n\",\n \"\\n\",\n \"* T1: relaxation time, is a decay constant that measures how quickly a qubit in state $|1\\\\rangle$ decays into state $|0\\\\rangle$.\\n\",\n \"* T2: dephasing time, is a decay constant that measures how quickly a qubit in state $|+\\\\rangle$ becomes an indistinguishable mixture of $|+\\\\rangle$ and $|-\\\\rangle$ states.\\n\",\n \"* Single-qubit gate fidelity quantifies how closely the actual operation $\\\\mathcal{E}$ performed by the hardware matches the ideal single-qubit unitary $U$. A fidelity of 1 means the gate behaves exactly as intended, with no deviation from the ideal case.\\n\",\n \"* Two-qubit gate fidelity quantifies how closely the actual operation $\\\\mathcal{E}_2$ performed by the hardware matches the ideal two-qubit unitary $U_2$. Since these gates are more complex, they tend to have lower fidelities. As in the single-qubit case, a fidelity of 1 represents a perfect match.\\n\",\n \"* Readout assignment error: probability that a qubit is measured incorrectly, implying that the measured classical bit does not match the actual quantum state of the qubit just before measurement.\\n\",\n \"\\n\",\n \"You can see information about specific IBM quantum devices on the [IBM Quantum Cloud Platform](https://quantum.cloud.ibm.com/computers). Click the device you want to analyze to see detailed characteristics of each qubit, along with a summary of the machine's overall properties. The image below shows an example of this summary for the device `ibm_brisbane`.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"You can also access this information about the devices directly through code.\\n\",\n \"\\n\",\n \"Let's see how it's done!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1: Find the best qubits \\n\",\n \"\\n\",\n \"**Your Goal:** Find the qubits with longer T1, T2, higher gate fidelities, and smaller readout errors.\\n\",\n \"\\n\",\n \"In this first exercise, you will provide the best value of each of the following metrics, as well as the qubit, or pair of qubits, that have this value:\\n\",\n \"\\n\",\n \"- Relaxation time: T1\\n\",\n \"- Dephasing time: T2\\n\",\n \"- Readout error\\n\",\n \"- Single-qubit gate error: 'PauliX' error\\n\",\n \"- Two-qubit gate error: 'ECR' error \\n\",\n \"\\n\",\n \"To make your solution robust, you should write a routine that works for any backend.\\n\",\n \"\\n\",\n \"Note 1: The ECR gate is the two-qubit gate specific to this backend and may differ on other quantum devices, which might have CZ or CNOT gates. You can check the basis gate of backend by using [backend.basis_gates](/docs/api/qiskit-ibm-runtime/ibm-backend).\\n\",\n \"\\n\",\n \"Note 2: Since the best-performing qubits and their values can change over time, we recommend finding these values across all qubits directly using Python's `min()` and `max()` functions for each run.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"The code below demonstrates how to retrieve and store properties for all qubits from the `ibm_brisbane` backend into an array. Use this as a reference when implementing your function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Execute to make arrays of properties\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"# We define a specific backend\\n\",\n \"brisbane_backend = service.backend(\\\"ibm_brisbane\\\")\\n\",\n \"# We obtain the system properties, number of qubits and coupling map\\n\",\n \"properties = brisbane_backend.properties()\\n\",\n \"num_qubits = brisbane_backend.num_qubits\\n\",\n \"coupling_map = brisbane_backend.coupling_map\\n\",\n \"\\n\",\n \"# We define various lists of metrics for all the qubits of the backend\\n\",\n \"t1, t2, gate_error_x, readout_error, gate_error_ecr = [], [], [], [], []\\n\",\n \"for i in range(num_qubits):\\n\",\n \" t1.append(properties.t1(i))\\n\",\n \" t2.append(properties.t2(i))\\n\",\n \" gate_error_x.append(properties.gate_error(gate=\\\"x\\\", qubits=i))\\n\",\n \" readout_error.append(properties.readout_error(i))\\n\",\n \"for pair in coupling_map:\\n\",\n \" gate_error_ecr.append(properties.gate_error(gate=\\\"ecr\\\", qubits=pair))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def find_best_metrics(backend: QiskitRuntimeService.backend) -> list[tuple[int or list, float]]:\\n\",\n \" \\\"\\\"\\\"Finds the best-performing qubits and qubit pair based on various hardware metrics.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" #---- TODO : Task 0 ---\\n\",\n \" #Define metrics lists for the backend\\n\",\n \" t1, t2, gate_error_x, readout_error, gate_error_ecr = [], [], [], [], []\\n\",\n \" \\n\",\n \" \\n\",\n \" # ---- TODO : Task 1 ---\\n\",\n \" # Goal: Obtain the best value and the index or indices of the qubits of the following metrics:\\n\",\n \"\\n\",\n \" #find the best qubit (index_t1_max) with the longest T1 and its value (max_t1)\\n\",\n \" index_t1_max = \\n\",\n \" max_t1 = \\n\",\n \"\\n\",\n \" #find the best qubit (index_t2_max) with the longest T2 and its value (max_t2)\\n\",\n \" index_t2_max =\\n\",\n \" max_t2 = \\n\",\n \"\\n\",\n \" #find the best qubit (index_min_x_error) with the smallest x gate error and its value (min_x_error)\\n\",\n \" index_min_x_error =\\n\",\n \" min_x_error = \\n\",\n \"\\n\",\n \" #find the best qubit (index_min_readout) with the smallest readout error and its value (min_readout)\\n\",\n \" index_min_readout=\\n\",\n \" min_readout = \\n\",\n \"\\n\",\n \" #find the best qubit pairs with minimum ECR error (min_ecr_pair) and its value (min_ecr_error)\\n\",\n \" \\n\",\n \" min_ecr_pair=\\n\",\n \" min_ecr_error = \\n\",\n \" \\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" solutions = [\\n\",\n \" [int(index_t1_max), max_t1],\\n\",\n \" [int(index_t2_max), max_t2],\\n\",\n \" [int(index_min_x_error), min_x_error],\\n\",\n \" [int(index_min_readout), min_readout],\\n\",\n \" [list(min_ecr_pair), min_ecr_error],\\n\",\n \" ]\\n\",\n \" return solutions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex1(find_best_metrics)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Good job! You've learned how to access the device properties and search for specific characteristics among them. Perhaps at this point you might be wondering whether we cannot simply look for the qubits with the best metrics. Well, it is not that easy. \\n\",\n \"\\n\",\n \"If you print the indices of the qubits you've obtained, you can see that they aren't clustered in a specific region of the device. Instead, they're spread out across different areas. This highlights a key challenge: a qubit with long relaxation time might also have a large single-qubit gate error, or vice versa. These trade-offs mean that choosing qubits based only on one metric may not lead to optimal performance for certain quantum algorithms. Furthermore, you also need to take into account that not all the qubits are connected, as shown on the coupling map. These factors make the task of choosing the specific layout of qubits an intricate, but fascinating, challenge. Fortunately for us, in Qiskit there's a dedicated tool designed to handle this problem: **the transpiler**. \\n\",\n \"\\n\",\n \"We'll explore the transpiler in detail in a later section.\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2. The problem: Max-cut \\n\",\n \"\\n\",\n \"The maximum cut ([Max-cut](https://en.wikipedia.org/wiki/Maximum_cut)) problem is a graph problem that lies in the category of [NP-hard](https://en.wikipedia.org/wiki/NP-hardnes), which means no algorithm exists that can solve it in polynomial time. Max-cut is an optimization problem with a wide range of applications including clustering, network science, statistical physics, and machine learning. The goal of this problem is to divide the nodes of a graph into two sets using a single cut, in such a way that the number of edges traversed by this cut is maximized. \\n\",\n \"Let's visualize it with one example. \\n\",\n \"\\n\",\n \"In the picture below you see the max-cut solution of a five-node problem that exemplifies how the graph is divided, maximizing the number of edges cut. Another way to view this problem is to consider that finding the maximum cut in a graph means identifying a way to divide the nodes into two groups such that the number of edges connecting nodes from different groups is as large as possible.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"In this lab, we will solve a Max-cut problem using a quantum algorithm. We'll also study how quantum noise affects our solution and discuss strategies to reduce its impact, ensuring we can still obtain accurate results despite the noise.\\n\",\n \"\\n\",\n \"## 2.1 The problem: Define the graph \\n\",\n \"\\n\",\n \"Now that we have a little bit of context of the Max-cut problem, let's choose a specific graph for which we want to find the maximum cut. In particular, we will choose the graph of the coupling map of a hypothetical quantum computer with all-to-all connectivity $^*$.\\n\",\n \"\\n\",\n \"$^*$ *Note that the last qubit is not actually connected to the first one, to explicitly break the symmetry of the problem and reduce the number of solutions.*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We define the seed\\n\",\n \"seed = 43\\n\",\n \"# We define the number of nodes:\\n\",\n \"n = 5\\n\",\n \"# We define the graph\\n\",\n \"graph = rx.PyGraph()\\n\",\n \"graph.add_nodes_from(np.arange(0, n, 1))\\n\",\n \"generic_backend = GenericBackendV2(n, seed=seed)\\n\",\n \"weights = 1\\n\",\n \"# We make it explicitly asymmetrical to have a smaller set of solutions\\n\",\n \"graph.add_edges_from([(edge[0], edge[1], weights) for edge in generic_backend.coupling_map][:-1])\\n\",\n \"draw_graph(graph, node_size=200, with_labels=True, width=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2 The mapping: a graph into a quantum computer \\n\",\n \"\\n\",\n \"To solve our graph problem with a quantum computer, we must first translate it into a language the computer understands. A common and powerful approach is to reframe the optimization problem in terms of a **Hamiltonian**, a mathematical object from quantum mechanics that describes the total energy of a system. The solution to our problem will then be encoded in the lowest energy state (the \\\"ground state\\\") of this Hamiltonian.\\n\",\n \"\\n\",\n \"There are two closely related languages for formulating such problems:\\n\",\n \"\\n\",\n \"1. **QUBO (Quadratic Unconstrained Binary Optimization):** This is a language often used in computer science and classical optimization. It uses binary variables **xi ∈ {0, 1}** and seeks to minimize an objective function of the form:\\n\",\n \" \\n\",\n \" $$ \\\\text{minimize} \\\\sum_{i} Q_{ii}x_{i} + \\\\sum_{ii ∈ {+1, -1}** and seeks to minimize an energy Hamiltonian of the form:\\n\",\n \"\\n\",\n \" $$ H = -\\\\sum_{i} h_{i}s_{i} - \\\\sum_{ii = 2xi - 1**.\\n\",\n \"\\n\",\n \"For this lab, we will map our graph problem directly to an **Ising Hamiltonian**. This is the most direct path for a quantum computer because the eigenvalues of the Pauli Z operator are **+1** and **-1**, which perfectly correspond to the spin variables **si** in the Ising model.\\n\",\n \"\\n\",\n \"### From Max-cut to an Ising Hamiltonian\\n\",\n \"\\n\",\n \"For the Max-cut problem on a graph $G = (V, E)$, we want to partition the vertices $V$ into two sets. The goal is to maximize the number of edges connecting vertices in different sets. We can express this objective using our spin variables **si**. If two connected nodes `i` and `j` are in different partitions (si ≠ sj), the term $s_i * s_j$ will be -1. If they are in the same partition (si = sj), the term will be +1.\\n\",\n \"\\n\",\n \"To *maximize* the cuts, we want to *minimize* the sum of $s_i * s_j$ over all connected edges. This gives us our cost Hamiltonian:\\n\",\n \"\\n\",\n \"$$ H_{cost} = \\\\sum_{(i,j) \\\\in E} Z_i \\\\otimes Z_j $$\\n\",\n \"\\n\",\n \"Here we've replaced the classical spin variables `s_i` with their quantum counterparts, the Pauli **Zi** operators, which act on the `i`-th qubit. The ground state of this Hamiltonian is the specific arrangement of qubit states (`|0⟩` or `|1⟩`) that minimizes this energy, directly giving us the solution to the Max-cut problem.\\n\",\n \"\\n\",\n \"Please refer this [tutorial](https://quantum.cloud.ibm.com/docs/tutorials/quantum-approximate-optimization-algorithm) for more details.\\n\",\n \"\\n\",\n \"That's exactly what the next exercise is all about!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2: From Graph to Hamiltonian \\n\",\n \"\\n\",\n \"**Your Goal:** Convert the 'graph' object we just created into an Ising Hamiltonian, which is the cost function for our Max-cut problem.\\n\",\n \"\\n\",\n \"In this second exercise you must find the way of mapping the graph problem you are given to a Hamiltonian using the identity and Pauli gates.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \" Hint 💡: (Click to expand)\\n\",\n \" The intuition of how to build this Hamiltonian consists of considering a Hilbert space of size n x n in which we add as many terms as edges in the graph, and in which the nodes connected by each edge are represented by Pauli-Z matrices, whereas the rest of the nodes are represented by the identity.
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def graph_to_Pauli(graph: rx.PyGraph) -> list[tuple[str, float]]:\\n\",\n \" \\\"\\\"\\\"Convert the graph to Pauli list.\\\"\\\"\\\"\\n\",\n \" pauli_list = []\\n\",\n \"\\n\",\n \" # ---- TODO : Task 2 ---\\n\",\n \" # Goal: Convert the graph into a list like: [['PauliWord_1', weight_1], ['PauliWord_2', weight_2],...]\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" return pauli_list\\n\",\n \"\\n\",\n \"\\n\",\n \"max_cut_paulis = graph_to_Pauli(graph)\\n\",\n \"cost_hamiltonian = SparsePauliOp.from_list(max_cut_paulis)\\n\",\n \"print(\\\"Cost Function Hamiltonian:\\\", cost_hamiltonian)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex2(graph_to_Pauli)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2.3 QAOA solution \\n\",\n \"\\n\",\n \"Now that we have successfully mapped our Max-cut problem to an Ising Hamiltonian, we have translated a classical graph problem into a quantum ground state search problem. The solution to the original problem is encoded in the state with the lowest possible energy (the \\\"ground state\\\") of this Hamiltonian.\\n\",\n \"\\n\",\n \"Several quantum algorithms can be employed to find this ground state. One of the most prominent is the Quantum Approximate Optimization Algorithm ([QAOA](https://en.wikipedia.org/wiki/Quantum_optimization_algorithms)). QAOA is known for its adaptability, relatively shallow circuit depth, and strong performance across a range of optimization tasks.\\n\",\n \"\\n\",\n \"\\n\",\n \"If you want a full description of QAOA, we recommend this [tutorial](https://quantum.cloud.ibm.com/docs/tutorials/quantum-approximate-optimization-algorithm) as well as the [Utility-scale QAOA](https://quantum.cloud.ibm.com/learning/courses/quantum-computing-in-practice/utility-scale-qaoa) lesson from the Quantum computing in practice course. Here, we will briefly comment on the main idea behind it. \\n\",\n \"\\n\",\n \"QAOA is a variational quantum algorithm inspired by the adiabatic theorem, which states that a quantum system remains in its ground state if it evolves slowly enough. QAOA mimics this by starting in the ground state of a known Hamiltonian (the mixer) and evolving toward the ground state of a Hamiltonian that encodes our problem (the cost). This is done through alternating layers of the two Hamiltonians, with each step controlled by tunable parameters that guide the system toward the optimal solution.\\n\",\n \"\\n\",\n \"After this little explanation of the theory, let's get some hands-on practice using `QAOAAnsatz` from Qiskit to implement QAOA and solve our Max-cut problem.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"layers = 2\\n\",\n \"qaoa_circuit = QAOAAnsatz(cost_operator=cost_hamiltonian, reps=layers)\\n\",\n \"qaoa_circuit.measure_all()\\n\",\n \"qaoa_circuit.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we define the QAOA circuit, we must pass it to the pass manager to transpile it to the native gates of the backend.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create pass manager for transpilation\\n\",\n \"\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=generic_backend, seed_transpiler=seed\\n\",\n \")\\n\",\n \"\\n\",\n \"qaoa_circuit_transpiled = pm.run(qaoa_circuit)\\n\",\n \"qaoa_circuit_transpiled.draw(\\\"mpl\\\", fold=False, idle_wires=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we define the initial parameters of our QAOA model.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"init_params = np.zeros(2 * layers)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We define and execute an optimization method using the library `scipy`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"objective_func_vals = []\\n\",\n \"\\n\",\n \"\\n\",\n \"def cost_func_estimator(\\n\",\n \" params: list, ansatz: QuantumCircuit, isa_hamiltonian: SparsePauliOp, estimator: Estimator\\n\",\n \") -> float:\\n\",\n \" \\\"\\\"\\\"Compute the cost function value using a parameterized ansatz and an estimator for a given Hamiltonian.\\\"\\\"\\\"\\n\",\n \" if isa_hamiltonian.num_qubits != ansatz.num_qubits:\\n\",\n \" isa_hamiltonian = isa_hamiltonian.apply_layout(ansatz.layout)\\n\",\n \" pub = (ansatz, isa_hamiltonian, params)\\n\",\n \" job = estimator.run([pub])\\n\",\n \" results = job.result()[0]\\n\",\n \" cost = results.data.evs\\n\",\n \" objective_func_vals.append(cost)\\n\",\n \" return cost\\n\",\n \"\\n\",\n \"\\n\",\n \"def train_qaoa(\\n\",\n \" params: list,\\n\",\n \" circuit: QuantumCircuit,\\n\",\n \" hamiltonian: SparsePauliOp,\\n\",\n \" backend: QiskitRuntimeService.backend,\\n\",\n \") -> tuple:\\n\",\n \" \\\"\\\"\\\"Optimize QAOA parameters using COBYLA and an estimator on a given backend.\\\"\\\"\\\"\\n\",\n \" with Session(backend=backend) as session:\\n\",\n \" options = {\\\"simulator\\\": {\\\"seed_simulator\\\": seed}}\\n\",\n \" estimator = Estimator(mode=session, options=options)\\n\",\n \" estimator.options.default_shots = 100000\\n\",\n \"\\n\",\n \" result = minimize(\\n\",\n \" cost_func_estimator,\\n\",\n \" params,\\n\",\n \" args=(circuit, hamiltonian, estimator),\\n\",\n \" method=\\\"COBYLA\\\",\\n\",\n \" options={\\\"maxiter\\\": 200, \\\"rhobeg\\\": 1, \\\"catol\\\": 1e-3, \\\"tol\\\": 0.0001},\\n\",\n \" )\\n\",\n \" print(result)\\n\",\n \" return result, objective_func_vals\\n\",\n \"\\n\",\n \"\\n\",\n \"result_qaoa, objective_func_vals = train_qaoa(\\n\",\n \" init_params, qaoa_circuit_transpiled, cost_hamiltonian, generic_backend\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After the optimization routine is executed, we can see how the cost function evolves with the number of iterations to check how the algorithm has converged.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(12, 6))\\n\",\n \"plt.plot(objective_func_vals)\\n\",\n \"plt.xlabel(\\\"Iteration\\\")\\n\",\n \"plt.ylabel(\\\"Cost\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Nice! As you can see, our circuit has trained quite well, converging to a value that should correspond to the minimum energy of the cost Hamiltonian representing our problem. But are we done yet? How can we be sure that this really is the minimum energy? And equally important, although we might have found the minimum energy, what ground state does it correspond to? Or in other words, what is the actual solution to the Max-cut problem?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2.4 Checking our solution \\n\",\n \"\\n\",\n \"After training our QAOA circuit using the Estimator primitive to obtain the ground energy of the cost Hamiltonian, we can obtain the ground state by using the Sampler primitive running the circuit with the optimized parameters of the QAOA.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get the optimized parameters from the result\\n\",\n \"opt_params = result_qaoa.x\\n\",\n \"SHOTS = 10000\\n\",\n \"\\n\",\n \"\\n\",\n \"def sample_qaoa(opt_params, circuit, backend):\\n\",\n \"\\n\",\n \" # Submit the circuit to Sampler\\n\",\n \" options = {\\\"simulator\\\": {\\\"seed_simulator\\\": seed}}\\n\",\n \" sampler = Sampler(mode=backend, options=options)\\n\",\n \" job = sampler.run([(circuit, opt_params)], shots=SHOTS)\\n\",\n \" results_sampler = job.result()\\n\",\n \" counts_list = results_sampler[0].data.meas.get_counts()\\n\",\n \" display(plot_histogram(counts_list, title=f\\\"Max cut with {backend.name}\\\"))\\n\",\n \"\\n\",\n \" return counts_list\\n\",\n \"\\n\",\n \"\\n\",\n \"counts_list = sample_qaoa(opt_params, qaoa_circuit_transpiled, generic_backend)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It looks we have 8 different states with significant higher probabilities than the rest, which suggests there might be 8 different ground states. But how can we check that? Luckily for us, this Max-cut problem is not very big in size, so it can still be solved analytically to help us discern if our solution is a good or bad approximation. Let's take a look!\\n\",\n \"\\n\",\n \"To solve this Max-cut problem analytically, we diagonalize the cost Hamiltonian. Note that this can be done because the size of the problem is not too large; however, the complexity of this problem scales exponentially with the number of nodes (qubits), so the problem becomes intractable for a large number of nodes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eigenvalues, eigenvectors = np.linalg.eig(cost_hamiltonian)\\n\",\n \"ground_energy = min(eigenvalues).real\\n\",\n \"num_solutions = eigenvalues.tolist().count(ground_energy)\\n\",\n \"index_solutions = np.where(eigenvalues == ground_energy)[0].tolist()\\n\",\n \"print(f\\\"The ground energy of the Hamiltonian is {ground_energy}\\\")\\n\",\n \"print(f\\\"The number of solutions of the problem is {num_solutions}\\\")\\n\",\n \"print(f\\\"The list of the solutions based on their index is {index_solutions}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Great! We obtained 8 solutions as well, so let us now do some post-processing to check if the 8 possible solutions match with ours.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def decimal_to_binary(decimal_list, n):\\n\",\n \" return [bin(num)[2:].zfill(n) for num in decimal_list]\\n\",\n \"\\n\",\n \"\\n\",\n \"# Convert the solutions to quantum states\\n\",\n \"states_solutions = decimal_to_binary(index_solutions, n)\\n\",\n \"# Sort the dictionary items by their counts in descending order\\n\",\n \"sorted_states = sorted(counts_list.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"# Take the top 'num_solutions' entries\\n\",\n \"top_states = sorted_states[:num_solutions]\\n\",\n \"# Extract only the states keys from the top entries\\n\",\n \"qaoa_ground_states = sorted([state for state, count in top_states])\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions}\\\")\\n\",\n \"print(f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Hurray! We've successfully found the right solution of the Max-cut problem using QAOA!\\n\",\n \"\\n\",\n \"However, it's important to note that these results were obtained using ideal quantum simulators, which do not account for the noise and imperfections present in real quantum hardware. In the next section, we'll explore how QAOA performs when executed on a noisy quantum simulator, which provides a practical view on how noise affects our quantum algorithms.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 3. Noisy quantum simulator \\n\",\n \"\\n\",\n \"In this section, we'll run the QAOA algorithm on noisy quantum simulators. These simulators are useful tools that aim to mimic the behaviour and noise of real quantum processors. They allow us to test and evaluate how our quantum algorithms could perform on actual quantum hardware, without consuming valuable quantum resources. Additionally, noisy simulators often run faster since they are executed locally on your machine, avoiding the potential delays and queues associated with cloud-based quantum devices.\\n\",\n \"\\n\",\n \"However, before we proceed, an important question arises. Which quantum device should I use? This is a key question that is sometimes overlooked. In the following, we'll explore how to choose the most suitable device (or simulator) for running a specific quantum algorithm.\\n\",\n \"\\n\",\n \"## 3.1 Choosing the backend \\n\",\n \"\\n\",\n \"Here we'll introduce a quantum backend that corresponds to a noisy simulator that mimics the noise of a quantum device.\\n\",\n \"\\n\",\n \"Let's see which backends are available for you.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"real_backends = service.backends()\\n\",\n \"print(f\\\"The quantum computers available for you are {real_backends}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you are using the Open Plan (which is free), you should have `ibm_brisbane`, `ibm_sherbrooke`, and `ibm_torino`. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now create a noisy simulator for each backend you have access to, and see which gates they have.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Backend Availability\\n\",\n \"\\n\",\n \"Depending on your account, you may have different backends available. Use only 3 backends - if possible, use ibm_brisbane, ibm_sherbrooke, and ibm_torino - but if they are not available to you, feel free to use 3 different ones.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# backends=[service.backend(\\\"alt_brisbane\\\"),service.backend(\\\"alt_kawasaki\\\"),service.backend(\\\"alt_torino\\\")]\\n\",\n \"real_backends = [\\n\",\n \" service.backend(\\\"ibm_brisbane\\\"),\\n\",\n \" service.backend(\\\"ibm_sherbrooke\\\"),\\n\",\n \" service.backend(\\\"ibm_torino\\\"),\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"noisy_fake_backends = []\\n\",\n \"for backend in real_backends:\\n\",\n \" noisy_fake_backends.append(AerSimulator.from_backend(backend, seed_simulator=seed))\\n\",\n \"print(f\\\"The noisy simulators are {noisy_fake_backends}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we transpile the QAOA circuit to the different backends and do a relatively simple analysis of their potential errors.\\n\",\n \"\\n\",\n \"In the next exercise, you will count the accumulated total error of applying the different quantum circuits to the three backends. We can do that using [`backend.properties()`](https://quantum.cloud.ibm.com/docs/en/guides/get-qpu-information#dynamic-backend-information), [`circuit.data`](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.QuantumCircuit) and [`backend.configuration()`](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.QuantumCircuit), as they will provide us with the required information to account for the various errors introduced by each instruction.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 3: Error Counting \\n\",\n \"\\n\",\n \"**Your Goal:** Estimate the total error of the quantum circuit. \\n\",\n \"\\n\",\n \"In this third exercise you will estimate the total error introduced by all the instructions when executing quantum circuits on different backends by completing the `accululated_errors` function. \\n\",\n \"In particular, you'll account for the errors introduced by:\\n\",\n \"\\n\",\n \"- Single-qubit gates: Depending on the backend, these may include 'rz','x', or 'sx', and you'll have to access each instruction name.\\n\",\n \"- Two-qubit gates: Depending on the backend, these may include 'cz', 'cx', or 'ecr' gates. You’ll need to identify which gate is used on each backend.\\n\",\n \"- Readout errors: These contribute a constant error added at the end of the total accumulated error, only on the physical qubits where your circuit is transpiled.\\n\",\n \"\\n\",\n \"Keep in mind that each qubit has different error rates, so you cannot simply count all operations and multiply by average error values you see on the Compute resources page on IBM Quantum Platform. \\n\",\n \"While this approximation might yield similar results, the goal of this exercise is to teach you how to access detailed backend information and perform a more accurate estimation of the specific error rates per qubit.\\n\",\n \"\\n\",\n \"Also, don't count the measure gates as single-qubit gates, as their error is already included in the readout error.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define a function that calculates the accumulated total errors of single and two qubit gates and readout\\n\",\n \"\\n\",\n \"\\n\",\n \"def accumulated_errors(backend: QiskitRuntimeService.backend, circuit: QuantumCircuit) -> list:\\n\",\n \" \\\"\\\"\\\"Compute accumulated gate and readout errors for a given circuit on a specific backend.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Initializing quantities\\n\",\n \" acc_single_qubit_error = 0\\n\",\n \" acc_two_qubit_error = 0\\n\",\n \" single_qubit_gate_count = 0\\n\",\n \" two_qubit_gate_count = 0\\n\",\n \" acc_readout_error = 0\\n\",\n \"\\n\",\n \" # Defining useful variables\\n\",\n \" properties = backend.properties()\\n\",\n \" qubit_layout = list(circuit.layout.initial_layout.get_physical_bits().keys())[:n]\\n\",\n \"\\n\",\n \" # ---- TODO : Task 3 ---\\n\",\n \" # Goal: Define the following quantities:\\n\",\n \" # acc_total_error, acc_two_qubit_error, acc_single_qubit_error, acc_readout_error, single_qubit_gate_count, two_qubit_gate_count\\n\",\n \"\\n\",\n \" # TODO Define readout error (only for qubits in qubit_layout) using `properties.readout_error`\\n\",\n \" acc_readout_error=0\\n\",\n \" for q in qubit_layout:\\n\",\n \" acc_readout_error+= # TODO\\n\",\n \" # TODO Define two qubit gates for the different backends using `backend.configuration()`\\n\",\n \" if \\\"ecr\\\" in (): # TODO\\n\",\n \" two_qubit_gate = \\\"ecr\\\"\\n\",\n \" elif \\\"cz\\\" in (): # TODO\\n\",\n \" two_qubit_gate = \\\"cz\\\"\\n\",\n \" # TODO Loop over the instructions in `circuit.data` to account for the single and two-qubit errors and single and two qubit gate counts\\n\",\n \" for instruction in circuit.data:\\n\",\n \" if(): # TODO Count and add errors for one qubit gates\\n\",\n \"\\n\",\n \" elif(): # TODO Count and add errors for two qubit gates\\n\",\n \" \\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" acc_total_error = acc_two_qubit_error + acc_single_qubit_error + acc_readout_error\\n\",\n \" results = [\\n\",\n \" acc_total_error,\\n\",\n \" acc_two_qubit_error,\\n\",\n \" acc_single_qubit_error,\\n\",\n \" acc_readout_error,\\n\",\n \" single_qubit_gate_count,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" ]\\n\",\n \" return results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, generate `errors_and_counts_list[]` and pass it to the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qaoa_transpiled_list = []\\n\",\n \"errors_and_counts_list = []\\n\",\n \"for noisy_fake_backend in noisy_fake_backends:\\n\",\n \" pm = generate_preset_pass_manager(\\n\",\n \" backend=noisy_fake_backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed,\\n\",\n \" )\\n\",\n \" circuit = pm.run(qaoa_circuit)\\n\",\n \" qaoa_transpiled_list.append(circuit)\\n\",\n \"\\n\",\n \" errors_and_counts = accumulated_errors(noisy_fake_backend, circuit)\\n\",\n \" errors_and_counts_list.append(errors_and_counts)\\n\",\n \"# You can print your results to visualize if they are correct\\n\",\n \"for backend, (\\n\",\n \" acc_total_error,\\n\",\n \" acc_two_qubit_error,\\n\",\n \" acc_single_qubit_error,\\n\",\n \" acc_readout_error,\\n\",\n \" single_qubit_gate_count,\\n\",\n \" two_qubit_gate_count,\\n\",\n \") in zip(noisy_fake_backends, errors_and_counts_list):\\n\",\n \" print(f\\\"Backend {backend.name}\\\")\\n\",\n \" print(f\\\"Accumulated two-qubit error of {two_qubit_gate_count} gates: {acc_two_qubit_error:.3f}\\\")\\n\",\n \" print(\\n\",\n \" f\\\"Accumulated one-qubit error of {single_qubit_gate_count} gates: {acc_single_qubit_error:.3f}\\\"\\n\",\n \" )\\n\",\n \" print(f\\\"Accumulated readout error: {acc_readout_error:.3f}\\\")\\n\",\n \" print(f\\\"Accumulated total error: {acc_total_error:.3f}\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's plot the results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_backend_errors_and_counts(noisy_fake_backends, errors_and_counts_list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex3(accumulated_errors)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We have seen that `ibm_torino` has a smaller accumulated error than the other noisy backends. Let's put these results into practice by running the whole QAOA algorithm with the different backends!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"Warning: 2 minutes needed. Do not skip!\\n\",\n \"\\n\",\n \"Running the following code will take approximately 2 minutes to execute, and will block this notebook during that time. \\n\",\n \"\\n\",\n \"
\\n\",\n \"Warning: 8 minutes needed. \\n\",\n \"\\n\",\n \"Running the following code will take approximately 8 minutes to execute, and will block this notebook during that time. \\n\",\n \"\\n\",\n \"If you want to skip this cell, go directly to [Section 4](#transpiler).\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for noisy_fake_backend, circuit in zip(noisy_fake_backends[1:], qaoa_transpiled_list[1:]):\\n\",\n \" result_backend, _ = train_qaoa(init_params, circuit, cost_hamiltonian, noisy_fake_backend)\\n\",\n \" opt_params = result_backend.x\\n\",\n \" opt_params_list.append(opt_params)\\n\",\n \" counts_list_backend = sample_qaoa(opt_params, circuit, noisy_fake_backend)\\n\",\n \" counts_list_backends.append(counts_list_backend)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It appears that the three backends give the correct result. However, it looks like the `ibm_torino` backend presents lower noise levels, since the probabilities of measuring a state that is not a solution are lower.\\n\",\n \"\\n\",\n \"At this point, we can define a metric based on the probability of measuring a correct solution to compare backend performance. Since all three backends have predicted the same 8 solutions as the most probable outcomes, this metric provides a useful way to identify which backend is more reliable in practice.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for noisy_fake_backend, counts_list_backend in zip(noisy_fake_backends, counts_list_backends):\\n\",\n \" solutions_counts = [counts_list_backend[key] for key in states_solutions]\\n\",\n \" print(\\n\",\n \" f\\\"Probability of measuring a solution for {noisy_fake_backend.name} is {float(sum(solutions_counts)/SHOTS)}\\\"\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As expected, `ibm_torino` provides us with the highest probability of measuring a solution. This confirms that our earlier error estimation analysis was correct in predicting which backend would execute the algorithm with minimum errors. To further understand the impact of noise, we can also compare these probabilities to that of the `GenericBackend`, which simulates an ideal, noise-free scenario.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"solutions_counts_noiseless = [counts_list[key] for key in states_solutions]\\n\",\n \"print(\\n\",\n \" f\\\"Probability of measuring a solution for {generic_backend.name} is {float(sum(solutions_counts_noiseless)/SHOTS)}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The probability of measuring a correct solution on the noise-free `GenericBackend` is, as expected, higher than on the noisy backends. However, the difference is not that big. This highlights an important point. Even with current noisy quantum computers, as long as we are careful and smart in our circuit design and use the right tools, we can still solve many problems with a good degree of accuracy!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 3.2 Estimating errors using NEAT \\n\",\n \"\\n\",\n \"The error estimation we have done above is a nice approximation of how noisy a device is going to be. Qiskit offers us another alternative, the Noisy Estimator Analyzer Tool (NEAT). NEAT is a function designed to help users understand and predict the performance of quantum estimation tasks, particularly when using the Estimator primitive. It leverages the `qiskit-aer` simulator to perform both ideal (noiseless) and noisy simulations of quantum circuits. \\n\",\n \"\\n\",\n \"A key feature of NEAT is its ability to simulate Clifford circuits efficiently. For non-Clifford circuits, NEAT can automatically convert them to their nearest Clifford equivalents that approximate their quantum state. This makes NEAT especially useful for performing a noise analysis of our quantum circuits before running them on real quantum hardware. However, this approximation introduces limitations, since Clifford equivalents do not perfectly replicate the behavior of non-Clifford circuits, and therefore the simulation may lose accuracy in certain cases.\\n\",\n \"\\n\",\n \"From a practical point of view, one way to use NEAT is to simulate a quantum circuit in a noisy and noiseless scenario measuring an observable whose expectation value is exactly 1 in the ideal (noiseless) case. In this setup, any deviation from 1 in the noisy simulation directly reflects the impact of noise on the circuit. An easy way to ensure that the observable meets the requirements is choosing:\\n\",\n \"$$\\n\",\n \"\\\\hat{O}=|\\\\psi\\\\rangle\\\\langle \\\\psi|, \\\\quad \\\\textrm{since} \\\\quad \\\\langle \\\\psi |\\\\hat{O}|\\\\psi \\\\rangle=1, \\n\",\n \"$$\\n\",\n \"where $|\\\\psi\\\\rangle$ is the quantum state generated by the circuit we want to simulate. \\n\",\n \"\\n\",\n \"Let's apply NEAT to our different noisy backends to see how they perform.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results = []\\n\",\n \"for backend, opt_params in zip(noisy_fake_backends, opt_params_list):\\n\",\n \" print(f\\\"\\\\nRunning on backend: {backend.name}\\\")\\n\",\n \"\\n\",\n \" # Prepare the QAOA circuit with optimized parameters\\n\",\n \" qaoa_neat = QAOAAnsatz(cost_operator=cost_hamiltonian, reps=layers)\\n\",\n \" qc = qaoa_neat.assign_parameters(opt_params)\\n\",\n \"\\n\",\n \" # Transpile the circuit\\n\",\n \" qc_transpiled = generate_preset_pass_manager(\\n\",\n \" optimization_level=3,\\n\",\n \" basis_gates=backend.configuration().basis_gates[:n],\\n\",\n \" seed_transpiler=seed,\\n\",\n \" ).run(qc)\\n\",\n \" # Use cost Hamiltonian as observable for Cliffordization\\n\",\n \" obs = cost_hamiltonian\\n\",\n \" analyzer = Neat(backend)\\n\",\n \" clifford_pub = analyzer.to_clifford([(qc_transpiled, obs)])[0]\\n\",\n \"\\n\",\n \" # Construct observable from Clifford circuit\\n\",\n \" state_clifford = Statevector.from_instruction(clifford_pub.circuit)\\n\",\n \" obs_clifford = SparsePauliOp.from_operator(Operator(DensityMatrix(state_clifford).data))\\n\",\n \"\\n\",\n \" # Apply layout\\n\",\n \" pm = generate_preset_pass_manager(backend=backend, optimization_level=1, seed_transpiler=seed)\\n\",\n \" isa_qc = pm.run(clifford_pub.circuit)\\n\",\n \" isa_obs = obs_clifford.apply_layout(isa_qc.layout)\\n\",\n \"\\n\",\n \" # Prepare pubs and simulate\\n\",\n \" pubs = [(isa_qc, isa_obs)]\\n\",\n \" result_noiseless = (\\n\",\n \" analyzer.ideal_sim(pubs, cliffordize=True, seed_simulator=seed)._pub_results[0].vals\\n\",\n \" )\\n\",\n \" noisy_results = (\\n\",\n \" analyzer.noisy_sim(pubs, cliffordize=True, seed_simulator=seed)._pub_results[0].vals\\n\",\n \" )\\n\",\n \"\\n\",\n \" # Store and print results\\n\",\n \" results.append({\\\"backend\\\": backend.name, \\\"noiseless\\\": result_noiseless, \\\"noisy\\\": noisy_results})\\n\",\n \" print(f\\\"Ideal results on {backend.name}:\\\\n{result_noiseless:.3f}\\\")\\n\",\n \" print(f\\\"Noisy results on {backend.name}:\\\\n{noisy_results:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using the NEAT tool, we can observe how the expectation value in a noisy simulation deviates from the ideal value of 1, in a way that agrees with the noise analysis performed in [Section 3.1](#choosing-backend). \\n\",\n \"\\n\",\n \"However, as mentioned before, we should be aware that here we are analyzing Cliffordized versions of the quantum circuits, which means that some gates have been transformed to ensure the circuit belongs to the Clifford group. As a result, while the transformed circuit is similar, it does not produce exactly the same quantum state. This means that NEAT provides a useful approximation of how much noise a circuit might accumulate on a given backend, but it doesn't offer exact predictions or guarantees. This is important to keep in mind!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 4. Transpiler \\n\",\n \"\\n\",\n \"The transpiler is one of the most important and useful tools for running quantum circuits on real quantum hardware. It serves as a bridge between the abstract, idealized version of a quantum circuit and the physical implementation on the actual quantum device. When you design a circuit, you typically use virtual qubits and ideal gates without considering the hardware's specific limitations. The transpiler translates this high-level circuit into a version that can be implemented on a real quantum computer, using only the physical qubit's gate operations available on the device.\\n\",\n \"\\n\",\n \"For example, suppose your circuit includes a CNOT gate between virtual qubits 0 and 1. On a real device, these two qubits might not be directly connected. In such cases, the transpiler inserts a series of SWAP gates to move the quantum states to physically adjacent qubits, enabling the CNOT operation. Alternatively, the transpiler might find a more efficient qubit mapping, such that virtual qubits 0 and 1 are reassigned to physical qubits that are directly connected - for example, qubits 3 and 4, hence avoiding the need for additional SWAP gates.\\n\",\n \"\\n\",\n \"Up to now we have been using the transpiler implicitly when executing the pass manager in `generate_preset_pass_manager`. However, now we will focus on understanding it better and leveraging it to its fullest for better circuit design.\\n\",\n \"\\n\",\n \"# 4.1 Minimizing the two-qubit gates \\n\",\n \"\\n\",\n \"One of the most important tasks of a quantum transpiler is to determine an optimal qubit layout for executing a quantum circuit. This involves finding the best mapping between the circuit's virtual qubits and the device's physical qubits. To do so, there are a few things to take into consideration. \\n\",\n \"\\n\",\n \"First, the transpiler must check for all the two-qubit gates in the circuit and ensure that the selected physical qubits are connected in a way that enables these operations and reduces the necessity of using additional SWAP gates. This requires inspecting the coupling map, which shows how physical qubits are connected.\\n\",\n \"\\n\",\n \"First we will perform a transpilation of the quantum circuit to a layout in the quantum computer that minimizes the number of two-qubit gates, since that will be the main source of error.\\n\",\n \"\\n\",\n \"In particular, we will consider the `ibm_brisbane` backend, where, as shown in [Section 3.1](#choosing-backend), two-qubit gate errors account for the majority of the total accumulated error.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We select the `ibm_brisbane` backend\\n\",\n \"num_backend = 0\\n\",\n \"noisy_fake_backend = noisy_fake_backends[num_backend]\\n\",\n \"\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" backend=noisy_fake_backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed,\\n\",\n \" layout_method=\\\"sabre\\\",\\n\",\n \")\\n\",\n \"circuit_transpiled = pm.run(qaoa_circuit)\\n\",\n \"\\n\",\n \"\\n\",\n \"def two_qubit_gate_errors_per_circuit_layout(\\n\",\n \" circuit: QuantumCircuit, backend: QiskitRuntimeService.backend\\n\",\n \") -> tuple:\\n\",\n \" \\\"\\\"\\\"Calculate accumulated two-qubit gate errors and related metrics for a given circuit layout.\\\"\\\"\\\"\\n\",\n \" pair_list = []\\n\",\n \" error_pair_list = []\\n\",\n \" error_acc_pair_list = []\\n\",\n \" two_qubit_gate_count = 0\\n\",\n \" properties = backend.properties()\\n\",\n \" if \\\"ecr\\\" in (backend.configuration().basis_gates):\\n\",\n \" two_qubit_gate = \\\"ecr\\\"\\n\",\n \" elif \\\"cz\\\" in (backend.configuration().basis_gates):\\n\",\n \" two_qubit_gate = \\\"cz\\\"\\n\",\n \" for instruction in circuit.data:\\n\",\n \" if instruction.operation.num_qubits == 2:\\n\",\n \" two_qubit_gate_count += 1\\n\",\n \" pair = [instruction.qubits[0]._index, instruction.qubits[1]._index]\\n\",\n \" error_pair = properties.gate_error(gate=two_qubit_gate, qubits=pair)\\n\",\n \" if pair not in (pair_list):\\n\",\n \" pair_list.append(pair)\\n\",\n \" error_pair_list.append(error_pair)\\n\",\n \" error_acc_pair_list.append(error_pair)\\n\",\n \" else:\\n\",\n \" pos = pair_list.index(pair)\\n\",\n \" error_acc_pair_list[pos] += error_pair\\n\",\n \"\\n\",\n \" acc_two_qubit_error = sum(error_acc_pair_list)\\n\",\n \" return (\\n\",\n \" acc_two_qubit_error,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" pair_list,\\n\",\n \" error_pair_list,\\n\",\n \" error_acc_pair_list,\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \"(\\n\",\n \" acc_two_qubit_error,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" pair_list,\\n\",\n \" error_pair_list,\\n\",\n \" error_acc_pair_list,\\n\",\n \") = two_qubit_gate_errors_per_circuit_layout(circuit_transpiled, noisy_fake_backend)\\n\",\n \"two_qubit_ops_list = [int(a / b) for a, b in zip(error_acc_pair_list, error_pair_list)]\\n\",\n \"# We print the results\\n\",\n \"print(f\\\"The pairs of qubits that need to perform two-qubit operations are:\\\\n {pair_list}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The errors introduced by each of the two-qubit operations are:\\\\n {[round(err,3) for err in error_pair_list]}\\\"\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The accumulated errors introduced by each of the two-qubit operations are:\\\\n {[round(err,3) for err in error_acc_pair_list]}\\\"\\n\",\n \")\\n\",\n \"print(f\\\"The repetitions of each one of the two-qubit operations is:\\\\n {two_qubit_ops_list}\\\")\\n\",\n \"print(f\\\"The number of two-qubit operations in total:\\\\n {two_qubit_gate_count}\\\")\\n\",\n \"print(f\\\"The total accumulated error by two-qubit operations is:\\\\n {acc_two_qubit_error:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 4.2 Find the optimal layout \\n\",\n \"\\n\",\n \"Next, the transpiler must choose the \\\"best\\\" physical qubits that fulfill the connectivity constraints. However, defining \\\"best\\\" in this context is a challenging task, as it depends on multiple hardware-specific metrics such as coherence times ($T_1$, $T_2$), single-qubit gate error rates, readout errors, and two-qubit gate error rates. Optimizing across all these metrics at the same time is not straightforward, and it often involves trade-offs.\\n\",\n \"\\n\",\n \"In this exercise, we simplify the problem by focusing on two criteria: \\n\",\n \"1. The selected qubits must satisfy the required connectivity given by the transpiler\\n\",\n \"2. We choose the qubits with the lowest two-qubit gate error rates\\n\",\n \"\\n\",\n \"As discussed in [Section 3.1](#choosing-backend), two-qubit gate errors are typically the predominant source of noise in many quantum devices.\\n\",\n \"\\n\",\n \"In this exercise, look for a different qubit configuration (layout) that gives you a smaller total accumulated error when accounting only for the two-qubit gates. However, this may be too easy, and since we're approaching the end of the lab, you can increase the difficulty by looking for all the possible layouts that offer a smaller total accumulated error of two-qubit gates operations.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Building the following graph will help you visualize the possible configurations, and will also serve to find all the layouts that fulfill the desired conditions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We build a graph with the connectivity constraints of our backend that includes the two-qubit gate errors as weights in the edges\\n\",\n \"graph = rx.PyDiGraph()\\n\",\n \"graph.add_nodes_from(np.arange(0, noisy_fake_backend.num_qubits, 1))\\n\",\n \"two_qubit_gate = \\\"ecr\\\"\\n\",\n \"graph.add_edges_from(\\n\",\n \" [\\n\",\n \" (\\n\",\n \" edge[0],\\n\",\n \" edge[1],\\n\",\n \" noisy_fake_backend.properties().gate_error(\\n\",\n \" gate=two_qubit_gate, qubits=(edge[0], edge[1])\\n\",\n \" ),\\n\",\n \" )\\n\",\n \" for edge in noisy_fake_backend.coupling_map\\n\",\n \" ]\\n\",\n \")\\n\",\n \"draw_graph(graph, node_size=150, with_labels=True, width=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Also take into account that we have to look for layouts that have the connectivity allowing for the gates in `pair_list`, which can be converted to a more interpretable list using `logical_pair_list`:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def remap_nodes(original_labels: list, edge_list: list[list]) -> list[list[int]]:\\n\",\n \" \\\"\\\"\\\"Remap node labels to a new sequence starting from 0 based on their order in original_labels.\\\"\\\"\\\"\\n\",\n \" label_mapping = {label: idx for idx, label in enumerate(original_labels)}\\n\",\n \" remapped = [[label_mapping[src], label_mapping[dst]] for src, dst in edge_list]\\n\",\n \" return remapped\\n\",\n \"\\n\",\n \"\\n\",\n \"layout_list = list(circuit_transpiled.layout.initial_layout.get_physical_bits().keys())[:5]\\n\",\n \"logical_pair_list = remap_nodes(layout_list, pair_list)\\n\",\n \"print(f\\\"Physical qubit layout list:\\\\n {layout_list}\\\")\\n\",\n \"print(f\\\"\\\\nOriginal two-qubit gates list:\\\\n {pair_list}\\\")\\n\",\n \"print(f\\\"\\\\nRemapped two-qubit gates list (in logical qubits):\\\\n {logical_pair_list}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 4: Good Mapping \\n\",\n \"\\n\",\n \"**Your Goal:** Find all the good qubit layouts. \\n\",\n \"\\n\",\n \"In this fourth exercise you will find ALL the possible qubit layouts that have a smaller two-qubit gate accumulated error than the layout transpilation of the above, whose value is stored in the variable `acc_two_qubit_error`. This problem can be reformulated as a graph problem in which you need to find ALL the paths in the graph above which the total edge weights sum to less than the specified threshold and that have the same connectivity of that in `logical_pair_list`.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"Warning: Don't try to solve it by brute force!\\n\",\n \"\\n\",\n \"If you try a brute force approach, it might take hours of computation, as there are too many paths. Instead, restrict the search to only the paths that have the same connectivity of `logical_pair_list`, which are <100, and your computation will take less than 1 second.\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \" Hint 💡: (Click to expand)\\n\",\n \" \\n\",\n \"The proposed workflow for this exercise is:\\n\",\n \"\\n\",\n \"1. Start from each node in the graph and iteratively build paths by extending them one step at a time. \\n\",\n \"\\n\",\n \"2. At each step, use the `logical_pair_list` to decide which node in the current path to expand from.\\n\",\n \"\\n\",\n \"3. Depending on whether the node we are expanding from in the path corresponds to the control or the target qubit (based on `logical_pair_list`), we choose how to access its neighbors:\\n\",\n \"\\n\",\n \" 3.1 If it's the control qubit, we use directed edges (`graph.neighbors`).\\n\",\n \"\\n\",\n \" 3.2 If it's the target qubit, we use undirected edges (`graph.neighbors_undirected`).\\n\",\n \"\\n\",\n \"4. As you extend each path, calculate the cumulative weight by multiplying the edge weight (`graph.get_edge_data(edge_1,edge_2)`) by the corresponding value in `two_qubit_ops_list`. Note that if you are using the undirected neighbors, you should use `graph.get_edge_data(edge_2,edge_1)`.\\n\",\n \"\\n\",\n \"5. When the path is complete, i.e., when it gets to the length of the `two_qubit_ops_list`, check to only keep paths whose total weight stays below the threshold.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You might find these functions useful for the next exercise: [rx.PyDiGraph.neighbors](https://www.rustworkx.org/dev/apiref/rustworkx.PyDiGraph.neighbors.html), [rx.PyDiGraph.neighbors_undirected](https://www.rustworkx.org/dev/apiref/rustworkx.PyDiGraph.neighbors_undirected.html), and [rx.PyDiGraph.has_edge](https://www.rustworkx.org/apiref/rustworkx.PyDiGraph.has_edge.html), which were used in the code below.\\n\",\n \"\\n\",\n \"Additionally, you might find [rx.PyDiGraph.get_edge_data](https://www.rustworkx.org/apiref/rustworkx.PyDiGraph.get_edge_data.html) particularly useful. It allows you to obtain the two-qubit gate error associated with each interaction in the graph.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def find_paths_with_weight_sum_below_threshold(\\n\",\n \" graph: rx.PyDiGraph,\\n\",\n \" threshold: float,\\n\",\n \" two_qubit_ops_list: list[int],\\n\",\n \" logical_pair_list: list[list[int]],\\n\",\n \") -> tuple[list[list[int]], list[float]]:\\n\",\n \" \\\"\\\"\\\"Find all valid paths through a graph whose weighted sum is below a given threshold.\\\"\\\"\\\"\\n\",\n \" valid_paths = []\\n\",\n \" valid_weights = []\\n\",\n \"\\n\",\n \" # Task 4 ---\\n\",\n \" # Goal: Find all valid paths through a graph such that the total weighted sum of the path is below a given threshold.\\n\",\n \"\\n\",\n \" # Iterate over all possible starting nodes in the graph\\n\",\n \" for start_node in range(graph.num_nodes()):\\n\",\n \" # Initialize the list of paths with a single-node path starting from the current node\\n\",\n \" paths = [[start_node]]\\n\",\n \" # Initialize the corresponding weights for each path (starting with 0)\\n\",\n \" weights = [0]\\n\",\n \" # Iterate through each step in the sequence of two-qubit operations\\n\",\n \" for i in range(len(two_qubit_ops_list)):\\n\",\n \" new_paths = [] \\n\",\n \" new_weights = [] \\n\",\n \" # Go through each current path and its weight\\n\",\n \" for path, weight in zip(paths, weights):\\n\",\n \" # We need to determine which node in the current path is involved in the next logical operation, so we can expand from it.\\n\",\n \" # Determine which node in the path we are going to expand from using logical_pair_list\\n\",\n \" if logical_pair_list[i][0] < logical_pair_list[i][1]:\\n\",\n \" index_of_expanding_node = logical_pair_list[i][0] # control qubit\\n\",\n \" node_to_expand_from = path[index_of_expanding_node]\\n\",\n \" # Explore all neighbors of the node_to_expand_from\\n\",\n \" for neighbor in graph.neighbors(node_to_expand_from):\\n\",\n \" # Ensure we don't revisit nodes and the edge exists\\n\",\n \" if neighbor not in path and graph.has_edge(node_to_expand_from, neighbor):\\n\",\n \" # Calculate the edge weight, scaled by the number of times the gate is applied which is in two_qubit_ops_list\\n\",\n \" \\n\",\n \" #### TODO ####\\n\",\n \" \\n\",\n \" edge_weight = \\n\",\n \" \\n\",\n \" #### End of TODO ####\\n\",\n \" \\n\",\n \" # Extend the path and update the weight\\n\",\n \" new_paths.append(path + [neighbor])\\n\",\n \" new_weights.append(weight + edge_weight)\\n\",\n \" else:\\n\",\n \" index_of_expanding_node = logical_pair_list[i][1] # target qubit\\n\",\n \" node_to_expand_from = path[index_of_expanding_node]\\n\",\n \" # Explore all undirected_neighbors of the node_to_expand_from\\n\",\n \" for neighbor in graph.neighbors_undirected(node_to_expand_from):\\n\",\n \" # Ensure we don't revisit nodes and the edge exists\\n\",\n \" if neighbor not in path and graph.has_edge(neighbor, node_to_expand_from):\\n\",\n \" # Calculate the edge weight, scaled by the number of times the gate is applied which is in two_qubit_ops_list\\n\",\n \" \\n\",\n \" #### TODO ####\\n\",\n \" \\n\",\n \" edge_weight = \\n\",\n \" \\n\",\n \" #### End of TODO ####\\n\",\n \" \\n\",\n \" # Extend the path and update the weight\\n\",\n \" new_paths.append(path + [neighbor])\\n\",\n \" new_weights.append(weight + edge_weight)\\n\",\n \" # Update paths and weights for the next iteration\\n\",\n \" paths = new_paths\\n\",\n \" weights = new_weights\\n\",\n \"\\n\",\n \" # After building all possible paths, filter those under the threshold\\n\",\n \" for path, weight in zip(paths, weights):\\n\",\n \"\\n\",\n \" #### TODO: Complete the condition for `if` ####\\n\",\n \" \\n\",\n \" if( ): \\n\",\n \" valid_paths.append(path)\\n\",\n \" valid_weights.append(weight)\\n\",\n \"\\n\",\n \" #### End of TODO ####\\n\",\n \" \\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" return valid_paths, valid_weights\\n\",\n \"\\n\",\n \"\\n\",\n \"threshold = acc_two_qubit_error\\n\",\n \"\\n\",\n \"valid_paths, valid_weights = find_paths_with_weight_sum_below_threshold(\\n\",\n \" graph, threshold, two_qubit_ops_list, logical_pair_list\\n\",\n \")\\n\",\n \"# Note that there could be no other paths with smaller errors\\n\",\n \"if valid_weights:\\n\",\n \" minimum_weight_index = valid_weights.index(min(valid_weights))\\n\",\n \" opt_layout = valid_paths[minimum_weight_index]\\n\",\n \"else:\\n\",\n \" minimum_weight_index = None\\n\",\n \" opt_layout = layout_list\\n\",\n \"print(f\\\"We found {len(valid_paths)} valid paths\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"init_layout = Layout({q: phys for q, phys in zip(qaoa_circuit.qubits, opt_layout)})\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" backend=noisy_fake_backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed,\\n\",\n \" initial_layout=init_layout,\\n\",\n \" layout_method=\\\"sabre\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"circuit_opt = pm.run(qaoa_circuit)\\n\",\n \"\\n\",\n \"(\\n\",\n \" acc_total_error_opt,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" pair_list,\\n\",\n \" error_pair_list,\\n\",\n \" error_acc_pair_list,\\n\",\n \") = two_qubit_gate_errors_per_circuit_layout(circuit_opt, noisy_fake_backend)\\n\",\n \"\\n\",\n \"print(\\n\",\n \" f\\\"The path with smaller errors in its two-qubit gates is: {opt_layout} \\\\n\\\",\\n\",\n \" f\\\"With total accumulated error of {acc_total_error_opt:.3f}\\\",\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex4(find_paths_with_weight_sum_below_threshold)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Well done, you've completed the most complicated part of the work. Now all of the found combinations are better than the trivial threshold.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To this point, we've seen how optimizing the layout of qubits can significantly reduce the accumulated error from two-qubit gates. But can you imagine having to do this manually every time we want to run a quantum circuit on real hardware? That would be incredibly tedious. But don't worry - the transpiler can handle this task for us. By using one of the `layout_methods` provided, it smartly selects a layout of qubits that satisfies the connectivity necessities of our circuit, while also aiming to minimize the number of two-qubit gates.\\n\",\n \"\\n\",\n \"In particular, we will use the `sabre` method, which is a stochastic technique that aims to minimize the number of SWAP gates. To make the most of it, we'll perform a sweep over different values of the transpiler seed. This allows us to explore multiple layout configurations and choose the one that minimizes the two-qubit error rate, as we wanted. Note that here we could have selected to minimize the circuit depth or the number of two-qubit gates, the readout error, or any other metric we wanted to use.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 5: Best Mapping \\n\",\n \"\\n\",\n \"**Your Goal:** Use the transpiler to find the optimal layout.\\n\",\n \"\\n\",\n \"In this fifth exercise you will use the `sabre` `layout_method` to identify the layout that has the lowest two-qubit gate accumulated error. \\n\",\n \"\\n\",\n \"To do so, perform a sweep over the `seed_transpiler` values from 0 to 500 and use the `generate_preset_pass_manager` with `optimization_level=3` to select the best layout. Remember you can count the accumulated error of the two-qubit gates and the two-qubit gate number using the `two_qubit_gate_errors_per_circuit_layout` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def finding_best_seed(\\n\",\n \" circuit: QuantumCircuit, backend: QiskitRuntimeService.backend\\n\",\n \") -> tuple[QuantumCircuit, int, float, int]:\\n\",\n \" \\\"\\\"\\\"Find the transpiler seed that minimizes two-qubit gate error for a given circuit and backend.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # We initialize the minimum error accumulated\\n\",\n \" min_err_acc_seed_loop = 100\\n\",\n \" circuit_opt_best_seed = None\\n\",\n \" # First we loop over 500 seeds and transpile the circuit\\n\",\n \" for seed_transpiler in range(0, 500):\\n\",\n \" pm = generate_preset_pass_manager(\\n\",\n \" backend=backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed_transpiler,\\n\",\n \" layout_method=\\\"sabre\\\",\\n\",\n \" )\\n\",\n \" circuit_opt_seed = pm.run([circuit])[0]\\n\",\n \" # ---- TODO : Task 5 ---\\n\",\n \" # Goal: Find the transpiler seed that minimizes two-qubit gate error for a given circuit and backend looping from 0 to 500\\n\",\n \"\\n\",\n \" # TODO Use the `two_qubit_gate_errors_per_circuit_layout` function to count for the error of the transpile circuit\\n\",\n \"\\n\",\n \" # TODO Check if the error accounted above is smaller than min_err_acc_seed_loop. If so, assign the variables that this function returns\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" return (\\n\",\n \" circuit_opt_best_seed,\\n\",\n \" best_seed_transpiler,\\n\",\n \" min_err_acc_seed_loop,\\n\",\n \" two_qubit_gate_count_seed_loop,\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(\\n\",\n \" circuit_opt_seed_loop,\\n\",\n \" best_seed_transpiler,\\n\",\n \" min_err_acc_seed_loop,\\n\",\n \" two_qubit_gate_count_seed_loop,\\n\",\n \") = finding_best_seed(qaoa_circuit, noisy_fake_backend)\\n\",\n \"\\n\",\n \"best_layout = list(circuit_opt_seed_loop.layout.initial_layout.get_physical_bits().keys())[:n]\\n\",\n \"print(f\\\"Best transpiler seed: {best_seed_transpiler}\\\")\\n\",\n \"print(f\\\"Minimum accumulated two-qubit gate error: {min_err_acc_seed_loop:.3f}\\\")\\n\",\n \"print(f\\\"Two-qubit gate count for best seed: {two_qubit_gate_count_seed_loop}\\\")\\n\",\n \"print(f\\\"Best layout (first n logical qubits mapped to physical qubits):\\\\n {best_layout}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex5(finding_best_seed)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, we sample the QAOA circuit with these circuits to highlight the importance of good transpilation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Warning: 5 minutes needed.\\n\",\n \"\\n\",\n \"Running the following code will take approximately 5 minutes to execute, and will block this notebook during that time. You can skip to the next cell.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"counts_list_transpiled_circuits = []\\n\",\n \"circuit_transpiled_list = [circuit_transpiled, circuit_opt_seed_loop]\\n\",\n \"opt_params_list_transpiled_circuits = []\\n\",\n \"for circuit in circuit_transpiled_list:\\n\",\n \" result_backend, _ = train_qaoa(init_params, circuit, cost_hamiltonian, noisy_fake_backend)\\n\",\n \" opt_params = result_backend.x\\n\",\n \" opt_params_list_transpiled_circuits.append(opt_params)\\n\",\n \" counts_list_transpiled_circuit = sample_qaoa(opt_params, circuit, noisy_fake_backend)\\n\",\n \" counts_list_transpiled_circuits.append(counts_list_transpiled_circuits)\\n\",\n \" solutions_counts = [counts_list_transpiled_circuit[key] for key in states_solutions]\\n\",\n \" print(f\\\"Probability of measuring a solution for is {float(sum(solutions_counts)/SHOTS)}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the following, remember to use the `sabre` layout method and loop over different seeds to minimize a specific metric - such as two-qubit gate counts, depth, or accumulated error - in order to maximize performance.\\n\",\n \"\\n\",\n \"In this section we have seen that transpilation is a key step in preparing quantum circuits to be executed on real hardware. Since different devices have different layouts and gate sets, a good transpiler helps to adapt your circuit to fit the hardware while trying to keep things like quantum depth and error rates low, thus achieving better performance from your quantum algorithms.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 5. Error Mitigation (EM) \\n\",\n \"\\n\",\n \"One of the main areas of research to address the inevitable noise in quantum devices is **error mitigation (EM)**. EM consists of a set of intelligent techniques designed to reduce the impact of noise without requiring complex error correction codes or additional qubits, resources that remain limited in today's quantum hardware. Instead of correcting errors as they occur, EM uses strategies such as loop repetition, calibration-based adjustments, and classical post-processing to improve the quality of the final results, leading to an improved performance in our quantum algorithms.\\n\",\n \"\\n\",\n \"This approach is especially valuable for current devices, which are small- to medium-scale and inherently noisy. Fully fault-tolerant quantum computing is still beyond our reach, but EM offers a practical way to take full advantage of the devices we have now.\\n\",\n \"EM integrates naturally with hybrid quantum-classical algorithms, those that alternate between quantum and classical computation, such as:\\n\",\n \"\\n\",\n \"- Variational Quantum Eigensolver (VQE),\\n\",\n \"- Quantum Approximate Optimization Algorithm (QAOA), and\\n\",\n \"- Quantum-enhanced machine learning models.\\n\",\n \"\\n\",\n \"These types of algorithms are especially sensitive to noise, and EM can greatly improve their reliability and accuracy.\\n\",\n \"\\n\",\n \"Remarkably, EM does not eliminate all imperfections, but it refines the result enough to make it useful and actionable. It is a tool for narrowing the gap between noisy quantum results and meaningful insights, paving the way for practical quantum applications even before large-scale error-correcting machines become a reality.\\n\",\n \"\\n\",\n \"There are several well-established techniques used in EM, each tailored to address different types of noise and imperfections in quantum computations.\\n\",\n \"\\n\",\n \"One of the most widely used methods is Zero Noise Extrapolation (ZNE). In this approach, the same quantum circuit is executed multiple times with deliberately increased noise levels. Then, mathematical extrapolation techniques are applied to estimate what the outcome would have been in the absence of noise. This method was introduced by [Temme et al. in 2017](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509).\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"## 5.1 Zero Noise Extrapolation (ZNE) \\n\",\n \"\\n\",\n \"ZNE is a powerful quantum error mitigation technique designed to reduce the impact of noise in quantum circuits without requiring additional qubits or full error correction. The process consists of three essential stages: noise amplification, execution at various noise levels, and classical extrapolation back to the zero-noise limit.\\n\",\n \"\\n\",\n \"\\n\",\n \"### Noise amplification\\n\",\n \"\\n\",\n \"The first stage, Noise Amplification, lies at the heart of ZNE. The idea is to run the quantum circuit in versions that have more noise than usual, but in a controlled and reversible way. By comparing how the output changes as noise increases, it becomes possible to infer what the result would be with no noise at all. This is typically achieved using a technique called circuit folding.\\n\",\n \"\\n\",\n \"Circuit folding artificially increases the noise in a quantum computation by inserting additional gates that, in theory, do not alter the logical outcome. These additional gates are the adjoint (inverse) operations of previously applied gates. For example, a unitary operation $U$ can be transformed into $ U \\\\cdot U^\\\\dagger \\\\cdot U$, which logically still computes $U$, but takes longer to execute, thus being exposed to more noise from the hardware.\\n\",\n \"\\n\",\n \"\\n\",\n \"There are two common types of folding: **Global Folding** and **Local Folding**. \\n\",\n \"\\n\",\n \"\\n\",\n \"#### Global folding\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"In Global folding, the entire quantum circuit is folded as a single block. This means the full unitary operation $U$ that the circuit represents is wrapped with its adjoint operation, yielding the transformation:\\n\",\n \"\\n\",\n \"$$U \\\\rightarrow U \\\\cdot U^\\\\dagger \\\\cdot U$$\\n\",\n \"\\n\",\n \"This global transformation is logically equivalent to the original circuit, since $U^\\\\dagger \\\\cdot U$ is the identity operation. However, because the circuit is now longer and includes more gate operations, it becomes more susceptible to environmental noise and hardware imperfections.\\n\",\n \"\\n\",\n \"Global folding is particularly useful for quickly applying a uniform increase in noise across the full circuit. It is straightforward to implement and does not require knowledge of the internal structure of the circuit. As such, it serves as a coarse-grained approach to noise amplification, suitable for general-purpose extrapolation when fine control is not needed.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 6a: Global Folding\\n\",\n \"\\n\",\n \"**Your Goal:** Implement global folding on the quantum circuits.\\n\",\n \"\\n\",\n \"In this part of Exercise 6 (Section a), you will create a function that applies global folding to any quantum circuit. Your implementation should allow evaluation of the circuit at different noise scaling factors, which simulate increased noise levels while preserving the circuit's logical output. The noise scaling factor represents the number of times a circuit $U$ or $U^\\\\dagger$ is applied, with 1 being the non-folding case.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def fold_global_circuit(circuit: QuantumCircuit, scale_factor: int) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"Apply global circuit folding for Zero Noise Extrapolation (ZNE).\\\"\\\"\\\"\\n\",\n \" if scale_factor % 2 == 0 or scale_factor < 1:\\n\",\n \" raise ValueError(\\\"scale_factor must be an odd positive integer (1, 3, 5, ...)\\\")\\n\",\n \" # We define the number of times we are going to \\\"fold\\\" the circuit\\n\",\n \" n_repeat = (scale_factor - 1) // 2\\n\",\n \" folded_circuit = QuantumCircuit(circuit.qubits, circuit.clbits)\\n\",\n \"\\n\",\n \" def remove_all_measurements(qc: QuantumCircuit) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"Remove all measurements from a quantum circuit.\\\"\\\"\\\"\\n\",\n \" clean_qc = QuantumCircuit(qc.num_qubits)\\n\",\n \" for instr in qc.data:\\n\",\n \" if instr.operation.name != \\\"measure\\\":\\n\",\n \" clean_qc.append(instr.operation, instr.qubits)\\n\",\n \" return clean_qc\\n\",\n \"\\n\",\n \" # Make a quantum circuit as a U (Unitary) by removing measurements, since measurements are not unitary\\n\",\n \" clean_circuit = remove_all_measurements(circuit)\\n\",\n \"\\n\",\n \" # ---- TODO : Task 6a ---\\n\",\n \" # Implement the global circuit folding. Use `QuantumCircuit.append` and `QuantumCircuit.inverse` functions\\n\",\n \" # add U^† (inverse of clean_circuit) then U(clean_circuit) to the main circuit (folded_circuit)\\n\",\n \"\\n\",\n \" # --- End of TODO --\\n\",\n \"\\n\",\n \" return folded_circuit\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex6a(fold_global_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Local Folding\\n\",\n \"\\n\",\n \"\\n\",\n \"Local folding focuses on selectively increasing the noise in specific parts of the quantum circuit, usually around the most error-prone gates or subcircuits. Instead of folding the entire circuit as a block, this method focuses on individual quantum gates or groups of gates. This provides greater control and more precise calibration of the noise amplification process. Its operation is to take from the quantum circuit each quantum gate $G$. We can apply local folding by wrapping it with its inverse operation as follows:\\n\",\n \"\\n\",\n \"$$ G \\\\rightarrow G \\\\cdot G^\\\\dagger \\\\cdot G $$\\n\",\n \"\\n\",\n \"This transformation logically cancels the inserted $G^\\\\dagger \\\\cdot G$ pair, leaving the computation unchanged. However, the execution now involves three times as many gate operations, thereby increasing the local noise exposure. This makes it ideal for simulating different noise levels selectively and systematically.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 6b: Local Folding\\n\",\n \"\\n\",\n \"\\n\",\n \"**Your Goal:** Implement local folding on quantum circuits.\\n\",\n \"\\n\",\n \"In this second part of Exercise 6 (Section b), your task is to write a function that applies local folding to a quantum circuit. Unlike global folding, this method focuses on individual gates and selectively folds them to amplify noise in specific areas of the circuit. The function should allow flexible control of the gates being folded (for example, we cannot fold a measurement gate) and their evaluation at different noise amplification scales.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def fold_local_circuit(circuit: QuantumCircuit, scale_factor: int) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"Performs Zero-Noise Folding at the level of individual circuit instructions.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" if scale_factor % 2 == 0:\\n\",\n \" raise ValueError(\\\"scale must be an odd positive integer (1, 3, 5, ...)\\\")\\n\",\n \" # We define the number of times we are going to \\\"fold\\\" each instruction\\n\",\n \" n_repeat = (scale_factor - 1) // 2\\n\",\n \" qc_folded = QuantumCircuit(circuit.qubits, circuit.clbits)\\n\",\n \"\\n\",\n \" if scale_factor == 1:\\n\",\n \" return circuit\\n\",\n \" else:\\n\",\n \" for instrction in circuit.data:\\n\",\n \" # ---- TODO : Task 6b ---\\n\",\n \" # Implement the local circuit folding. Don't fold measurement gates!\\n\",\n \" # \\n\",\n \" \\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" return qc_folded\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex6b(fold_local_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Extrapolation\\n\",\n \"\\n\",\n \"\\n\",\n \"After amplifying the noise through folding, the quantum circuit is executed multiple times at different noise levels. Finally, in the third step, **Extrapolation**, the results from the noisy runs are post-processed using classical fitting methods, such as linear, polynomial, or exponential extrapolation, to estimate what the outcome would be in a hypothetical zero-noise scenario. Through this pipeline, ZNE helps recover higher-fidelity results from current noisy quantum hardware, making it a valuable tool in the near-term quantum computing landscape.\\n\",\n \"\\n\",\n \"\\n\",\n \"Now, we will integrate ZNE into the execution of a QAOA circuit, improving the accuracy of results on noisy quantum hardware.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def basic_zne(\\n\",\n \" circuit,\\n\",\n \" scales,\\n\",\n \" backend,\\n\",\n \" opt_params,\\n\",\n \" observable,\\n\",\n \"):\\n\",\n \" \\\"\\\"\\\"Basic Zero Noise Extrapolation (ZNE) loop using local folding.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" exp_vals = []\\n\",\n \" xdata = np.array(scales)\\n\",\n \" estimator = Estimator(mode=backend)\\n\",\n \"\\n\",\n \" for scale in scales:\\n\",\n \" # Apply local folding\\n\",\n \" folded = fold_local_circuit(circuit, scale)\\n\",\n \"\\n\",\n \" # Transpile for the backend\\n\",\n \" basis_gates = backend.target.operation_names\\n\",\n \" transpiled_folded = generate_preset_pass_manager(\\n\",\n \" basis_gates=basis_gates, optimization_level=0, seed_transpiler=seed\\n\",\n \" ).run(folded)\\n\",\n \" pub = (\\n\",\n \" transpiled_folded,\\n\",\n \" observable.apply_layout(circuit.layout),\\n\",\n \" opt_params,\\n\",\n \" )\\n\",\n \" # Evaluate the expectation value\\n\",\n \" job = estimator.run([pub])\\n\",\n \" results = job.result()[0]\\n\",\n \" exp_vals.append(results.data.evs)\\n\",\n \"\\n\",\n \" return xdata, exp_vals, pub\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"scales = [1, 3, 5, 7, 9, 11, 13]\\n\",\n \"xdata, ydata, pub = basic_zne(\\n\",\n \" qaoa_circuit_transpiled,\\n\",\n \" scales,\\n\",\n \" noisy_fake_backend,\\n\",\n \" opt_params_list[num_backend],\\n\",\n \" cost_hamiltonian,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To validate and analyze the results of the ZNE we just implemented, we can apply three types of extrapolation methods: linear, polynomial, and exponential. These approaches help estimate the circuit's output in the zero-noise limit based on the data collected at higher noise levels. To illustrate how this can be done, consider the following example code:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"methods = [\\\"linear\\\", \\\"quadratic\\\", \\\"exponential\\\"]\\n\",\n \"\\n\",\n \"for method in methods:\\n\",\n \" print(f\\\"\\\\n Extrapolation Method: {method.upper()}\\\")\\n\",\n \"\\n\",\n \" # Perform the extrapolation\\n\",\n \" zero_val, fitted_vals, fit_params, fit_fn = zne_method(method=method, xdata=xdata, ydata=ydata)\\n\",\n \"\\n\",\n \" # Print the extrapolated expectation value\\n\",\n \" print(f\\\"⟨Z⟩ (ZNE Estimate): {zero_val:.3f}\\\")\\n\",\n \"\\n\",\n \" # Plot the results\\n\",\n \" plot_zne(xdata, fitted_vals, zero_val, fit_fn, fit_params, method)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Conclusions \\n\",\n \"\\n\",\n \"Good job completing Lab 2! \\n\",\n \"\\n\",\n \"In this lab we explored the various sources of noise that can affect your quantum circuit, and - more importantly - the dedicated tools we can use to *cut through the noise*. With these tools at hand, you're now well-equipped to tackle any of your ongoing quantum algorithms and execute them on real quantum hardware. Remember, the key steps to follow are:\\n\",\n \"\\n\",\n \"- **Choose the right hardware** that is well-suited for your circuit.\\n\",\n \"- **Use the transpiler** to select the best possible layout and minimize the number of two-qubit gates and other metrics.\\n\",\n \"- **Apply error mitigation and suppression** to further reduce the impact of noise and improve the performance.\\n\",\n \"\\n\",\n \"Keep that in mind and you are all set to tackle your quantum challenges ahead!\\n\",\n \"\\n\",\n \"That's all, folks! Or is it?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check your submission status with the code below\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Bonus challenge: Scaling it up! \\n\",\n \"\\n\",\n \"As a bonus, we've included a more complicated version of this problem, in which you'll implement the Max-cut problem on IBM quantum hardware. This is your chance to put everything you've learned into practice, including the error mitigation techniques!\\n\",\n \"\\n\",\n \"### The problem\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We define the number of nodes:\\n\",\n \"n_ext = 7\\n\",\n \"# We define the graph\\n\",\n \"graph_ext = rx.PyGraph()\\n\",\n \"graph_ext.add_nodes_from(np.arange(0, n_ext, 1))\\n\",\n \"generic_backend_ext = GenericBackendV2(n_ext, seed=seed)\\n\",\n \"weights = 1\\n\",\n \"# We make it explicitly asymmetrical to have a smaller set of solutions\\n\",\n \"graph_ext.add_edges_from(\\n\",\n \" [(edge[0], edge[1], weights) for edge in generic_backend_ext.coupling_map][:-7]\\n\",\n \")\\n\",\n \"draw_graph(graph_ext, node_size=200, with_labels=True, width=1)\\n\",\n \"max_cut_paulis_ext = graph_to_Pauli(graph_ext)\\n\",\n \"cost_hamiltonian_ext = SparsePauliOp.from_list(max_cut_paulis_ext)\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=generic_backend_ext, seed_transpiler=seed\\n\",\n \")\\n\",\n \"layers = 2\\n\",\n \"init_params = np.zeros(2 * layers)\\n\",\n \"qaoa_circuit_ext = QAOAAnsatz(cost_operator=cost_hamiltonian_ext, reps=layers)\\n\",\n \"qaoa_circuit_ext.measure_all()\\n\",\n \"qaoa_circuit_ext_transpiled = pm.run(qaoa_circuit_ext)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Choose the backend\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qaoa_circuit_transpiled_ext_list = []\\n\",\n \"acc_error_list = []\\n\",\n \"\\n\",\n \"\\n\",\n \"for backend_hw in real_backends:\\n\",\n \"\\n\",\n \" pm = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend_hw, seed_transpiler=seed\\n\",\n \" )\\n\",\n \" qaoa_circuit_transpiled_ext = pm.run([qaoa_circuit_ext])[0]\\n\",\n \" qaoa_circuit_transpiled_ext_list.append(qaoa_circuit_transpiled_ext)\\n\",\n \"\\n\",\n \" # ---- TODO : Bonus Task ---\\n\",\n \"\\n\",\n \" # Use the accumulated_errors function, `backend_hw` and `qaoa_circuit_transpiled_ext` and get the [0] value\\n\",\n \" acc_error = \\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" acc_error_list.append(acc_error)\\n\",\n \"# We choose the backend with smallest error\\n\",\n \"best_backend_ext = real_backends[acc_error_list.index(min(acc_error_list))]\\n\",\n \"print(\\n\",\n \" f\\\"The backend {best_backend_ext.name} has the circuit with the smallest accumulated error: {min(acc_error_list):.3f}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### We optimize the layout and minimize the accumulated error\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"circuit_ext_opt_seed, best_seed_transpiler, min_err_acc_seed, _ = finding_best_seed(\\n\",\n \" qaoa_circuit_ext, best_backend_ext\\n\",\n \")\\n\",\n \"print(f\\\"Best transpiler seed: {best_seed_transpiler}\\\")\\n\",\n \"print(f\\\"Minimum accumulated two-qubit gate error: {min_err_acc_seed:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### We execute the circuit on simulator\\n\",\n \"\\n\",\n \"First, we must optimize the parameters of this QAOA problem. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"best_backend_sim = AerSimulator.from_backend(best_backend_ext, seed_simulator=seed)\\n\",\n \"result_qaoa_sim, objective_func_vals_sim = train_qaoa(\\n\",\n \" init_params, circuit_ext_opt_seed, cost_hamiltonian_ext, best_backend_sim\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can sample from the QAOA circuit.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"counts_list_sim = sample_qaoa(opt_params_sim, circuit_ext_opt_seed, best_backend_sim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Checking the results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eigenvalues_ext, eigenvectors_ext = np.linalg.eig(cost_hamiltonian_ext)\\n\",\n \"ground_energy_ext = min(eigenvalues_ext).real\\n\",\n \"num_solutions_ext = eigenvalues_ext.tolist().count(ground_energy_ext)\\n\",\n \"index_solutions_ext = np.where(eigenvalues_ext == ground_energy_ext)[0].tolist()\\n\",\n \"print(f\\\"The ground energy of the Hamiltonian is {ground_energy_ext}\\\")\\n\",\n \"print(f\\\"The number of solutions of the problem is {num_solutions_ext}\\\")\\n\",\n \"print(f\\\"The list of the solutions based on their index is {index_solutions_ext}\\\")\\n\",\n \"\\n\",\n \"states_solutions_ext = decimal_to_binary(index_solutions_ext, n_ext)\\n\",\n \"# Sort the dictionary items by their counts in descending order\\n\",\n \"sorted_states_sim = sorted(counts_list_sim.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"# Take the top 'num_solutions' entries\\n\",\n \"top_states_sim = sorted_states_sim[:num_solutions_ext]\\n\",\n \"# Extract only the states keys from the top entries\\n\",\n \"qaoa_ground_states_sim = sorted([state for state, count in top_states_sim])\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions_ext}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_sim} for {best_backend_sim.name}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Running on Hardware\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" Resource limit: \\n\",\n \"\\n\",\n \"When running the section below, you will execute the above problem on real hardware, which could consume approximately 2-3 minutes of the your Open Plan allotment. Please proceed only if you're comfortable with this potential usage. Also, please try to maintain these settings if possible, to make sure you don't use too much time. \\n\",\n \"
\\n\",\n \"\\n\",\n \"Finally we can execute the problem on hardware, and compare the results.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Estimator\\n\",\n \"\\n\",\n \"We will not train the QAOA circuit on hardware, as it will consume a lot of your Open Plan allotment. Instead, we'll execute the estimator with the optimized parameters and will apply error mitigation techniques later to see how we can improve the results.\\n\",\n \"\\n\",\n \"First we compute the ground state energy on hardware without error mitigation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO : Bonus Task ---\\n\",\n \"\\n\",\n \"# Based on previous section, choose the best backend for executing the code on hardware\\n\",\n \"best_backend_hw = \\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"options = EstimatorOptions(default_shots=100000)\\n\",\n \"estimator = Estimator(mode=best_backend_hw, options=options)\\n\",\n \"ground_energy_ext_hw = cost_func_estimator(\\n\",\n \" opt_params_sim, circuit_ext_opt_seed, cost_hamiltonian_ext, estimator\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The ground energy of the QAOA circuit executed on {best_backend_hw.name} without EM is: {ground_energy_ext_hw}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we apply different error mitigation and error suppression techniques.\\n\",\n \"The different techniques we'll apply are:\\n\",\n \"\\n\",\n \"- **Dynamical decoupling**: this error suppression technique involves applying a sequence of control pulses to idle qubits to cancel out unwanted interactions and coherent errors. It helps preserve quantum coherence by effectively \\\"decoupling\\\" the system from noise sources over time.\\n\",\n \"- **Pauli twirling**: this error mitigation technique transforms arbitrary noise into simpler Pauli noise by randomly applying and undoing Pauli gates around operations, reducing the impact of coherent errors and enabling more effective noise modeling and correction.\\n\",\n \"- **TREX**: Twirled Readout Error eXtinction is a readout error mitigation technique that reduces measurement errors by randomly flipping qubits before measurement and classically correcting the results, effectively diagonalizing the readout-error matrix and simplifying its inversion for more accurate expectation value estimation.\\n\",\n \"- **ZNE**: as explained before, ZNE is an error mitigation technique that estimates the result of a quantum computation as if it were run on a noise-free device. It does this by deliberately increasing the noise in a controlled way, measuring the results, and then extrapolating back to the zero-noise limit.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"options = EstimatorOptions(default_shots=100000)\\n\",\n \"# Dynamical Decoupling\\n\",\n \"options.dynamical_decoupling.enable = True\\n\",\n \"\\n\",\n \"# Probabilistic Twirling\\n\",\n \"options.twirling.enable_gates = True\\n\",\n \"options.twirling.num_randomizations = 10\\n\",\n \"options.twirling.shots_per_randomization = 10000\\n\",\n \"\\n\",\n \"# TREX\\n\",\n \"options.resilience.measure_mitigation = True\\n\",\n \"options.resilience.measure_noise_learning.num_randomizations = 10\\n\",\n \"options.resilience.measure_noise_learning.shots_per_randomization = 10000\\n\",\n \"\\n\",\n \"# ZNE setup\\n\",\n \"options.resilience.zne_mitigation = True\\n\",\n \"options.resilience.zne.amplifier = \\\"gate_folding\\\"\\n\",\n \"options.resilience.zne.extrapolator = \\\"polynomial_degree_2\\\"\\n\",\n \"options.resilience.zne.noise_factors = (1, 3, 5)\\n\",\n \"\\n\",\n \"# We execute on hardware\\n\",\n \"estimator_EM = Estimator(mode=best_backend_hw, options=options)\\n\",\n \"ground_energy_ext_hw_EM = cost_func_estimator(\\n\",\n \" opt_params_sim, circuit_ext_opt_seed, cost_hamiltonian_ext, estimator_EM\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The ground energy of the QAOA circuit executed on {best_backend_hw.name} with EM is: {ground_energy_ext_hw_EM}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can use the Sampler primitive on real hardware without any error mitigation and suppression techniques.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"# We execute on hardware\\n\",\n \"sampler = Sampler(mode=best_backend_hw)\\n\",\n \"job = sampler.run([(circuit_ext_opt_seed, opt_params_sim)], shots=SHOTS)\\n\",\n \"results_sampler = job.result()\\n\",\n \"counts_list_hw = results_sampler[0].data.meas.get_counts()\\n\",\n \"display(plot_histogram(counts_list_hw, title=f\\\"Max cut with {best_backend_hw.name} \\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we include error suppression techniques in the Sampler.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"# We execute on hardware\\n\",\n \"sampler = Sampler(mode=best_backend_hw)\\n\",\n \"# Set runtime options directly on the sampler\\n\",\n \"sampler.options.dynamical_decoupling.enable = True\\n\",\n \"job = sampler.run([(circuit_ext_opt_seed, opt_params_sim)], shots=SHOTS)\\n\",\n \"results_sampler = job.result()\\n\",\n \"counts_list_hw_EM = results_sampler[0].data.meas.get_counts()\\n\",\n \"display(plot_histogram(counts_list_hw_EM, title=f\\\"Max cut with {best_backend_hw.name} with EM\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Check results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sorted_states_hw = sorted(counts_list_hw.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"top_states_hw = sorted_states_hw[:num_solutions_ext]\\n\",\n \"qaoa_ground_states_hw = sorted([state for state, count in top_states_hw])\\n\",\n \"\\n\",\n \"sorted_states_hw_EM = sorted(counts_list_hw_EM.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"top_states_hw_EM = sorted_states_hw_EM[:num_solutions_ext]\\n\",\n \"qaoa_ground_states_hw_EM = sorted([state for state, count in top_states_hw_EM])\\n\",\n \"\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions_ext}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_hw} for {best_backend_hw.name}\\\"\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_hw_EM} for {best_backend_hw.name} with EM\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Congratulations on reaching the end of the lab!\\n\",\n \"If you still have some time left for running experiments on hardware, feel free to explore a few additional things:\\n\",\n \"\\n\",\n \"- Experiment with error mitigation techniques: try rerunning your execution on hardware using different error mitigation and suppression strategies. You can enable or disable specific techniques or tweak various parameters to study their impact.\\n\",\n \"\\n\",\n \"- Redo the bonus challenge: try rerunning the entire bonus challenge section using a different backend to compare results and performance.\\n\",\n \"\\n\",\n \"- Scale it up even more: try scaling the problem to a larger system, such as 10 qubits, and rerun your experiments. However, this might take some time in both the simulator and hardware, so we encourage you to try the first two other points first.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# References:\\n\",\n \"\\n\",\n \"[1] Fahri et al., \\\"A Quantum Approximate Optimization Algorithm\\\" (2014). [arXiv:quant-ph/1411.4028](https://arxiv.org/abs/1411.4028)\\n\",\n \"\\n\",\n \"[2] Nation et al., \\\"Suppressing Quantum Circuit Errors Due to System Variability\\\" (2023). [PRX Quantum 4, 010327](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.4.010327)\\n\",\n \"\\n\",\n \"[3] Temme et al., \\\"Error Mitigation for Short-Depth Quantum Circuits\\\" (2017). [Phys. Rev. Lett. 119, 180509](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509)\\n\",\n \"\\n\",\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Jorge Martínez de Lejarza, Alberto Maldonado\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Paul Nation, Junye Huang, Sophy Shin\\n\",\n \"\\n\",\n \"**Version:** 1.0.1\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.11\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "lab-2/Lab2_ja.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# ラボ2：ノイズを切り抜ける\\n\",\n \"\\n\",\n \"このラボでは、量子ノイズの世界と、現在の量子コンピュータで良い結果を得るためにノイズの課題を乗り越えるための様々な手法について学びます。小規模な Max-cut 問題の文脈で、量子アルゴリズムを量子コンピュータで実行するために量子ノイズを低減するために踏むべきステップを一つずつ案内します。誤りを最小限に抑え、ノイズにもかかわらず結果が正しいままであることを保証するために、様々なトランスパイルやエラー緩和技術を探ります。最後に、ラボのまとめとして、これまでに紹介した全ての技術を実際の量子ハードウェア上で実装するボーナス課題を用意しています。\\n\",\n \"\\n\",\n \"# 目次\\n\",\n \"\\n\",\n \"0. [要件](#requirements)\\n\",\n \"1. [イントロダクション](#intro) \\n\",\n \" - [ラボの精神](#spirit)\\n\",\n \" - [量子ノイズとは？](#quantum-noise) \\n\",\n \" - [ノイズの発生源](#sources)\\n\",\n \" * [演習1: T1, T2が長く、ゲート忠実度が高く、読み出しエラーが小さい量子ビットを探す](#exercise_1)\\n\",\n \"2. [Max-cut問題](#max-cut)\\n\",\n \" - [問題: グラフの定義](#the-graph)\\n\",\n \" - [マッピング: グラフを量子コンピュータに変換](#the-mapping)\\n\",\n \" * [演習2: Max-cut問題のイジングハミルトニアンを求める](#exercise_2)\\n\",\n \" - [QAOAによる解法](#qaoa-solution)\\n\",\n \" - [解の検証](#checking)\\n\",\n \"3. [ノイズ付き量子シミュレータ](#noisy-simulator)\\n\",\n \" - [バックエンドの選択](#choosing-backend)\\n\",\n \" * [演習3: エラーのカウント](#exercise_3)\\n\",\n \" - [NEATによるエラー推定](#neat)\\n\",\n \"4. [トランスパイラ](#transpiler)\\n\",\n \" - [2量子ビットゲートの最小化](#min-two-qubit)\\n\",\n \" - [最適なレイアウトの探索](#opt-layout)\\n\",\n \" * [演習4: 良いマッピングを全て見つける](#exercise_4)\\n\",\n \" * [演習5: トランスパイラで最適レイアウトを見つける](#exercise_5)\\n\",\n \"5. [エラー緩和](#em)\\n\",\n \" - [ゼロノイズ外挿（ZNE）](#zne)\\n\",\n \" * [演習6a: 量子回路のグローバルフォールディングを実装](#exercise_6a)\\n\",\n \" * [演習6b: 量子回路のローカルフォールディングを実装](#exercise_6b)\\n\",\n \" * [演習7: ローカルゼロノイズ外挿（ZNE）を実装](#exercise_7)\\n\",\n \"6. [まとめ](#conclusions)\\n\",\n \"7. [ボーナスチャレンジ: スケールアップ！](#bonus)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 0. 要件 \\n\",\n \"\\n\",\n \"このチュートリアルを開始する前に、以下のライブラリがインストールされていることを確認してください。\\n\",\n \"\\n\",\n \"* Qiskit SDK v2.0 （visualization サポート付） (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime v0.22 以降 (`pip install qiskit-ibm-runtime`)\\n\",\n \"* Rustworkx (`pip install rustworkx`)\\n\",\n \"* Qiskit Aer v0.17\\n\",\n \"* Pylatexenc\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install -U qiskit \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Qiskit のバージョンが `>=2.0.0` で、Grader が `>=0.22.9` であることを確認してください。バージョンが低い場合は、カーネルを再起動して Grader を再インストールしてください。 \\n\",\n \"また、Lab 0に従ってすべてをセットアップし、以下のセルでテストしてください。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# アカウントが正しく保存されていることを確認してください\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import rustworkx as rx\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from rustworkx.visualization import mpl_draw as draw_graph\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from scipy.optimize import minimize\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.providers.fake_provider import GenericBackendV2\\n\",\n \"from qiskit.quantum_info import SparsePauliOp, Statevector, DensityMatrix, Operator\\n\",\n \"from qiskit.circuit.library import QAOAAnsatz\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.transpiler import Layout\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import (\\n\",\n \" Session,\\n\",\n \" EstimatorV2 as Estimator,\\n\",\n \" SamplerV2 as Sampler,\\n\",\n \" EstimatorOptions,\\n\",\n \")\\n\",\n \"from qiskit_ibm_runtime.debug_tools import Neat\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from utils import zne_method, plot_zne, plot_backend_errors_and_counts\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab2_ex1,\\n\",\n \" grade_lab2_ex2,\\n\",\n \" grade_lab2_ex3,\\n\",\n \" grade_lab2_ex4,\\n\",\n \" grade_lab2_ex5,\\n\",\n \" grade_lab2_ex6a,\\n\",\n \" grade_lab2_ex6b,\\n\",\n \")\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. イントロダクション \\n\",\n \"\\n\",\n \"### 1.1 ラボの精神 \\n\",\n \"\\n\",\n \"*IBM量子ハードウェア上でのパフォーマンスを最大化するために、適切なトランスパイルとエラー緩和技術を使って実際の問題に取り組むプロセスを案内します。*\\n\",\n \"\\n\",\n \"### 1.2 量子ノイズとは？ \\n\",\n \"\\n\",\n \"ノイズは量子コンピュータの設計や利用において中心的な話題であり、現在のデバイスの主な特徴の一つです。しかし、量子情報においてノイズとは一体何を意味するのでしょうか？ \\n\",\n \"通常、ノイズとは量子状態の測定結果に期待される結果を乱す、望ましくない変換全般を指します。これには、エラーを引き起こす様々な要因が含まれ、主に2つのグループに分類できます：\\n\",\n \"\\n\",\n \"- コヒーレントエラー：量子操作（ゲート）の適用における小さく一貫したミスによって引き起こされます。これらのエラーはユニタリーであり、情報を破壊せず、理論的には逆転可能です。典型的な例は、あるゲートが本来 $\\\\theta$ だけ回転させるべきところを、キャリブレーションの不完全さにより $\\\\theta+\\\\varepsilon$ だけ回転させてしまう場合です。例えば、Hadamard ゲートがわずかにミスキャリブレーションされていると、完全な重ね合わせを作れず、測定結果にバイアスが生じます。\\n\",\n \"- インコヒーレントエラー：これらのエラーの起源は量子的であり、量子システムが環境と相互作用することで発生します。インコヒーレントエラーは、現在の量子コンピュータの多くはミリケルビンオーダーの低温に冷却され、不要な相互作用を減らしコヒーレンスを維持しています。これらの相互作用は混合状態を生み出し、出力にランダム性を導入するため、特に問題となります。具体例としては $T_1$ 緩和時間があり、励起状態 $|1\\\\rangle$ の量子ビットが時間とともに基底状態 $|0\\\\rangle$ へ自発的に崩壊する現象を記述します。測定が遅れると、この崩壊により量子ビットが状態を反転させ、誤った結果につながることがあります。\\n\",\n \"\\n\",\n \"### 1.3 ノイズの発生源 \\n\",\n \"\\n\",\n \"上述の通り、ノイズの発生源には、電磁パルスとしての量子ゲートの不完全な実装、読み出しエラー、量子システムのデコヒーレンスなど、様々な要因があります。\\n\",\n \"\\n\",\n \"量子コンピュータ（または量子ビット）がどれだけノイズに強いかを評価するための指標は様々ありますが、その中からいくつかを選んで扱います：\\n\",\n \"\\n\",\n \"* T1：緩和時間。状態 $|1\\\\rangle$ の量子ビットが状態 $|0\\\\rangle$ へ崩壊する速さを測る減衰定数\\n\",\n \"* T2：位相緩和時間。状態 $|+\\\\rangle$ の量子ビットが $|+\\\\rangle$ と $|-\\\\rangle$ の区別がつかない混合状態になるまでの速さを測る減衰定数\\n\",\n \"* 1量子ビットゲート忠実度：ハードウェアが実際に行う操作 $\\\\mathcal{E}$ が理想的な1量子ビットユニタリー$U$とどれだけ近いかを定量化します。忠実度1は理想通りの動作を意味します。\\n\",\n \"* 2量子ビットゲート忠実度：ハードウェアが実際に行う操作 $\\\\mathcal{E}_2$ が理想的な2量子ビットユニタリー $U_2$ とどれだけ近いかを定量化します。これらのゲートはより複雑なため、忠実度は低くなりがちです。1なら完全一致です。\\n\",\n \"* 読み出し割り当てエラー：量子ビットが誤って測定される確率。測定された古典ビットが、測定直前の量子ビットの実際の量子状態と一致しないことを意味します。\\n\",\n \"\\n\",\n \"ある特定の IBM Quantum デバイスの情報は [IBM Quantum Cloud Platform](https://quantum.cloud.ibm.com/computers) から確認できます。分析したいデバイスを選択すると、各量子ビットの詳細な特性やマシン全体の概要をインタラクティブに確認できます。下の画像は `ibm_brisbane` デバイスの概要例です。\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"このデバイスの情報はコードを通して直接アクセスすることもできます。\\n\",\n \"\\n\",\n \"実際にやってみましょう！\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"最後に、ハードウェア上で問題を実行し、得られる結果を比較できます。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Estimator\\n\",\n \"\\n\",\n \"私達はハードウェア上でQAOA回路をトレーニングしていません。なぜなら、IBMハードウェアでの実行に無料プランでかなりの時間を消費する可能性があるからです。代わりに、最適化されたパラメータで Estimator を実行し、後でエラー緩和技術を適用して結果を改善する方法を確認します。\\n\",\n \"\\n\",\n \"まずは、エラー緩和なしでハードウェア上で基底状態エネルギーを計算します。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO : Bonus Task ---\\n\",\n \"\\n\",\n \"# これまでのセクションに基づいて、ハードウェアでコードを実行するための最適なバックエンドを選択してください\\n\",\n \"best_backend_hw = \\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"options = EstimatorOptions(default_shots=100000)\\n\",\n \"estimator = Estimator(mode=best_backend_hw, options=options)\\n\",\n \"ground_energy_ext_hw = cost_func_estimator(\\n\",\n \" opt_params_sim, circuit_ext_opt_seed, cost_hamiltonian_ext, estimator\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The ground energy of the QAOA circuit executed on {best_backend_hw.name} without EM is: {ground_energy_ext_hw}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"ここでは、さまざまなエラー緩和およびエラー抑制技術を適用します。\\n\",\n \"今回適用する技術は以下の通りです：\\n\",\n \"\\n\",\n \"- **Dynamical decoupling**：このエラー抑制技術は、アイドル状態の量子ビットに制御パルスのシーケンスを適用し、不要な相互作用やコヒーレントエラーを打ち消します。これにより、量子コヒーレンスを維持し、時間とともにノイズ源からシステムを「デカップリング」する効果があります。\\n\",\n \"- **Pauli twirling**：このエラー緩和技術は、任意のノイズをより単純なパウリノイズに変換します。操作の前後にランダムにパウリゲートを適用・解除することで、コヒーレントエラーの影響を減らし、より効果的なノイズモデリングと補正を可能にします。\\n\",\n \"- **TREX（Twirled Readout Error eXtinction）**：これは読み出しエラー緩和技術で、測定前に量子ビットをランダムに反転させ、結果を古典的に補正することで測定エラーを低減します。これにより、読み出しエラーマトリクスが対角化され、その逆行列の計算が簡単になり、より正確な期待値推定が可能になります。\\n\",\n \"- **ZNE（Zero Noise Extrapolation）**：前述の通り、ZNE は量子計算の結果をノイズのないデバイスで実行した場合の値として推定するエラー緩和技術です。ノイズを意図的に制御して増加させ、その結果を測定し、ゼロノイズ極限まで外挿することで推定します。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"options = EstimatorOptions(default_shots=100000)\\n\",\n \"# Dynamical Decoupling\\n\",\n \"options.dynamical_decoupling.enable = True\\n\",\n \"\\n\",\n \"# Probabilistic Twirling\\n\",\n \"options.twirling.enable_gates = True\\n\",\n \"options.twirling.num_randomizations = 10\\n\",\n \"options.twirling.shots_per_randomization = 10000\\n\",\n \"\\n\",\n \"# TREX\\n\",\n \"options.resilience.measure_mitigation = True\\n\",\n \"options.resilience.measure_noise_learning.num_randomizations = 10\\n\",\n \"options.resilience.measure_noise_learning.shots_per_randomization = 10000\\n\",\n \"\\n\",\n \"# ZNE setup\\n\",\n \"options.resilience.zne_mitigation = True\\n\",\n \"options.resilience.zne.amplifier = \\\"gate_folding\\\"\\n\",\n \"options.resilience.zne.extrapolator = \\\"polynomial_degree_2\\\"\\n\",\n \"options.resilience.zne.noise_factors = (1, 3, 5)\\n\",\n \"\\n\",\n \"# ハードウェアで実行\\n\",\n \"estimator_EM = Estimator(mode=best_backend_hw, options=options)\\n\",\n \"ground_energy_ext_hw_EM = cost_func_estimator(\\n\",\n \" opt_params_sim, circuit_ext_opt_seed, cost_hamiltonian_ext, estimator_EM\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The ground energy of the QAOA circuit executed on {best_backend_hw.name} with EM is: {ground_energy_ext_hw_EM}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"これで、エラー緩和や抑制技術なしで実際のハードウェア上で Sampler プリミティブを使用できます。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"# ハードウェアで実行\\n\",\n \"sampler = Sampler(mode=best_backend_hw)\\n\",\n \"job = sampler.run([(circuit_ext_opt_seed, opt_params_sim)], shots=SHOTS)\\n\",\n \"results_sampler = job.result()\\n\",\n \"counts_list_hw = results_sampler[0].data.meas.get_counts()\\n\",\n \"display(plot_histogram(counts_list_hw, title=f\\\"Max cut with {best_backend_hw.name} \\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sampler を使用して、エラー抑制技術を含めます。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"# ハードウェアで実行\\n\",\n \"sampler = Sampler(mode=best_backend_hw)\\n\",\n \"# Sampler乗でruntime optionsを直接設定\\n\",\n \"sampler.options.dynamical_decoupling.enable = True\\n\",\n \"job = sampler.run([(circuit_ext_opt_seed, opt_params_sim)], shots=SHOTS)\\n\",\n \"results_sampler = job.result()\\n\",\n \"counts_list_hw_EM = results_sampler[0].data.meas.get_counts()\\n\",\n \"display(plot_histogram(counts_list_hw_EM, title=f\\\"Max cut with {best_backend_hw.name} with EM\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 結果の確認\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sorted_states_hw = sorted(counts_list_hw.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"top_states_hw = sorted_states_hw[:num_solutions_ext]\\n\",\n \"qaoa_ground_states_hw = sorted([state for state, count in top_states_hw])\\n\",\n \"\\n\",\n \"sorted_states_hw_EM = sorted(counts_list_hw_EM.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"top_states_hw_EM = sorted_states_hw_EM[:num_solutions_ext]\\n\",\n \"qaoa_ground_states_hw_EM = sorted([state for state, count in top_states_hw_EM])\\n\",\n \"\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions_ext}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_hw} for {best_backend_hw.name}\\\"\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_hw_EM} for {best_backend_hw.name} with EM\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Labの最後への到達、おめでとうございます！\\n\",\n \"もしハードウェアでの実験を実行する時間がまだ残っている場合は、いくつかの追加のことを試してみてください：\\n\",\n \"\\n\",\n \"- エラー緩和技術を試す：ハードウェアでの実行を再度行い、異なるエラー緩和および抑制戦略を使用してみてください。特定の技術を有効または無効にしたり、さまざまなパラメータを調整して、その影響を研究できます。\\n\",\n \"\\n\",\n \"- ボーナス課題をやり直す：異なるバックエンドを使用してボーナス課題のセクション全体を再実行し、結果とパフォーマンスを比較してみてください。\\n\",\n \"\\n\",\n \"- さらにスケールアップする：問題を10量子ビットなどの大規模なシステムにスケールアップし、実験を再実行してみてください。ただし、これはシミュレーターとハードウェアの両方で時間がかかる可能性があるため、最初に他の2つのポイントを試すことをお勧めします。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 参考文献:\\n\",\n \"\\n\",\n \"[1] Fahri et al., \\\"A Quantum Approximate Optimization Algorithm\\\" (2014). [arXiv:quant-ph/1411.4028](https://arxiv.org/abs/1411.4028)\\n\",\n \"\\n\",\n \"[2] Nation et al., \\\"Suppressing Quantum Circuit Errors Due to System Variability\\\" (2023). [PRX Quantum 4, 010327](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.4.010327)\\n\",\n \"\\n\",\n \"[3] Temme et al., \\\"Error Mitigation for Short-Depth Quantum Circuits\\\" (2017). [Phys. Rev. Lett. 119, 180509](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509)\\n\",\n \"\\n\",\n \"# 追加情報\\n\",\n \"\\n\",\n \"**作成:** Jorge Martínez de Lejarza, Alberto Maldonado\\n\",\n \"\\n\",\n \"**監修:** Marcel Pfaffhauser, Paul Nation, Junye Huang, Sophy Shin\\n\",\n \"\\n\",\n \"**翻訳:** Daiki Murata\\n\",\n \"\\n\",\n \"**Version:** 1.0\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "lab-2/utils.py",
"content": "from qiskit.quantum_info import SparsePauliOp\nimport numpy as np\nfrom qiskit import transpile\nfrom qiskit import QuantumCircuit\nfrom scipy.optimize import curve_fit\nimport matplotlib.pyplot as plt\nfrom qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n\n\n\n\ndef linear_model(x, a, b):\n return a * x + b\n\n\ndef quadratic_model(x, a, b, c):\n return a * x**2 + b * x + c\n\n\ndef exponential_model(x, a, b, c):\n return a * np.exp(-b * x) + c\n\n\ndef zne_method(method=\"linear\", xdata=[], ydata=[]):\n\n if method == \"linear\":\n popt, _ = curve_fit(linear_model, xdata, ydata)\n zero_val = linear_model(0, *popt)\n fit_fn = linear_model\n elif method == \"quadratic\":\n popt, _ = curve_fit(quadratic_model, xdata, ydata)\n zero_val = quadratic_model(0, *popt)\n fit_fn = quadratic_model\n elif method == \"exponential\":\n popt, _ = curve_fit(exponential_model, xdata, ydata, p0=(1, 0.1, 0), maxfev=5000)\n zero_val = exponential_model(0, *popt)\n fit_fn = exponential_model\n else:\n raise ValueError(f\"Unknown method '{method}'. Use 'linear', 'quadratic', or 'exponential'.\")\n\n return zero_val, ydata, popt, fit_fn\n\n\ndef plot_zne(scales, values, zero_val, fit_fn, fit_params, method):\n x_plot = np.linspace(0, max(scales), 200)\n y_plot = fit_fn(x_plot, *fit_params)\n\n plt.figure(figsize=(8, 5))\n plt.plot(scales, values, \"o\", label=\"Noisy measurements\")\n plt.plot(x_plot, y_plot, \"-\", label=f\"{method.capitalize()} fit\")\n plt.axvline(0, linestyle=\"--\", color=\"gray\")\n plt.axhline(zero_val, linestyle=\"--\", color=\"red\", label=\"Zero-noise estimate\")\n plt.xlabel(\"Noise scaling factor\")\n plt.ylabel(\"⟨Z⟩ Expectation Value\")\n plt.title(f\"Zero-Noise Extrapolation ({method})\")\n plt.legend()\n plt.grid(True)\n plt.show()\n\n\ndef plot_backend_errors_and_counts(backends, errors_and_counts_list):\n\n # Unpack errors and counts from the list\n (\n acc_total_errors,\n acc_two_qubit_errors,\n acc_single_qubit_errors,\n acc_readout_errors,\n single_qubit_gate_counts,\n two_qubit_gate_counts,\n ) = np.array(errors_and_counts_list).T.tolist()\n\n errors = np.array(\n [\n acc_total_errors,\n acc_two_qubit_errors,\n acc_single_qubit_errors,\n acc_readout_errors,\n ]\n )\n error_labels = [\n \"Total Error\",\n \"Two-Qubit Error\",\n \"Single-Qubit Error\",\n \"Readout Error\",\n ]\n counts = np.array([single_qubit_gate_counts, two_qubit_gate_counts])\n count_labels = [\"Single-Qubit Gate Count\", \"Two-Qubit Gate Count\"]\n\n # Transpose errors and counts to align with plotting requirements\n errors = errors.T\n counts = counts.T\n # Plot for accumulated errors\n x = np.arange(len(error_labels)) # the label locations\n width = 0.2 # the width of the bars\n fig, ax = plt.subplots(figsize=(10, 5))\n for i in range(len(backends)):\n ax.bar(x + i * width, errors[i], width, label=backends[i].name)\n ax.set_xlabel(\"Error Type\")\n ax.set_ylabel(\"Accumulated Error\")\n ax.set_title(\"Accumulated Errors by Backend\")\n ax.set_xticks(x + width)\n ax.set_xticklabels(error_labels)\n ax.legend()\n plt.show()\n\n # Plot for gate counts\n x = np.arange(len(count_labels)) # the label locations\n\n fig, ax = plt.subplots(figsize=(10, 5))\n for i in range(len(backends)):\n ax.bar(x + i * width, counts[i], width, label=backends[i].name)\n\n ax.set_xlabel(\"Gate Type\")\n ax.set_ylabel(\"Gate Count\")\n ax.set_title(\"Gate Counts by Backend\")\n ax.set_xticks(x + width)\n ax.set_xticklabels(count_labels)\n ax.legend()\n plt.show()\n"
},
{
"path": "lab-3/lab3-ko.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8cdbc826-5411-42cd-bf85-8f3ef3e40feb\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# Lab 3: The Power of 'good sampling' for simulating a chemistry Hamiltonian with SQD\\n\",\n \"\\n\",\n \"Qiskit Global Summer School 세 번째 코딩 챌린지에 오신 것을 환영합니다. 이번 랩에서는 양자 컴퓨팅의 가장 유망한 응용 분야 중 하나인 양자 화학을 탐구해보도록 하겠습니다. 실제 양자 화학 연구자들이 사용하는 워크플로우를 기반으로 양자컴퓨터에서 분자를 시뮬레이션하는 실제 예제를 소개하며, 각 단계가 목표 달성에 어떤 역할을 하는지 전체 워크플로우를 단계적으로 알아보도록 하겠습니다.\\n\",\n \"\\n\",\n \"**추천 학습 자료** \\n\",\n \"이 챌린지를 진행하기 전에 잠깐 시간을 내서, IBM Quantum Learning에서 제공하는 [Variational Algorithm Design](https://quantum.cloud.ibm.com/learning/en/courses/variational-algorithm-design) 코스와 최근에 공개된 코스 중 하나인 [Quantum Diagonalization Algorithms](https://quantum.cloud.ibm.com/learning/en/courses/quantum-diagonalization-algorithms/skqd) 코스를 훑어보고 오시길 추천드립니다.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9e49e912-bc84-44e2-a715-94ec1e95eba2\",\n \"metadata\": {},\n \"source\": [\n \"# 목차\\n\",\n \"\\n\",\n \"* [Part 1: 개요](#part-1-introduction)\\n\",\n \" * 1.1 목표\\n\",\n \" * 1.2 원자란 무엇인가?\\n\",\n \" * 1.3 슈뢰딩거 방정식\\n\",\n \" * 1.4 기저 집합 근사법 - 효율적인 구현법\\n\",\n \" * 1.5 해밀토니안\\n\",\n \" * [연습문제 1](#exercise1): $O_2$ 분자의 해밀토니안 크기 측정하기\\n\",\n \"* [Part 2: 변분 양자 고유상태 계산기](#part-2-variational-quantum-eigensolver)\\n\",\n \" * VQE – 고전 컴퓨터와 양자 컴퓨터의 협력\\n\",\n \"* [Part 3: 샘플 기반 양자 대각화(SQD)란 무엇인가?](#part-3-what-is-sample-based-quantum-diagonalization-(SQD)?)\\n\",\n \" * SQD 접근법: 양질의 샘플을 이용한 해밀토니안 재구성\\n\",\n \" * 관련성 있는 전자 배치의 선택 방법\\n\",\n \" * 전자 배치 복원을 통한 노이즈 영항 처리 방법\\n\",\n \"* [Part 4: SQD를 이용한 $N_2$ 분자의 시뮬레이션](#part-4-how-to-simulate-a-$N_2$-molecule-with-SQD)\\n\",\n \" * [연습문제 2](#exercise2): 전자 배치 복원을 통한 비트 교정\\n\",\n \"* [Part 5: 가설해 개선](#part-5-improve-the-ansatz)\\n\",\n \" * [연습문제 3](#exercise3): 기저 집합 변경\\n\",\n \" * [연습문제 4](#exercise4): 최적의 큐비트 배치 선택\\n\",\n \" * [연습문제 5](#exercise5): LUCJ 가설해에 추가적인 상호작용 도입\\n\",\n \"* [보너스: 실제 하드웨어에서 실행 및 오류 완화](Bonus-real-hardware-execution-with-error-mitigation )\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c7df7f65-7bc0-405b-b937-0d1203f7204a\",\n \"metadata\": {},\n \"source\": [\n \"## 요구사항\\n\",\n \"\\n\",\n \"본 튜토리얼을 시작하기 전에, 다음의 패키지들이 설치되어 있는지 확인해 주세요.\\n\",\n \"\\n\",\n \"* Qiskit SDK 2.0 or later with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime 0.40 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"* Qiskit Addons SQD 0.11.0 or later (`pip install qiskit-addon-sqd`)\\n\",\n \"* ffsim (`pip install ffsim`)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"723fb00e-8dfa-421e-95bc-dcabffdf3cec\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"⚠️ **참고:** 이번 랩을 진행하기 위해서는 아래 패키지들이 설치되어 있어야 합니다. 이 중 일부는 Windows 운영체제에서는 사용 할 수 없습니다. 만약 Windows를 사용하고 계신다면 [온라인 실습 환경](https://quantum.cloud.ibm.com/docs/en/guides/online-lab-environments#online-lab-environments)을 사용하시기를 추천드립니다.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"128fe3fe-9b55-4696-87ce-4d8d1ad1474c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"# %pip install pyscf\\n\",\n \"# %pip install ffsim\\n\",\n \"# %pip install qiskit_addon_sqd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"242ba214-1bb5-4028-8122-c89b28e3564b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d2d72ab3-882f-4789-a414-504e0c46190d\",\n \"metadata\": {},\n \"source\": [\n \"Qiskit 버전 `>=2.0.0` 그레이더 버전 `>=0.22.12` 이상이 설치되어 있어야 합니다. 만약 버전이 다르게 표시된다면 커널을 재시작해보시고, 그래도 안 된다면 그레이더를 다시 설치해야 할 수 있습니다.\\n\",\n \"랩 0에 따라 환경설정을 완료했는지, IBM Quantum 계정이 잘 저장되어 있는지 아래의 셀을 실행해 확인해주세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9c98e978\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 계정이 잘 저장되어 있는지 확인합니다\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f58bfa2-295b-4831-bb96-9e6c2761bb25\",\n \"metadata\": {},\n \"source\": [\n \"# 불러오기\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e92c99a3-1a03-4fca-a476-edc4b4692ed4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 공통적으로 사용하는 패키지들을 먼저 불러오기\\n\",\n \"import numpy as np\\n\",\n \"from math import comb\\n\",\n \"import warnings\\n\",\n \"import pyscf\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pickle\\n\",\n \"from functools import partial\\n\",\n \"\\n\",\n \"# qiskit 클래스들을 불러오기\\n\",\n \"from qiskit import QuantumCircuit, QuantumRegister\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_gate_map\\n\",\n \"from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian, solve_sci_batch\\n\",\n \"\\n\",\n \"# qiskit ecosystem 패키지들을 불러오기\\n\",\n \"import ffsim\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\\n\",\n \"from qiskit_ibm_runtime import SamplerOptions\\n\",\n \"\\n\",\n \"# grader 불러오기\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab3_ex1, \\n\",\n \" grade_lab3_ex2, \\n\",\n \" grade_lab3_ex3,\\n\",\n \" grade_lab3_ex4,\\n\",\n \" grade_lab3_ex5\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5421b095-f20c-494c-a8c9-a33ed120fa3b\",\n \"metadata\": {},\n \"source\": [\n \"# 1. 개요\\n\",\n \"\\n\",\n \"## 1.1 목표\\n\",\n \"\\n\",\n \"이번 랩의 목표는 양자화학 계산의 기본 개념과 일반적인 워크플로우를 학습하는 것입니다. 또한, 이 과정에서 양자-고전 하이브리드 알고리즘 중 하나인 샘플 기반 양자 대각화 알고리즘 (Sample-based Quantum Diagonalization, SQD)에 대해 배우게 됩니다. SQD는 양자 프로세서(QPU)에서 양자회로를 실행하여 얻은 샘플들을 기반으로 고전적인 대각화 연산을 수행하는 기술입니다. 이 알고리즘은 양자 시스템의 해밀토니안과 같은 양자 연산자의 고윳값을 찾는 데 매우 유용하며, 양자 컴퓨팅과 분산 고전 컴퓨팅 테크닉을 함께 활용하는 방식입니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"3123cd89-2682-4da9-9322-ee40376bec6c\",\n \"metadata\": {},\n \"source\": [\n \"## 1.2 원자란 무엇인가?\\n\",\n \"여러분 주변의 모든 것은 원자로 이루어져 있습니다. 원자는 물질을 구성하는 아주 작은 기본 단위입니다. \\\"원자(atom)\\\"라는 말은 그리스어로 \\\"더 이상 나눌 수 없다\\\"라는 뜻에서 나왔습니다. 오래전 데모크리토스라는 사람은 세상의 모든 것이 더 이상 쪼갤 수 없는 아주 작은 입자인 \\\"원자\\\"로 이루어져 있다고 생각했습니다.\\n\",\n \"\\n\",\n \"1913년, 닐스 보어는 전자가 원자 중심 주변을 마치 태양 주위를 도는 행성처럼 원형 궤도를 따라 움직인다고 말했습니다.\\n\",\n \"하지만 1926년, 에르빈 슈뢰딩거는 전자가 실제로는 완벽한 경로를 따라 움직이는 것이 아니라, 전자가 발견될 가능성이 가장 높은 \\\"구름\\\" 형태의 영역 안에서 윙윙거리며 움직인다고 설명했습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 1. 보어의 원자 모형(왼쪽)과 전자의 양자역학적 및 파동적 특성에 기초한 슈뢰딩거의 원자 모형(오른쪽)의 스케치. \\n\",\n \"\\n\",\n \"이러한 구름(오비탈)은 각기 다른 모양과 에너지 준위를 가지고 있습니다. 전자들은 일반적으로 가장 낮은 에너지 준위(바닥 상태)에 머물러 있지만, 에너지를 받으면 더 높은 에너지 준위로 올라갈 수 있습니다. 다시 원래 상태로 떨어질 때는 그만큼의 에너지를 방출하게 됩니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f39f52bc-d1c5-4bbf-a4db-4a46d8cdf403\",\n \"metadata\": {},\n \"source\": [\n \"## 1.3 슈뢰딩거 방정식\\n\",\n \"\\n\",\n \"에르빈 슈뢰딩거는 양자역학 분야에 단순히 새로운 전자 모형을 소개하는 것을 넘어, 유명한 슈뢰딩거 방정식을 만들었습니다. 시간 독립 슈뢰딩거 방정식은 다음과 같습니다:\\n\",\n \"$$\\n\",\n \"\\\\hat{H}|\\\\psi\\\\rangle = {E}|\\\\psi\\\\rangle\\n\",\n \"$$\\n\",\n \"\\n\",\n \"**$\\\\hat{H}$** : 해밀토니안 연산자 \\n\",\n \"**$|\\\\psi\\\\rangle$** : 파동함수 (계의 상태) \\n\",\n \"**${E}$** : 측정된 에너지 (고윳값) \\n\",\n \"\\n\",\n \"**양자화학의 목표는 슈뢰딩거 방정식을 풀어서 파동함수를 얻는 것입니다.** 슈뢰딩거 방정식을 만족하는 파동함수를 알게 되면 양자계의 에너지, 운동량, 스핀, 자화 등 다양한 흥미로운 특성들을 알아낼 수 있습니다.\\n\",\n \"\\n\",\n \"양자화학에서는 흔히 **바닥상태 에너지(ground state energy)** 를 찾는 데 관심을 둡니다. 이는 분자의 바닥상태 에너지를 알면 다음과 같은 많은 정보를 얻을 수 있기 때문입니다.\\n\",\n \"\\n\",\n \"- 분자가 안정된 형태로 취할 가능성이 높은 모양(\\\"평형 상태\\\"라 부름)\\n\",\n \"- 화학 반응 중 분자들이 어떻게 변화하거나 반응할 수 있는지\\n\",\n \"- 약물이 체내의 단백질과 결합하는 방식 (Docking 시뮬레이션 등에서 활용)\\n\",\n \"\\n\",\n \"간단히 말해, 바닥 상태를 찾는 것은 분자가 할 수 있는 모든 흥미로운 행동이 시작되는 시작점을 찾는 것과 같습니다.\\n\",\n \"\\n\",\n \"하지만 큰 분자들의 경우, 전자의 공간적 분포를 설명하는 파동함수가 매우 복잡하여 **슈뢰딩거 방정식을 정확히 푸는 것이 매우 어렵습니다.** 그래서 완벽히 푸는 대신에, 과학자들은 충분히 근접한 좋은 추정이나 근사를 사용합니다.\\n\",\n \"\\n\",\n \"## 1.4 기저 집합 근사법 - 효율적인 구현법\\n\",\n \"양자화학에서 중요한 근사법 중 하나는 **기저 집합(basis set)** 접근법입니다. 기저함수는 **원자나 분자 내 전자의 오비탈(파동함수)을 근사하기 위해 사용되는 수학적 함수들의 집합입니다.** 대부분의 계에 대해 슈뢰딩거 방정식을 정확하게 풀 수 없으므로, 기저 집합을 이용하여 문제를 계산할 수 있도록 만듭니다. 연속적인 원자 오비탈을 완벽히 다루는 대신, 미리 정의된 수학적 함수들(주로 가우시안형 또는 슬레이터형 오비탈)을 사용하여 근사합니다.\\n\",\n \"\\n\",\n \"이 방법은 선형 결합 원자 오비탈(LCAO: Linear Combination of Atomic Orbitals)로 알려져 있습니다. 이 아이디어는 간단한데 마치 간단하고 알려진 도형들을 더해 복잡한 형태를 만드는 것과 같이 기저 함수들을 결합하여 분자 오비탈을 만듭니다.\\n\",\n \"- 작은 기저 집합(적은 수의 함수)는 빠르지만 거친 근사치를 제공합니다.\\n\",\n \"- 큰 기저 집합은 정확도를 향상하지만 계산 비용이 매우 커집니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"기저함수의 종류 \\n\",\n \"\\n\",\n \"- **슬레이터형 오비탈(STOs)**: 실제 원자 오비탈과 매우 비슷한 형태의 수학적 함수입니다. 이는 핵으로부터 멀어질수록 전자 밀도가 감소하는 것을 잘 나타냅니다.\\n\",\n \"- **가우시안형 오비탈(GTOs)**: 실제 오비탈과 형태적으로는 덜 비슷하지만 계산이 더 용이하여 실제 실습에서 자주 사용됩니다.
\\n\",\n \"\\n\",\n \"슬레이터형 오비탈(STO)은 여러 개의 가우시안형 오비탈(GTO)을 결합하여 근사할 수 있습니다. 즉, 폭이 서로 다른 여러 개의 가우시안을 결함하여 STO의 형태를 모방합니다. 이런 결합을 **축약된 가우시안 함수(contracted Gaussian function)** 라고 부릅니다. 이 아이디어는 최소 기저 집합(minimal basis set) 및 분리 원자가 기저 집합(split-valence basis set)과 같은 다양한 종류의 기저함수의 기반이 됩니다.\\n\",\n \"\\n\",\n \"- **최소 기저 집합(Minimal Basis Set)**: 오비탈 당 한 개의 함수만을 사용하는 기저 집합입니다. (예: 1s에 한 개의 함수, 2s에 한개의 함수 등).\\n\",\n \"STO-3G의 경우 각 STO가 3개의 가우시안으로 근사됩니다.\\n\",\n \"- **분리 원자가 기저 집합(Split-Valence Basis Set)**: 최소 기저집합보다 더 유연하며 정확한 표현을 제공합니다. 원자가 오비탈(결합에 참여하는 최외각 오비탈)을 두 개 이상의 부분으로 나누고 각각 다른 가우시안 조합으로 근사합니다(예: 6-31G). \\n\",\n \"이는 화학 결합과 반응의 정확도를 높여줍니다.\\n\",\n \"\\n\",\n \"### 자주 사용되는 기저 집합들\\n\",\n \"\\n\",\n \"| 기저 집합 | 설명\\t| 사용 예시 | 계산 비용 |\\n\",\n \"| ----------------- | ----------- | ----------- | ----------- |\\n\",\n \"| **STO-3G**\\t| 3 개의 가우시안(\\\"3G\\\")로 1 개의 STO를 근사한다. 흔히 최소 기저 집합으로 불린다.| 소형 분자에 대한 빠르고 대략적인 초기계산| 낮음 |\\n\",\n \"| **6-31G**\\t | 각 핵심 오비탈마다 6개의 가우시안(\\\"6\\\")로 1 STO를 근사한다. 각 원자가 오비탈에는 3 개의 가우시안으로 수축된 함수(\\\"3\\\")와 1개의 비수축 가우스 함수(\\\"1\\\")로 사용된다.\\t| 속도와 정확성의 우수한 균형| 중간 |\\n\",\n \"| **cc-pVDZ**\\t| 전자 상관 계산을 개선하기 위해 설계된 기저. 각 원자가 오비탈에 1 개 대신 2개의 기저 함수를 두고 분극 함수를 추가한다.\\t| 상관 효과가 큰 시스템 시뮬레이션에서 높은 정확도 제공 (STO-3G와 6-31G보다 정밀하나 더 비용 집약적)| 높음 |\\n\",\n \"\\n\",\n \"> 참고: 위 표에서 말하는 계산 비용은 분자 적분을 수행할 때 드는 고전적 계산 지원을 가리킵니다. 기저 함수의 수가 많아질수록 결과의 정확도는 높아지지만, 계산에 소요되는 시간도 그만큼 길어집니다.\\n\",\n \"\\n\",\n \"요약하자면, 분자 시스템에 대해 슈뢰딩거 방정식을 정확히 푸는 일은 거의 불가능하므로, 이미 알려진 함수들을 결합해 유연한 근사 파동함수를 구성한 뒤, 그 파동함수를 조정하여 시스템의 에너지를 최소화합니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"13c35f0d-e9ac-490b-898e-490adcdd8bdd\",\n \"metadata\": {},\n \"source\": [\n \"## 1.5 해밀토니안\\n\",\n \"\\n\",\n \"슈뢰딩거 방정식의 핵심이자 모든 양자화학 문제의 중심은 **해밀토니안**이 자리합니다. 해밀토니안은 고전역학과 양자역학 모두에서 물리계의 총 에너지(운동 + 위치)를 나타내는 함수입니다.\\n\",\n \"\\n\",\n \"양자역학에서 해밀토니안 $\\\\hat{H}$는 파동함수 $|\\\\psi\\\\rangle$ 위에서 작용하는 연산자로 정의됩니다. 기저를 선택하면 파동함수 $|\\\\psi\\\\rangle$는 벡터로, 해밀토니안은 행렬 형식으로 표현할 수 있습니다.\\n\",\n \"\\n\",\n \"대부분의 양자화학 문제에서 첫 번째이자 가장 중요한 목표는 해밀토니안의 바닥 상태 에너지를 계산하는 것입니다. 이 계산은 해밀토니안(행렬)을 대각화하여 고윳값과 고유벡터를 구함으로써 수행할 수 있습니다.\\n\",\n \"\\n\",\n \"분자 해밀토니안의 크기(차원)는 전자들이 공간 오비탈에 배치될 수 있는 조합의 수로 결정됩니다. 이 조합 수는 지수적으로 증가하므로, 대부분의 실제 유용한 분자에서 해밀토니안을 대각화하는 것은 사실상 불가능해집니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d354e83a-fe0e-477e-b7fa-6d1bf6045186\",\n \"metadata\": {},\n \"source\": [\n \"### 예시: $N_2$ 해밀토니안의 사이즈 계산\\n\",\n \"비교적 작은 분자인 질소($N_2$)라도 시뮬레이션의 목표 정확도에 따라 해밀토니안 행렬 $H$의 차원이 얼마나 커질 수 있는지 살펴보기 위해 이 예시를 제시합니다. 아래에서는 계산에 사용할 $N_2$의 공간 오비탈 수, 스핀 오비탈 수, 그리고 전자 수를 임의로 설정하였습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dc20e23f-f68c-4fd8-9f79-059ac6405b2f\",\n \"metadata\": {},\n \"source\": [\n \"아래 표에 제시된 공간 오비탈 수와 전자 수를 이용하여 전자들이 공간 오비탈에 배치될 수 있는 경우의 수 (스핀 배치)를 계산하면, 이는 $N_2$ 분자 해밀토니안의 행렬 크기를 가늠하는 지표가 됩니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"| | STO-3G | 6-31G | cc-pVDZ | \\n\",\n \"|:-------|:------:|:-------:|:-------:|\\n\",\n \"|공간 오비탈 수 | (10) **8**| (18) **16** | (28) **26** |\\n\",\n \"|스핀 오비탈 수 | (20) **16**| (36) **32**| (56) **52** |\\n\",\n \"| α-스핀 전자 수 | (7) **5**| (7) **5**| (7) **5** |\\n\",\n \"| β-스핀 전자 수 | (7) **5**| (7) **5**| (7) **5** |\\n\",\n \"\\n\",\n \"\\n\",\n \"
Table 1: $N_2$ 분자에 대해 선택한 기저집합별 오비탈 및 전자 수
\\n\",\n \"\\n\",\n \"- (#) : 본 예시에서 설정한 오비탈 및 전자 수\\n\",\n \"- **#** : 핵심 오비탈 (*1s*)를 고정한 뒤 전자 수와 공간 오비탈 수를 각각 2개씩 줄였을 때의 값\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"79f14c83-fe07-476c-b091-456458ec9f04\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"⚠️ **참고:** 이 예시에서는 $1s$ 핵심 오비탈을 화학적으로 비활성으로 간주합니다. 따라서 핵심 오비탈을 고정하여 전자 2개와 공간 오비탈 2개, 즉 스핀 궤조 4개(큐비트 4개)를 절약할 수 있습니다. 이 방법은 실제 화학 시뮬레이션에서 자주 사용되는 유용한 근사 기법중 하나입니다. 이 기법을 적용할 떄는 **굵게 표기된** 수치를 사용하여 $N_2$ 해밀토니안의 모든 가능한 전자 배치를 계산해주세요.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"363fddb2-7208-4192-8c1f-ee60870e925e\",\n \"metadata\": {},\n \"source\": [\n \"보시다시피, 가능한 모든 스핀 배치를 결정하는 문제는 본질적으로 조합(combination)의 문제입니다. 이제 주어진 오비탈에 대해 **$\\\\alpha$-스핀 (스핀 업)** 전자와 **$\\\\beta$-스핀 (스핀 다운)** 전자가 각각 얼마나 다양한 방식으로 채워질 수 있는지 수학적으로 계산해 보겠습니다. 전체 가능한 스핀 배치의 수는 두 가지를 곱하여 얻을 수 있습니다.\\n\",\n \"\\n\",\n \"
전체 전자 배치의 수 = (전체 α-스핀 배치의 수) × (전체 β-스핀 배치의 수)
\\n\",\n \"\\n\",\n \"$$ \\\\Large{n}\\\\Large{C}n_α \\\\times n\\\\Large{C}n_β = \\\\Large\\\\frac{n!}{n_α!(n-n_α)!} \\\\times \\\\Large\\\\frac{n!}{n_β!(n-n_β)!} $$\\n\",\n \"\\n\",\n \"여기서\\n\",\n \"**$n$**: 공간 오비탈의 개수,\\n\",\n \"**$n_α$**: α-스핀 전자의 개수,\\n\",\n \"**$n_β$**: β-스핀 전자의 개수\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"46459f2f-05a4-40e9-9a48-a32e2391d067\",\n \"metadata\": {},\n \"source\": [\n \"이제 전체 가능한 스핀 배치의 수를 구하는 방법을 알게 되었습니다. 각 기저 집합에 대해 이 값을 직접 계산한 뒤, 그 결과를 그래프로 나타내어 해밀토니안의 행렬 크기가 계산 정확도가 증가함에 따라 어떻게 커지는지를 살펴보겠습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"74899db7-baca-47f9-9ead-3bba945b6fc1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 가능한 스핀 배치의 수 계산\\n\",\n \"# 예시: N2 분자에 대한 STO-3G, 6-31G, cc-pVDZ 기저 집합 사용\\n\",\n \"# 전자 14개, 스핀 오비탈 20개 (공간 오비탈 10개 × 2)\\n\",\n \"\\n\",\n \"# 각 기저 집합에서의 전체 가능한 전자 배치 수 계산\\n\",\n \"y1 = comb(8, 5) * comb(8, 5) # STO-3G\\n\",\n \"y2 = comb(16, 5) * comb(16, 5) # 6-31G\\n\",\n \"y3 = comb(26, 5) * comb(26, 5) # cc-pVDZ\\n\",\n \"\\n\",\n \"# 데이터 정의\\n\",\n \"y = [y1, y2, y3]\\n\",\n \"x = list(range(len(y)))\\n\",\n \"labels = ['STO-3G', '6-31G', 'cc-pVDZ']\\n\",\n \"\\n\",\n \"# 그래프 그리기 (y축은 로그 스케일로 표현)\\n\",\n \"plt.figure(figsize=(6, 4))\\n\",\n \"plt.plot(x, y, 'o')\\n\",\n \"\\n\",\n \"plt.yscale('log')\\n\",\n \"plt.xticks(x, labels)\\n\",\n \"plt.xlabel('Basis sets')\\n\",\n \"plt.ylabel('Number of spin configurations (log scale)')\\n\",\n \"plt.title('Hamiltonian size of N2 molecule at different basis sets')\\n\",\n \"\\n\",\n \"# 각 점 위에 레이블 추가\\n\",\n \"for i in range(len(x)):\\n\",\n \" plt.text(x[i], y[i], f'{labels[i]}', fontsize=9, ha='center', va='bottom', color='red')\\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c8b45733-4801-4e7c-9264-5699e730cb87\",\n \"metadata\": {},\n \"source\": [\n \"위 그래프에서 Y축은 로그 눈금으로 표현된 점을 참고해주세요. 그래프를 통해, 더 정밀한 근사를 위해 기저 집합의 크기를 증가시키면 가능한 스핀 배치의 수가 기하급수적으로 증가함을 확인할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"79a50b43-5c0d-413e-8010-ccb48e07aec0\",\n \"metadata\": {},\n \"source\": [\n \"## 연습문제 1: $O_2$ 분자의 해밀토니안 크기 측정하기\\n\",\n \"\\n\",\n \"이번에는 산소 분자($O_2$)의 해밀토니안 크기를 `6-31G` 기저 집합을 사용하여 계산해 보겠습니다. 산소 분자는 앞서 다룬 질소 분자($N_2$)의 동일한 수의 오비탈을 가지지만 **$\\\\alpha$-스핀 (스핀 업)** 전자가 2개 더 많습니다. 이 연습문제에서 계산에 사용할 숫자들은 아래의 표에 제공되어 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"43b3c014-fe64-424b-b045-d32dae44159f\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 1: $O_2$ 분자의 해밀토니안 크기 측정하기 \\n\",\n \"\\n\",\n \"아래 표에 제시된 공간 오비탈과 전자의 수를 이용하여, 산소 분자($O_2$)가 `6-31G`기저 집합에 있을 때 가능한 총 전자 스핀 배치의 수를 계산하시오. **직접 코드를 작성하거나 수작업으로 계산하여 답을 구해도 됩니다.**\\n\",\n \"\\n\",\n \"\\n\",\n \"| | STO-3G | 6-31G | cc-pVDZ | \\n\",\n \"|:-------|:------:|:-------:|:-------:|\\n\",\n \"|공간 오비탈 수 | (10) **8**| (18) **16** | (28) **26** |\\n\",\n \"|스핀 오비탈 수 | (20) **16**| (36) **32**| (56) **52** |\\n\",\n \"| α-스핀 전자 수 | (9) **7**| (9) **7**| (9) **7** |\\n\",\n \"| β-스핀 전자 수 | (7) **5**| (7) **5**| () **5** |\\n\",\n \"\\n\",\n \"\\n\",\n \"
Table 1: $O_2$ 분자의 각 기저 집합별 오비탈과 전자의 수
\\n\",\n \"\\n\",\n \"- (#) : 각 기저 집합에서 실제 원자 구조를 바탕으로 한 오비탈 혹은 전자의 수\\n\",\n \"- **#** : 핵심 오비탈(*1s*)을 고정하고 전자 및 공간 오비탈의 수를 각각 2개씩 줄인 경우\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **주의:** 앞서 본 예시에서와 같이, 이 연습문제에서도 핵심 오비탈($1s$ 오비탈) 은 화학적으로 비활성이라고 가정하겠습니다. 이는 핵심 오비탈을 고정하여 전재 2개와 공간 오비탈 2개(즉, 스핀 오비탈 4개 -> 큐비트 4개)를 절약할 수 있음을 의미합니다. 실제 양자화학 시뮬레이션에서 자주 사용되는 유용한 근사법이며 이 근사법을 적용할 때는 위 표에서 **굵게** 표시된 숫자들을 사용하여 $O_2$ 분자의 해밀토니안에 대한 전체 가능한 전자 배치 수를 계산해 주세요.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b3e26a71-1cdd-4d3c-be48-fb85eb942520\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 연습문제 1: 가능한 스핀 배치 수 계산\\n\",\n \"# 예시: O2 분자에 대한 6-31G 기저 사용\\n\",\n \"# 전자 16개, 스핀 오비탈 20개 (공간 오비탈 10개 × 2)\\n\",\n \"\\n\",\n \"# 가능한 전체 전자 배치 수를 계산합니다.\\n\",\n \"# 힌트: 조합론적(combinatorial) 문제입니다. 수작업으로 계산하거나, 아래의 math.comb() 함수를 사용하여 계산할 수 있습니다.\\n\",\n \"\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"### 코드를 작성하여 전체 가능한 스핀 배치의 수를 계산해보세요\\n\",\n \"\\n\",\n \"α_config = \\n\",\n \"β_config = \\n\",\n \"total_config = (α_config)*(β_config)\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \"print(f\\\"Total physical configurations for O2 in the given basis : {α_config:} x {β_config:} = {total_config}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8501c714-9965-469f-98d3-d607506b1564\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# total_config = # 수작업으로 계산한 경우 이곳에 정답을 입력하세요. 답안을 제출하기 전에 이 부분의 주석 처리를 해제하세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0e236dfe-a6bf-41fa-9001-9501699c6126\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"\\n\",\n \"grade_lab3_ex1(total_config) # Expected result type: integer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"45e0bf86-70ed-499e-bc12-d9a005b22c6e\",\n \"metadata\": {},\n \"source\": [\n \"6-31G 기저 세트로 계산한 답안을 제출한 후에는, 동일한 방식으로 다른 기저 세트(STO-3G 및 cc-pVDZ)에 대해서도 계산하여 결과를 서로 비교해 보십시오. 필요에 따라 계산한 결과를 그래프로 나타내면, 기저 세트의 크기가 증가할수록 가능한 스핀 배치의 수, 즉 해밀토니안 행렬의 크기가 어떻게 증가하는지 한눈에 확인할 수 있습니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"757e79f6-2a94-4c6a-9ce0-43f02ed5792d\",\n \"metadata\": {},\n \"source\": [\n \"# 2. 변분 양자 고유상태 계산기(Variational Quantum Eigensolver)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"ea1b9d0d-e5be-468c-9efa-7010b4b4e483\",\n \"metadata\": {},\n \"source\": [\n \"지금까지 슈뢰딩거 방정식, 기저 세트, 그리고 실제 물리적 시스템을 더 정확하기 시뮬레이션하려고 할수록 해밀토니안의 크기가 기하급수적으로 증가한다는 점을 학습했습니다. 이제부터는 이를 효율적으로 해결하기 위한 알고리즘에 대해 살펴보겠습니다.\\n\",\n \"\\n\",\n \"현재의 잡음이 있는 중간 규모 양자컴퓨터(near-term quantum computer)를 활용하여 계산 화학을 연구하는 연구자들에게 잘 알려진 알고리즘 중 하나는 바로 **변분 양자 고유상태 계산기(Variational Quantum Eigensolver, VQE)** 입니다.\\n\",\n \"\\n\",\n \"VQE는 변분 원리에 따라 정의된 비용함수 형태의 해밀토니안을 최적화하는 하이브리드 양자-고전 알고리즘입니다. 이번 실습의 주된 목표는 VQE와는 다른 형태의 하이브리드 양자-고전 알고리즘인 샘플 기반 양자 대각화(Sample-based Quantum Diagonalizatoin, SQD)를 학습하는 것이지만, SQD의 주요 구성요소 가운데 많은 부분이 VQE의 핵심 구성요소를 포함하고 있으므로, 이들을 간략히 알아보는 것이 유용할 것입니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 2. VQE 알고리즘의 계산 워크플로우를 나타낸 도식\\n\",\n \"\\n\",\n \"VQE의 계산 과정 요약:\\n\",\n \"\\n\",\n \"1. 양자 상태 $|\\\\psi(𝜃)\\\\rangle$를 준비한다 (양자 단계).\\n\",\n \"2. 준비된 상태에 대해 해밀토니안의 기댓값 $\\\\langle\\\\psi(𝜃)|\\\\hat{H}|\\\\psi(𝜃)\\\\rangle$ 을 측정한다 (양자 단계).\\n\",\n \"3. 측정된 결과를 이용하여 비용 함수 $𝐸(𝜃)$ 를 계산한다 (고전 단계).\\n\",\n \"4. 고전 최적화 기법을 이용해 파라미터 $𝜃$ 를 조정한다 (고전 단계).\\n\",\n \"5. 비용 함수 $𝐸(𝜃)$ 가 수렴할 때까지 위 단계를 반복한다.\\n\",\n \"\\n\",\n \"위 과정에서 확인할 수 있듯이, VQE 알고리즘을 실행하기 위한 계산 과정은 본질적으로 반복(iterative) 과정입니다. 즉, 이 일련의 단계를 수차례 반복해야 합니다. 계산 비용이 매우 크기 때문에, 분자 시스템의 크기가 커질수록 계산 비용이 급격히 증가하며, 결과적으로 큰 분자 시스템에 대해 VQE가 효율적으로 확장되는 데 한계로 작용하게 됩니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"74c08c83-f1e2-4012-b10f-a97c01eb6976\",\n \"metadata\": {},\n \"source\": [\n \"# 3. 샘플 기반 양자 대각화(Sample-based Quantum Diagonalization, SQD)란 무엇인가?\\n\",\n \"\\n\",\n \"VQE는 2014년 처음 제안된 이후 폭넓게 연구되어 온 알고리즘이지만, 몇 가지 명확한 한계점을 가지고 있습니다. 가장 대표적인 문제점은 시스템의 크기가 증가하여 해밀토니안이 커질수록 변분법을 통한 기댓값의 계산이 기하급수적으로 많은 계산 자원을 요구한다는 것입니다. 따라서 큰 분자 시스템에 대해서는 효율적인 확장성을 확보하기 어렵다는 단점이 있습니다. 이러한 VQE의 확장성 문제는 곧 샘플 기반 양자 대각화(Sample-based Quantum Diagonalization, SQD) 알고리즘을 연구 개발하게 되는 계기가 되었습니다.\\n\",\n \"\\n\",\n \"SQD는 [이 논문](https://arxiv.org/abs/2302.11320)에서 처음 논의된 양자 선택적 배치 상호작용(Quantum-selected Configuration Interaction, QSCI)이라는 하이브리드 양자-고전 방법에서 영감을 얻어 개발된 알고리즘입니다. QSCI와 마찬가지로, SQD 역시 하이브리드 양자-고전 알고리즘이며, 그 기본 원리는 다음과 같습니다. 먼저 양자 컴퓨터는 특정 양자회로를 통해 전자 배치를 양자 상태로부터 샘플링합니다. 이렇게 샘플링을 통해 얻은 전자 배치는 해밀토니안의 표현을 위한 부분공간을 형성하는 데 사용되며, 최종적으로는 이 부분공간으로 해밀토니안을 투영(project)하고 대각화하여 고유 에너지를 계산하는 방식입니다.\\n\",\n \"\\n\",\n \"이렇게 구성된 부분공간은 원래 해밀토니안의 차원보다 훨씬 작으므로, 고전적인 방법으로 대각화하는 과정이 크게 단순해지고 계산 부담이 줄어들게 됩니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 3. 원래의 해밀토니안은 N개의 큐비트로 구성된 힐베르트 공간에서 지수적으로 큰 행렬 형태로 나타나지만, 전자 배치 추출 및 샘플링을 수행한 이후에는 그 크기가 훨씬 작은 행렬로 축소될 수 있음을 나타낸 도식.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"35ea499c-9d5b-4655-905b-a120eb14cbf4\",\n \"metadata\": {},\n \"source\": [\n \"세 개의 원자 오비탈인 1s, 2s, 3p가 있고, 낮은 에너지에서 높은 에너지 순으로 배열된 이 오비탈을 두 개의 전자가 점유한다고 가정해 봅시다. 일반적으로, 낮은 에너지의 전자 배치는 전자들이 주로 하위 오비탈에 존재하는 상태를 의미하며, 높은 에너지의 전자 배치는 전자들이 주로 상위 오비탈에 존재하는 상태를 의미합니다. SQD 알고리즘의 중요한 가정 중 하나는 높은 에너지에 해당하는 전자 배치가 바닥상태에 크게 기여하지 한다는 것입니다. 이와 같은 가정을 통해, 바닥상태의 에너지 계산에서 기여도가 미미한 높은 에너지의 전자 배치를 효과적으로 제거할 수 있게 됩니다. 이렇게 하면 낮은 에너지의 오비탈들을 주로 점유하는, 바닥상태에 가장 관련성이 높은 전자 배치만으로 이루어진 부분공간에 초점을 맞추는 것이 가능해집니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 4. 바닥상태와 관련이 적은 전자 배치를 제거함으로써 행렬(해밀토니안)의 크기를 효과적으로 축소할 수 있음을 나타낸 도식\\n\",\n \"\\n\",\n \"\\n\",\n \"이러한 방식으로 해밀토니안을 더 작은 크기로 재구성하면, 바닥상태 에너지와 같은 목표 해(solution)에 도달하기까지 소요되는 계산 시간이 현저히 단축됩니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"26b3cc45-85ec-49b6-a1dc-963e8e0543f3\",\n \"metadata\": {},\n \"source\": [\n \"## 관련성 있는 전자 배치의 선택 방법\\n\",\n \"\\n\",\n \"관련성 있는 전자 배치를 식별하기 위해, 도식에서 $|\\\\psi\\\\rangle$로 표현된 특정 양자 회로(ansatz)를 사용합니다. 이 양자 회로를 계산 기저에서 측정하면, 힐베르트 공간 전체에 걸친 확률 분포를 얻게 되고, 이를 바탕으로 필요한 비트열을 샘플링 할 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 5. 양자 'ansatz' 회로로부터 측정된 비트열을 샘플링하는 과정을 나타낸 도식\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c1da7f72-e948-4c26-87fc-15a80308d70f\",\n \"metadata\": {},\n \"source\": [\n \"## 전자 배치 복원(Configuration Recovery)을 통한 노이즈 영향 처리 방법\\n\",\n \"\\n\",\n \"양자 회로로부터 생성된 비트열에서 샘플링을 수행하기 이전에, 양자 시스템의 노이즈로 인해 에너지 계산 정확도에 영향을 미칠 수 있는 '노이즈가 포함된 양자 샘플(noisy quantum samples)'을 다룰 필요가 있습니다. 이때 중요한 역할을 하는 것이 바로 전자 배치 복원 기법입니다. 예를 들어, 특정 비트열의 전자 배치에 전자 하나가 빠져 있는 경우를 생각해 봅시다. 이 경우는 해밍 무계가 정확한 값과 다르기 때문에 쉽게 식별할 수 있습니다. 이때 서로 다른 오비탈의 점유율의 기댓값(아래 도식에서 $n$ 으로 표현됨)을 이용하여, 잘못된 비트가 무엇인지 판단하고, 해당 비트를 다시 뒤집어 올바른 비트열로 복원할 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 6. 전자의 개수 및 오비탈의 점유 기댓값을 확인함으로써, 잘못된 비트를 찾아 뒤집을 수 있음을 나타낸 도식\\n\",\n \"\\n\",\n \"양질의 샘플을 사용하여 해밀토니안을 재구성한 이후, 이제 대각화가 가능한 축소된 크기의 행렬을 가지게 됩니다. 이렇게 형성된 부분공간에서 해밀토니안을 대각화하면, 대각화 알고리즘은 문제의 바닥상태에 기여하는 계산기저 상태들에 대해 0이 아닌 파동함수 진폭을 부여하게 됩니다. 같은 과정에서, 바닥상태에 거의 기여하지 않는 비트열에는 0에 가까운 진폭을 할당합니다. 이러한 일련의 과정을 모든 배치(batch)에 대해 반복하여 얻은 결과로부터, SQD 알고리즘은 각 오비탈의 전자 점유율 및 최저 에너지 추정값을 얻게 되며, 이 정보를 다음번 전자 배치 복원 과정에서 활용할 데이터로 업데이트합니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f9feaa87-b5cc-4c59-aea9-fcc35c3a7348\",\n \"metadata\": {},\n \"source\": [\n \"# 4. SQD를 이용한 $N_2$ 분자의 시뮬레이션\\n\",\n \"\\n\",\n \"이번 세션에서는 노이즈가 포함된 양자 샘플을 후처리하여 화학적 해밀토니안의 바닥상태를 근사적으로 나타내는 과정을 시연해 보겠습니다. 예시로 다룰 시스템은 평형 상태(equilibrium)에 있는 질소 분자($N_2$)이며, 6-31G 기저 집합을 사용하였습니다. 구체적으로, 36개의 큐비트로 이루어진 양자 ansatz 회로(이 경우 LUCJ 회로)를 이용하여 얻은 샘플을 처리하는 데 SQD 알고리즘을 적용할 것입니다. 양자 노이즈의 영향을 보정하기 위해 전자 배치 복원 기법을 사용합니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"02847627-1ba8-4677-91d5-92c621f87ece\",\n \"metadata\": {},\n \"source\": [\n \"## 분자 해밀토니안\\n\",\n \"\\n\",\n \"분자의 다양한 화학적 성질은 주로 그 내부 전자 구조에 의해 결정됩니다. 전자는 페르미온이라는 입자의 특성을 가지며, 페르미온의 물리적 상태를 표현하는 수식으로 2차 양자화(second quantization)라는 방법이 널리 사용됩니다. 2차 양자화의 핵심 아이디어는 다음과 같습니다. 분자 시스템을 구성하는 여러 개의 오비탈이 있고, 각 오비탈은 페르미온이 점유하거나 점유하지 않는 상태 중 하나를 취할 수 있습니다. 총 $N$ 개의 오비탈로 구성된 시스템은 일련의 페르미온 소멸 연산자(fermionic annihilation operators) $\\\\{\\\\hat{a}_p\\\\}_{p=1}^N$ 로 기술되며, 다음과 같은 페르미온 반교환 관계(fermionic anticommutation relations)를 만족합니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\hat{a}_p \\\\hat{a}_q + \\\\hat{a}_q \\\\hat{a}_p &= 0, \\\\\\\\\\n\",\n \"\\\\hat{a}_p \\\\hat{a}_q^\\\\dagger + \\\\hat{a}_q^\\\\dagger \\\\hat{a}_p &= \\\\delta_{pq}.\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"연산자 $\\\\hat{a}_p$의 에르미트 수반(Hermitian adjoint)인 $\\\\hat{a}_p^\\\\dagger$는 생성 연산자(creation operator)라고 부릅니다.\\n\",\n \"\\n\",\n \"지금까지의 설명에서는 페르미온의 근본적 특성 중 하나인 스핀을 고려하지 않았습니다. 스핀을 고려하게 되면, 오비탈은 쌍을 이루어 *공간 오비탈*로 표현됩니다. 각 공간 오비탈은 두 개의 *스핀 오비탈*로 구성되며, 이 두 스핀 오비탈은 각각 \\\"스핀-$\\\\alpha$\\\"와 \\\"스핀-$\\\\beta$\\\"로 표시됩니다. 이때, 공간 오비탈 $p$에 속하면서 스핀 $\\\\sigma$ ($\\\\sigma \\\\in \\\\{\\\\alpha, \\\\beta\\\\}$) 를 가지는 스핀 오비탈에 대응되는 소멸 연산자를 $\\\\hat{a}_{p\\\\sigma}$ 로 표기합니다. 만약 공간 오비탈의 총 개수를 $N$ 이라고 하면, 전체 스핀 오비탈의 수는 $2N$ 개가 됩니다. 이러한 시스템의 힐베르트 공간은 다음과 같은 두 부분의 비트열로 표기되는 총 $2^{2N}$ 개의 직교 정규 기저(orthonormal basis)에 의해 생성됩니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\lvert z \\\\rangle = \\\\lvert z_\\\\beta z_\\\\alpha \\\\rangle = \\\\lvert z_{\\\\beta, N} \\\\cdots z_{\\\\beta, 1} z_{\\\\alpha, N} \\\\cdots z_{\\\\alpha, 1} \\\\rangle.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"분자 시스템의 해밀토니안은 다음과 같은 형태로 표현할 수 있습니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{H} = \\\\sum_{ \\\\substack{pr\\\\\\\\\\\\sigma} } h_{pr} \\\\, \\\\hat{a}^\\\\dagger_{p\\\\sigma} \\\\hat{a}_{r\\\\sigma}\\n\",\n \"+ \\\\frac12\\n\",\n \"\\\\sum_{ \\\\substack{prqs\\\\\\\\\\\\sigma\\\\tau} }\\n\",\n \"h_{prqs} \\\\, \\n\",\n \"\\\\hat{a}^\\\\dagger_{p\\\\sigma}\\n\",\n \"\\\\hat{a}^\\\\dagger_{q\\\\tau}\\n\",\n \"\\\\hat{a}_{s\\\\tau}\\n\",\n \"\\\\hat{a}_{r\\\\sigma}.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"여기서 $h_{pr}$ 및 $h_{prqs}$는 분자 적분(molecular integrals) 이라 불리는 복소수로, 이 값들을 주어진 분자의 구조와 특성을 컴퓨터 프로그램을 사용하여 계산할 수 있습니다. 본 실습에서는 이 분자 적분을 [PySCF](https://pyscf.org/) 소프트웨어 패키지를 이용하여 계산합니다.\\n\",\n \"\\n\",\n \"분자의 해밀토니안이 유도되는 자세한 과정에 관해서는 양자화학 교과서(예: *Modern Quantum Chemistry* by Szabo and Ostlund)를 참고하시기를 바랍니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"b5e32180-3ff6-46bb-baa3-1c16a3ec443f\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"## 국소 유니터리 클러스터 자스트로(Local Unitary Cluster Jastrow, LUCJ) 가설해\\n\",\n \"\\n\",\n \"SQD 알고리즘을 적용하기 위해서는 샘플을 추출할 수 있는 양자 회로 가설해가 필요합니다. 본 실습에서는 물리적 타당성과 하드웨어 친화성을 모두 만족하는 [국소 유니터리 클러스트 자스트로(Local Unitary Cluster Jastrow, LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) 가설해[1]를 사용하겠습니다.\\n\",\n \"\\n\",\n \"LUCJ 가설해는 일반적인 유니터리 클러스터 자스트로(UCJ) 가설해의 특수한 형태로, UCJ 가설해는 다음과 같은 형식으로 표현됩니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \" \\\\lvert \\\\Psi \\\\rangle = \\\\prod_{\\\\mu=1}^L e^{\\\\hat{K}_\\\\mu} e^{i \\\\hat{J}_\\\\mu} e^{-\\\\hat{K}_\\\\mu} | \\\\Phi_0 \\\\rangle\\n\",\n \"$$\\n\",\n \"\\n\",\n \"여기서 $\\\\lvert \\\\Phi_0 \\\\rangle$는 참조 상태(reference state)로, 일반적으로 하트리-폭(Hartree-Fock, HF) 상태를 사용합니다. 또한 연산자 $\\\\hat{K}_\\\\mu$ 와 $\\\\hat{J}_\\\\mu$ 는 각각 다음과 같이 정의됩니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{K}_\\\\mu = \\\\sum_{pq, \\\\sigma} K_{pq}^\\\\mu \\\\, \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{q \\\\sigma}\\n\",\n \"\\\\;,\\\\;\\n\",\n \"\\\\hat{J}_\\\\mu = \\\\sum_{pq, \\\\sigma\\\\tau} J_{pq,\\\\sigma\\\\tau}^\\\\mu \\\\, \\\\hat{n}_{p \\\\sigma} \\\\hat{n}_{q \\\\tau}\\n\",\n \"\\\\;\\n\",\n \"$$\\n\",\n \"\\n\",\n \"여기서 $\\\\hat{n}_{p \\\\sigma}$는 오비탈 점유 수 연산자(number operator)로, 다음과 같이 정의됩니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{n}_{p \\\\sigma} = \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{p \\\\sigma}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"연산자 $e^{\\\\hat{K}_\\\\mu}$ 는 오비탈 회전을 나타내며, 알려진 알고리즘을 통해 선형 깊이와 선형 연결성으로 효율적으로 구현할 수 있습니다.\\n\",\n \"반면, UCJ 가설해의 $e^{i \\\\mathcal{J}_k}$ 항은 모든 큐비트 간 상호 연결성(all-to-all connectivity)을 필요로 하거나 페르미온 교환 네트워크(fermionic swap network)를 사용해야 하므로, 연걸성이 제한적인 노이즈가 있는 결함 허용 전의 (pre-fault-tolerant) 양자 프로세서에서는 구현하기가 어렵습니다.\\n\",\n \"이러한 문제를 해결하기 위해 제안된 것이 바로 *국소* UCJ 가설해이며, 이 가설해는 $\\\\mathbf{J}^{\\\\alpha\\\\alpha}$ 와 $\\\\mathbf{J}^{\\\\alpha\\\\beta}$에 희소성(sparsity) 제약 조건을 적용함으로써 제한된 연결성을 가진 큐비트 위상에서도 일정한 깊이로 구현이 가능합니다. 여기서 $\\\\mathbf{J}^{\\\\alpha\\\\alpha}$ 와 $\\\\mathbf{J}^{\\\\alpha\\\\beta}$는 각각 다음과 같이 정의됩니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha}= J_{p q,\\\\alpha\\\\alpha}^1 \\\\qquad \\\\mathbf{J}^{\\\\alpha\\\\beta}= J_{p q,\\\\alpha\\\\beta}^1\\n\",\n \"$$\\n\",\n \"\\n\",\n \"IBM의 하드웨어에서는 중 육각 격자(heavy-hex lattice)라는 큐비트 위상구조를 사용하며, 이 경우 다음과 같은 \\\"지그재그\\\" 패턴을 적용할 수 있습니다.\\n\",\n \"이 지그재그 패턴에서 동일한 스핀을 가진 오비탈을 선형 위상을 가진 큐비트에 대응하여 배치되며(빨간색과 파란색 원), 서로 다른 스핀을 가진 오비탈 간 연결성은 매 4번째 공간 오비탈마다 이루어지고, 이 연결은 보조 큐비트(ancillary qubit, 보라색 원)를 통해 실현됩니다. 이때 적용되는 인덱스 제약 조건은 다음과 같습니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha} &: \\\\{(p, p+1) \\\\; , \\\\; p = 0, \\\\ldots, N-2\\\\} \\\\\\\\\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\beta} &: \\\\{(p, p) \\\\;, \\\\; p = 0, 4, 8, \\\\ldots,\\\\; p \\\\leq N-1\\\\}\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 7. IBM 양자 하드웨어의 중 육각 격자 큐비트 위상구조를 나타낸 도식\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"8a20c704-0564-4bc3-b24a-64ce11179489\",\n \"metadata\": {},\n \"source\": [\n \"## 샘플 기반 양자 대각화(Sample-based Quantum Diagonalization, SQD)\\n\",\n \"\\n\",\n \"자기 일관적 전자 배치 복원(self-consistent configuration recovery) 과정은 노이즈가 포함된 양자 샘플로부터 가능한 많은 유의미한 신호를 추출하기 위해 설계된 방법입니다. 이 과정은 반복적인 루프를 통해 실행되며, 각 반복은 다음의 네 가지 단계로 구성됩니다.\\n\",\n \"\\n\",\n \"1. **전자 배치 복원**: 특정한 대칭성을 위반하는 모든 비트열에 대해, 현재 추정된 평균 오비탈 점유율에 더 가까워지도록 설계된 확률적 방법을 이용해 비트를 뒤집어 새로운 비트열을 생성합니다.\\n\",\n \"2. **부분 샘플링**: 원본 비트열과 새롭게 복원된 비트열 중, 대칭성 조건을 만족하는 비트열을 모두 모으고, 미리 정해진 크기의 부분집합으로 샘플링 합니다.\\n\",\n \"3. **부분공간에서의 대각화**: 각 비트열 부분집합에 대해, 해당 비트열로 정의된 기저 벡터에 의해 생성되는 부분공간으로 해밀토니안을 투영하고, 이를 고전 컴퓨터로 대각화하여 부분공간 내의 바닥상태 에너지를 근사적으로 계산합니다.\\n\",\n \"4. **최저 에너지 결정**: 계산된 모든 바닥상태 추정값들 중에서 가장 낮은 에너지 값을 갖는 상태를 선택하여, 이 정보를 바탕으로 평균 오비탈 점유율의 추정치를 업데이트합니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"204b5142-3f9b-4d8f-8408-d6d32e9c9a13\",\n \"metadata\": {},\n \"source\": [\n \"SQD 알고리즘의 전체적인 워크플로우는 다음의 도식에서 나타냅니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 8. SQD 워크플로우를 나타내는 도식\\n\",\n \"\\n\",\n \"SQD 알고리즘은 목표로 하는 고유상태의 파동함수가 희소성을 가질 때 특히 우수한 성능을 나타냅니다. 즉, 파동함수의 진폭이 0이 아닌 값을 가지는 기저 상태들의 집합 $\\\\mathcal{S} = \\\\{|x\\\\rangle \\\\}$ 의 크기가 문제의 크기 (size of the problem)가 증가함에 따라 기하급수적으로 늘어나지 않을 때, SQD가 효과적으로 작동하는 것으로 알려져 있습니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"1dce8aba-af4c-4a9a-a33a-3d8175085da8\",\n \"metadata\": {},\n \"source\": [\n \"### 전자 배치 복원 루프 1: 전자 배치 복원 단계\\n\",\n \"\\n\",\n \"전자 배치 복원 루프의 첫 번째 단계는 전자 배치를 복원하는 것입니다. 즉, 이 단계에서는 오류완화(error mitigation)가 수행됩니다.\\n\",\n \"\\n\",\n \"위에서 구성된 LUCJ 가설해에 해당하는 양자 회로를 실제 양자 컴퓨터에서 실행하면, 왼쪽 하단 그림과 같은 비트열과 그 출현 횟수 형태의 결과를 얻을 수 있습니다. 그러나 현재의 양자 컴퓨터는 노이즈로 인해 결과에 오류가 포함될 수밖에 없습니다. 문제로 주어진 분자는 \\\"스핀-$\\\\alpha$\\\" 와 \\\"스핀-$\\\\beta$\\\" 각각의 입자 수가 고정되어 있으므로, 입자 수가 정확히 표현되도록 결과로 얻은 비트열의 일부 비트를 뒤집어 오류를 교정할 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 9. LUCJ 가설해 회로로부터 초기 생성된 비트열 (왼쪽) 과 오류 교정을 통해 복원된 비트열 (오른쪽)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"857741ce-d6d9-490a-9c64-44ce60217815\",\n \"metadata\": {},\n \"source\": [\n \"예를 들어, 4개의 스핀 오비탈을 고려하는 문제에서 \\\"스핀-$\\\\alpha$\\\" 전자 2개와 \\\"스핀-$\\\\beta$\\\" 전자 2개를 가진 분자를 생각해 보겠습니다. 만약 오비탈이 낮은 에너지 준위부터 차례대로 채워진다면, 양자 상태는 아래 왼쪽 그림과 같이 |0011 0011>로 표현될 것입니다.\\n\",\n \"이때 양자 컴퓨터 실행 결과가 |1110 0011>로 나타났다고 한다면, \\\"스핀-$\\\\beta$\\\" 전자가 3개로 너무 많으므로, 이중 하나의 비트를 1에서 0으로 뒤집어 전자의 수가 정확히 2개가 되도록 교정할 수 있습니다.\\n\",\n \"또한 |0011 0001> 이 관측된 경우를 생각해 보면, \\\"스핀-$\\\\alpha$\\\" 전자가 1개로 부족한 상태이므로, 0으로 표현된 비트 중 하나를 1로 뒤집어서 정확히 두 개의 \\\"스핀-$\\\\alpha$\\\" 전자가 존재하도록 비트열을 교정할 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 10. 전자의 수와 각 오비탈의 점유 기댓값을 바탕으로 최대 가중 확률 방법을 사용하여 비트열을 교정하는 과정의 도식\\n\",\n \"\\n\",\n \"이때, 어떤 전자에 해당하는 비트를 뒤집을지 결정할 때는 각 오비탈의 평균 점유율을 사용하여, 비트를 뒤집을 확률이 최대인 전자를 선택합니다.\\n\",\n \"평균 오비탈 점유율은 전자 배치 복원 루프의 마지막 단계에서 계산됩니다. 따라서 첫 번째 루프에서는 아직 계산된 평균 점유율이 존재하지 않으므로 이 방법을 사용하지 않으며, 잘못된 입자 수를 가진 샘플들은 모두 버리게 됩니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"aa2b0d64-63b6-455e-999e-f35c7d52b9e1\",\n \"metadata\": {},\n \"source\": [\n \"### 전자 배치 복원 루프 2: 부분 샘플링\\n\",\n \"\\n\",\n \"다음 단계는 앞 단계에서 오류가 교정된 샘플들 중에서 일부를 선택하는 것입니다.\\n\",\n \"예를 들어, 이번 경우에는 100,000회의 측정이 이루어졌으므로, 여기서 무작위 50개의 샘플을 추출하는 과정을 5회 반복하여, 총 5개의 배치(batch)를 얻을 수 있습니다. 큰 분자 시스템을 다룰 경우, 배치 단위로 처리하면서 슈퍼컴퓨터에서 병렬 계산이 가능합니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 11. 오류가 정정된 샘플들로부터의 부분 샘플링 과정\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"fe817330-971e-4b02-b1a5-eb9c89a27a9e\",\n \"metadata\": {},\n \"source\": [\n \"### 전자 배치 복원 루프 3: 부분공간에서의 대각화 \\n\",\n \"\\n\",\n \"다음 단계는 부분공간에서의 해밀토니안 대각화입니다. 앞서 예시로 든 문제에서 원래 해밀토니안 $H$ 은 $2^{32}\\\\times2^{32}$ 크기의 행렬입니다. 이 행렬을 50개의 비트열, 즉 $|x_i\\\\rangle$ 로 생성된 부분공간으로 투영하여 크기를 축소한 후 이를 대각화합니다.\\n\",\n \"이 투영 행렬 $P_S$는 이론적으로 0...0 부터 1...1 까지 총 $2^{32}$ 개의 가능한 모든 비트열을 가질 수 있으나, 실제로 측정된 최대 50개의 비트열만을 포함하게 됩니다. $P_S$는 대각에 50개의 원소만이 1을 가지고 나머지 원소는 모두 0으로 구성된 행렬입니다.\\n\",\n \"따라서, 원래 해밀토니안 $H$ 와 투영 행렬 $P_S$ 를 곱하여 얻은 축소된 해밀토니안은 오직 $50\\\\times50$개 이고, 나머지 원소들은 전부 0입니다. 결국, 원래 크기인 $2^{32}\\\\times2^{32}$ 의 거대한 행렬 대신 $50\\\\times50$ 크기의 작은 행렬만을 대각화하면 됩니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 12. 부분공간에서의 행렬 대각화 과정을 나타낸 도식\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"52d3b688-89d0-4451-a03b-db232442a1a3\",\n \"metadata\": {},\n \"source\": [\n \"### 전자 배치 복원 루프 4: 최저 에너지 계산\\n\",\n \"\\n\",\n \"마지막으로, 앞서 수행한 모든 배치의 결과로부터 평균 오비탈 점유율에 대해 추정값과 바닥상태에 해당하는 최저 에너지를 얻습니다. 이 정보를 다음 반복을 위한 데이터로 업데이트합니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"2566ce1e-cf95-4915-b986-0c1a68662afa\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskit pattern\\n\",\n \"\\n\",\n \"SQD 알고리즘을 다음과 같이 Qiskit pattern을 활용하여 구현할 수 있습니다:\\n\",\n \"\\n\",\n \"1. **Step 1: 양자 문제로 정의**\\n\",\n \" - 바닥상태를 추정하기 위해 가설해를 생성합니다.\\n\",\n \"2. **Step 2: 문제 최적화**\\n\",\n \" - 사용하고자 하는 양자 하드웨어(backend)에 맞게 가설해 회로를 트랜스파일합니다.\\n\",\n \"3. **Step 3: 실험 실행**\\n\",\n \" - Qiskit의 ``Sampler`` primitive을 이용하여 가설해 회로로부터 비트열 샘플을 수집합니다.\\n\",\n \"4. **Step 4: 결과 후처리**\\n\",\n \" - 자기 일관적 전자 배치 복원 루프를 수행합니다.\\n\",\n \" - 입자 수에 대한 사전 정보와 이전 반복에서 계산된 평균 오비탈 점유율을 사용하여 전체 비트열 샘플을 후처리 합니다.\\n\",\n \" - 복원된 비트열로부터 부분 샘플을 확률적으로 생성하여 배치를 구성합니다.\\n\",\n \" - 각 부분공간에 대해 분자 해밀토니안을 투영하고, 축소된 행렬을 고전적으로 대각화합니다.\\n\",\n \" - 모든 배치에서 얻어진 바닥상태 에너지 추정값 중 최솟값을 선택하고, 이를 바탕으로 평균 오비탈 점유율을 업데이트합니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"157e65a0-06a9-4ac9-bafa-702824a84eb2\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Map classical inputs to a quantum problem\\n\",\n \"\\n\",\n \"We will find an approximation to the ground state of the molecule at equilibrium in the 6-31G basis set. First, we specify the molecule and its properties.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c5c49c74-d658-42f6-a729-ec1c7687e9aa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"\\n\",\n \"# 분자의 성질 특정\\n\",\n \"open_shell = False\\n\",\n \"spin_sq = 0\\n\",\n \"\\n\",\n \"# N2 분자 구축\\n\",\n \"mol = pyscf.gto.Mole()\\n\",\n \"mol.build(\\n\",\n \" atom=[[\\\"N\\\", (0, 0, 0)], [\\\"N\\\", (1.0, 0, 0)]],\\n\",\n \" basis=\\\"6-31g\\\",\\n\",\n \" symmetry=\\\"Dooh\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# 활성 공간 정의\\n\",\n \"n_frozen = 2\\n\",\n \"active_space = range(n_frozen, mol.nao_nr())\\n\",\n \"\\n\",\n \"# 분자 적분 계산\\n\",\n \"scf = pyscf.scf.RHF(mol).run()\\n\",\n \"num_orbitals = len(active_space)\\n\",\n \"n_electrons = int(sum(scf.mo_occ[active_space]))\\n\",\n \"num_elec_a = (n_electrons + mol.spin) // 2\\n\",\n \"num_elec_b = (n_electrons - mol.spin) // 2\\n\",\n \"cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\\n\",\n \"mo = cas.sort_mo(active_space, base=0)\\n\",\n \"hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\\n\",\n \"eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\\n\",\n \"\\n\",\n \"# 분자의 정확한 에너지 계산\\n\",\n \"exact_energy = cas.run().e_tot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91fa0157-366c-4c0e-b040-6a18f5546416\",\n \"metadata\": {},\n \"source\": [\n \"`LUCJ` 가설해 회로를 구성하기 전에, 다음 코드 셀에서 CCSD 계산을 먼저 수행합니다. 여기서 얻어진 [$t_1$ 및 $t_2$ 진폭](https://en.wikipedia.org/wiki/Coupled_cluster#Cluster_operator)은 이후 가설해의 매개변수 초기화에 사용됩니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f72b68e1-065f-49fc-a7cb-7afe3d1c3f65\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 가설해의 초기 매개변수로 사용할 CCSD의 t1, t2 진폭을 계산\\n\",\n \"ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\\n\",\n \"t1 = ccsd.t1\\n\",\n \"t2 = ccsd.t2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"adb2fd92-294b-4f87-a438-f42d6589cd73\",\n \"metadata\": {},\n \"source\": [\n \"이제, [ffsim](https://github.com/qiskit-community/ffsim)을 이용하여 가설해 회로를 구성합니다. 고려하는 분자는 닫힌 껍질(closed-shell)의 하트리-폭 상태를 가지므로, 스핀 균형을 이루는 UCJ 가설해 변형인 [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced)를 사용합니다. 이때 상호작용 쌍은 중 육각 격자 큐비트 위상구조에 적합하도록 설정하여 전달합니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"527f5873-f5be-4890-960e-4ad24c1e423d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"n_reps = 1\\n\",\n \"alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\\n\",\n \"alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\\n\",\n \"\\n\",\n \"ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\\n\",\n \" t2=t2,\\n\",\n \" t1=t1,\\n\",\n \" n_reps=n_reps,\\n\",\n \" interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\\n\",\n \")\\n\",\n \"\\n\",\n \"nelec = (num_elec_a, num_elec_b)\\n\",\n \"\\n\",\n \"# 빈 양자회로 생성\\n\",\n \"qubits = QuantumRegister(2 * num_orbitals, name=\\\"q\\\")\\n\",\n \"circuit = QuantumCircuit(qubits)\\n\",\n \"\\n\",\n \"# 하트리-폭 상태 상태를 준비하여 양자회로에 추가\\n\",\n \"circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\\n\",\n \"\\n\",\n \"# UCJ 연산자 적용\\n\",\n \"circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\\n\",\n \"circuit.measure_all()\\n\",\n \"\\n\",\n \"circuit.decompose().decompose().draw(\\\"mpl\\\", fold =-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5a385896-0c00-425a-b06f-dc929371678a\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: 양자 실행을 위한 문제 최적화\\n\",\n \"다음 단계는 양자 하드웨어에서 실행할 수 있도록 양자 회로를 최적화하는 것입니다. 여기서는 127-큐비트 QPU인 `ibm_brisbane`을 사용합니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ff55ea1b-22ef-4082-af12-d663ba3b7d6c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"backend = service.backend(\\\"ibm_brisbane\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"11c41725-68be-4289-95ee-562763547ca6\",\n \"metadata\": {},\n \"source\": [\n \"가설해를 최적화하고 양자 하드웨어와의 호환성을 높이기 위해 다음과 같은 절차를 권장합니다.\\n\",\n \"\\n\",\n \"* 목표 하드웨어로부터 위에서 설명한 지그재그 패턴에 적합한 물리적 큐비트 배치(`initial_layout`)를 선택합니다. 이 방식으로 큐비트를 배치하면 하드웨어와 호환되는 효율적인 양자 회로를 구성할 수 있으며, 회로의 전체 게이트 수를 최소화할 수 있습니다.\\n\",\n \"* 선택한 `backend`와 위에서 결정한 `initial_layout`을 사용하여 Qiskit의 [generate\\\\_preset\\\\_pass\\\\_manager](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.transpiler.generate_preset_pass_manager)함수를 통해 단계화된 패스 매니저(pass manager)를 생성합니다.\\n\",\n \"* 생성된 단계화된 패스 매니저의 `pre_init`단계를 `ffsim.qiskit.PRE_INIT`으로 설정합니다. `ffsim.qiskit.PRE_INIT`은 게이트를 오비탈 회전으로 분해한 뒤, 병합하는 Qsikit 트랜스파일러 패스를 포함하며, 그 결과 최종 양자 회로의 게이트 수가 감소하게 됩니다.\\n\",\n \"* 마지막으로 위에서 구성된 패스 매니저를 양자 회로에 실행합니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b77ff882-7da3-40be-bbef-cadab8f9c841\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\\n\",\n \"spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\\n\",\n \"initial_layout = spin_a_layout + spin_b_layout\\n\",\n \"\\n\",\n \"pass_manager = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend, initial_layout=initial_layout\\n\",\n \")\\n\",\n \"\\n\",\n \"# 하드웨어 실행을 위해 이 패스 매니저에 의해 생성된 회로를 사용\\n\",\n \"pass_manager.pre_init = ffsim.qiskit.PRE_INIT\\n\",\n \"isa_circuit = pass_manager.run(circuit)\\n\",\n \"print(f\\\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"04a7a60b-e87a-4b13-9f35-573db129526c\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Qiskit Primitive를 이용한 양자 회로 실행\\n\",\n \"\\n\",\n \"하드웨어 실행을 위해 최적화된 양자 회로가 준비되었으므로, 이제 이 회로를 실제 양자 하드웨어에서 실행하여 바닥상태 에너지 추정을 위한 샘플을 수집할 수 있습니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **주의:** QPU에서 회로를 실행하는 부분의 코드는 주석 처리하여 사용자 참고용으로 남겨 두었습니다. 본 실습에서는 실제 하드웨어에서 회로를 실행하는 대신, 이전에 `ibm_brisbane` 양자 프로세서에서 미리 추출해 둔 100,000개의 샘플을 불러와 사용합니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8a4169e6-870d-4c30-a07b-207a09c3f444\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# from qiskit_ibm_runtime import SamplerV2 as Sampler\\n\",\n \"\\n\",\n \"# sampler = Sampler(mode=backend)\\n\",\n \"# job = sampler.run([isa_circuit], shots=10_000)\\n\",\n \"# primitive_result = job.result()\\n\",\n \"# pub_result = primitive_result[0]\\n\",\n \"# bit_array = pub_result.data.meas\\n\",\n \"\\n\",\n \"bit_array = np.load('utils/N2_device_bitarray.npy', allow_pickle=True).item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6e1521e1-7474-4267-9364-738404e367a7\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: 후처리 및 고전적인 결과 형식으로 반환\\n\",\n \"\\n\",\n \"앞서 말했듯이, 자기 일관적 전자 배치 복원 과정은 반복 루프를 통해 실행됩니다.\\n\",\n \"다음 코드 셀에서는 루프의 첫 번째 반복에서 원본 샘플을 그대로 사용합니다(단, 대칭성을 위반하는 샘플을 배제한 이후). 이후, 이 샘플을 대각화 절차의 입력으로 전달하여 평균 오비탈 점유율에 대한 초기 추정값을 얻습니다.\\n\",\n \"두 번째 반복부터는 이전에 얻은 평균 오비탈 점유율을 이용하여, 원본 샘플 중 대칭성을 위반하는 샘플을 수정하여 새로운 전자 배치를 생성합니다.\\n\",\n \"이렇게 얻어진 새로운 전자 배치를 수집한 후 다시 부분 샘플링 하여 배치를 구성하고, 이 배치를 이용하여 해밀토니안을 축소 투영한 뒤, 축소된 해밀토니안의 고유상태 계산기(eigenstate solver)를 통해 바닥상태 에너지를 근사적으로 계산합니다.\\n\",\n \"\\n\",\n \"이 기법에서 사용자가 제어할 수 있는 몇 가지 중요한 옵션들이 있습니다:\\n\",\n \"\\n\",\n \"* `max_iterations`: 자기 일관적 전자 배치 복원 루프를 수행할 총 반복의 횟수입니다.\\n\",\n \"* `num_batches`: 부분 샘플링으로 생성할 전자 배치들의 배치 개수이며, 이는 고유상태 계산기를 호출하는 횟수와 동일합니다.\\n\",\n \"* `samples_per_batch`: 각 배치에 포함할 서로 다른 고유한 전자 배치의 개수입니다.\\n\",\n \"* `max_cycles`: 고유상태 계산기가 수행할 Davidson 알고리즘의 최대 사이클 횟수입니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f585a9a-acd4-437e-b2af-eb672e0eb0c4\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"경고: 약 5분의 실행 시간 소요\\n\",\n \"\\n\",\n \"아래의 코드를 실행할 경우, 사용 중인 컴퓨터의 성능에 따라 최대 5분 정도의 시간이 걸릴 수 있으며, 실행이 완료될 때까지 이 노트북에서 다른 작업을 수행할 수 없습니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f026c1fb-e159-47b3-9b00-062cc07f1271\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"# SQD 옵션 설정\\n\",\n \"energy_tol = 1e-3 \\n\",\n \"occupancies_tol = 1e-3 \\n\",\n \"max_iterations = 5\\n\",\n \"\\n\",\n \"# 고유상태 계산기 옵션\\n\",\n \"num_batches = 5\\n\",\n \"samples_per_batch = 50\\n\",\n \"symmetrize_spin = True \\n\",\n \"carryover_threshold = 1e-4 \\n\",\n \"max_cycle = 200\\n\",\n \"rng = np.random.default_rng(24)\\n\",\n \"\\n\",\n \"\\n\",\n \"# 고유상태 계산기에 전달할 옵션 설정\\n\",\n \"# 기본값을 그대로 사용하고 싶다면 이 부분을 생략할 수 있으며,\\n\",\n \"# 이 경우 아래 diagonalize_fermionic_hamiltonian 함수 호출에서 sci_solver 옵션을 지정하지 않아야 합니다.\\n\",\n \"sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle)\\n\",\n \"\\n\",\n \"\\n\",\n \"# 중간 결과를 저장하기 위한 리스트\\n\",\n \"result_history = [] \\n\",\n \"\\n\",\n \"def callback(results: list[SCIResult]): \\n\",\n \" result_history.append(results)\\n\",\n \" iteration = len(result_history)\\n\",\n \" print(f\\\"Iteration {iteration}\\\")\\n\",\n \" for i, result in enumerate(results):\\n\",\n \" print(f\\\"\\\\tSubsample {i}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tEnergy: {result.energy + nuclear_repulsion_energy}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\\\")\\n\",\n \"\\n\",\n \"result = diagonalize_fermionic_hamiltonian(\\n\",\n \" hcore,\\n\",\n \" eri,\\n\",\n \" bit_array,\\n\",\n \" samples_per_batch=samples_per_batch,\\n\",\n \" norb=num_orbitals,\\n\",\n \" nelec=nelec,\\n\",\n \" num_batches=num_batches,\\n\",\n \" energy_tol=energy_tol,\\n\",\n \" occupancies_tol=occupancies_tol,\\n\",\n \" max_iterations=max_iterations,\\n\",\n \" sci_solver=sci_solver,\\n\",\n \" symmetrize_spin=symmetrize_spin,\\n\",\n \" carryover_threshold=carryover_threshold,\\n\",\n \" callback=callback,\\n\",\n \" seed=rng,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b7e28f80-675f-4ef4-bf86-2a6c696cef23\",\n \"metadata\": {},\n \"source\": [\n \"## 결과 시각화\\n\",\n \"\\n\",\n \"SQD 알고리즘의 결과를 시각적으로 확인하기 위해, 다음과 같이 `plot_energy_and_occupancy`라는 함수를 생성합니다.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bd6d96b3-cfd6-490b-bd20-4c5530071f86\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def plot_energy_and_occupancy(result_history, exact_energy):\\n\",\n \"\\n\",\n \" # 에너지를 시각화하기 위한 데이터\\n\",\n \" x1 = range(len(result_history))\\n\",\n \" min_e = [\\n\",\n \" min(result, key=lambda res: res.energy).energy + nuclear_repulsion_energy\\n\",\n \" for result in result_history\\n\",\n \" ]\\n\",\n \" e_diff = [abs(e - exact_energy) for e in min_e]\\n\",\n \" yt1 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4]\\n\",\n \" \\n\",\n \" # 화학적 정밀도 (+/- 1 milli-Hartree)\\n\",\n \" chem_accuracy = 0.001\\n\",\n \" \\n\",\n \" # 평균 공간 오비탈 점유율을 위한 데이터\\n\",\n \" y2 = np.sum(result.orbital_occupancies, axis=0)\\n\",\n \" x2 = range(len(y2))\\n\",\n \" \\n\",\n \" fig, axs = plt.subplots(1, 2, figsize=(12, 6))\\n\",\n \" \\n\",\n \" # 에너지 시각화\\n\",\n \" axs[0].plot(x1, e_diff, label=\\\"energy error\\\", marker=\\\"o\\\")\\n\",\n \" axs[0].set_xticks(x1)\\n\",\n \" axs[0].set_xticklabels(x1)\\n\",\n \" axs[0].set_yticks(yt1)\\n\",\n \" axs[0].set_yticklabels(yt1)\\n\",\n \" axs[0].set_yscale(\\\"log\\\")\\n\",\n \" axs[0].set_ylim(1e-4)\\n\",\n \" axs[0].axhline(y=chem_accuracy, color=\\\"#BF5700\\\", linestyle=\\\"--\\\", label=\\\"chemical accuracy\\\")\\n\",\n \" axs[0].set_title(\\\"Approximated Ground State Energy Error vs SQD Iterations\\\")\\n\",\n \" axs[0].set_xlabel(\\\"Iteration Index\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" axs[0].set_ylabel(\\\"Energy Error (Ha)\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" axs[0].legend()\\n\",\n \" \\n\",\n \" # 오비탈 점유율 시각화\\n\",\n \" axs[1].bar(x2, y2, width=0.8)\\n\",\n \" axs[1].set_xticks(x2)\\n\",\n \" axs[1].set_xticklabels(x2)\\n\",\n \" axs[1].set_title(\\\"Avg Occupancy per Spatial Orbital\\\")\\n\",\n \" axs[1].set_xlabel(\\\"Orbital Index\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" axs[1].set_ylabel(\\\"Avg Occupancy\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" \\n\",\n \" print(f\\\"Exact energy: {exact_energy:.5f} Ha\\\")\\n\",\n \" print(f\\\"SQD energy: {min_e[-1]:.5f} Ha\\\")\\n\",\n \" print(f\\\"Absolute error: {e_diff[-1]:.5f} Ha\\\")\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f05f1766-38de-410b-ab8e-0ded552c77bc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_energy_and_occupancy(result_history, exact_energy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8c8fc4ab-1ab4-4e76-9fbb-a069ed759085\",\n \"metadata\": {},\n \"source\": [\n \"첫 번째 그래프에서는 몇 번의 반복을 거친 이후, 바닥상태 에너지를 약 `~100 mH` 범위 내에서 예측하는 것을 확인 할 수 있습니다. 일반적으로 화학적 정밀도는 `1 kcal/mol` $\\\\approx$ `1.6 mH`로 정의되므로, 현재의 정확도는 이에 비하면 다소 부족합니다. 양자 회로에서 더 많은 샘플을 추출하거나, 각 배치에서 사용하는 샘플 수를 증가시킴으로써 이 에너지 추정값을 개선할 수 있습니다.\\n\",\n \"\\n\",\n \"두 번째 그래프는 최종 반복 이후 각 공간 오비탈의 평균 점유율을 나타냅니다. 이 결과를 통해 스핀 업 및 스핀 다운 전자 모두가 첫 5개의 오비탈을 높은 확률로 점유하는 것을 확인할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86541d66-d12d-4049-abd6-0c3469a155b4\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 2: 전자 배치 복원을 통한 비트 교정 \\n\",\n \"\\n\",\n \"STO-3G 기저 집합을 사용하여 준비된 $N_2$ 분자에 대해 전자 배치 복원을 수행한다고 가정하겠습니다. 평균 오비탈 점유도 $n$ 이 다음과 같을 때, 주어진 비트열 $x$ 를 복원 과정을 통해 교정하려고 합니다. 이 경우, 주어진 비트열은 어떤 비트열로 수정될 가능성이 높을지 결정하십시오.\\n\",\n \"\\n\",\n \"$n = [0.007, 0.029, 0.029, 0.995, \\n\",\n \" 0.976, 0.976, 0.993, 0.997, \\n\",\n \" 0.007, 0.029, 0.029, 0.995,\\n\",\n \" 0.976, 0.976, 0.993, 0.997]$\\n\",\n \"\\n\",\n \"$x = [1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0]$\\n\",\n \"\\n\",\n \"비트를 뒤집을 때의 가중 확률 $w(y)$은 다음의 변형된 ReLU 함수를 사용하여 계산합니다 [2].\\n\",\n \"$$\\n\",\n \"\\\\begin{align}\\n\",\n \" w(y) = \\\\begin{cases} \\n\",\n \" \\\\delta\\\\cdot \\\\frac{y}{h} & \\\\text{if } y \\\\leq h\\\\\\\\ \\n\",\n \" \\\\delta + (1 - \\\\delta)\\\\cdot \\\\frac{y - h}{1 - h} & \\\\text{if } y > h\\n\",\n \"\\\\end{cases}\\n\",\n \"\\\\end{align}\\n\",\n \"$$ \\n\",\n \"\\n\",\n \"여기서, $y$는 비트를 뒤집을 확률로 정의되며, $i$-번째 스핀 오비탈에 대해 $y[i] =|x[i]-n[i]|$ 로 나타낼 수 있습니다.\\n\",\n \"여기서 $h$는 ReLU 함수의 꺾임 지점(\\\"corner\\\")을 나타내며, $\\\\delta$는 꺾임 지점에서의 ReLU 함수의 값을 정의하는 매개변수입니다. 본 예시에서는 [2]와 동일하게 $\\\\delta = 0.01$을 사용하고, $h$는 알파(또는 베타) 스핀 오비탈 수로 나눈 값, 즉 채움 인자(filling factor)로 정의됩니다.\\n\",\n \"\\n\",\n \"실제 전자 배치 복원 과정에서는 $w(y)$를 가중치로 하여 무작위로 비트를 뒤집습니다. 본 연습문제에서는 가장 큰 $w(y[i])$ 값을 갖는 비트 $i$를 뒤집었을 때 얻어질 비트열을 최종 정답으로 하여, 얻어질 확률이 가장 높은 비트열을 답안으로 제시하십시오.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0ecea848-e0cb-4b35-87cd-b4e75318352c\",\n \"metadata\": {},\n \"source\": [\n \"참고:\\n\",\n \"- 주어진 비트열에서 오른쪽 절반은 스핀 업 오비탈을 나타내며, 왼쪽 절반은 스핀 다운 오비탈을 나타냅니다. 여기서 `1`은 해당 오비탈이 전자에 의해 점유된 상태를 나타내고, `0`은 오비탈이 비어 있는 상태를 나타냅니다.\\n\",\n \"- 더 자세한 내용은 IBM Quantum Learning의 \\\"Quantum Diagonalization Algorithm\\\" 코스 중 [\\\"4.1 Configuration recovery overview\\\" in Lesson 4: SQD application](https://quantum.cloud.ibm.com/learning/en/courses/quantum-diagonalization-algorithms/sqd-implementation) 부분을 참조하십시오.\\n\",\n \"- 본 연습문제의 경우에는 현재 스핀-베타 전자가 1개 더 필요한 상황이므로, 만약 특정 오비탈 $i$가 이미 점유된 상태라서 뒤집을 필요가 없다면, 해당 오비탈의 y_beta[i] 값을 0으로 설정합니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cd04526f-eee0-4f96-8f52-d437899af540\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"n = [0.007, 0.029, 0.029, 0.995, \\n\",\n \" 0.976, 0.976, 0.993, 0.997, \\n\",\n \" 0.007, 0.029, 0.029, 0.995,\\n\",\n \" 0.976, 0.976, 0.993, 0.997]\\n\",\n \"\\n\",\n \"x = [1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"95e5f030-47af-495a-a8c5-823a24d666ea\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x = np.array(x)\\n\",\n \"n = np.array(n)\\n\",\n \"\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"\\n\",\n \"# 알파 스핀과 베타 스핀 나누기\\n\",\n \"x_alpha =\\n\",\n \"x_beta =\\n\",\n \"\\n\",\n \"# 뒤집히는 확률 정의하기\\n\",\n \"y = \\n\",\n \"y_alpha =\\n\",\n \"y_beta =\\n\",\n \"\\n\",\n \"# 이 경우, 한 개의 베타 입자가 더 필요하므로, 만약 x_beta[i]가 이미 1이라면 y_beta[i]를 0으로 설정합니다.\\n\",\n \"for i in range(len(y_beta)):\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \"print(y_beta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9e9f16a3-d459-41e4-b79d-bc7bbd91de1f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"h = 5/8\\n\",\n \"delta = 0.01\\n\",\n \"w = np.zeros(len(y_beta))\\n\",\n \"\\n\",\n \"# 최대 w 값을 찾기\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"for i in range(len(y_beta)):\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\\n\",\n \"# print(max_index, max_w)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d23e1599-e5cf-467d-a58b-e22952e0dfc7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 비트열에서 가장 큰 w 값을 갖는 인덱스의 비트를 뒤집기\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"for i in range(len(y_beta)):\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"x = np.concatenate([x_beta, x_alpha])\\n\",\n \"corrected_x = \\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\\n\",\n \"# print(corrected_x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b028186e-039f-4f6d-a9a6-2a4cc85f6119\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"\\n\",\n \"grade_lab3_ex2(corrected_x) # Expected result type: list\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7e08dfec-1433-4166-9b56-53e722059917\",\n \"metadata\": {},\n \"source\": [\n \"# 5. 가설해 개선\\n\",\n \"\\n\",\n \"양자 회로에서의 샘플링 정확도가 높을수록, SQD 알고리즘을 통해 얻는 결과 역시 향상됩니다. 따라서 샘플링을 수행하는 가설해를 어떻게 구성하는가는 SQD 성능을 좌우하는 핵심 요소 중 하나입니다. 가설해 설정에 따라 샘플링되는 비트열이 달라지며, 궁극적으로 얻을 수 있는 에너지 추정값의 정확도에 직접적인 영향을 미치게 됩니다. 이번 세션에서는 서로 다른 형태의 가설해를 탐구하여 최종 결과를 개선하는 방안에 대해 살펴보겠습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b948384-3b96-4c37-9676-b6619d0c6ee6\",\n \"metadata\": {},\n \"source\": [\n \"## 5.1 기저 집합의 변경\\n\",\n \"\\n\",\n \"먼저, 기저 집합을 변경하여 분자 시스템을 더욱 자연스럽게 모델링해 보겠습니다. 이는 더 많은 전자 오비탈을 계산에 포함하도록 합니다. 더 많은 오비탈을 계산에 포함하면 계산 복잡성이 증가하지만, 일반적으로 에너지 추정값의 정확도를 높이고 결과적으로 더 낮은 에너지 값을 얻을 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e3d406e6-a664-4890-b8cc-36265ac72c66\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 3: 기저 집합 변경 \\n\",\n \"\\n\",\n \"분자의 기저 집합을 기존의 `6-31G` 에서 `cc-pvdz`로 변경해 보십시오. 이때 양자 하드웨어 백엔드로 `ibm_torino`를 사용하여, 6개의 보조 큐비트를 포함하는 LUCJ 가설해를 구성한다고 가정할 때, 필요한 총 큐비트 수는 얼마인지 계산하십시오.\\n\",\n \"참고: `ibm_torino` 하드웨어의 기하학적 제한으로 인해 본 예시에서는 사용할 수 있는 보조 큐비트의 최대개수가 6개로 제한되어 있습니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"28aacef0-e91e-4b29-9d10-d298c699bf4b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"\\n\",\n \"# 분자의 성질 특정\\n\",\n \"open_shell = False\\n\",\n \"spin_sq = 0\\n\",\n \"\\n\",\n \"# N2 분자 구축\\n\",\n \"mol = pyscf.gto.Mole()\\n\",\n \"mol.build(\\n\",\n \" atom=[[\\\"N\\\", (0, 0, 0)], [\\\"N\\\", (1.0, 0, 0)]],\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" basis= ### 여기에 코드를 작성해주세요 ###,\\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \" symmetry=\\\"Dooh\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# 활성 공간 정의\\n\",\n \"n_frozen = 2\\n\",\n \"active_space = range(n_frozen, mol.nao_nr())\\n\",\n \"\\n\",\n \"# 분자 적분 계산\\n\",\n \"scf = pyscf.scf.RHF(mol).run()\\n\",\n \"num_orbitals = len(active_space)\\n\",\n \"n_electrons = int(sum(scf.mo_occ[active_space]))\\n\",\n \"num_elec_a = (n_electrons + mol.spin) // 2\\n\",\n \"num_elec_b = (n_electrons - mol.spin) // 2\\n\",\n \"cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\\n\",\n \"mo = cas.sort_mo(active_space, base=0)\\n\",\n \"hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\\n\",\n \"eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\\n\",\n \"\\n\",\n \"print(num_orbitals)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1afdd410-3288-4df9-927e-c00afd50daac\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"n_qubits = ### 결과를 입력하세요 ###\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"14d5fd85-612a-4878-be7b-52aa2b8e6f3e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# # 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"\\n\",\n \"grade_lab3_ex3(n_qubits) # Expected result type: integer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae2587d2-d5d1-473c-8cbc-5112642c30d1\",\n \"metadata\": {},\n \"source\": [\n \"## 5.2 최적의 큐비트 배치 선택\\n\",\n \"\\n\",\n \"이제 총 큐비트의 개수를 결정했으므로, 다음 단계는 실제 양자 하드웨어에서 사용할 물리적 큐비트의 배치(layout)를 결정합니다. 더 큰 기저 집합을 효과적으로 활용하기 위해, 보다 우수한 성능을 가진 Heron 장치를 사용하여 최적의 배치를 선택하도록 하겠습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"13f326d6-16c2-4395-bc02-9d4499b50c9e\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 4: 최적의 큐비트 배치 선택 \\n\",\n \"\\n\",\n \"최적의 결과를 얻기 위해 초기 큐비트 배치를 어떻게 선택해야 할까요? 이를 결정하려면 각 큐비트의 오류 수치를 확인해야 합니다. 다음의 큐비트 맵에서 읽기 오류(readout error)가 0.1보다 큰 큐비트는 검은색으로 표기되어 있으며, CZ 오류가 0.1보다 큰 연결(edge)은 흰색으로 나타나 있습니다.\\n\",\n \"이 정보를 바탕으로 ISA 회로를 생성하기 위한 pass_manager의 인자인 `initial_layout`을 가장 최적의 방식으로 설정하십시오.\\n\",\n \"백엔드로는 `ibm_torino`를 사용할 것이며, 이 장치의 기하학적인 제약으로 인해 사용할 수 있는 보조 큐비트의 최대 개수는 6개로 제한됩니다.\\n\",\n \"\\n\",\n \"최적의 큐비트 배치를 선택하여 더 우수한 샘플링을 얻기 위해, 다음과 같은 절차를 따르십시오:\\n\",\n \"1. 먼저, `backend_target`에서 읽기 오류가 0.1 이상인 큐비트를 찾아 `bad_readout_qubits` 리스트로 정리합니다.\\n\",\n \"2. 다음으로, CZ 게이트 오류가 0.1 이상인 연결을 찾아 `bad_czgate_edges` 리스트로 정리합니다.\\n\",\n \"3. 이 결과를 이용하여 큐비트의 연결도를 시각화하며, 이때 읽기 오류가 높은 큐비트 (`bad_readout_qubits`)는 검은색으로, CZ 오류가 높은 연결(`bad_czgate_edges`)은 흰색으로 나타내어 구분합니다.\\n\",\n \"4. 마지막으로, 오류가 가장 적은 최적의 초기 큐비트 배치(initial qubit layout)를 선택합니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **주의:** **이 연습문제의 일관된 채점을 위해 벡엔드 정보로 미리 제공된 pickle 파일인 `backend_target_v20.pkl` 또는 `backend_target_v21.pkl`중 하나를 불러와서 이를 `backend_target`으로 사용해주십시오**. Lab 2에서는 실제 백엔드의 정보를 얻기 위해 `backend.properties()`, `backend.target` 등을 사용했지만, 이번 실습에서는 이 방법을 사용하지 않습니다. 참고로 실제 백엔드를 호출하는 코드는 주석 처리되어 있습니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **주의:** Qiskit 버전 2.1.x를 사용하는 경우, 다음 셀에서 `backend_target_v21.pkl` 파일을 여십시오.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"60db7fe7-01b0-42c9-8569-e729cfde5f3c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Qiskit version 2.0.x 사용자\\n\",\n \"with open(\\\"utils/backend_target_v20.pkl\\\", \\\"rb\\\") as f:\\n\",\n \"# Qiskit version 2.1.x 사용자\\n\",\n \"# with open(\\\"utils/backend_target_v21.pkl\\\", \\\"rb\\\") as f:\\n\",\n \" backend_target = pickle.load(f)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bc9dc488-8232-44eb-8b14-a28603948212\",\n \"metadata\": {},\n \"source\": [\n \"주의: `backend_target`으로부터 \\\"measure\\\"와 \\\"cz\\\"와 같은 명령어의 속성을 얻는 방법은 [\\\"Get QPU information with Qiskit\\\"의 \\\"Instruction properties\\\" 부분](https://quantum.cloud.ibm.com/docs/en/guides/get-qpu-information#instruction-properties)을 참조하십시오.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a5906475-2c7e-46b3-9799-b29a6b6b77bd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"BAD_READOUT_ERROR_THRESHOLD = 0.1\\n\",\n \"BAD_CZGATE_ERROR_THRESHOLD = 0.1\\n\",\n \"backend_num_qubits = 133\\n\",\n \"\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"bad_readout_qubits = ### 여기에 코드를 작성해주세요 ###\\n\",\n \"bad_czgate_edges = ### 여기에 코드를 작성해주세요 ###\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\\n\",\n \"print(\\\"Bad readout qubits:\\\", bad_readout_qubits)\\n\",\n \"print(\\\"Bad CZ gates:\\\", bad_czgate_edges)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ac01a0ea-2d7b-4225-8694-349341663b4b\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"주의: Graphviz 라이브러리 필요\\n\",\n \"\\n\",\n \"`plot_coupling_map`을 사용하려면 'Graphviz' 라이브러리가 필요합니다. 설치하려면 다음의 안내 페이지를 참조하십시오.\\n\",\n \"\\n\",\n \"\\n\",\n \"https://graphviz.org/download \\n\",\n \"\\n\",\n \"\\n\",\n \"설치를 원치 않을 경우 다음 코드 블록을 건너뛰어도 됩니다. 해당 코드는 시각화를 위한 용도로만 사용됩니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b80881e2-9a28-4d16-8d8b-ca14811ea780\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qubit_color = []\\n\",\n \"for i in range(backend_num_qubits):\\n\",\n \" if i in bad_readout_qubits:\\n\",\n \" qubit_color.append(\\\"#000000\\\") # 검은색\\n\",\n \" else:\\n\",\n \" qubit_color.append(\\\"#8c00ff\\\") # 보라색\\n\",\n \"line_color = []\\n\",\n \"for e in backend_target.build_coupling_map().get_edges():\\n\",\n \" if e in bad_czgate_edges:\\n\",\n \" line_color.append(\\\"#ffffff\\\") # 흰색\\n\",\n \" else:\\n\",\n \" line_color.append(\\\"#888888\\\") # 회색\\n\",\n \"plot_gate_map(backend, qubit_color=qubit_color, line_color=line_color, qubit_size=50, font_size=25, figsize=(14,14))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c43f5d1f-c7cc-4584-81e9-43db1a56e2dd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 초기 큐비트 배치를 선택합니다\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"spin_a_layout = ### 큐비트 리스트를 작성해주세요 ###\\n\",\n \"spin_b_layout = ### 큐비트 리스트를 작성해주세요 ###\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\\n\",\n \"initial_layout = spin_a_layout + spin_b_layout\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c929a180-c518-4cee-abd5-67b239f381ea\",\n \"metadata\": {},\n \"source\": [\n \"여기서 큐비트 배치를 확인하세요:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b3d191ac-68b0-4c1d-846c-30730856b876\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qubit_color = []\\n\",\n \"for i in range(backend_num_qubits):\\n\",\n \" if i in bad_readout_qubits:\\n\",\n \" qubit_color.append(\\\"#000000\\\") # 검은색\\n\",\n \" elif i in initial_layout:\\n\",\n \" qubit_color.append(\\\"#ff00dd\\\") # 분홍색\\n\",\n \" else:\\n\",\n \" qubit_color.append(\\\"#8c00ff\\\") # 보라색\\n\",\n \"line_color = []\\n\",\n \"for e in backend_target.build_coupling_map().get_edges():\\n\",\n \" if e in bad_czgate_edges:\\n\",\n \" line_color.append(\\\"#ffffff\\\") # 흰색\\n\",\n \" else:\\n\",\n \" line_color.append(\\\"#888888\\\") # 회색\\n\",\n \"plot_gate_map(backend, qubit_color=qubit_color, line_color=line_color, qubit_size=50, font_size=25, figsize=(14,14))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cc2bf757-2fa1-47b9-9993-36cf163d99f0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"\\n\",\n \"grade_lab3_ex4(initial_layout) # Expected result type: lists\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"531344cb-0b6f-45fe-83ee-9b99801f2dd5\",\n \"metadata\": {},\n \"source\": [\n \"## 5.3 LUCJ 가설해에 추가적인 상호작용 도입\\n\",\n \"\\n\",\n \"세션 4에서 사용한 LUCJ 가설해는 다음과 같은 형태로 표현됩니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \" \\\\lvert \\\\Psi \\\\rangle = \\\\prod_{\\\\mu=1}^L e^{\\\\hat{K}_\\\\mu} e^{i \\\\hat{J}_\\\\mu} e^{-\\\\hat{K}_\\\\mu} | \\\\Phi_0 \\\\rangle\\n\",\n \"$$\\n\",\n \"\\n\",\n \"여기서 $\\\\lvert \\\\Phi_0 \\\\rangle$ 는 참조 상태로서 일반적으로 하트리-폭 상태를 사용합니다. 또한 연산자 $\\\\hat{K}_\\\\mu$ 와 $\\\\hat{J}_\\\\mu$는 다음과 같이 정의됩니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{K}_\\\\mu = \\\\sum_{pq, \\\\sigma} K_{pq}^\\\\mu \\\\, \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{q \\\\sigma}\\n\",\n \"\\\\;,\\\\;\\n\",\n \"\\\\hat{J}_\\\\mu = \\\\sum_{pq, \\\\sigma\\\\tau} J_{pq,\\\\sigma\\\\tau}^\\\\mu \\\\, \\\\hat{n}_{p \\\\sigma} \\\\hat{n}_{q \\\\tau}\\n\",\n \"\\\\;,\\n\",\n \"$$\\n\",\n \"\\n\",\n \"여기서 $\\\\hat{n}_{p \\\\sigma}$ 는 다음과 같이 정의된 입자 수 연산자 입니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{n}_{p \\\\sigma} = \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{p \\\\sigma}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"IBM의 하드웨어는 중 육각 격자의 큐비트 위상구조를 가지며, 이에 따라 $\\\\mathbf{J}$ 행렬에 대한 인덱스 제약 조건은 다음과 같이 주어집니다.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha} &: \\\\{(p, p+1) \\\\; , \\\\; p = 0, \\\\ldots, N-2\\\\} \\\\\\\\\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\beta} &: \\\\{(p, p) \\\\;, \\\\; p = 0, 4, 8, \\\\ldots,\\\\; p \\\\leq N-1\\\\}\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\\n\",\n \"여기서 $\\\\mathbf{J}^{\\\\alpha\\\\alpha}= J_{p q,\\\\alpha\\\\alpha}^1$ 및 $\\\\mathbf{J}^{\\\\alpha\\\\beta}= J_{p q,\\\\alpha\\\\beta}^1$로 정의됩니다.\\n\",\n \"\\n\",\n \"기존의 방식에서는 $\\\\mathbf{J}^{\\\\alpha\\\\alpha}$ 행렬이 같은 스핀($\\\\alpha$ 또는 $\\\\beta$)을 가진 인접한 오비탈 간의 상호작용만을 나타냅니다. 이번에는 자연의 물리적 상호작용을 보다 정확히 모델링하기 위해, 인접 오비탈뿐 아니라 다음으로 가까운 오비탈(next adjacent)과의 상호작용도 포함하도록 $\\\\mathbf{J}^{\\\\alpha\\\\alpha}$를 수정하여 다음과 같이 정의하겠습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha} &: \\\\{(p, p+1, p+2) \\\\; , \\\\; p = 0, \\\\ldots, N-3\\\\} \\\\\\\\\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\beta} &: \\\\{(p, p) \\\\;, \\\\; p = 0, 4, 8, \\\\ldots,\\\\; p \\\\leq N-1\\\\}\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c0292b43-2e65-43bf-855b-2fda443b8c82\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"연습문제 5: LUCJ 가설해에 추가적인 상호작용 도입 \\n\",\n \"\\n\",\n \"LUCJ 가설해의 코드에서 `alpha_alpha_indices`를 수정하여, $\\\\mathbf{J}$ 행렬이 $\\\\alpha$ 또는 $\\\\beta$ 스핀 오비탈의 바로 인접한 스핀뿐만 아니라, 두 단계 떨어져 있는 스핀과도 상호작용을 하도록 설정하십시오. 수정한 후, 최종적으로 완성된 `alpha_alpha_indices` 리스트를 제출하십시오.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fad7278a-9178-4ac0-9c7f-01648d46cd9f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 가설해의 초기 매개변수로 사용할 CCSD의 t1, t2 진폭을 계산\\n\",\n \"ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\\n\",\n \"t1 = ccsd.t1\\n\",\n \"t2 = ccsd.t2\\n\",\n \"\\n\",\n \"n_reps = 1\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"alpha_alpha_indices = ### 여기에 코드를 작성해주세요 ###\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\\n\",\n \"alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\\n\",\n \"\\n\",\n \"ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\\n\",\n \" t2=t2,\\n\",\n \" t1=t1,\\n\",\n \" n_reps=n_reps,\\n\",\n \" interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\\n\",\n \")\\n\",\n \"\\n\",\n \"nelec = (num_elec_a, num_elec_b)\\n\",\n \"\\n\",\n \"# 빈 양자회로 생성\\n\",\n \"qubits = QuantumRegister(2 * num_orbitals, name=\\\"q\\\")\\n\",\n \"circuit = QuantumCircuit(qubits)\\n\",\n \"\\n\",\n \"# 하트리-폭 상태 상태를 준비하여 양자회로에 추가\\n\",\n \"circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\\n\",\n \"\\n\",\n \"# UCJ 연산자 적용\\n\",\n \"circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\\n\",\n \"circuit.measure_all()\\n\",\n \"circuit.decompose().decompose().draw(\\\"mpl\\\", fold=-1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f01ad27a-5a0a-46f8-afaf-da18af7d1956\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"\\n\",\n \"grade_lab3_ex5(alpha_alpha_indices) # Expected result type: list[tuple[int, int]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5db71e0e-4c4c-460d-99d2-8b13acfa9bfd\",\n \"metadata\": {},\n \"source\": [\n \"축하합니다! 이번 랩도 성공적으로 완료하셨군요. 다음 세션은 보너스 세션으로 점수에는 반영되지 않습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"644cd6fb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 현재의 진도 상황을 확인해보세요\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"109cb3e9-987f-4990-acb1-eac3c706627f\",\n \"metadata\": {},\n \"source\": [\n \"# 보너스: 실제 하드웨어에서 실행 및 오류 완화\\n\",\n \"\\n\",\n \"마지막으로, 수정된 가설해 회로를 실제 양자 하드웨어에서 실행한 후, 전자 배치 복원 루프를 통해 최종적으로 에너지를 계산해 봅시다. 이 과정에서, 양자 하드웨어 실행 시 발생할 수 있는 오류의 영향을 최대한 줄이기 위해 오류 완화 기법을 함께 사용할 것입니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2581d200-3327-418e-afb8-b71413c05d56\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"backend = service.backend('ibm_torino')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1a7c1c3d-19fa-430e-898d-4bdcec737922\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pass_manager = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend, initial_layout=initial_layout\\n\",\n \")\\n\",\n \"\\n\",\n \"# 이 패스 매니저에 의해 생성된 회로를 하드웨어 실행에 사용할 것입니다.\\n\",\n \"pass_manager.pre_init = ffsim.qiskit.PRE_INIT\\n\",\n \"isa_circuit = pass_manager.run(circuit)\\n\",\n \"print(f\\\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e713e002-0187-4c4f-9b7f-17843bd49eb3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opts = SamplerOptions()\\n\",\n \"opts.dynamical_decoupling.enable = True\\n\",\n \"opts.twirling.enable_measure = True\\n\",\n \"\\n\",\n \"sampler = Sampler(mode=backend, options=opts)\\n\",\n \"job = sampler.run([isa_circuit], shots=100_000)\\n\",\n \"print(\\\"job id:\\\", job.job_id())\\n\",\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d3b6559f-2bce-4d05-8ec5-fd818c4535e9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"\\n\",\n \"# 위의 셀에서 job id를 가져오세요\\n\",\n \"job = service.job('job_id를_입력하세요')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"21f262fb-e8fd-414a-98d0-7e497dfb79c0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"59f86a08-c703-4362-8957-e30aeb94e3c5\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"주의: 약 20분의 실행 시간 소요\\n\",\n \"\\n\",\n \"아래의 코드를 실행하면 사용 중인 컴퓨터의 성능에 따라 20분 정도의 시간이 걸릴 수 있으며, 실행이 완료될 때까지 이 노트북에서 다른 작업을 수행할 수 없습니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f329e74f-b905-46df-af8c-e0044e6606d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"primitive_result = job.result()\\n\",\n \"pub_result = primitive_result[0]\\n\",\n \"bit_array = pub_result.data.meas\\n\",\n \"\\n\",\n \"# SQD 옵션 설정\\n\",\n \"energy_tol = 1e-3 \\n\",\n \"occupancies_tol = 1e-3 \\n\",\n \"max_iterations = 3\\n\",\n \"\\n\",\n \"# 고유상태 계산기 옵션\\n\",\n \"num_batches = 3\\n\",\n \"samples_per_batch = 200\\n\",\n \"symmetrize_spin = True \\n\",\n \"carryover_threshold = 1e-4 \\n\",\n \"max_cycle = 200\\n\",\n \"\\n\",\n \"# 고유상태 계산기에 전달할 옵션 설정\\n\",\n \"# 기본값을 그대로 사용하고 싶다면 이 부분을 생략할 수 있으며,\\n\",\n \"# 이 경우 아래 diagonalize_fermionic_hamiltonian 함수 호출에서 sci_solver 옵션을 지정하지 않아야 합니다.\\n\",\n \"sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle) ###NEW###\\n\",\n \"\\n\",\n \"# 중간 결과를 저장하기 위한 리스트\\n\",\n \"result_history = [] \\n\",\n \"\\n\",\n \"def callback(results: list[SCIResult]): \\n\",\n \" result_history.append(results)\\n\",\n \" iteration = len(result_history)\\n\",\n \" print(f\\\"Iteration {iteration}\\\")\\n\",\n \" for i, result in enumerate(results):\\n\",\n \" print(f\\\"\\\\tSubsample {i}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tEnergy: {result.energy + nuclear_repulsion_energy}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\\\")\\n\",\n \"\\n\",\n \"result = diagonalize_fermionic_hamiltonian(\\n\",\n \" hcore,\\n\",\n \" eri,\\n\",\n \" bit_array,\\n\",\n \" samples_per_batch=samples_per_batch,\\n\",\n \" norb=num_orbitals,\\n\",\n \" nelec=nelec,\\n\",\n \" num_batches=num_batches,\\n\",\n \" energy_tol=energy_tol,\\n\",\n \" occupancies_tol=occupancies_tol,\\n\",\n \" max_iterations=max_iterations,\\n\",\n \" sci_solver=sci_solver,\\n\",\n \" symmetrize_spin=symmetrize_spin,\\n\",\n \" carryover_threshold=carryover_threshold,\\n\",\n \" callback=callback,\\n\",\n \" seed=rng,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a7424751-f167-4006-9d48-f738791dab09\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"exact_energy=-109.2177884189209 # CCSD 에너지\\n\",\n \"plot_energy_and_occupancy(result_history, exact_energy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c9894969-4c9b-45c0-91a8-097f17d7462b\",\n \"metadata\": {},\n \"source\": [\n \"# Reference \\n\",\n \"\\n\",\n \"\\\\[1] M. Motta et al., “Bridging physical intuition and hardware efficiency for correlated electronic states: the local unitary cluster Jastrow ansatz for electronic structure” (2023). [Chem. Sci., 2023, 14, 11213](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k)\\n\",\n \"\\n\",\n \"\\\\[2] J. Robledo-Moreno et al., \\\"Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer\\\" (2024). [arXiv:quant-ph/2405.05068](https://arxiv.org/abs/2405.05068).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fbfca50d-c810-4f56-a0fc-ab3b6fc64228\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Yuri Kobayashi, Kifumi Numata\\n\",\n \"\\n\",\n \"**Advised by:** Yukio Kawashima, Toshinari Itoko\\n\",\n \"\\n\",\n \"**Reviewed by:** Kevin Sung, Jennifer Glick\\n\",\n \"\\n\",\n \"**Translated by:** Inho Choi\\n\",\n \"\\n\",\n \"**Version:** 1.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"38eb8499-55ca-4d5c-8ca9-2441b03b52b1\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit packages versions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8588a958-8208-4f81-88e1-cae2e27d04ee\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_runtime\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit IBM Runtime: {qiskit_ibm_runtime.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.11\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-3/lab3.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8cdbc826-5411-42cd-bf85-8f3ef3e40feb\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# Lab 3: The Power of 'good sampling' for simulating a chemistry Hamiltonian with SQD\\n\",\n \"\\n\",\n \"Welcome to the third coding challenge of the Qiskit Global Summer School. In this lab, we explore one of the most promising applications of quantum computing, which is quantum chemistry. We present you with a real-life example of simulating a molecule using a quantum computer based on a workflow that quantum chemists may usually follow. We will explain what each step takes to achieve this and walk you through the entire workflow.\\n\",\n \"\\n\",\n \"**Recommended study** \\n\",\n \"Prior to going through this challenge, we recommend that you take a moment and check out the [Variational Algorithm Design](https://quantum.cloud.ibm.com/learning/en/courses/variational-algorithm-design) course and one of our latest courses on IBM Quantum Learning, [Quantum Diagonalization Algorithms](https://quantum.cloud.ibm.com/learning/en/courses/quantum-diagonalization-algorithms/skqd). \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9e49e912-bc84-44e2-a715-94ec1e95eba2\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"* [Part 1: Introduction](#part-1-introduction)\\n\",\n \" * 1.1 Objective\\n\",\n \" * 1.2 What Are Atoms?\\n\",\n \" * 1.3 The Schrödinger Equation\\n\",\n \" * 1.4 Basis Set Approximation - Smart Building\\n\",\n \" * 1.5 The Hamiltonian\\n\",\n \" * [Exercise 1](#exercise1): Measure the size of the $O_2$ Hamiltonian\\n\",\n \"* [Part 2: Variational Quantum Eigensolver](#part-2-variational-quantum-eigensolver)\\n\",\n \" * VQE – A Team-Up Between Classical and Quantum Computers\\n\",\n \"* [Part 3: What is Sample-based Quantum Diagonalization (SQD)?](#part-3-what-is-sample-based-quantum-diagonalization-(SQD)?)\\n\",\n \" * SQD Approach: Reconstruct the Hamiltonian with 'good samples'\\n\",\n \" * Choosing relevant configurations\\n\",\n \" * Dealing with the effects of noise with configuration recovery\\n\",\n \"* [Part 4: How to simulate a $N_2$ molecule with SQD](#part-4-how-to-simulate-a-$N_2$-molecule-with-SQD)\\n\",\n \" * [Exercise 2](#exercise2): Flip a bit by configuration recovery\\n\",\n \"* [Part 5: Improve the ansatz](#part-5-improve-the-ansatz)\\n\",\n \" * [Exercise 3](#exercise3): Change the basis set\\n\",\n \" * [Exercise 4](#exercise4): Select the best layout\\n\",\n \" * [Exercise 5](#exercise5): Add more interaction to LUCJ ansatz\\n\",\n \"* [Bonus: Real hardware execution with error mitigation ](Bonus-real-hardware-execution-with-error-mitigation )\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c7df7f65-7bc0-405b-b937-0d1203f7204a\",\n \"metadata\": {},\n \"source\": [\n \"## Requirements\\n\",\n \"\\n\",\n \"Before starting this tutorial, please make sure that you have the following installed:\\n\",\n \"\\n\",\n \"* Qiskit SDK 2.0 or later with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime 0.40 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"* Qiskit Addons SQD 0.11.0 or later (`pip install qiskit-addon-sqd`)\\n\",\n \"* ffsim (`pip install ffsim`)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"723fb00e-8dfa-421e-95bc-dcabffdf3cec\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** You need to have the below packages installed in order to run this lab, of which some are not available on Windows. If you are using Windows we recommend you to use [an online lab environment.](https://quantum.cloud.ibm.com/docs/en/guides/online-lab-environments#online-lab-environments)\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"128fe3fe-9b55-4696-87ce-4d8d1ad1474c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"# %pip install pyscf\\n\",\n \"# %pip install ffsim\\n\",\n \"# %pip install qiskit_addon_sqd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"242ba214-1bb5-4028-8122-c89b28e3564b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d2d72ab3-882f-4789-a414-504e0c46190d\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.12`. If you see a lower version, you need to reinstall the grader and restart the kernel.\\n\",\n \"Also make sure you have set up everything according to lab 0 and test it with the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9c98e978\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f58bfa2-295b-4831-bb96-9e6c2761bb25\",\n \"metadata\": {},\n \"source\": [\n \"# Imports\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e92c99a3-1a03-4fca-a476-edc4b4692ed4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import common packages first\\n\",\n \"import numpy as np\\n\",\n \"from math import comb\\n\",\n \"import warnings\\n\",\n \"import pyscf\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pickle\\n\",\n \"from functools import partial\\n\",\n \"\\n\",\n \"# Import qiskit classes\\n\",\n \"from qiskit import QuantumCircuit, QuantumRegister\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_gate_map\\n\",\n \"from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian, solve_sci_batch\\n\",\n \"\\n\",\n \"# Import qiskit ecosystems\\n\",\n \"import ffsim\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\\n\",\n \"from qiskit_ibm_runtime import SamplerOptions\\n\",\n \"\\n\",\n \"# Import grader\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab3_ex1, \\n\",\n \" grade_lab3_ex2, \\n\",\n \" grade_lab3_ex3,\\n\",\n \" grade_lab3_ex4,\\n\",\n \" grade_lab3_ex5\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5421b095-f20c-494c-a8c9-a33ed120fa3b\",\n \"metadata\": {},\n \"source\": [\n \"# 1. Introduction \\n\",\n \"\\n\",\n \"## 1.1 Objective\\n\",\n \"\\n\",\n \"The objective of this Lab is to learn the basics and general workflow of quantum chemistry calculations. You will also learn about a useful hybrid quantum-classical algorithm called Sample-based Quantum Diagonalization (SQD) algorithm. SQD is a classical post-processing technique which acts on samples obtained from a quantum circuit after execution on a QPU. It is useful for finding eigenvalues and eigenvectors of quantum operators, such as the Hamiltonian of a quantum system, and uses quantum and distributed classical computing together. \\n\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"3123cd89-2682-4da9-9322-ee40376bec6c\",\n \"metadata\": {},\n \"source\": [\n \"## 1.2 What Are Atoms?\\n\",\n \"Everything around you is made of atoms—the tiny building blocks of matter. The word “atom” comes from a Greek word that means “can’t be split.” A long time ago, a man named Democritus thought that all things were made of tiny, unbreakable pieces called atoms.\\n\",\n \"\\n\",\n \"In 1913, Niels Bohr said electrons move in circles (orbits) around the center of the atom, like planets around the sun. \\n\",\n \"But in 1926, Erwin Schrödinger said electrons don’t really move in perfect paths. Instead, they buzz around in \\\"clouds\\\" where they’re most likely to be found.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 1. A sketch of Bohr’s atomic model (left) and Schrödinger’s atomic model based on quantum mechanical and wave nature of electrons (right). \\n\",\n \"\\n\",\n \"\\n\",\n \"These clouds (called orbitals) have different shapes and energy levels. Electrons usually stay in the lowest energy level (the ground state), but they can jump to higher ones if they get energy. When they fall back down, they give off energy.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f39f52bc-d1c5-4bbf-a4db-4a46d8cdf403\",\n \"metadata\": {},\n \"source\": [\n \"## 1.3 The Schrödinger Equation\\n\",\n \"\\n\",\n \"Erwin Schrödinger's contributions to quantum mechanics go beyond introducing a new electronic model; he established the famous Schrödinger equation. The time-independent Schrödinger equation is:\\n\",\n \"$$\\n\",\n \"\\\\hat{H}|\\\\psi\\\\rangle = {E}|\\\\psi\\\\rangle\\n\",\n \"$$\\n\",\n \"\\n\",\n \"**$\\\\hat{H}$** : Hamiltonian operator \\n\",\n \"**$|\\\\psi\\\\rangle$** : Wave function (state of the system) \\n\",\n \"**${E}$** : Measured energy (eigenvalue) \\n\",\n \"\\n\",\n \"**The goal of quantum chemistry is to solve the Schrödinger Equation** for the wave function. The wave function that satisfies the Schrödinger Equation can help us find out interesting properties of that quantum system such as energy, momentum, spin, magnetization, and more. \\n\",\n \"\\n\",\n \"In quantum chemistry, we are often interested in finding **the ground state energy**. This is because once we know the ground state energy of a molecule, we can learn a lot such as:\\n\",\n \"\\n\",\n \"- the shape a molecule will most likely take in a stable form. (often called the \\\"equilibrium\\\" state)\\n\",\n \"- how molecules might change or react during chemical reactions.\\n\",\n \"- how a drug might stick to a protein in the body seen in Docking simulations.\\n\",\n \" \\n\",\n \"In short, finding the ground state is like finding the starting point for all the interesting things molecules can do.\\n\",\n \"\\n\",\n \"But for big molecules, **solving the Schrödinger Equation exactly is extremely difficult** because the wave function, which describes the spatial distribution of electrons, is highly complex. So, instead of solving it perfectly, scientists make good guesses or approximations that are close enough.\\n\",\n \"\\n\",\n \"## 1.4 Basis Set Approximation - Smart Building\\n\",\n \"One of the key approximations in quantum chemistry is the use of a **basis set** approach. A basis set is **a collection of mathematical functions used to approximate the orbitals (wavefunctions) of electrons** in atoms and molecules. Since we can't solve the Schrödinger equation exactly for most systems, we use basis sets to make the problem computationally tractable. Instead of working with full, continuous atomic orbitals, we approximate them using a set of predefined mathematical functions—usually Gaussian-type or Slater-type orbitals.\\n\",\n \"\\n\",\n \"This method is known as the Linear Combination of Atomic Orbitals (LCAO). The idea is simple: we build molecular orbitals by combining basis functions. It’s like building a complex shape using a sum of simpler, known shapes.\\n\",\n \"- A small basis set (fewer functions) gives a rough but fast approximation.\\n\",\n \"- A larger basis set improves accuracy but requires much more computational power.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Types of Basis Functions \\n\",\n \"\\n\",\n \"- **Slater-type orbitals (STOs)**: These are mathematical functions that look very similar to real atomic orbitals (the shapes electrons naturally form around atoms). They describe how electron density drops off with distance from the nucleus.\\n\",\n \"- **Gaussian-type orbitals (GTOs)**: Easier to work with computationally, even though they don't resemble orbitals as closely. Often used in practice.
\\n\",\n \"\\n\",\n \"\\n\",\n \"The Slater-type orbital (STO) can be approximated by a combination of Gaussian-type orbitals (GTOs). In other words, we combine multiple Gaussians with different widths to mimic the shape of an STO. This combination is called a **contracted Gaussian function**. This idea is the basis for different kinds of basis sets, like minimal (simple) and split-valence (more accurate) sets.\\n\",\n \"\\n\",\n \"- **Minimal Basis Set**:\\n\",\n \"A basis set that uses just one function per orbital (e.g., one function for $1s$, one for $2s$, etc.).\\n\",\n \"In STO-3G, for example, each STO is approximated using 3 Gaussians.\\n\",\n \"- **Split-Valence Basis Set**:\\n\",\n \"These are more flexible and provide more accurate representations than minimal basis sets.\\n\",\n \"They split the valence orbitals (the outermost orbital that contain electrons involved in bonding with other atoms) into two or more parts, each approximated with different combinations of Gaussians (like 6-31G).\\n\",\n \"This allows for better accuracy in chemical bonding and reactions.\\n\",\n \"\\n\",\n \"### Common Basis Sets\\n\",\n \"\\n\",\n \"| Basis\\tSet | Description\\t| Example Use | Computational Cost |\\n\",\n \"| ----------------- | ----------- | ----------- | ----------- |\\n\",\n \"| **STO-3G**\\t| Uses 3 Gaussians (the \\\"3G\\\") to mimic 1 STO. (aka 'Minimal basis')| Fast, rough estimate for small molecules| Low |\\n\",\n \"| **6-31G**\\t | Uses 6 Gaussians to mimic 1 STO (the \\\"6\\\") for each core orbital. For each valence (outer) orbital, a contracted function made of 3 Gaussians (the \\\"3\\\") and a single uncontracted Gaussian (the \\\"1\\\") are used.\\t| Good balance between speed and accuracy| Medium |\\n\",\n \"| **cc-pVDZ**\\t| Designed to improve electron correlation calculations. Adds polarization functions. Uses 2 basis functions for each valence orbitals instead of 1.\\t| More accurate than STO-3G and 6-31G when simulating systems that have stronger correlations, but more computationally expensive.| High |\\n\",\n \"\\n\",\n \"> NOTE: The computational cost referred to in the table above is the classical cost of computing the molecular integrals. The more functions in your basis set, the more accurate your results—but computations take longer.\\n\",\n \"\\n\",\n \"To summarize, instead of solving the full Schrödinger equation exactly (which is almost always impossible for molecules), we build a flexible, approximate wave function using known functions—and then tune it to minimize the system’s energy.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"13c35f0d-e9ac-490b-898e-490adcdd8bdd\",\n \"metadata\": {},\n \"source\": [\n \"## 1.5 The Hamiltonian\\n\",\n \"\\n\",\n \"At the heart of the Schrödinger equation, and to that extent in every quantum chemistry problem, there is a **Hamiltonian**. It’s a central object in both classical mechanics and quantum mechanics, and is essentially a function that represents the total energy (kinetic + potential) of a physical system. \\n\",\n \"\\n\",\n \"In quantum mechanics, the Hamiltonian $\\\\hat{H}$ becomes an operator acting on the wave function $|\\\\psi\\\\rangle$. In a chosen basis, these wave functions $|\\\\psi\\\\rangle$ can be described as vectors and Hamiltonians in matrix form. \\n\",\n \"\\n\",\n \"In most quantum chemistry problems, the first and most important goal was to calculate its ground state energy. This calculation can be done by diagonalizing a Hamiltonian (matrix) and computing its eigenvalues and eigenvectors.\\n\",\n \"\\n\",\n \"The size or dimensions of a molecular Hamiltonian can be determined by finding the number of combinations the electrons can occupy the spatial orbitals. This is essentially a combination problem that can quickly grow exponential in size making the diagonalization uncontractable for most interesting molecules. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d354e83a-fe0e-477e-b7fa-6d1bf6045186\",\n \"metadata\": {},\n \"source\": [\n \"### Example: Calculate the size of the $N_2$ Hamiltonian\\n\",\n \"We would like you to get a sense of how large a Hamiltonian matrix $H$ can grow, even for a relatively small molecule like nitrogen ($N_2$), depending on how accurately you want to simulate it. In the following example, we chose the number of spatial orbitals, spin orbitals, and electrons of $N_2$ to use in our calculations. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dc20e23f-f68c-4fd8-9f79-059ac6405b2f\",\n \"metadata\": {},\n \"source\": [\n \"Using the number of spatial orbitals and electrons in the table below, let's compute the number of ways the electrons can occupy the spatial orbitals (spin configurations), which indicates the size of the $N_2$ molecule Hamiltonian.\\n\",\n \"\\n\",\n \"\\n\",\n \"| | STO-3G | 6-31G | cc-pVDZ | \\n\",\n \"|:-------|:------:|:-------:|:-------:|\\n\",\n \"|Spatial orbitals | (10) **8**| (18) **16** | (28) **26** |\\n\",\n \"|Spin orbitals | (20) **16**| (36) **32**| (56) **52** |\\n\",\n \"| α-spin electrons | (7) **5**| (7) **5**| (7) **5** |\\n\",\n \"| β-spin electrons | (7) **5**| (7) **5**| (7) **5** |\\n\",\n \"\\n\",\n \"\\n\",\n \"
Table 1: Number of orbitals and electrons in specified basis sets for $N_2$
\\n\",\n \"\\n\",\n \"- (#) : number of orbitals and electrons we specifically chose for the purpose of this example.\\n\",\n \"- **#** : number of orbitals or electrons when freezing the core orbital (*1s*) and reducing the number of electrons and spatial orbitals each by 2.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"79f14c83-fe07-476c-b091-456458ec9f04\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** In this example we treat the $1s$ (core) orbital as chemically inactive. This means we can freeze the core orbital and save 2 electrons and 2 spatial orbitals (i.e. 4 spin orbitals -> 4 qubits). This is another useful approximation technique you will frequently see in actual chemistry simulations. Applying this technique, please make sure to use the numbers in **bold** for calculating for all possible electron configurations for the $N_2$ Hamiltonian.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"363fddb2-7208-4192-8c1f-ee60870e925e\",\n \"metadata\": {},\n \"source\": [\n \"As you can tell, solving for all possible spin configurations is essentially a combination problem. Let's take a look at the math to calculate how many ways the **$\\\\alpha$-spin (spin-up)** electrons and **$\\\\beta$-spin (spin-down)** electrons can each occupy given spatial orbitals. For the total spin configurations we simply need to multiply them together. \\n\",\n \"\\n\",\n \"
Total electron configurations = (total α-spin configurations) × (total β-spin configurations)
\\n\",\n \"\\n\",\n \"$$ \\\\Large{n}\\\\Large{C}n_α \\\\times n\\\\Large{C}n_β = \\\\Large\\\\frac{n!}{n_α!(n-n_α)!} \\\\times \\\\Large\\\\frac{n!}{n_β!(n-n_β)!} $$\\n\",\n \"\\n\",\n \"Where\\n\",\n \"**$n$**: number of spatial orbitals,\\n\",\n \"**$n_α$**: number of α-spins,\\n\",\n \"**$n_β$**: number of β-spins\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"46459f2f-05a4-40e9-9a48-a32e2391d067\",\n \"metadata\": {},\n \"source\": [\n \"Now that we know how to obtain the total spin configurations, let's calculate this in each basis set and plot the results to see how the size of the Hamiltonian grows with more accuracy.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"74899db7-baca-47f9-9ead-3bba945b6fc1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Number of possible spin configurations\\n\",\n \"# Example: N2 molecule in STO-3G, 6-31G, and cc-pVDZ basis sets\\n\",\n \"# 14 electrons, 20 spin orbitals (from 10 spatial orbitals × 2)\\n\",\n \"\\n\",\n \"# Calculate total electron configurations for each basis set\\n\",\n \"y1 = comb(8, 5) * comb(8, 5) # STO-3G\\n\",\n \"y2 = comb(16, 5) * comb(16, 5) # 6-31G\\n\",\n \"y3 = comb(26, 5) * comb(26, 5) # cc-pVDZ\\n\",\n \"\\n\",\n \"# Data\\n\",\n \"y = [y1, y2, y3]\\n\",\n \"x = list(range(len(y)))\\n\",\n \"labels = ['STO-3G', '6-31G', 'cc-pVDZ']\\n\",\n \"\\n\",\n \"# Plot with logarithmic y-scale\\n\",\n \"plt.figure(figsize=(6, 4))\\n\",\n \"plt.plot(x, y, 'o')\\n\",\n \"\\n\",\n \"plt.yscale('log')\\n\",\n \"plt.xticks(x, labels)\\n\",\n \"plt.xlabel('Basis sets')\\n\",\n \"plt.ylabel('Number of spin configurations (log scale)')\\n\",\n \"plt.title('Hamiltonian size of N2 molecule at different basis sets')\\n\",\n \"\\n\",\n \"# Add labels above points\\n\",\n \"for i in range(len(x)):\\n\",\n \" plt.text(x[i], y[i], f'{labels[i]}', fontsize=9, ha='center', va='bottom', color='red')\\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c8b45733-4801-4e7c-9264-5699e730cb87\",\n \"metadata\": {},\n \"source\": [\n \"Note that the Y-axis above is in logarithmic scale. You can see how the number of spin configurations grows exponentially with a basis set choice of better approximation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"79a50b43-5c0d-413e-8010-ccb48e07aec0\",\n \"metadata\": {},\n \"source\": [\n \"## Exercise 1: Measure the size of the $O_2$ Hamiltonian\\n\",\n \"\\n\",\n \"Now let's calculate the size of oxygen $O_2$ in the `6-31G` basis. Oxygen has the same number of orbitals as $N_2$ but has 2 additional **$\\\\alpha$-spin (spin-up)** electrons. The numbers you need to use in this exercise are provided in the table below. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"43b3c014-fe64-424b-b045-d32dae44159f\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1: Measure the size of the $O_2$ Hamiltonian \\n\",\n \"\\n\",\n \"Using the number of spatial orbitals and electrons in the table below, compute the total number of electron spin configurations for the oxygen $O_2$ molecule in the `6-31G` basis. **You may write your own code or calculate by hand to get the answer.**\\n\",\n \"\\n\",\n \"\\n\",\n \"| | STO-3G | 6-31G | cc-pVDZ | \\n\",\n \"|:-------|:------:|:-------:|:-------:|\\n\",\n \"|Spatial orbitals | (10) **8**| (18) **16** | (28) **26** |\\n\",\n \"|Spin orbitals | (20) **16**| (36) **32**| (56) **52** |\\n\",\n \"| α-spin electrons | (9) **7**| (9) **7**| (9) **7** |\\n\",\n \"| β-spin electrons | (7) **5**| (7) **5**| () **5** |\\n\",\n \"\\n\",\n \"\\n\",\n \"
Table 1: Number of orbitals and electrons in specified basis sets for $O_2$
\\n\",\n \"\\n\",\n \"- (#) : number of orbitals or electrons based on actual atomic structure in the specified basis set.\\n\",\n \"- **#** : number of orbitals or electrons when freezing the core orbital (*1s*) and reducing the number of electrons and spatial orbitals each by 2.\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** As we already saw in the previous example, we will treat the $1s$ (core) orbital as chemically inactive in this exercise. This means we can freeze the core orbital and save 2 electrons and 2 spatial orbitals (i.e. 4 spin orbitals -> 4 qubits). This is another useful approximation technique you will frequently see in actual chemistry simulations. Applying this technique, please make sure to use the numbers in **bold** for calculating for all possible electron configurations for the $O_2$ Hamiltonian.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b3e26a71-1cdd-4d3c-be48-fb85eb942520\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Exercise 1: Number of possible spin configurations\\n\",\n \"# Example: O2 molecule in 6-31G basis\\n\",\n \"# 16 electrons, 20 spin orbitals (from 10 spatial orbitals × 2)\\n\",\n \"\\n\",\n \"# Calculate all valid electron configurations\\n\",\n \"# Hint: This is a combinatorial problem. You can calculate by hand and provide the answer or use the math.comb() method below.\\n\",\n \"\\n\",\n \"# ---- TODO : Task 1 ---\\n\",\n \"### Provide your code below to calculate the total configurations\\n\",\n \"\\n\",\n \"α_config = \\n\",\n \"β_config = \\n\",\n \"total_config = (α_config)*(β_config)\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"print(f\\\"Total physical configurations for O2 in the given basis : {α_config:} x {β_config:} = {total_config}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8501c714-9965-469f-98d3-d607506b1564\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# total_config = #provide your answer here if calculated by hand. Then uncomment this section before you submit your answer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0e236dfe-a6bf-41fa-9001-9501699c6126\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex1(total_config) # Expected result type: integer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"45e0bf86-70ed-499e-bc12-d9a005b22c6e\",\n \"metadata\": {},\n \"source\": [\n \"After submitting your answer calculated in the 6-31G basis, try calculating the same in other basis sets (e.g. STO-3G, and cc-pVDZ) for comparison. Feel free to plot your results to see how the size grows.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"757e79f6-2a94-4c6a-9ce0-43f02ed5792d\",\n \"metadata\": {},\n \"source\": [\n \"# 2. Variational Quantum Eigensolver\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"ea1b9d0d-e5be-468c-9efa-7010b4b4e483\",\n \"metadata\": {},\n \"source\": [\n \"So far, we've learned about the Schrödinger Equation, basis sets, and how the Hamiltonian of a physical system can grow exponentially in size depending on how accurately you want to simulate your system. Now let's talk about algorithms. \\n\",\n \"\\n\",\n \"One of the well-known algorithms among computational chemists who work with near-term quantum computers is perhaps the **Variational Quantum Eigensolver (VQE)**.\\n\",\n \"\\n\",\n \"The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm used to optimize a cost function Hamiltonian based on the variational principle. Although this lab's main focus is on learning about a different kind of hybrid quantum-classical algorithm called Sample-based Quantum Diagonalization (SQD), it would be useful to briefly review the components of VQE as they form some of the important building blocks for SQD as well.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 2. A diagram that shows the computational workflow of VQE\\n\",\n \"\\n\",\n \"Summary of Computational Steps in VQE:\\n\",\n \"\\n\",\n \"1. Prepare the quantum state $|\\\\psi(𝜃)\\\\rangle$(Quantum).\\n\",\n \"2. Measure expectation values $\\\\langle\\\\psi(𝜃)|\\\\hat{H}|\\\\psi(𝜃)\\\\rangle$(Quantum). \\n\",\n \"3. Compute the cost function $𝐸(𝜃)$(Classical).\\n\",\n \"4. Adjust the parameter $𝜃$ using classical optimization (Classical).\\n\",\n \"5. Repeat the steps until cost function $𝐸(𝜃)$ converges.\\n\",\n \"\\n\",\n \"As you can tell from the above, the computational steps for executing VQE is an 'iterative' process - you need to repeat these steps many, many, times. This is a computationally costly procedure and becomes a limitation for an algorithm like VQE to scale and work with larger molecules. \"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"74c08c83-f1e2-4012-b10f-a97c01eb6976\",\n \"metadata\": {},\n \"source\": [\n \"# 3. What is Sample-based Quantum Diagonalization (SQD)?\\n\",\n \"\\n\",\n \"While VQE has been widely explored since its original proposal in 2014, it has certain limitations. As the Hamiltonian grows larger, obtaining the expectation value through the variational method becomes increasingly resource-intensive, and the algorithm does not scale well for larger molecules. This challenge motivates the study and implementation of Sample-based Quantum Diagonalization (SQD).\\n\",\n \"\\n\",\n \"SQD was inspired by a hybrid quantum-classical method called quantum-selected configuration interaction (QSCI) introduced and published in [this paper](https://arxiv.org/abs/2302.11320). Similar to QSCI, SQD is also a hybrid quantum-classical algorithm in which a quantum computer is used to generate electronic configurations by sampling them from a quantum circuit, and then those configurations are used to form a subspace in which to project and diagonalize the electronic Hamiltonian.\\n\",\n \"\\n\",\n \"This subspace will have dimensions that are smaller than the original Hamiltonian making it much easier to classically diagonalize.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 3. An illustration of the original Hamiltonian shown as an exponentially large matrix in the Hilbert space of N qubits then reduced to a smaller size after configuration recovery and subsampling.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"35ea499c-9d5b-4655-905b-a120eb14cbf4\",\n \"metadata\": {},\n \"source\": [\n \"Let's say we have three atomic orbitals 1s, 2p, 3d, and two electrons that can occupy these orbitals that are ranked from low to high energy. Low energy configurations mean most electrons occupy the lower orbitals while high energy configurations will have electrons in the higher orbitals. \\n\",\n \"One of the important assumptions of the SQD algorithm is that high-energy configurations will have little contributions to the ground state. This allows us to remove these less relevant configurations so we can focus on a subspace of relevant configurations only - in our case configurations that show occupancies of electrons in the lower orbitals. \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 4. Removing electron configurations that are not relevant to the ground state allows us to reduce the size of the matrix (Hamiltonian)\\n\",\n \"\\n\",\n \"\\n\",\n \"This reconstruction of Hamiltonian to a smaller size significantly reduces the time to arrive at the solution. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"26b3cc45-85ec-49b6-a1dc-963e8e0543f3\",\n \"metadata\": {},\n \"source\": [\n \"## Choosing relevant configurations\\n\",\n \"\\n\",\n \"For identifying the relevant electron configuration, we use a quantum circuit (ansatz) denoted by $|\\\\psi\\\\rangle$ in this illustration, which upon measurement in the computational basis will give us a probability distribution over the Hilbert space to sample necessary bitstrings from. \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 5. A quantum 'ansatz' circuit will produce the bitstrings we sample from\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c1da7f72-e948-4c26-87fc-15a80308d70f\",\n \"metadata\": {},\n \"source\": [\n \"## Dealing with the effects of noise with configuration recovery\\n\",\n \"\\n\",\n \"Before we subsample from the pool of bitstrings generated by the quantum circuit, we need to deal with 'noisy quantum samples' that may affect the accuracy of our energy calculations. This is where configuration recovery comes into play. Let's say we have a bitstring that is missing one electron from its configuration. This is easy to identify as the hamming weight is wrong. We can also exploit the expectation of occupancies in the different orbitals (given by 'n' in the illustration below) to determine which bit to flip to correct the bitstrings.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 6. By looking at the electron numbers and occupancy expectation, we can determine which bit to flip\\n\",\n \"\\n\",\n \"Having reconstructed your Hamiltonian with good samples, you now have a reduced size matrix to diagonalize. Upon diagonalizing the Hamiltonian in the subspace, what the diagonalization subroutine does is assign a non-zero wavefunction amplitude to the computational basis states that contribute to the ground state of the problem. The same routine will assign a zero weight to those bit strings that do not represent the ground state. From the results of all batches, the SQD algorithm obtains the orbital occupancy by electrons and lowest energy estimation and updates the data for the next configuration recovery loop.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f9feaa87-b5cc-4c59-aea9-fcc35c3a7348\",\n \"metadata\": {},\n \"source\": [\n \"# 4. How to simulate an $N_2$ molecule with SQD\\n\",\n \"\\n\",\n \"In this section, we will demonstrate how to post-process noisy quantum samples to find an approximation to the ground state of a chemistry Hamiltonian: the $N_2$ molecule at equilibrium in the 6-31G basis set. We will follow a SQD approach to process samples taken from a 36-qubit quantum circuit ansatz (in this case, an LUCJ circuit). In order to account for the effect of quantum noise, the configuration recovery technique is used.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"02847627-1ba8-4677-91d5-92c621f87ece\",\n \"metadata\": {},\n \"source\": [\n \"## Molecular Hamiltonian\\n\",\n \"\\n\",\n \"The properties of molecules are largely determined by the structure of the electrons within them. As fermionic particles, electrons can be described using a mathematical formalism called second quantization. The idea is that there are a number of *orbitals*, each of which can be either empty or occupied by a fermion. A system of $N$ orbitals is described by a set of fermionic annihilation operators $\\\\{\\\\hat{a}_p\\\\}_{p=1}^N$ that satisfy the fermionic anticommutation relations,\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\hat{a}_p \\\\hat{a}_q + \\\\hat{a}_q \\\\hat{a}_p &= 0, \\\\\\\\\\n\",\n \"\\\\hat{a}_p \\\\hat{a}_q^\\\\dagger + \\\\hat{a}_q^\\\\dagger \\\\hat{a}_p &= \\\\delta_{pq}.\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"The adjoint $\\\\hat{a}_p^\\\\dagger$ is called a creation operator.\\n\",\n \"\\n\",\n \"So far, our exposition has not accounted for spin, which is a fundamental property of fermions. When accounting for spin, the orbitals come in pairs called *spatial orbitals*. Each spatial orbital is composed of two *spin orbitals*, one that is labeled \\\"spin-$\\\\alpha$\\\" and one that is labeled \\\"spin-$\\\\beta$\\\". We then write $\\\\hat{a}_{p\\\\sigma}$ for the annihilation operator associated with the spin-orbital with spin $\\\\sigma$ ($\\\\sigma \\\\in \\\\{\\\\alpha, \\\\beta\\\\}$) in spatial orbital $p$. If we take $N$ to be the number of spatial orbitals, then there are a total of $2N$ spin-orbitals. The Hilbert space of this system is spanned by $2^{2N}$ orthonormal basis vectors labeled with two-part bitstrings $\\\\lvert z \\\\rangle = \\\\lvert z_\\\\beta z_\\\\alpha \\\\rangle = \\\\lvert z_{\\\\beta, N} \\\\cdots z_{\\\\beta, 1} z_{\\\\alpha, N} \\\\cdots z_{\\\\alpha, 1} \\\\rangle$.\\n\",\n \"\\n\",\n \"The Hamiltonian of a molecular system can be written as\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{H} = \\\\sum_{ \\\\substack{pr\\\\\\\\\\\\sigma} } h_{pr} \\\\, \\\\hat{a}^\\\\dagger_{p\\\\sigma} \\\\hat{a}_{r\\\\sigma}\\n\",\n \"+ \\\\frac12\\n\",\n \"\\\\sum_{ \\\\substack{prqs\\\\\\\\\\\\sigma\\\\tau} }\\n\",\n \"h_{prqs} \\\\, \\n\",\n \"\\\\hat{a}^\\\\dagger_{p\\\\sigma}\\n\",\n \"\\\\hat{a}^\\\\dagger_{q\\\\tau}\\n\",\n \"\\\\hat{a}_{s\\\\tau}\\n\",\n \"\\\\hat{a}_{r\\\\sigma},\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where the $h_{pr}$ and $h_{prqs}$ are complex numbers called molecular integrals that can be calculated from the specification of the molecule using a computer program. In this tutorial, we compute the integrals using the [PySCF](https://pyscf.org/) software package.\\n\",\n \"\\n\",\n \"For details about how the molecular Hamiltonian is derived, consult a textbook on quantum chemistry (for example, *Modern Quantum Chemistry* by Szabo and Ostlund). \"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"b5e32180-3ff6-46bb-baa3-1c16a3ec443f\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"## Local unitary cluster Jastrow (LUCJ) ansatz\\n\",\n \"\\n\",\n \"SQD requires a quantum circuit ansatz to draw samples from. In this lab, we'll use the [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz [1] due to its combination of physical motivation and hardware-friendliness.\\n\",\n \"\\n\",\n \"The LUCJ ansatz is a specialized form of the general unitary cluster Jastrow (UCJ) ansatz, which has the form\\n\",\n \"\\n\",\n \"$$\\n\",\n \" \\\\lvert \\\\Psi \\\\rangle = \\\\prod_{\\\\mu=1}^L e^{\\\\hat{K}_\\\\mu} e^{i \\\\hat{J}_\\\\mu} e^{-\\\\hat{K}_\\\\mu} | \\\\Phi_0 \\\\rangle\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $\\\\lvert \\\\Phi_0 \\\\rangle$ is a reference state, often taken to be the Hartree-Fock state, and the $\\\\hat{K}_\\\\mu$ and $\\\\hat{J}_\\\\mu$ have the form\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{K}_\\\\mu = \\\\sum_{pq, \\\\sigma} K_{pq}^\\\\mu \\\\, \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{q \\\\sigma}\\n\",\n \"\\\\;,\\\\;\\n\",\n \"\\\\hat{J}_\\\\mu = \\\\sum_{pq, \\\\sigma\\\\tau} J_{pq,\\\\sigma\\\\tau}^\\\\mu \\\\, \\\\hat{n}_{p \\\\sigma} \\\\hat{n}_{q \\\\tau}\\n\",\n \"\\\\;,\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where we have defined the number operator\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{n}_{p \\\\sigma} = \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{p \\\\sigma}.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"The operator $e^{\\\\hat{K}_\\\\mu}$ is an orbital rotation, which can be implemented using known algorithms in linear depth and using linear connectivity.\\n\",\n \"Implementing the $e^{i \\\\mathcal{J}_k}$ term of the UCJ ansatz requires either all-to-all connectivity or the use of a fermionic swap network, making it challenging for noisy pre-fault-tolerant quantum processors that have limited connectivity. The idea of the *local* UCJ ansatz is to impose sparsity constraints on the\\n\",\n \"$\\\\mathbf{J}^{\\\\alpha\\\\alpha}$ and $\\\\mathbf{J}^{\\\\alpha\\\\beta} $\\n\",\n \"matrices which allow them to be implemented in constant depth on qubit topologies with limited connectivity. (Here, $\\\\mathbf{J}^{\\\\alpha\\\\alpha}= J_{p q,\\\\alpha\\\\alpha}^1$ and $\\\\mathbf{J}^{\\\\alpha\\\\beta}= J_{p q,\\\\alpha\\\\beta}^1$.)\\n\",\n \"\\n\",\n \"The IBM hardware has a heavy-hex lattice qubit topology, in which case we can adopt a \\\"zigzag\\\" pattern, depicted below. In this pattern, orbitals with the same spin are mapped to qubits with a line topology (red and blue circles), and a connection between orbitals of different spin is present at every 4th spatial orbital, with the connection being facilitated by an ancillary qubit (purple circles). In this case, the index constraints are\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha} &: \\\\{(p, p+1) \\\\; , \\\\; p = 0, \\\\ldots, N-2\\\\} \\\\\\\\\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\beta} &: \\\\{(p, p) \\\\;, \\\\; p = 0, 4, 8, \\\\ldots,\\\\; p \\\\leq N-1\\\\}\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 7. IBM's quantum hardware with a heavy-hex lattice qubit topology\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"8a20c704-0564-4bc3-b24a-64ce11179489\",\n \"metadata\": {},\n \"source\": [\n \"## Sample-based Quantum Diagonalization (SQD)\\n\",\n \"\\n\",\n \"The self-consistent configuration recovery procedure is designed to extract as much signal as possible from noisy quantum samples. The procedure is run in a loop, and each iteration has the following steps:\\n\",\n \"\\n\",\n \"1. **Recover the configuration**: For each bitstring that violates the specified symmetries, flip its bits with a probabilistic procedure designed to bring the bitstring closer to the current estimate of the average orbital occupancies, to obtain a new bitstring.\\n\",\n \"2. **Subsample**: Collect all of the old and new bitstrings that satisfy the symmetries, and subsample subsets of a fixed size, chosen in advance.\\n\",\n \"3. **Diagonalize in subspace**: For each subset of bitstrings, project the Hamiltonian into the subspace spanned by the corresponding basis vectors and compute a ground state estimate of the projected Hamiltonian on a classical computer.\\n\",\n \"4. **Find the lowest energy**: Update the estimate of the average orbital occupancies with the ground state estimate with the lowest energy.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"204b5142-3f9b-4d8f-8408-d6d32e9c9a13\",\n \"metadata\": {},\n \"source\": [\n \"The SQD workflow is depicted in the following diagram:\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 8. A diagram showing the SQD workflow\\n\",\n \"\\n\",\n \"SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\\\\mathcal{S} = \\\\{|x\\\\rangle \\\\}$ whose size does not increase exponentially with the size of the problem.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"1dce8aba-af4c-4a9a-a33a-3d8175085da8\",\n \"metadata\": {},\n \"source\": [\n \"### Configuration recovery loop 1: Recover the configuration\\n\",\n \"\\n\",\n \"The first step in the configuration recovery loop is to recover the configuration. In other words, error mitigation is performed in this part.\\n\",\n \"\\n\",\n \"If you run the quantum circuit that is the LUCJ ansatz built above on a quantum computer, you will get a bitstring and its count number as shown in the figure on the lower left. Because current noisy quantum computers give the result with the errors. Since the particle number of \\\"spin-$\\\\alpha$\\\" and \\\"spin-$\\\\beta$\\\" of the molecule in problem is fixed, we will correct the error by flipping a part of the resulting bitstring so that the particle number is stored correctly.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 9. Bitstrings that are initially produced by the LUCJ ansatz circuit (left) and the corrected bitstrings (right)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"857741ce-d6d9-490a-9c64-44ce60217815\",\n \"metadata\": {},\n \"source\": [\n \"For example, let's think about a problem that considers up to four spin orbitals for a molecule with two \\\"spin-$\\\\alpha$\\\" and two \\\"spin-$\\\\beta$\\\". If the orbitals are filled from the bottom of energy levels, the quantum state is |0011 0011> as shown in the figure on the lower left.\\n\",\n \"If the result of running the quantum computer contains |1110 0011> then three \\\"spin-$\\\\beta$\\\" is too many, so one of the bits is flipped from 1 to 0 so that there are two.\\n\",\n \"Also, if |0011 0001> is observed, one \\\"spin-$\\\\alpha$\\\" is missing, so one of these three electrons is inverted from 0 to 1.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 10. The strings are being corrected based on electron numbers and occupancy expectation with maxim weighted probability\\n\",\n \"\\n\",\n \"At this time, when choosing which electron to be flipped, the average orbital occupancy is used and flip the bit with the maximum weighted probability of flipping.\\n\",\n \"The average orbital occupancy is calculated at the end of the configuration recovery loop. So, in the first loop, we don't do this because we don't have an average orbital occupancy, and we discard the sample with the wrong particle numbers.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"aa2b0d64-63b6-455e-999e-f35c7d52b9e1\",\n \"metadata\": {},\n \"source\": [\n \"### Configuration recovery loop 2: Subsample\\n\",\n \"\\n\",\n \"The next step is to select some from the corrected samples.\\n\",\n \"This time, we have 100,000 shots, so for example, we randomly select 50 samples from these 5 times, that is, 5 batches. When handling large molecules in batches, this part can be computed in parallel on a supercomputer.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 11. Subsampling from corrected samples\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"fe817330-971e-4b02-b1a5-eb9c89a27a9e\",\n \"metadata\": {},\n \"source\": [\n \"### Configuration recovery loop 3: Diagonalize in subspace\\n\",\n \"\\n\",\n \"Next is diagonalizing in subspace. In the previous example, the original Hamiltonian $H$, $2^{32}\\\\times2^{32}$ matrix, is projected into the subspace created by $50$ bitstrings, $|x_i\\\\rangle$, and diagonalized.\\n\",\n \"This projection matrix $P_S$ can have $2^{32}$ different measured bitstrings, from 0...0 to 1...1, but a maximum of $50$ bitstrings can be measured. $P_S$ is a matrix with only $50$ diagonal components and the rest of the matrix with $0$. Therefore, by multiplying $P_S$ by the original Hamiltonian $H$, the projected Hamiltonian $H_s$ has only $50\\\\times50$ components and all other elements are 0. So, we can diagonalize only $50\\\\times50$ matrix instead of the original huge $2^{32}\\\\times2^{32}$ matrix.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 12. Matrix diagonalization in the subspace\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"52d3b688-89d0-4451-a03b-db232442a1a3\",\n \"metadata\": {},\n \"source\": [\n \"### Configuration recovery loop 4: Find the lowest energy\\n\",\n \"\\n\",\n \"Finally, from the results of all batches, we obtain an estimate of the average orbital occupancy, and the lowest energy as an estimate of the ground state. And update these data.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"2566ce1e-cf95-4915-b986-0c1a68662afa\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskit patterns\\n\",\n \"\\n\",\n \"We implement a Qiskit patterns showing how SQD process:\\n\",\n \"\\n\",\n \"1. **Step 1: Map to quantum problem**\\n\",\n \" - Generate an ansatz for estimating the ground state\\n\",\n \"2. **Step 2: Optimize the problem**\\n\",\n \" - Transpile the ansatz for the backend\\n\",\n \"3. **Step 3: Execute experiments**\\n\",\n \" - Draw samples from the ansatz using the ``Sampler`` primitive\\n\",\n \"4. **Step 4: Post-process results**\\n\",\n \" - Self-consistent configuration recovery loop\\n\",\n \" - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration.\\n\",\n \" - Probabilistically create batches of subsamples from recovered bitstrings.\\n\",\n \" - Project and diagonalize the molecular Hamiltonian over each sampled subspace.\\n\",\n \" - Save the minimum ground state energy found across all batches and update the avg orbital occupancy.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"157e65a0-06a9-4ac9-bafa-702824a84eb2\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Map classical inputs to a quantum problem\\n\",\n \"\\n\",\n \"We will find an approximation to the ground state of the molecule at equilibrium in the 6-31G basis set. First, we specify the molecule and its properties.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c5c49c74-d658-42f6-a729-ec1c7687e9aa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"\\n\",\n \"# Specify molecule properties\\n\",\n \"open_shell = False\\n\",\n \"spin_sq = 0\\n\",\n \"\\n\",\n \"# Build N2 molecule\\n\",\n \"mol = pyscf.gto.Mole()\\n\",\n \"mol.build(\\n\",\n \" atom=[[\\\"N\\\", (0, 0, 0)], [\\\"N\\\", (1.0, 0, 0)]],\\n\",\n \" basis=\\\"6-31g\\\",\\n\",\n \" symmetry=\\\"Dooh\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# Define active space\\n\",\n \"n_frozen = 2\\n\",\n \"active_space = range(n_frozen, mol.nao_nr())\\n\",\n \"\\n\",\n \"# Get molecular integrals\\n\",\n \"scf = pyscf.scf.RHF(mol).run()\\n\",\n \"num_orbitals = len(active_space)\\n\",\n \"n_electrons = int(sum(scf.mo_occ[active_space]))\\n\",\n \"num_elec_a = (n_electrons + mol.spin) // 2\\n\",\n \"num_elec_b = (n_electrons - mol.spin) // 2\\n\",\n \"cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\\n\",\n \"mo = cas.sort_mo(active_space, base=0)\\n\",\n \"hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\\n\",\n \"eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\\n\",\n \"\\n\",\n \"# Compute exact energy\\n\",\n \"exact_energy = cas.run().e_tot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91fa0157-366c-4c0e-b040-6a18f5546416\",\n \"metadata\": {},\n \"source\": [\n \"Before constructing the `LUCJ` ansatz circuit, we first perform a CCSD calculation in the following code cell. The [$t_1$ and $t_2$ amplitudes](https://en.wikipedia.org/wiki/Coupled_cluster#Cluster_operator) from this calculation will be used to initialize the parameters of the ansatz.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f72b68e1-065f-49fc-a7cb-7afe3d1c3f65\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get CCSD t2 amplitudes for initializing the ansatz\\n\",\n \"ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\\n\",\n \"t1 = ccsd.t1\\n\",\n \"t2 = ccsd.t2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"adb2fd92-294b-4f87-a438-f42d6589cd73\",\n \"metadata\": {},\n \"source\": [\n \"Now, we use [ffsim](https://github.com/qiskit-community/ffsim) to create the ansatz circuit. Since our molecule has a closed-shell Hartree-Fock state, we use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced). We pass interaction pairs appropriate for a heavy-hex lattice qubit topology.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"527f5873-f5be-4890-960e-4ad24c1e423d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"n_reps = 1\\n\",\n \"alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\\n\",\n \"alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\\n\",\n \"\\n\",\n \"ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\\n\",\n \" t2=t2,\\n\",\n \" t1=t1,\\n\",\n \" n_reps=n_reps,\\n\",\n \" interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\\n\",\n \")\\n\",\n \"\\n\",\n \"nelec = (num_elec_a, num_elec_b)\\n\",\n \"\\n\",\n \"# create an empty quantum circuit\\n\",\n \"qubits = QuantumRegister(2 * num_orbitals, name=\\\"q\\\")\\n\",\n \"circuit = QuantumCircuit(qubits)\\n\",\n \"\\n\",\n \"# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\\n\",\n \"circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\\n\",\n \"\\n\",\n \"# apply the UCJ operator to the reference state\\n\",\n \"circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\\n\",\n \"circuit.measure_all()\\n\",\n \"\\n\",\n \"circuit.decompose().decompose().draw(\\\"mpl\\\", fold =-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5a385896-0c00-425a-b06f-dc929371678a\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Optimize problem for quantum execution\\n\",\n \"Next, we optimize the circuit for a target hardware. We'll use the 127-qubit `ibm_brisbane` QPU.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ff55ea1b-22ef-4082-af12-d663ba3b7d6c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"backend = service.backend(\\\"ibm_brisbane\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"11c41725-68be-4289-95ee-562763547ca6\",\n \"metadata\": {},\n \"source\": [\n \"We recommend the following steps to optimize the ansatz and make it hardware-compatible.\\n\",\n \"\\n\",\n \"* Select physical qubits (`initial_layout`) from the target hardware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with less gates.\\n\",\n \"* Generate a staged pass manager using the [generate\\\\_preset\\\\_pass\\\\_manager](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.transpiler.generate_preset_pass_manager) function from qiskit with your choice of `backend` and `initial_layout`.\\n\",\n \"* Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes qiskit transpiler passes that decompose gates into orbital rotations and then merges the orbital rotations, resulting in fewer gates in the final circuit.\\n\",\n \"* Run the pass manager on your circuit.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b77ff882-7da3-40be-bbef-cadab8f9c841\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\\n\",\n \"spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\\n\",\n \"initial_layout = spin_a_layout + spin_b_layout\\n\",\n \"\\n\",\n \"pass_manager = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend, initial_layout=initial_layout\\n\",\n \")\\n\",\n \"\\n\",\n \"# We will use the circuit generated by this pass manager for hardware execution\\n\",\n \"pass_manager.pre_init = ffsim.qiskit.PRE_INIT\\n\",\n \"isa_circuit = pass_manager.run(circuit)\\n\",\n \"print(f\\\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"04a7a60b-e87a-4b13-9f35-573db129526c\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Execute using Qiskit Primitives\\n\",\n \"\\n\",\n \"After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. \\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** We have commented out the code for running the circuit on a QPU and left it for the user's reference. Instead of running on real hardware in this walkthrough, we will just read in 100k samples drawn from ``ibm_brisbane`` at an earlier time.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8a4169e6-870d-4c30-a07b-207a09c3f444\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# from qiskit_ibm_runtime import SamplerV2 as Sampler\\n\",\n \"\\n\",\n \"# sampler = Sampler(mode=backend)\\n\",\n \"# job = sampler.run([isa_circuit], shots=10_000)\\n\",\n \"# primitive_result = job.result()\\n\",\n \"# pub_result = primitive_result[0]\\n\",\n \"# bit_array = pub_result.data.meas\\n\",\n \"\\n\",\n \"bit_array = np.load('utils/N2_device_bitarray.npy', allow_pickle=True).item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6e1521e1-7474-4267-9364-738404e367a7\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Post-process and return result to desired classical format\\n\",\n \"\\n\",\n \"Recall that the self-consistent configuration recovery is an iterative procedure that runs in a loop. In the following code cell, the first iteration of the loop simply uses the raw samples (after post-selection on symmetries) as input to the diagonalization procedure to obtain an estimate of the average orbital occupancies. Later iterations of the loop use these occupancies to generate new configurations from raw samples that violate the symmetries. These configurations are collected and then subsampled to produce batches of configurations, which are then used to project the Hamiltonian and compute a ground state estimate with an eigenstate solver.\\n\",\n \"\\n\",\n \"There are a few user-controlled options which are important for this technique:\\n\",\n \"\\n\",\n \"* `max_iterations`: Number of iterations of the self-consistent recovery loop.\\n\",\n \"* `num_batches`: Number of batches of configurations to subsample (this will be the number of separate calls to the eigenstate solver)\\n\",\n \"* `samples_per_batch`: Number of unique configurations to include in each batch\\n\",\n \"* `max_cycles`: Maximum number of Davidson cycles run by the eigenstate solver\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f585a9a-acd4-437e-b2af-eb672e0eb0c4\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"Warning: 5 minutes needed\\n\",\n \"\\n\",\n \"When running the code below it will take up to 5 minutes (depending on your computer) to execute and will block this notebook for this time. \\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f026c1fb-e159-47b3-9b00-062cc07f1271\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"# SQD options\\n\",\n \"energy_tol = 1e-3 \\n\",\n \"occupancies_tol = 1e-3 \\n\",\n \"max_iterations = 5\\n\",\n \"\\n\",\n \"# Eigenstate solver options\\n\",\n \"num_batches = 5\\n\",\n \"samples_per_batch = 50\\n\",\n \"symmetrize_spin = True \\n\",\n \"carryover_threshold = 1e-4 \\n\",\n \"max_cycle = 200\\n\",\n \"rng = np.random.default_rng(24)\\n\",\n \"\\n\",\n \"\\n\",\n \"# Pass options to the built-in eigensolver. If you just want to use the defaults,\\n\",\n \"# you can omit this step, in which case you would not specify the sci_solver argument\\n\",\n \"# in the call to diagonalize_fermionic_hamiltonian below.\\n\",\n \"sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle)\\n\",\n \"\\n\",\n \"\\n\",\n \"# List to capture intermediate results\\n\",\n \"result_history = [] \\n\",\n \"\\n\",\n \"def callback(results: list[SCIResult]): \\n\",\n \" result_history.append(results)\\n\",\n \" iteration = len(result_history)\\n\",\n \" print(f\\\"Iteration {iteration}\\\")\\n\",\n \" for i, result in enumerate(results):\\n\",\n \" print(f\\\"\\\\tSubsample {i}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tEnergy: {result.energy + nuclear_repulsion_energy}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\\\")\\n\",\n \"\\n\",\n \"result = diagonalize_fermionic_hamiltonian(\\n\",\n \" hcore,\\n\",\n \" eri,\\n\",\n \" bit_array,\\n\",\n \" samples_per_batch=samples_per_batch,\\n\",\n \" norb=num_orbitals,\\n\",\n \" nelec=nelec,\\n\",\n \" num_batches=num_batches,\\n\",\n \" energy_tol=energy_tol,\\n\",\n \" occupancies_tol=occupancies_tol,\\n\",\n \" max_iterations=max_iterations,\\n\",\n \" sci_solver=sci_solver,\\n\",\n \" symmetrize_spin=symmetrize_spin,\\n\",\n \" carryover_threshold=carryover_threshold,\\n\",\n \" callback=callback,\\n\",\n \" seed=rng,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b7e28f80-675f-4ef4-bf86-2a6c696cef23\",\n \"metadata\": {},\n \"source\": [\n \"## Visualize the results\\n\",\n \"\\n\",\n \"To see the result, we first create a function `plot_energy_and_occupancy`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bd6d96b3-cfd6-490b-bd20-4c5530071f86\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def plot_energy_and_occupancy(result_history, exact_energy):\\n\",\n \"\\n\",\n \" # Data for energies plot\\n\",\n \" x1 = range(len(result_history))\\n\",\n \" min_e = [\\n\",\n \" min(result, key=lambda res: res.energy).energy + nuclear_repulsion_energy\\n\",\n \" for result in result_history\\n\",\n \" ]\\n\",\n \" e_diff = [abs(e - exact_energy) for e in min_e]\\n\",\n \" yt1 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4]\\n\",\n \" \\n\",\n \" # Chemical accuracy (+/- 1 milli-Hartree)\\n\",\n \" chem_accuracy = 0.001\\n\",\n \" \\n\",\n \" # Data for avg spatial orbital occupancy\\n\",\n \" y2 = np.sum(result.orbital_occupancies, axis=0)\\n\",\n \" x2 = range(len(y2))\\n\",\n \" \\n\",\n \" fig, axs = plt.subplots(1, 2, figsize=(12, 6))\\n\",\n \" \\n\",\n \" # Plot energies\\n\",\n \" axs[0].plot(x1, e_diff, label=\\\"energy error\\\", marker=\\\"o\\\")\\n\",\n \" axs[0].set_xticks(x1)\\n\",\n \" axs[0].set_xticklabels(x1)\\n\",\n \" axs[0].set_yticks(yt1)\\n\",\n \" axs[0].set_yticklabels(yt1)\\n\",\n \" axs[0].set_yscale(\\\"log\\\")\\n\",\n \" axs[0].set_ylim(1e-4)\\n\",\n \" axs[0].axhline(y=chem_accuracy, color=\\\"#BF5700\\\", linestyle=\\\"--\\\", label=\\\"chemical accuracy\\\")\\n\",\n \" axs[0].set_title(\\\"Approximated Ground State Energy Error vs SQD Iterations\\\")\\n\",\n \" axs[0].set_xlabel(\\\"Iteration Index\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" axs[0].set_ylabel(\\\"Energy Error (Ha)\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" axs[0].legend()\\n\",\n \" \\n\",\n \" # Plot orbital occupancy\\n\",\n \" axs[1].bar(x2, y2, width=0.8)\\n\",\n \" axs[1].set_xticks(x2)\\n\",\n \" axs[1].set_xticklabels(x2)\\n\",\n \" axs[1].set_title(\\\"Avg Occupancy per Spatial Orbital\\\")\\n\",\n \" axs[1].set_xlabel(\\\"Orbital Index\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" axs[1].set_ylabel(\\\"Avg Occupancy\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" \\n\",\n \" print(f\\\"Exact energy: {exact_energy:.5f} Ha\\\")\\n\",\n \" print(f\\\"SQD energy: {min_e[-1]:.5f} Ha\\\")\\n\",\n \" print(f\\\"Absolute error: {e_diff[-1]:.5f} Ha\\\")\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f05f1766-38de-410b-ab8e-0ded552c77bc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_energy_and_occupancy(result_history, exact_energy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8c8fc4ab-1ab4-4e76-9fbb-a069ed759085\",\n \"metadata\": {},\n \"source\": [\n \"The first plot shows that after a couple of iterations we estimate the ground state energy within `~100 mH` (chemical accuracy is typically accepted to be `1 kcal/mol` $\\\\approx$ `1.6 mH`). The energy can be improved by drawing more samples from the circuit or increasing the number of samples per batch.\\n\",\n \"\\n\",\n \"The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86541d66-d12d-4049-abd6-0c3469a155b4\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2: Flip a bit by configuration recovery \\n\",\n \"\\n\",\n \"In a problem in which an $N_2$ molecule is prepared using the STO-3G basis set, we perform configuration recovery. When the average orbital occupancy $n$ is as follows, we will correct the bitstring $x$ as follows. What bitstring is most likely to be modified to?\\n\",\n \"\\n\",\n \"$n = [0.007, 0.029, 0.029, 0.995, \\n\",\n \" 0.976, 0.976, 0.993, 0.997, \\n\",\n \" 0.007, 0.029, 0.029, 0.995,\\n\",\n \" 0.976, 0.976, 0.993, 0.997]$\\n\",\n \"\\n\",\n \"$x = [1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0]$\\n\",\n \"\\n\",\n \"A weighted probability of flipping $w(y)$ using a modified ReLU function is calculated as follows from [2]. \\n\",\n \"$$\\n\",\n \"\\\\begin{align}\\n\",\n \" w(y) = \\\\begin{cases} \\n\",\n \" \\\\delta\\\\cdot \\\\frac{y}{h} & \\\\text{if } y \\\\leq h\\\\\\\\ \\n\",\n \" \\\\delta + (1 - \\\\delta)\\\\cdot \\\\frac{y - h}{1 - h} & \\\\text{if } y > h\\n\",\n \"\\\\end{cases}\\n\",\n \"\\\\end{align}\\n\",\n \"$$ \\n\",\n \"\\n\",\n \"Here, $y$ is a probability of flipping, and defined as $y[i] =|x[i]-n[i]|$ for the $i$ -th spin orbital. $h$ defines the location of the \\\"corner\\\" of the ReLU function, and the parameter $\\\\delta$ defines the value of the ReLU function at the corner. We use $\\\\delta = 0.01$, same as [2], and $h = $number of alpha(or beta) particles$/$number of alpha(or beta) spin orbitals$ = N/M$ (filling factor).\\n\",\n \"\\n\",\n \"In the actual configuration recovery, a bit is randomly inverted with a weight of $w(y)$. In this exercise, answer the result of inverting the bit $i$ with the largest $w(y[i])$ as the bitstring with the highest probability of obtaining.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0ecea848-e0cb-4b35-87cd-b4e75318352c\",\n \"metadata\": {},\n \"source\": [\n \"Note: \\n\",\n \"- The right half of a bitstring represents spin-up orbitals, and the left half represents spin-down orbitals. A `1` means the orbital is occupied by an electron, and a `0` means the orbital is empty.\\n\",\n \"- Please refer to the section [\\\"4.1 Configuration recovery overview\\\" in Lesson 4: SQD application](https://quantum.cloud.ibm.com/learning/en/courses/quantum-diagonalization-algorithms/sqd-implementation) of \\\"Quantum Diagonalization Algorithm\\\" of IBM Quantum Learning.\\n\",\n \"- In this case, one more beta particle is needed, so if the i-th orbital is already occupied and need not to be flipped, you set its y_beta[i] to 0.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cd04526f-eee0-4f96-8f52-d437899af540\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"n = [0.007, 0.029, 0.029, 0.995, \\n\",\n \" 0.976, 0.976, 0.993, 0.997, \\n\",\n \" 0.007, 0.029, 0.029, 0.995,\\n\",\n \" 0.976, 0.976, 0.993, 0.997]\\n\",\n \"\\n\",\n \"x = [1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"95e5f030-47af-495a-a8c5-823a24d666ea\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x = np.array(x)\\n\",\n \"n = np.array(n)\\n\",\n \"\\n\",\n \"# ---- TODO : Task 2 ---\\n\",\n \"\\n\",\n \"# Divide into alpha spin and beta spin\\n\",\n \"x_alpha =\\n\",\n \"x_beta =\\n\",\n \"\\n\",\n \"# probability of flipping\\n\",\n \"y = \\n\",\n \"y_alpha =\\n\",\n \"y_beta =\\n\",\n \"\\n\",\n \"# In this case, one more beta particle is needed, so set y_beta[i] to 0 if x_beta[i] is already 1. \\n\",\n \"for i in range(len(y_beta)):\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"print(y_beta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9e9f16a3-d459-41e4-b79d-bc7bbd91de1f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"h = 5/8\\n\",\n \"delta = 0.01\\n\",\n \"w = np.zeros(len(y_beta))\\n\",\n \"\\n\",\n \"# find the maximum w\\n\",\n \"# ---- TODO : Task 2 ---\\n\",\n \"for i in range(len(y_beta)):\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"# print(max_index, max_w)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d23e1599-e5cf-467d-a58b-e22952e0dfc7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Flip the bit of the index with the largest w\\n\",\n \"# ---- TODO : Task 2 ---\\n\",\n \"for i in range(len(y_beta)):\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"x = np.concatenate([x_beta, x_alpha])\\n\",\n \"corrected_x = \\n\",\n \"# --- End of TODO ---\\n\",\n \"# print(corrected_x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b028186e-039f-4f6d-a9a6-2a4cc85f6119\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex2(corrected_x) # Expected result type: list\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7e08dfec-1433-4166-9b56-53e722059917\",\n \"metadata\": {},\n \"source\": [\n \"# 5. Improve the ansatz\\n\",\n \"\\n\",\n \"The more accurate the sampling from the quantum circuit, the better the results. Therefore, how to make the ansatz for sampling is one of the key points in SQD performance. Depending on how the ansatz is set, different bit strings are sampled, which affects the accuracy of the energy estimate value that can be obtained. Here, we will explore the different ansatz to improve the result.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b948384-3b96-4c37-9676-b6619d0c6ee6\",\n \"metadata\": {},\n \"source\": [\n \"## 5.1 Change basis set\\n\",\n \"\\n\",\n \"First, let's try to model the molecule in a more natural way by changing the basis set of the molecule to account for more electron orbitals. Incorporating more orbitals into the calculation will increase the computational complexity, but should lower the value of the energy estimation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e3d406e6-a664-4890-b8cc-36265ac72c66\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 3: Change basis set \\n\",\n \"\\n\",\n \"Change the basis set from `6-31G` to `cc-pvdz`. How many qubits are needed if we use the LUCJ ansatz with **6** ancillary qubits in case we use `ibm_torino` as a backend? \\n\",\n \"Note: Because of the geometric limitation of `ibm_torino`, the number of ancillary qubits is limited to 6 here.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"28aacef0-e91e-4b29-9d10-d298c699bf4b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"\\n\",\n \"# Specify molecule properties\\n\",\n \"open_shell = False\\n\",\n \"spin_sq = 0\\n\",\n \"\\n\",\n \"# Build N2 molecule\\n\",\n \"mol = pyscf.gto.Mole()\\n\",\n \"mol.build(\\n\",\n \" atom=[[\\\"N\\\", (0, 0, 0)], [\\\"N\\\", (1.0, 0, 0)]],\\n\",\n \" # ---- TODO : Task 3 ---\\n\",\n \" basis= ### input your code here ###,\\n\",\n \" # --- End of TODO ---\\n\",\n \" symmetry=\\\"Dooh\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# Define active space\\n\",\n \"n_frozen = 2\\n\",\n \"active_space = range(n_frozen, mol.nao_nr())\\n\",\n \"\\n\",\n \"# Get molecular integrals\\n\",\n \"scf = pyscf.scf.RHF(mol).run()\\n\",\n \"num_orbitals = len(active_space)\\n\",\n \"n_electrons = int(sum(scf.mo_occ[active_space]))\\n\",\n \"num_elec_a = (n_electrons + mol.spin) // 2\\n\",\n \"num_elec_b = (n_electrons - mol.spin) // 2\\n\",\n \"cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\\n\",\n \"mo = cas.sort_mo(active_space, base=0)\\n\",\n \"hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\\n\",\n \"eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\\n\",\n \"\\n\",\n \"print(num_orbitals)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1afdd410-3288-4df9-927e-c00afd50daac\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO : Task 3 ---\\n\",\n \"n_qubits = ### input your result ###\\n\",\n \"# --- End of TODO ---\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"14d5fd85-612a-4878-be7b-52aa2b8e6f3e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex3(n_qubits) # Expected result type: integer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae2587d2-d5d1-473c-8cbc-5112642c30d1\",\n \"metadata\": {},\n \"source\": [\n \"## 5.2 Select the best layout\\n\",\n \"\\n\",\n \"Now that we have obtained the number of qubits, the next step is to determine the layout of the physical qubits. To use a larger basis, we will use a Heron device with better performance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"13f326d6-16c2-4395-bc02-9d4499b50c9e\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 4: Select the best layout \\n\",\n \"\\n\",\n \"Which qubits should we choose as the initial placement to get the best results? To select the qubits, you need to check the errors of each qubit. In the following map, qubits with a readout error greater than 0.1 are shown in black, and edges with a CZ error greater than 0.1 are shown in white. Answer the best `initial_layout`, which is used as an argument of pass_manger to create the ISA circuit. \\n\",\n \"We will use `ibm_torino` as a backend. Because of the geometric limitation of `ibm_torino`, the number of ancillary qubits limit to 6 here.\\n\",\n \"\\n\",\n \"Select the best initial qubit layout to get better sampling by following:\\n\",\n \"1. List the qubits with a readout error of 0.1 or more as `bad_readout_qubits` from `backend_target`.\\n\",\n \"2. List of edges with a CZ error of 0.1 or more as `bad_czgate_edges` from `backend_target`.\\n\",\n \"3. Display the coupling map with `bad_readout_qubits` in black and `bad_czgate_edges` in white.\\n\",\n \"4. Select the best initial qubit layout.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** **You need to use the preloaded pickle file `backend_target_v20.pkl` or `backend_target_v21.pkl` as `backend_target` for backend information in order to pass the grader of this exercise**. In Lab 2, we used `backend.properties()`, `backend.target`, etc., but we won’t use them this time. We have commented out the code for a real backend a for the user's reference. \\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** If you are using Qiskit version 2.1.x, open `backend_target_v21.pkl` in the next cell. \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"60db7fe7-01b0-42c9-8569-e729cfde5f3c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# for Qiskit version 2.0.x users\\n\",\n \"with open(\\\"utils/backend_target_v20.pkl\\\", \\\"rb\\\") as f:\\n\",\n \"# for Qiskit version 2.1.x users\\n\",\n \"# with open(\\\"utils/backend_target_v21.pkl\\\", \\\"rb\\\") as f:\\n\",\n \" backend_target = pickle.load(f)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bc9dc488-8232-44eb-8b14-a28603948212\",\n \"metadata\": {},\n \"source\": [\n \"Note: Please refer [\\\"Instruction properties\\\" part of \\\"Get QPU information with Qiskit\\\"](https://quantum.cloud.ibm.com/docs/en/guides/get-qpu-information#instruction-properties) to get the properties like \\\"measure\\\" and \\\"cz\\\" from `backend_target`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a5906475-2c7e-46b3-9799-b29a6b6b77bd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"BAD_READOUT_ERROR_THRESHOLD = 0.1\\n\",\n \"BAD_CZGATE_ERROR_THRESHOLD = 0.1\\n\",\n \"backend_num_qubits = 133\\n\",\n \"\\n\",\n \"# ---- TODO : Task 4 ---\\n\",\n \"bad_readout_qubits = ### build your code here ###\\n\",\n \"bad_czgate_edges = ### build your code here ###\\n\",\n \"# --- End of TODO ---\\n\",\n \"print(\\\"Bad readout qubits:\\\", bad_readout_qubits)\\n\",\n \"print(\\\"Bad CZ gates:\\\", bad_czgate_edges)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ac01a0ea-2d7b-4225-8694-349341663b4b\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"Warning: Graphviz Library needed\\n\",\n \"\\n\",\n \"The 'Graphviz' library is required to use 'plot_coupling_map'. To install, follow the instructions at:\\n\",\n \"\\n\",\n \"\\n\",\n \"https://graphviz.org/download \\n\",\n \"\\n\",\n \"\\n\",\n \"If you dont want to install it, you can just skip the next block of code, it is only needed for visualization. \\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b80881e2-9a28-4d16-8d8b-ca14811ea780\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qubit_color = []\\n\",\n \"for i in range(backend_num_qubits):\\n\",\n \" if i in bad_readout_qubits:\\n\",\n \" qubit_color.append(\\\"#000000\\\") #black\\n\",\n \" else:\\n\",\n \" qubit_color.append(\\\"#8c00ff\\\") #purple\\n\",\n \"line_color = []\\n\",\n \"for e in backend_target.build_coupling_map().get_edges():\\n\",\n \" if e in bad_czgate_edges:\\n\",\n \" line_color.append(\\\"#ffffff\\\") #white\\n\",\n \" else:\\n\",\n \" line_color.append(\\\"#888888\\\") #gray\\n\",\n \"plot_gate_map(backend, qubit_color=qubit_color, line_color=line_color, qubit_size=50, font_size=25, figsize=(14,14))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c43f5d1f-c7cc-4584-81e9-43db1a56e2dd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# select the initial layout\\n\",\n \"# ---- TODO : Task 4 ---\\n\",\n \"spin_a_layout = ### add your qubits list ###\\n\",\n \"spin_b_layout = ### add your qubits list ###\\n\",\n \"# --- End of TODO ---\\n\",\n \"initial_layout = spin_a_layout + spin_b_layout\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c929a180-c518-4cee-abd5-67b239f381ea\",\n \"metadata\": {},\n \"source\": [\n \"Check your layout here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b3d191ac-68b0-4c1d-846c-30730856b876\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qubit_color = []\\n\",\n \"for i in range(backend_num_qubits):\\n\",\n \" if i in bad_readout_qubits:\\n\",\n \" qubit_color.append(\\\"#000000\\\") #black\\n\",\n \" elif i in initial_layout:\\n\",\n \" qubit_color.append(\\\"#ff00dd\\\") #pink\\n\",\n \" else:\\n\",\n \" qubit_color.append(\\\"#8c00ff\\\") #purple\\n\",\n \"line_color = []\\n\",\n \"for e in backend_target.build_coupling_map().get_edges():\\n\",\n \" if e in bad_czgate_edges:\\n\",\n \" line_color.append(\\\"#ffffff\\\") #white\\n\",\n \" else:\\n\",\n \" line_color.append(\\\"#888888\\\") #gray\\n\",\n \"plot_gate_map(backend, qubit_color=qubit_color, line_color=line_color, qubit_size=50, font_size=25, figsize=(14,14))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cc2bf757-2fa1-47b9-9993-36cf163d99f0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex4(initial_layout) # Expected result type: lists\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"531344cb-0b6f-45fe-83ee-9b99801f2dd5\",\n \"metadata\": {},\n \"source\": [\n \"## 5.3 Add more interaction to LUCJ ansatz\\n\",\n \"\\n\",\n \"The LUCJ ansatz used in section 4 has the form\\n\",\n \"\\n\",\n \"$$\\n\",\n \" \\\\lvert \\\\Psi \\\\rangle = \\\\prod_{\\\\mu=1}^L e^{\\\\hat{K}_\\\\mu} e^{i \\\\hat{J}_\\\\mu} e^{-\\\\hat{K}_\\\\mu} | \\\\Phi_0 \\\\rangle\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $\\\\lvert \\\\Phi_0 \\\\rangle$ is a reference state, often taken to be the Hartree-Fock state, and the$\\\\hat{K}_\\\\mu$ and $\\\\hat{J}_\\\\mu$ have the form\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{K}_\\\\mu = \\\\sum_{pq, \\\\sigma} K_{pq}^\\\\mu \\\\, \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{q \\\\sigma}\\n\",\n \"\\\\;,\\\\;\\n\",\n \"\\\\hat{J}_\\\\mu = \\\\sum_{pq, \\\\sigma\\\\tau} J_{pq,\\\\sigma\\\\tau}^\\\\mu \\\\, \\\\hat{n}_{p \\\\sigma} \\\\hat{n}_{q \\\\tau}\\n\",\n \"\\\\;,\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where we have defined the number operator\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{n}_{p \\\\sigma} = \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{p \\\\sigma}.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"The IBM hardware has a heavy-hex lattice qubit topology, and it yields the following index constraints on the $\\\\mathbf{J}$ matrices:\\n\",\n \"\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha} &: \\\\{(p, p+1) \\\\; , \\\\; p = 0, \\\\ldots, N-2\\\\} \\\\\\\\\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\beta} &: \\\\{(p, p) \\\\;, \\\\; p = 0, 4, 8, \\\\ldots,\\\\; p \\\\leq N-1\\\\}\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\\n\",\n \"Here, $\\\\mathbf{J}^{\\\\alpha\\\\alpha}= J_{p q,\\\\alpha\\\\alpha}^1$ and $\\\\mathbf{J}^{\\\\alpha\\\\beta}= J_{p q,\\\\alpha\\\\beta}^1$.\\n\",\n \"\\n\",\n \"In this case, $\\\\mathbf{J}^{\\\\alpha\\\\alpha}$ only interacts with adjacent spins on the $\\\\alpha$ or $\\\\beta$ orbit. This time, to model close to nature, let's change $\\\\mathbf{J}^{\\\\alpha\\\\alpha}$ so that the interaction also works with the next adjacent spin.\\n\",\n \"\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha} &: \\\\{(p, p+1, p+2) \\\\; , \\\\; p = 0, \\\\ldots, N-3\\\\} \\\\\\\\\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\beta} &: \\\\{(p, p) \\\\;, \\\\; p = 0, 4, 8, \\\\ldots,\\\\; p \\\\leq N-1\\\\}\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c0292b43-2e65-43bf-855b-2fda443b8c82\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 5: Add more interaction to LUCJ ansatz \\n\",\n \"\\n\",\n \"In the LUCJ ansatz, modify the code of `alpha_alpha_indices` so that the $\\\\mathbf{J}$ matrix interacts not only with adjacent spins on the $\\\\alpha$ or $\\\\beta$ orbit, but also with spins two steps ahead. Submit the list of `alpha_alpha_indices`.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fad7278a-9178-4ac0-9c7f-01648d46cd9f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get CCSD t2 amplitudes for initializing the ansatz\\n\",\n \"ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\\n\",\n \"t1 = ccsd.t1\\n\",\n \"t2 = ccsd.t2\\n\",\n \"\\n\",\n \"n_reps = 1\\n\",\n \"# ---- TODO : Task 5 ---\\n\",\n \"alpha_alpha_indices = ### input your code here ###\\n\",\n \"# --- End of TODO ---\\n\",\n \"alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\\n\",\n \"\\n\",\n \"ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\\n\",\n \" t2=t2,\\n\",\n \" t1=t1,\\n\",\n \" n_reps=n_reps,\\n\",\n \" interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\\n\",\n \")\\n\",\n \"\\n\",\n \"nelec = (num_elec_a, num_elec_b)\\n\",\n \"\\n\",\n \"# create an empty quantum circuit\\n\",\n \"qubits = QuantumRegister(2 * num_orbitals, name=\\\"q\\\")\\n\",\n \"circuit = QuantumCircuit(qubits)\\n\",\n \"\\n\",\n \"# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\\n\",\n \"circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\\n\",\n \"\\n\",\n \"# apply the UCJ operator to the reference state\\n\",\n \"circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\\n\",\n \"circuit.measure_all()\\n\",\n \"circuit.decompose().decompose().draw(\\\"mpl\\\", fold=-1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f01ad27a-5a0a-46f8-afaf-da18af7d1956\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex5(alpha_alpha_indices) # Expected result type: list[tuple[int, int]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5db71e0e-4c4c-460d-99d2-8b13acfa9bfd\",\n \"metadata\": {},\n \"source\": [\n \"Congratulations! You finished this lab. The next session is a bonus session and does not count towards your score.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"644cd6fb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check your submission status with the code below\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"109cb3e9-987f-4990-acb1-eac3c706627f\",\n \"metadata\": {},\n \"source\": [\n \"# Bonus: Real hardware execution with error mitigation \\n\",\n \"\\n\",\n \"Finally, let's run the modified ansatz circuit on a real device and run the configuration recovery loop to find the energy. In doing so, we will use error mitigation to reduce the effect of errors as much as possible.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2581d200-3327-418e-afb8-b71413c05d56\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"backend = service.backend('ibm_torino')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1a7c1c3d-19fa-430e-898d-4bdcec737922\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pass_manager = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend, initial_layout=initial_layout\\n\",\n \")\\n\",\n \"\\n\",\n \"# We will use the circuit generated by this pass manager for hardware execution\\n\",\n \"pass_manager.pre_init = ffsim.qiskit.PRE_INIT\\n\",\n \"isa_circuit = pass_manager.run(circuit)\\n\",\n \"print(f\\\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e713e002-0187-4c4f-9b7f-17843bd49eb3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opts = SamplerOptions()\\n\",\n \"opts.dynamical_decoupling.enable = True\\n\",\n \"opts.twirling.enable_measure = True\\n\",\n \"\\n\",\n \"sampler = Sampler(mode=backend, options=opts)\\n\",\n \"job = sampler.run([isa_circuit], shots=100_000)\\n\",\n \"print(\\\"job id:\\\", job.job_id())\\n\",\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d3b6559f-2bce-4d05-8ec5-fd818c4535e9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"\\n\",\n \"# Get job id from cell above\\n\",\n \"job = service.job('INPUT-YOUR-JOB-ID')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"21f262fb-e8fd-414a-98d0-7e497dfb79c0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"59f86a08-c703-4362-8957-e30aeb94e3c5\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Warning: 20 minutes needed\\n\",\n \"\\n\",\n \"When running the code below it will take up to 20 minutes (depending on your computer) to execute and will block this notebook for this time. \\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f329e74f-b905-46df-af8c-e0044e6606d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"primitive_result = job.result()\\n\",\n \"pub_result = primitive_result[0]\\n\",\n \"bit_array = pub_result.data.meas\\n\",\n \"\\n\",\n \"# SQD 設定\\n\",\n \"energy_tol = 1e-3 \\n\",\n \"occupancies_tol = 1e-3 \\n\",\n \"max_iterations = 3\\n\",\n \"\\n\",\n \"# 固有値ソルバー設定\\n\",\n \"num_batches = 3\\n\",\n \"samples_per_batch = 200\\n\",\n \"symmetrize_spin = True \\n\",\n \"carryover_threshold = 1e-4 \\n\",\n \"max_cycle = 200\\n\",\n \"\\n\",\n \"# ビルトイン固有値ソルバーにオプションを渡します。デフォルトを使用したい場合は、このステップを省略できます。\\n\",\n \"# その場合、diagonalize_fermionic_hamiltonian の呼び出しで sci_solver 引数は指定しません。\\n\",\n \"sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle) ###NEW###\\n\",\n \"\\n\",\n \"# 中間結果をキャプチャするリスト\\n\",\n \"result_history = [] \\n\",\n \"\\n\",\n \"def callback(results: list[SCIResult]): \\n\",\n \" result_history.append(results)\\n\",\n \" iteration = len(result_history)\\n\",\n \" print(f\\\"Iteration {iteration}\\\")\\n\",\n \" for i, result in enumerate(results):\\n\",\n \" print(f\\\"\\\\tSubsample {i}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tEnergy: {result.energy + nuclear_repulsion_energy}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\\\")\\n\",\n \"\\n\",\n \"result = diagonalize_fermionic_hamiltonian(\\n\",\n \" hcore,\\n\",\n \" eri,\\n\",\n \" bit_array,\\n\",\n \" samples_per_batch=samples_per_batch,\\n\",\n \" norb=num_orbitals,\\n\",\n \" nelec=nelec,\\n\",\n \" num_batches=num_batches,\\n\",\n \" energy_tol=energy_tol,\\n\",\n \" occupancies_tol=occupancies_tol,\\n\",\n \" max_iterations=max_iterations,\\n\",\n \" sci_solver=sci_solver,\\n\",\n \" symmetrize_spin=symmetrize_spin,\\n\",\n \" carryover_threshold=carryover_threshold,\\n\",\n \" callback=callback,\\n\",\n \" seed=rng,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a7424751-f167-4006-9d48-f738791dab09\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"exact_energy=-109.2177884189209 # CCSD エネルギー\\n\",\n \"plot_energy_and_occupancy(result_history, exact_energy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c9894969-4c9b-45c0-91a8-097f17d7462b\",\n \"metadata\": {},\n \"source\": [\n \"# 参考文献 \\n\",\n \"\\n\",\n \"\\\\[1] M. Motta et al., “Bridging physical intuition and hardware efficiency for correlated electronic states: the local unitary cluster Jastrow ansatz for electronic structure” (2023). [Chem. Sci., 2023, 14, 11213](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k)\\n\",\n \"\\n\",\n \"\\\\[2] J. Robledo-Moreno et al., \\\"Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer\\\" (2024). [arXiv:quant-ph/2405.05068](https://arxiv.org/abs/2405.05068).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fbfca50d-c810-4f56-a0fc-ab3b6fc64228\",\n \"metadata\": {},\n \"source\": [\n \"# 追加情報\\n\",\n \"\\n\",\n \"**作成** Yuri Kobayashi, Kifumi Numata\\n\",\n \"\\n\",\n \"**監修** Yukio Kawashima, Toshinari Itoko\\n\",\n \"\\n\",\n \"**査読** Kevin Sung, Jennifer Glick\\n\",\n \"\\n\",\n \"**翻訳:** Daiki Murata\\n\",\n \"\\n\",\n \"**Version:** 1.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"38eb8499-55ca-4d5c-8ca9-2441b03b52b1\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit パッケージバージョン\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8588a958-8208-4f81-88e1-cae2e27d04ee\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_runtime\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit IBM Runtime: {qiskit_ibm_runtime.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-4/lab4-ko.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9c80575e\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# Lab 4: Quantum Error Correction: From Core Concepts to the Road to Fault-Tolerant Quantum Computing\\n\",\n \"\\n\",\n \"Qiskit Global Summer School의 네 번째 코딩 챌린지까지 오신 것을 환영합니다. 이번 랩에서는 오류 정정 코드에서 기본적인 고전 오류 정정 코드와 주요 개념들로부터 시작을 해보도록 하겠습니다. 그리고는 결함 허용 양자컴퓨터의 근간을 이루는 양자 오류 정정(Quantum Error Correction, QEC)을 스태빌라이저 형식과 그와 관련된 예시를 통해 공부할 것입니다. 마지막으로 고급 QEC 아키텍쳐를 QLDPC, Toric, Gross 코드 등의 예시를 통해 살펴보도록 하겠습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"690bf7d6\",\n \"metadata\": {},\n \"source\": [\n \"# 목차\\n\",\n \"\\n\",\n \"- 챕터 1: 고전 오류 정정 코드 복기\\n\",\n \" - 1.1 고전 [n, k, d] 코드\\n\",\n \" - 1.2 해밍 거리\\n\",\n \" - 1.3 [3, 1, 3] 반복 코드\\n\",\n \" - 연습: [3, 1, 3] 코드의 lookup table 기반 디코더\\n\",\n \"- 챕터 2: [[n, k, d]] 양자 오류 정정 코드\\n\",\n \" - 2.1 스태빌라이저 형식\\n\",\n \" - 2.2 3-큐비트 비트 반전 코드 연습\\n\",\n \" - 연습문제 1: 3-비트 코드의 lookup table 기반 디코더\\n\",\n \" - 2.3 CSS 코드 (Calderbank-Shor-Steane)\\n\",\n \" - 2.4 [[7, 1, 3]] Steane 코드 연습\\n\",\n \" - 연습문제 2: [[7, 1, 3]] Steane 코드의 lookup table\\n\",\n \" - 연습문제 3: Lookup table을 사용한 오류 탐지\\n\",\n \"- 챕터 3: 고급 양자 오류 정정 부호와 그 효율성 탐구\\n\",\n \" - 3.1 비교를 위한 기본 개념과 주요 QLDPC 아키텍쳐\\n\",\n \" - 3.2 연습에서 사용할 큐비트 레이아웃과 표기 원칙\\n\",\n \" - 3.3 Toric 코드 연습\\n\",\n \" - 연습문제 4: Toric 코드의 패리티 검사 행렬 찾기\\n\",\n \" - 3.4 Gross 코드 연습\\n\",\n \" - 연습문제 5: Gross 코드의 패리티 검사 행렬 찾기\\n\",\n \" - 3.5 논리 큐비트의 개수 찾기\\n\",\n \" - 연습문제 6: Toric과 Gross 코드에서의 논리 큐비트 개수 세기\\n\",\n \" - 3.6 끝맺으며: 연결의 힘\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fa09aff1\",\n \"metadata\": {},\n \"source\": [\n \"## 요구사항\\n\",\n \"\\n\",\n \"해당 튜토리얼을 시작하기 전에 다음이 설치되어 있는지 확인 부탁드립니다:\\n\",\n \"\\n\",\n \"* Qiskit SDK v2.0 or later with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime v0.40 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"558e90cb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"48794705\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1d9769d1\",\n \"metadata\": {},\n \"source\": [\n \"Qiskit 버전 `>=2.0.0` 그레이더 버전 `>=0.22.12` 이상이 설치되어 있어야 합니다. 만약 버전이 다르게 표시된다면 커널을 재시작해보시고, 그래도 안 된다면 그레이더를 다시 설치해야 할 수 있습니다.\\n\",\n \"랩 0에 따라 환경설정을 완료했는지, IBM Quantum 계정이 잘 저장되어 있는지 아래의 셀을 실행해 확인해주세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a60cc22d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 계정이 잘 저장되어 있는지 확인합니다\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9984274d\",\n \"metadata\": {},\n \"source\": [\n \"# 불러오기\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"776ad97c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 일반적인 패키지부터 불러옵니다.\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"# Qiskit 클래스들을 불러옵니다.\\n\",\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"\\n\",\n \"# util과 ecosystem 패키지들을 불러옵니다.\\n\",\n \"from lab4_util import hamming_distance, minimum_distance, bring_states, matrixRank\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"from qiskit_ibm_runtime import SamplerV2 as Sampler\\n\",\n \"\\n\",\n \"# grader를 불러옵니다.\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab4_ex1, \\n\",\n \" grade_lab4_ex2, \\n\",\n \" grade_lab4_ex3,\\n\",\n \" grade_lab4_ex4,\\n\",\n \" grade_lab4_ex5,\\n\",\n \" grade_lab4_ex6\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2ac33837\",\n \"metadata\": {},\n \"source\": [\n \"# 챕터 1: 고전 오류 정정 코드 복기\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b51a2f0f\",\n \"metadata\": {},\n \"source\": [\n \"## 1.1 고전 [n, k, d] 코드\\n\",\n \"\\n\",\n \"코딩 이론에서 고전 선형 블록 코드는 종종 $[n, k, d]$ 코드로 표현됩니다. 이는 이진 (비트) 메시지를 부호화하는 코드로, 각 변수는 다음을 의미합니다:\\n\",\n \"\\n\",\n \"* **$n$**: **부호문의 길이**. 코드의 각 부호문은 길이 $n$ 짜리 이진 문자열이 됩니다.\\n\",\n \"* **$k$**: **메세지의 길이**. 부호문 내에 인코딩된 정보 비트의 수입니다. 길이 $k$ 짜리 문자열로 표현되는 $2^k$ 개의 메세지는 $2^k$ 개의 구별 가능한 부호문으로 인코딩 됩니다.\\n\",\n \"* **$d$**: 코드의 **최소 해밍 거리**. 코드 내의 임의의 *구별 가능한* 부호문 쌍 사이의 최소 해밍 거리입니다. 최소 거리 $d$ 는 코드의 오류 탐지와 정정 능력의 척도가 됩니다. $2^n$ 개의 가능한 모든 부호문 중 일부 ($2^k$ 개) 부호문만이 $k$ 비트의 정보를 담고 있는 유효한 부호문이 됩니다.\\n\",\n \"\\n\",\n \"## 1.2 해밍 거리\\n\",\n \"\\n\",\n \"같은 길이인 두 문자열 (혹은 벡터) 사이의 **해밍 거리**(Hamming distance)는 서로의 기호가 다른 위치의 개수입니다. 0과 1로 이루어진 이진 문자열의 경우에는 간단하게 한 쪽이 0이고 다른 쪽이 1인 위치의 개수를 세면 됩니다.\\n\",\n \"\\n\",\n \"수학적으로, 두 이진 벡터가 $x = (x_1, x_2, ..., x_n)$과 $y = (y_1, y_2, ..., y_n)$일 때 해밍 거리 $d_H(x,y)$는:\\n\",\n \"$$ d_H(x, y) = \\\\sum_{i=1}^{n} (x_i \\\\oplus y_i) $$\\n\",\n \"으로 쓰이며, 이때 $\\\\oplus$ 는 XOR 연산자(모듈러 2 덧셈)를 의미합니다. 이는 $x$ 와 $y$ 에 비트별 XOR 연산을 한 뒤 남아 있는 1의 개수를 세는 것과 같습니다. 이렇게 세어진 숫자는 XOR 연산 결과의 **해밍 가중치**(Hamming weight)라고도 불립니다.\\n\",\n \"\\n\",\n \"**중요**\\n\",\n \"\\n\",\n \"해밍 거리는 오류 정정 코드를 이해하는 데에 있어 매우 중요합니다:\\n\",\n \"1. **오류 탐지**: 최소 길이 $d$ 인 코드는 최대 $d-1$ 비트에 영향을 끼치는 모든 오류 패턴을 탐지할 수 있습니다. 만약 부호문의 전송 중에 $d$ 비트보다 적은 개수가 뒤집히면, 영향을 받은 문자열은 어떠한 유효한 부호문도 될 수 없으므로 오류가 탐지됩니다.\\n\",\n \"2. **오류 정정**: 최소 길이 $d$ 인 코드는 최대 $t$ 비트에 영향을 끼치는 모든 오류 패턴을 정정할 수 있습니다. 이때, $t$ 는 $t = \\\\lfloor \\\\frac{d-1}{2} \\\\rfloor$ 입니다. 이는 $t$ 개 이하의 오류가 발생하는 경우까지는 수신된 문자열이 여전히 다른 어떤 유효한 부호문보다는 원래의 부호문에 (해밍 거리 관점에서) 가깝기 때문입니다.\\n\",\n \"\\n\",\n \"다음은 두 이진 문자열 사이의 해밍 거리를 계산하는 파이썬 함수입니다:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ca1e251c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 사용 예시:\\n\",\n \"str1 = \\\"10110\\\"\\n\",\n \"str2 = \\\"11100\\\"\\n\",\n \"dist = hamming_distance(str1, str2)\\n\",\n \"print(f\\\"'{str1}'과 '{str2}'의 해밍 거리: {dist}\\\") # 결과: 2\\n\",\n \"\\n\",\n \"vec1 = [1, 0, 0, 1]\\n\",\n \"vec2 = [0, 0, 1, 1]\\n\",\n \"dist_vec = hamming_distance(vec1, vec2)\\n\",\n \"print(f\\\"{vec1}과 {vec2}의 해밍 거리: {dist_vec}\\\") # 결과: 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3ee79d97\",\n \"metadata\": {},\n \"source\": [\n \"### 코드의 최소 거리 ($d$)\\n\",\n \"\\n\",\n \"코드 $C$ 의 최소 해밍 거리는 코드 내의 임의의 구별 가능한 부호문 $C \\\\subset \\\\{0, 1\\\\}^n$ 쌍 사이의 해밍 거리의 최솟값입니다.\\n\",\n \"$$ d = \\\\min { d_H(c_1, c_2) \\\\mid c_1, c_2 \\\\in C, c_1 \\\\neq c_2 }. $$\\n\",\n \"선형 코드에서는 $c_1 \\\\in C$ 과 $c_2 \\\\in C$ 가 주어졌을 때 $(c_1 +c_2) \\\\in C$ 가 되기 때문에 최소 거리가 모든 (0이 아닌) 부호문의 최소 해밍 가중치와도 같습니다. 부호문의 해밍 가중치는 0을 표현하는 부호문과의 거리로 정의할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4fd8d205\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- 예시: 간단한 [4, 3, 2] 패리티 검사 코드 ---\\n\",\n \"# 이 코드는 3개의 메시지 비트(k=3)를 받고 패리티 검사 비트를 추가해\\n\",\n \"# 전체 부호문의 길이 n=4 로 만듭니다.\\n\",\n \"# 메시지: 000, 001, 010, 011, 100, 101, 110, 111\\n\",\n \"# 부호문 (패리티 비트를 더해줍니다)\\n\",\n \"parity_code_4_3 = [\\n\",\n \" \\\"0000\\\", # 000 + 0 (짝수 패리티)\\n\",\n \" \\\"0011\\\", # 001 + 1\\n\",\n \" \\\"0101\\\", # 010 + 1\\n\",\n \" \\\"0110\\\", # 011 + 0\\n\",\n \" \\\"1001\\\", # 100 + 1\\n\",\n \" \\\"1010\\\", # 101 + 0\\n\",\n \" \\\"1100\\\", # 110 + 0\\n\",\n \" \\\"1111\\\" # 111 + 1\\n\",\n \"]\\n\",\n \"\\n\",\n \"# 최소 거리 d를 계산합니다.\\n\",\n \"d_parity = minimum_distance(parity_code_4_3)\\n\",\n \"print(f\\\"부호문: {parity_code_4_3}\\\")\\n\",\n \"print(f\\\"계산된 최소 거리 d = {d_parity}\\\") # 결과: 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0c11207c\",\n \"metadata\": {},\n \"source\": [\n \"따라서 위의 코드는 [4, 3, 2] 코드입니다.\\n\",\n \"\\n\",\n \"예시 코드 `parity_code_4_3`에서 우리는 거리 $d=2$ 임을 찾아냈습니다.\\n\",\n \"\\n\",\n \"- 오류 탐지: $t_{detect} = d - 1 = 2 - 1 = 1$. 해당 코드는 어떤 하나의 비트에서 일어나는 오류를 탐지할 수 있습니다.\\n\",\n \"- 오류 정정: $t_{correct} = \\\\lfloor \\\\frac{d-1}{2} \\\\rfloor = \\\\lfloor \\\\frac{2-1}{2} \\\\rfloor = \\\\lfloor 0.5 \\\\rfloor = 0$.\\n\",\n \"\\n\",\n \"특히 어떤 하나의 비트에서 일어나는 오류는 유효한 코드 공간에 없는 문자열을 만들어 내므로, 우리는 오류가 일어난 것을 알고 탐지할 수 있습니다!\\n\",\n \"\\n\",\n \"한편, 이 코드는 오류를 감지할 수는 있지만 정정해주는 능력은 없습니다. 예를 들어, `0000` -> `0001` 와 같이 0번째 비트에서 비트 반전이 일어나면 전송된 문자열은 유효한 부호문이 아니므로 오류가 탐지됩니다. 하지만 `0001`은 `0000`, `0011`, `0101`, `1001`과 해밍 거리가 모두 같기 때문에 어느 하나로 정정될 수는 없습니다.\\n\",\n \"\\n\",\n \"따라서, [4, 3, 2] 코드에서는 하나의 비트 반전 오류가 유효 부호문이 아닌 이진 문자열을 만들지만, 오류 정정을 위해 필요한 구별 가능하며 가장 가까운 부호문을 찾을 수 없기에 오류를 신뢰성 있게 고치는 것은 불가능합니다 - 즉, 원래의 부호문이 무엇인지를 아는 것이 불가능합니다.\\n\",\n \"\\n\",\n \"일반적으로, 코드가 $d-1$ 개까지의 오류를 탐지할 수 있는 이유는, 이러한 종류의 비트 반전이 일어나서 전송되는 메시지를 교란시키는 경우, 전송된 메시지가 더 이상 유효한 부호문이 아니게 되고, 이를 통해 오류가 일어났음을 유추할 수 있기 때문입니다. 오류를 정정하는 것은 조금 더 힘들기는 하지만, 교란된 부호문를 보고 해밍 거리 $\\\\lfloor \\\\frac{d-1}{2} \\\\rfloor$ 내에 있는 유효한 모든 부호문을 비교하여 가장 가까운 하나의 가능성을 추려낼 수 있기에, 최대 가중치 $\\\\lfloor \\\\frac{d-1}{2} \\\\rfloor$ 내의 오류는 충분히 정정할 수 있습니다. 이러한 디코딩이라 불리는 작업을 효율적으로 해내는 것은 여전히 간단하지는 않고, 코드에 따라 활발한 연구가 이루어지고 있는 분야입니다.\\n\",\n \"\\n\",\n \"이를 조금 더 알아보기 위해, 다른 코드([3, 1, 3] 반복 코드 $C = { 000, 111 }$)를 고려해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e79adbe9\",\n \"metadata\": {},\n \"source\": [\n \"## 1.3 [3, 1, 3] 반복 코드\\n\",\n \"\\n\",\n \"가장 간단한 오류 정정 코드는 반복 코드입니다.\\n\",\n \"하나의 비트를 보내기 위해서 (k=1), 우리는 이것을 `n`번 반복해서 보낼 수 있습니다. `[3,1,3]` 코드에서는 이처럼 비트를 3번 반복하여 사용합니다.\\n\",\n \"- 메시지 `0`은 `000`으로 인코딩됩니다.\\n\",\n \"- 메시지 `1`은 `111`로 인코딩됩니다.\\n\",\n \"\\n\",\n \"따라서 이 경우에 n=3이고 k=1입니다.\\n\",\n \"부호문은 {000, 111}입니다.\\n\",\n \"000과 111의 해밍 거리는 3 이므로, $d=3$ 입니다.\\n\",\n \"이 코드는 $d-1=2$ 개 오류까지 탐지할 수 있습니다.\\n\",\n \"이 코드는 $\\\\lfloor((d-1)/2)\\\\rfloor = \\\\lfloor((3-1)/2)\\\\rfloor$ = 1 개 오류까지 정정할 수 있습니다.\\n\",\n \"\\n\",\n \"**인코딩**\\n\",\n \"- 메시지 비트가 `m`일 때, 부호문 `c = mmm`입니다.\\n\",\n \"\\n\",\n \"**오류와 디코딩 (다수결)**\\n\",\n \"부호문 `c`가 전송되고, `c'`을 받았다고 가정해 봅시다.\\n\",\n \"만약 많아야 하나의 비트가 뒤집혔으면, 원래의 메시지는 다수결을 통해 복구할 수 있습니다.\\n\",\n \"- 받은 메시지 `000` -> 디코딩 결과 `0`\\n\",\n \"- 받은 메시지 `001` -> 디코딩 결과 `0` (1개의 오류)\\n\",\n \"- 받은 메시지 `010` -> 디코딩 결과 `0` (1개의 오류)\\n\",\n \"- 받은 메시지 `100` -> 디코딩 결과 `0` (1개의 오류)\\n\",\n \"- 받은 메시지 `111` -> 디코딩 결과 `1`\\n\",\n \"- 받은 메시지 `110` -> 디코딩 결과 `1` (1개의 오류)\\n\",\n \"- 받은 메시지 `101` -> 디코딩 결과 `1` (1개의 오류)\\n\",\n \"- 받은 메시지 `011` -> 디코딩 결과 `1` (1개의 오류)\\n\",\n \"\\n\",\n \"만약 `000` -> `011`처럼 두 개의 비트가 뒤집혔으면 다수결은 틀린 메시지 (예시에서는 `1`)로 디코딩하게 됩니다. 이 코드는 이를 오류로 탐지는 할 수 있지만 고치지는 못합니다.\\n\",\n \"\\n\",\n \"먼저 이 코드의 최소 거리를 계산해보고, 오류 탐지 능력과 오류 정정 능력을 검증해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3d9af05b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- 예시: [3, 1, 3] 반복 코드 ---\\n\",\n \"repetition_code_3_1 = [\\\"000\\\", \\\"111\\\"]\\n\",\n \"d_repetition = minimum_distance(repetition_code_3_1)\\n\",\n \"print(f\\\"계산된 최소 거리 d = {d_repetition}\\\") # 결과: 3\\n\",\n \"\\n\",\n \"# d=3의 능력:\\n\",\n \"t_detect = d_repetition - 1\\n\",\n \"t_correct = int((d_repetition - 1) / 2) // 1\\n\",\n \"print(f\\\"오류 탐지 능력 (t_detect = d-1): {t_detect}\\\") # 결과: 2\\n\",\n \"print(f\\\"오류 정정 능력(t_correct = floor((d-1)/2)): {t_correct}\\\") # 결과: 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f32e68b\",\n \"metadata\": {},\n \"source\": [\n \"### 연습: [3, 1, 3] 코드의 lookup table 기반 디코더\\n\",\n \"\\n\",\n \"이 코드의 오류 정정 능력을 확인했으므로, 우리는 이제 오류 정정을 위해 딕셔너리 형태로 정의된 lookup table 기반 디코더를 만들 것입니다. Lookup table 기반 디코더는 가능한 모든 수신된 3-bit 문자열을 가장 가능성 높은 1-비트 메시지로 대응시킵니다. 우리는 이를 오류가 정정된 메시지로 여길 것입니다. 여기서 우리는 함수 `hamming_distance`를 이용해 해밍 거리가 어떻게 오류 정정에 영향을 끼치는지 살펴볼 것입니다. 먼저 하나의 예시를 통해 간단하게 연습해봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9dc8d7e7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"test_str = \\\"010\\\"\\n\",\n \"\\n\",\n \"print(\\\"010과 000 사이의 해밍 거리\\\", hamming_distance(test_str, \\\"000\\\"))\\n\",\n \"print(\\\"010과 111 사이의 해밍 거리\\\", hamming_distance(test_str, \\\"111\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"96a83fff\",\n \"metadata\": {},\n \"source\": [\n \"\\\\\\\"000\\\\\\\"은 논리적 `0`이고 \\\\\\\"111\\\\\\\"은 논리적 `1`이므로 `test_str`은 `0`으로 고쳐질 것입니다. [3, 1, 3] 고전 오류 정정 코드의 lookup table 기반 디코더를 아래의 딕셔너리를 채워 만들어 보세요.\\n\",\n \"\\n\",\n \"아래의 정답표를 보기 전에, 표의 각 행이 무엇을 의미하는지 아는 것이 도움이 될 것입니다: '수신된 문자열'은 (오류가 일어났을 수도 있는) 전체 3-비트 문자열입니다; '\\\"000\\\"과의 해밍 거리'와 '\\\"111\\\"과의 해밍 거리'는 수신된 문자열이 두 개의 유효한 부호문(000과 111)과 비교했을 때 비트가 얼마나 다른지를 보여줍니다; 그리고 '고쳐진 문자열'은 디코더가 원래의 메시지로 판단한 하나의 비트(0 또는 1)입니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5bad9fd5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hardcode_decoder_3_1_3 ={\\n\",\n \" '000': '0',\\n\",\n \" '001': '',\\n\",\n \" '010': '',\\n\",\n \" '011': '',\\n\",\n \" '100': '',\\n\",\n \" '101': '',\\n\",\n \" '110': '',\\n\",\n \" '111': '1'}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3f8018cb\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \" 만든 디코더를 다음 정답표와 비교해보세요! \\n\",\n \"\\n\",\n \"| 수신된 문자열 | \\\"000\\\"과의 해밍 거리 | \\\"111\\\"과의 해밍 거리 | 고쳐진 문자열 |\\n\",\n \"|---|---|---|---|\\n\",\n \"|000|0|3|0|\\n\",\n \"|001|1|2|0|\\n\",\n \"|010|1|2|0|\\n\",\n \"|011|2|1|1|\\n\",\n \"|100|1|2|0|\\n\",\n \"|101|2|1|1|\\n\",\n \"|110|2|1|1|\\n\",\n \"|111|3|0|1|\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fea7ef37\",\n \"metadata\": {},\n \"source\": [\n \"# 챕터 2: [[n, k, d]] 양자 오류 정정 코드\\n\",\n \"\\n\",\n \"양자 정보는 고전 정보보다 훨씬 취약합니다. 큐비트는 비트 반전 ($X$ 오류) 뿐만 아니라 위상 반전 ($Z$ 오류), 그리고 둘을 합친 오류 ($Y$ 오류) 모두에 취약합니다. 양자 오류 정정 코드는 양자 상태를 이러한 오류로부터 지키는 것을 목적으로 합니다.\\n\",\n \"\\n\",\n \"양자 오류 정정 코드는 보통 `[[n, k, d]]`로 표현되며, 각 변수는 다음을 의미합니다:\\n\",\n \"\\n\",\n \"* **`n`**: 코드를 구성하는데 필요한 **정보 큐비트**의 개수\\n\",\n \"* **`k`**: 코드에 인코딩되는 **논리 큐비트**의 개수. 이는 코드가 $2^k$-차원의 논리 상태 공간 (코드 공간)을 지켜준다는 것을 의미합니다.\\n\",\n \"* **`d`**: 코드의 **최소 거리**. 이는 논리 큐비트에 임의의 연산을 할 때 (I는 제외) 바꿔야 하는 큐비트의 최소 개수입니다. 앞으로 소개할 스태빌라이저 코드의 경우에 이는 논리 파울리 연산자를 구현하기 위해 파울리 연산자가 적용되야 할 큐비트의 최소 개수입니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"자원 요구량에 관해:\\n\",\n \"\\n\",\n \"`[[n, k, d]]` 표기에서 `n`은 보통 코드 블록을 이루는 정보 큐비트의 개수를 의미합니다. 이렇게 세어지는 큐비트의 개수는 보통 오류 감지와 정정에 필요한 보조 큐비트나 징후(syndrome) 큐비트를 *제외*합니다. 따라서, 양자 오류 정정을 위해 필요한 추가 큐비트의 수는 단순히 `n - k` 개의 정보 큐비트가 아니라, `(n - k)` (중복으로 사용되는 정보 큐비트의 수)에 징후 측정을 위해 필요한 보조 큐비트의 수를 *더한* 수입니다. 많은 스태빌라이저 코드에서는 독립적인 스태빌라이저의 수 (따라서 보조 큐비트의 수)는 `n - k`이나, 이는 코드에 따라 다를 수 있고, 몇몇 경우에는 필요한 보조 큐비트의 수가 `n`만큼 클 수도 있습니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"고전 오류 정정 코드와 비슷하게 코드의 오류 정정 능력은 최소 거리 $d$ 에 좌우됩니다. `[[n, k, d]]` 코드는 `d-1`만큼의 오류를 탐지할 수 있고, `floor((d-1)/2)`만큼의 개별 큐비트 오류를 정정할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9895c32e\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"## 2.1 스태빌라이저 형식\\n\",\n \"\\n\",\n \"스태빌라이저 형식은 수많은 양자 오류 정정 코드를 만들고 이해할 수 있는 아주 강력한 도구입니다.\\n\",\n \"\\n\",\n \"- $n$ 개의 큐비트에서 정의된 **파울리 군** $P_n$ 은 파울리 행렬 $\\\\{I, X, Y, Z\\\\}$ 의 n-텐서 곱에 계수 $\\\\{\\\\pm 1, \\\\pm i\\\\}$ 이 곱해진 항들로 구성됩니다.\\n\",\n \"- **스태빌라이저 코드**는 그의 **스태빌라이저 군 S**로 정의됩니다. 이 군은 $-I \\\\notin S$ 인 $P_n$ 의 아벨리안 부분군입니다.\\n\",\n \"- **코드 공간** C(S)는 $S$ 의 모든 원소에 의해 안정화(안정화) 되는 $(\\\\mathbb{C}^2)^{\\\\otimes n}$ 의 부분 공간입니다. 이는 임의의 상태 $|\\\\psi\\\\rangle \\\\in C(S)$ 와 임의의 $g \\\\in S$ 에 대해서, $g|\\\\psi\\\\rangle = |\\\\psi\\\\rangle$ 가 성립함을 의미합니다. 즉, 코드 공간 내에 있는 상태들은 모든 스태빌라이저 연산자에 대해 +1의 고유값을 갖는 고유상태입니다.\\n\",\n \"- 일반적으로 $S$ 는 $m = n-k$ 개의 독립적이고 교환 가능한 낳음 연산자(generator) $S = \\\\langle g_1, g_2, ..., g_m \\\\rangle$ 로 표기됩니다.\\n\",\n \"\\n\",\n \"이에 대한 자세한 내용은 John Watrous의 IBM Quantum Learning 코스: [Foundations of Quantum Error Correction](https://quantum.cloud.ibm.com/learning/en/courses/foundations-of-quantum-error-correction) 에서 다루고 있습니다. 만약 더 깊이 있는 수학적 설명과 예시들이 필요하시다면, 위 항목을 참고하세요. 요약을 보고 싶다면 아래 `스태빌라이저 형식에 대한 요약`을 클릭해보시고, 원치 않으시다면 넘기셔도 됩니다.\\n\",\n \"\\n\",\n \"\\n\",\n \" 스태빌라이저 형식에 대한 요약\\n\",\n \"\\n\",\n \" 앞으로 소개될 3-큐비트 반복 코드, 유명한 Steane 코드(`[[7, 1, 3]]`)와 Shor 코드(`[[9, 1, 3]]`)를 포함한 많은 강력한 양자 오류 정정 코드는 **스태빌라이저 코드**에 속합니다. 스태빌라이저 형식은 양자 코드를 정의하고 오류 탐지 및 정정 과정을 설계하는 강력한 도구입니다.\\n\",\n \"\\n\",\n \"**1. 안정화 되는 상태와 부분 공간:**\\n\",\n \"* 만약 양자 상태 $|\\\\psi\\\\rangle$가 연산자 $S$의 고유값 +1을 갖는 고유상태이면 연산자 $S$는 양자 상태 $|\\\\psi\\\\rangle$ 를 **안정화**한다고 합니다.\\n\",\n \"* 만약 연산자 $S$가 부분 공간($\\\\mathcal{C}$ 로 표기되는 **코드 공간**)에 있는 *모든* 상태 $|\\\\psi\\\\rangle$ 를 안정화 시키면 연산자 $S$ 는 해당 부분 공간을 안정화한다고 합니다. 이를 수학적으로 표현하면 다음과 같습니다: $S|\\\\psi\\\\rangle = |\\\\psi\\\\rangle$ for all $|\\\\psi\\\\rangle \\\\in \\\\mathcal{C}$.\\n\",\n \"\\n\",\n \"**2. 스태빌라이저 군 ($\\\\mathcal{S}$):**\\n\",\n \"* 주어진 스태빌라이저 코드에 대하여, 코드 공간 $\\\\mathcal{C}$ 를 안정화하는 모든 연산자의 집합은 **스태빌라이저 군**을 이루고, 우리는 이를 보통 $\\\\mathcal{S}$ 로 표기합니다.\\n\",\n \"* **주요 성질:**\\n\",\n \" * 군 $\\\\mathcal{S}$ 에 속하는 모든 연산자 $S_i, S_j$ 는 서로에 대하여 **교환 가능**하여야 합니다: $S_i S_j = S_j S_i$.\\n\",\n \" * 스태빌라이저 연산자는 $n$ 개의 큐비트에 작용하는 **Pauli 연산자** ($I, X, Y, Z$)의 텐서 곱으로 구성됩니다. 예를 들어 $n=4$ 인 경우에는 $X \\\\otimes Z \\\\otimes I \\\\otimes X$ 가 스태빌라이저 원소일 수 있습니다.\\n\",\n \" * 모든 경우에 항등 연산자 $I^{\\\\otimes n}$ 는 $\\\\mathcal{S}$ 의 원소입니다. 관습적으로 $-I^{\\\\otimes n}$ 는 군의 원소로 고려하지 않습니다.\\n\",\n \"\\n\",\n \"**3. 낳음 연산자 ($\\\\{g_i\\\\}$):**\\n\",\n \"* 굉장히 클 수 있는 군 $\\\\mathcal{S}$ 의 모든 원소를 나열하는 대신에, 우리는 보통 $\\\\mathcal{S}$ 를 **낳음 연산자** ($g_1, g_2, ..., g_{n-k}$)의 집합을 이용하여 정의합니다.\\n\",\n \"* 모든 원소 $S \\\\in \\\\mathcal{S}$ 는 이러한 낳음 연산자의 곱으로 표기할 수 있습니다. 예를 들면 $g_1 g_3$이나 $g_2 g_5 g_1$이 있겠습니다.\\n\",\n \"* **낳음 연산자의 주요 성질:**\\n\",\n \" * 항상 서로에 대해 **교환 가능**하여야 합니다: $[g_i, g_j] = 0$ for all $i, j$.\\n\",\n \" * 항상 **독립**하여야 합니다: 그 어떠한 낳음 연산자도 다른 낳음 연산자의 곱으로써 나타낼 수 없어야 합니다.\\n\",\n \" * `[[n, k, d]]` 코드에는 $n-k$ 개의 독립적인 낳음 연산자가 있습니다.\\n\",\n \"\\n\",\n \"**4. 코드 공간 ($\\\\mathcal{C}$) 정의하기:**\\n\",\n \"* 스태빌라이저 코드의 코드 공간 $\\\\mathcal{C}$ 은 스태빌라이저 군의 모든 낳음 연산자 원소에 대한 **+1 고유공간**입니다. (이는 코드 공간이 스태빌라이저 군 $\\\\mathcal{S}$ 의 모든 원소에 대해서도 +1 고유공간임을 의미합니다.)\\n\",\n \"* 만약 코드 공간에 상태 $|\\\\psi\\\\rangle$ 가 있다면, 이는 모든 낳음 연산자 $g_i$ (i=1, ..., n-k)에 대해 $g_i |\\\\psi\\\\rangle = |\\\\psi\\\\rangle$ 를 만족해야 합니다.\\n\",\n \"* $n$ 개의 물리적 큐비트로 이루어진 전체 $2^n$-차원의 힐베르트 공간에서 시작해서, 각각의 독립적인 낳음 연산자는 이들이 안정화하는 부분공간의 차원을 양분합니다. 따라서 $n-k$ 의 독립적인 낳음 연산자는 $2^{n-(n-k)} = 2^k$-차원의 코드 공간을 정의합니다. 이는 정확히 $k$ 개의 논리 큐비트를 정의하는데에 필요한 공간과 일치합니다.\\n\",\n \"\\n\",\n \"**5. 스태빌라이저를 이용한 오류 탐지:**\\n\",\n \"* 양자 오류 정정에 있어서 스태빌라이저의 주요 임무는 **오류 탐지**입니다. 이는 스태빌라이저 낳음 연산자의 고윳값을 측정함으로서 이루어집니다.\\n\",\n \"* **오류가 없을 때:** 만약 시스템이 유효한 코드 공간에 속하는 상태 $|\\\\psi\\\\rangle \\\\in \\\\mathcal{C}$ 에 있고 오류가 일어나지 않았다면, 임의의 낳음 연산자 $g_i$ 를 측정하여 얻는 결과는 $g_i|\\\\psi\\\\rangle=|\\\\psi\\\\rangle$ 에 따라 당연히 +1이 될 것입니다.\\n\",\n \"* **오류가 일어났을 때:** 만약 파울리 연산자의 텐서 곱으로 나타내질 수 있는 오류 $E$ 가 일어났다고 하면, 이는 상태를 $E|\\\\psi\\\\rangle$ 로 변환시킬 것입니다. 이제 낳음 연산자 $g_i$ 를 측정해봅시다.\\n\",\n \" * 만약 $g_i$ 가 오류 $E$ 와 **교환 가능**하다면 (즉 $g_i E = E g_i$):\\n\",\n \" - $g_i (E |\\\\psi\\\\rangle) = E g_i |\\\\psi\\\\rangle = E |\\\\psi\\\\rangle$. 측정 결과는 여전히 +1일 것이고, 따라서 오류 $E$ 는 $g_i$ 에 의해 **탐지되지 않습니다**.\\n\",\n \" * 만약 $g_i$가 오류 $E$ 와 **반교환 가능**하다면 (즉 $g_i E = -E g_i$):\\n\",\n \" - $g_i (E |\\\\psi\\\\rangle) = -E g_i |\\\\psi\\\\rangle = -E |\\\\psi\\\\rangle$. 측정 결과는 -1이고, 오류 $E$ 는 $g_i$ 에 의해 **탐지됩니다**.\\n\",\n \"* **오류 징후:** 모든 낳음 연산자 $\\\\{g_1, ..., g_{n-k}\\\\}$ 에 대한 측정 결과(+1 또는 -1, 일반적으로 0과 1로 측정됨)의 집합은 **오류 징후**을 구성합니다.\\n\",\n \" * 모두가 +1인 (혹은 모두 0인) 징후는 오류가 일어나지 않았거나, 일어난 오류가 모든 스태빌라이저와 교환 가능함을 의미합니다. (마지막 경우에는 유한한 코드 거리 $d$로 인해 오류가 탐지되지 않았거나 정확히 논리 연산자가 가해졌을 수 있습니다.)\\n\",\n \" * 적어도 하나의 -1(혹은 1)을 포함하는 징후는 탐지 가능한 오류가 일어났음을 의미합니다. 징후의 특정한 패턴은 어떤 오류 $E$ 가 일어났을 확률이 가장 높은지에 대한 정보를 줍니다. 해당 정보는 추후에 오류 정정에 이용됩니다.\\n\",\n \"\\n\",\n \"**`[[n, k, d]]`과의 관계**\\n\",\n \"\\n\",\n \"* `n`은 스태빌라이저가 작용하는 물리 큐비트의 개수입니다.\\n\",\n \"* `k`는 인코딩되는 논리 큐비트의 개수입니다. 이는 $k = n - (\\\\text{독립적인 낳음 연산자의 개수})$ 로 결정됩니다.\\n\",\n \"* `d`는 모든 스태빌라이저와 교환 가능하지만 (모든 i에 대해서 $[E, g_i]=0$) 그 자체로는 스태빌라이저의 곱이 *아닌* (즉, $E \\\\notin \\\\mathcal{S}$) 파울리 오류 $E$ 의 최소 가중치입니다. 그러한 연산자는 **논리 연산자** (인코딩된 정보에 비자명한 방식으로 작용한다는 의미에서) 이거나 탐지할 수 없는 오류가 됩니다. 거리 $d$ 는 코드가 가중치가 낮은 오류를 구분할 수 있는 능력을 정량화하여 나타냅니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"스태빌라이저 형식은 아래에 있는 `[[7, 1, 3]]` 예시와 같은 코드가 어떻게 구성되고 그러한 코드가 다양한 종류의 오류를 어떻게 탐지하는지에 대해 이해할 수 있는 좋은 기반이 됩니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88f289c0\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2 3-큐비트 비트 반전 코드 연습\\n\",\n \"\\n\",\n \"(주의: 해당 설명에서는 파울리 문자열의 가장 오른쪽에 위치한 문자가 0번째 큐비트를 의미하는 Qiskit의 little-endian 표기법을 따릅니다. 예를 들어 `IZZ`는 $I_2 \\\\otimes Z_1 \\\\otimes Z_0$ 를 의미합니다.)\\n\",\n \"\\n\",\n \"이 코드는 고전적인 [3,1,3] 반복 코드의 양자 버전이라고 보시면 됩니다. 이 코드는 그 어떠한 세 개의 물리 큐비트에서 일어나는 **하나의 비트 반전 (X) 오류**를 고칠 수 있으나, $Z$ 위상 오류는 고칠 수 없습니다.\\n\",\n \"- n=3 물리 큐비트, k=1 논리 큐비트. 이 코드의 목적은 물리 큐비트에서 일어나는 오류에도 불구하고 논리 큐비트의 상태를 유지하는 것에 있습니다.\\n\",\n \"- (정규화되지 않은) 논리 상태:\\n\",\n \" - $|0_L\\\\rangle \\\\equiv |000\\\\rangle$\\n\",\n \" - $|1_L\\\\rangle \\\\equiv |111\\\\rangle$\\n\",\n \" - 임의의 논리 상태는 $\\\\alpha|0_L\\\\rangle + \\\\beta|1_L\\\\rangle = \\\\alpha|000\\\\rangle + \\\\beta|111\\\\rangle$ 로 나타낼 수 있습니다.\\n\",\n \"\\n\",\n \"**스태빌라이저 낳음 연산자:** \\n\",\n \"해당 코드는 다음 스태빌라이저에 의해 안정화 됩니다:\\n\",\n \"- $S_0 = Z_2 Z_1 I_0$ (혹은 간단하게 $Z_1Z_2$)\\n\",\n \"- $S_1 = I_2 Z_1 Z_0$ (혹은 간단하게 $Z_0Z_1$)\\n\",\n \"이들은 Z 스태빌라이저이므로 비트 반전으로 인한 X 오류를 탐지하는데 쓰입니다.\\n\",\n \"\\n\",\n \"**논리 연산자:** \\n\",\n \"- 논리적 Z 연산: $\\\\bar{Z} = Z_0$ (혹은 $Z_1$ 또는 $Z_2$, 이는 $Z_0|0_L\\\\rangle = |0_L\\\\rangle, Z_0|1_L\\\\rangle = -|1_L\\\\rangle$ 과 같은 Z 논리 연산을 하기 때문입니다.) 흔히들 Z 논리 연산자로 $Z_2 Z_1 Z_0$ 를 선택합니다.\\n\",\n \"- 논리적 X 연산: $\\\\bar{X} = X_2 X_1 X_0$. $(\\\\bar{X}|0_L\\\\rangle = |1_L\\\\rangle, \\\\bar{X}|1_L\\\\rangle = |0_L\\\\rangle)$.\\n\",\n \"\\n\",\n \"**오류 탐지 (징후 추출):** \\n\",\n \"$s_0, s_1$ 이 스태빌라이저 $S_0, S_1$ 의 고윳값이라고 해봅시다. 만약 비트 반전이 일어났다면 -1일테고, 아니라면 +1일 것입니다. 이때 우리는 $(s_0, s_1)$ 을 관찰하여 징후를 확인합니다.\\n\",\n \"- 오류가 없을 때: $(+1, +1)$\\n\",\n \"- $X_0$ 오류 (0번째 큐비트에 비트 반전): $S_0$ 와는 교환 가능하고, $S_1$ 과는 반교환 가능합니다. ($Z_0Z_1X_0 = -X_0Z_0Z_1$). 징후: $(s_1,s_0) = (-1, +1)$. 해당 오류를 정정하기 위해서는 $X_0$ 연산을 가하면 됩니다.\\n\",\n \"- $X_1$ 오류 (1번째 큐비트에 비트 반전): $S_0$, $S_1$ 모두와 반교환 가능합니다. 징후: $(-1, -1)$. 해당 오류를 정정하기 위해서는 $X_1$ 연산을 가하면 됩니다.\\n\",\n \"- $X_2$ 오류 (1번째 큐비트에 비트 반전): $S_1$ 과는 교환 가능하고, $S_0$ 과는 반교환 가능합니다. 징후: $(+1, -1)$. 해당 오류를 정정하기 위해서는 $X_2$ 연산을 가하면 됩니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"**Z 오류에 대한 취약성**\\n\",\n \"\\n\",\n \"한 가지 기억할 것은 이 코드는 오직 비트 반전 (X) 오류로부터 양자 상태를 지키기 위해 고안되었다는 것입니다. 따라서, 이 코드는 모든 종류의 Z (위상 반전) 오류에는 취약합니다. 어느 물리 큐비트에 단 하나의 Z 오류라도 일어나면 해당 오류는 X 오류를 탐지하는 스태빌라이저 (에를 들어 $Z_i Z_j$)와 교환 가능할 것이기에, 인코딩된 큐비트에 고칠 수 없는 Z 논리 연산자가 가해진 효과를 낼 것입니다. 이는 해당 논리 큐비트의 위상 정보가 감지하지 못하는 새에 바뀐 것과 같습니다.\\n\",\n \"
\\n\",\n \"연습문제 1: 3-비트 코드의 lookup table 기반 디코더\\n\",\n \"\\n\",\n \"앞서 설명한 대로, 스태빌라이저 낳음 연산자 $S_0=ZZI$ ($Z_2 \\\\otimes Z_1 \\\\otimes I_0$) 와 $S_1=IZZ$ ($I_2 \\\\otimes Z_1 \\\\otimes Z_0$) 로 정의되는 양자 비트 반전 코드에서 징후는 이러한 스태빌라이저를 측정함으로써 얻는 2-비트 결과입니다. 이러한 징후는 각 징후를 특정한 오류 정정 연산으로 이어주는 lookup table로 구현되는 디코더에 대한 입력값이 됩니다.\\n\",\n \"\\n\",\n \"당신의 임무는 하나의 비트 반전 오류(가중치 1)를 고려할 때 측정된 징후 비트를 해당하는 오류 코드와 연결시켜주는 딕셔너리를 완성하는 것입니다. 예를 들면 0번째 큐비트에 X 오류가 났다면 'X0', 1번째 큐비트에 X 오류가 났다면 'X1', 2번째 큐비트에 X 오류가 났다면 'X2', 그리고 오류가 없다면 'I'라고 표기하시면 됩니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2bae421e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hardcode_decoder_bit_flip_syndrome_map = {\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" # 디코더에서 비어있는 나머지 항들을 채워주세요 (''는 유지)\\n\",\n \" # {\\\"s1s0\\\": \\\"오류 코드\\\"}\\n\",\n \" '00': 'I',\\n\",\n \" '01': '', \\n\",\n \" '10': '', \\n\",\n \" '11': '' \\n\",\n \" # --- 코드 작성이 완료되었습니다 --- \\n\",\n \"}\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"87281376\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab4_ex1(hardcode_decoder_bit_flip_syndrome_map )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"53623200\",\n \"metadata\": {},\n \"source\": [\n \"## 2.3 CSS 코드 (Calderbank-Shor-Steane)\\n\",\n \"\\n\",\n \"Calderbank, Shor와 Steane이 만든 CSS 코드는 고전적인 선형 코드로부터 만들어진 양자 코드의 강력한 한 종류입니다.\\n\",\n \"여기서 우리는 해당 코드에 대해 피상적인 설명만을 할 것이므로, CSS 코드에 대한 구체적 설명이 필요하면 IBM Quantum Learning에서 제공하는 교육 자료 [Quantum Code Constructions (CSS Codes)](https://quantum.cloud.ibm.com/learning/en/courses/foundations-of-quantum-error-correction/quantum-code-constructions/css-codes)를 참고해주시기 바랍니다.\\n\",\n \"\\n\",\n \"**구성:**\\n\",\n \"CSS 코드는 $C_2^\\\\perp \\\\subseteq C_1$ 를 만족하는 $C_1 = [n, k_1, d_1]$ 와 $C_2 = [n, k_2, d_2]$ 이라는 두 개의 고전적인 이진 선형 코드를 이용하여 정의됩니다. 여기서 $C_2^\\\\perp$ 는 $C_2$ 의 dual 코드를 의미합니다.\\n\",\n \"\\n\",\n \"* **스태빌라이저:** CSS code $[[n, k, d]]$ (이때 $k = k_1 + k_2 - n$)를 위한 스태빌라이저 군 $S$ 는 다음 스태빌라이저로 생성됩니다:\\n\",\n \" * **X 스태빌라이저:** $C_1$ 의 패리티 검사 행렬 $H_1$ 으로부터 유도됩니다. 각 행 $h \\\\in H_1$ 에 대해 $\\\\bigotimes_{i: h_i=1} X_i$ 라는 스태빌라이저를 만듭니다. 이러한 스태빌라이저는 오직 파울리 X와 항등 연산자로 이루어지게 됩니다.\\n\",\n \" * **Z 스태빌라이저:** $C_2^\\\\perp$ 의 패리티 검사 행렬 $H_2^\\\\perp$ 로부터 유도됩니다. 각 행 $h' \\\\in H_2^\\\\perp$ 에 대해 $\\\\bigotimes_{i: h'_i=1} Z_i$ 라는 스태빌라이저를 만듭니다. 이러한 스태빌라이저는 오직 파울리 Z와 항등 연산자로 이루어지게 됩니다.\\n\",\n \" *(참고: 다른 표기법에서는 X와 Z 스태빌라이저에서의 $C_1$ 와 $C_2^\\\\perp$ 의 역할을 바꿀 수 있습니다.)\\n\",\n \"\\n\",\n \"**핵심 성질과 디코딩:**\\n\",\n \"CSS 코드의 핵심 성질은 스태빌라이저 종류의 구분성에 있습니다:\\n\",\n \"* **X 스태빌라이저**는 그들이 적용되는 큐비트에서 일어나는 X 오류와 교환 가능하지만 Z 오류와는 반교환 가능합니다. 이들은 **Z 오류** (또한 Y 오류의 Z 성분) 를 탐지하는데 사용됩니다.\\n\",\n \"* **Z 스태빌라이저**는 그들이 적용되는 큐비트에서 일어나는 Z 오류와 교환 가능하지만 X 오류와는 반교환 가능합니다. 이들은 **Ｘ 오류** (또한 Ｙ 오류의 Ｘ 성분) 를 탐지하는데 사용됩니다.\\n\",\n \"\\n\",\n \"이러한 구분성은 디코딩을 간단하게 합니다:\\n\",\n \"1. 완전한 징후을 얻기 위해 모든 스태빌라이저를 측정합니다.\\n\",\n \"2. **Z 스태빌라이저**에 해당하는 모든 징후 비트를 추출합니다. 이 비트들과 $C_1$ 코드 (위에서 보았듯이 해당 코드의 패리티 검사는 X 스태빌라이저 정의합니다)와 연관된 고전적인 디코딩 알고리즘을 이용하여 가장 가능성 높은 **X 혹은 Y 오류**를 인식하고 정정합니다.\\n\",\n \"3. **X 스태빌라이저**에 해당하는 모든 징후 비트를 추출합니다. 이 비트들과 $C_2^\\\\perp$ 코드 (위에서 보았듯이 해당 코드의 패리티 검사는 Z 스태빌라이저 정의합니다)와 연관된 고전적인 디코딩 알고리즘을 이용하여 가장 가능성 높은 **Z 혹은 Y 오류**를 인식하고 정정합니다.\\n\",\n \"\\n\",\n \"X와 Z 오류를 고전적인 코드의 원소들을 사용하여 따로따로 다루는 이 강력한 CSS 구성은 그 유명한 [[7,1,3]] Steane 코드의 예시를 통해 알 수 있습니다. 다음 장에서 우리는 이 중요한 코드의 특정 스태빌라이저와 실용적 측면을 살펴볼 예정입니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9da222c6\",\n \"metadata\": {},\n \"source\": [\n \"## 2.4 [[7, 1, 3]] Steane 코드 연습\\n\",\n \"\\n\",\n \"**[[7,1,3]] Steane 코드**는 대표적인 CSS 코드입니다. 이 코드는 $k=1$ 논리 큐비트를 $n=7$ 물리 큐비트에 인코딩하고 $d=3$ 이므로 임의의 한 개 큐비트에서 발생하는 오류를 고칠 수 있습니다. 이 코드는 고전적인 [7, 4, 3] 해밍 코드 $C_1$ 과 $C_2$ 를 사용하여 구성됩니다. 즉, $H_1 = H_2 = H_{\\\\text{Hamming}}$ 입니다.\\n\",\n \"\\n\",\n \"### 패리티 검사 행렬의 기초\\n\",\n \"\\n\",\n \"고전 선형 코드에 있어 **패리티 검사 행렬** $H$ 는 중요한 도구입니다. 만약 $c$ 가 부호문이면 $Hc^T = \\\\mathbf{0}$ (영 벡터) 가 무조건 성립해야 합니다. $H$ 의 각 행은 패리티 검사를 정의하고, 이는 해당 행을 모두 modulo 2로 더했을 때 0이 나오게 되는 부호문 비트들의 부분 집합을 특정함을 의미합니다. 만약 $Hc^T \\\\neq \\\\mathbf{0}$ 이면 해당 부호문에 오류가 있음을 의미하며, 이를 이용하여 오류를 고칠 수 있으므로 이를 **징후**라고 부릅니다. CSS 코드에서 고전 패리티 검사 행렬의 행은 양자 코드의 X와 Z 스태빌라이저 낳음 연산자를 정의하는데 사용됩니다.\\n\",\n \"\\n\",\n \"### 패리티 검사 행렬로부터 스태빌라이저 낳음 연산자 만들기\\n\",\n \"\\n\",\n \"큐비트 $q_0, ..., q_6$ 에 대하여 각 행이 Steane 코드의 스태빌라이저가 되는 패리티 검사 행렬 $H$ 는 다음과 같습니다:\\n\",\n \"$$ H = \\\\begin{pmatrix}\\n\",\n \"1 & 1 & 1 & 1 & 0 & 0 & 0 \\\\\\\\ % X0 X1 X2 X3 (그리고 Z0 Z1 Z2 Z3)에 해당합니다\\n\",\n \"1 & 1 & 0 & 0 & 1 & 1 & 0 \\\\\\\\ % X0 X1 X4 X5 (그리고 Z0 Z1 Z4 Z5)에 해당합니다\\n\",\n \"1 & 0 & 1 & 0 & 1 & 0 & 1 % X0 X2 X4 X6 (그리고 Z0 Z2 X4 X6)에 해당합니다\\n\",\n \"\\\\end{pmatrix} $$\\n\",\n \"위 행렬 $H$ 의 각 행 $(h_0, h_1, h_2, h_3, h_4, h_5, h_6)$ 은 스태빌라이저 낳음 연산자에 해당합니다. X-스태빌라이저에 대해서 $h_i=1$ 이면 $X_i$ 연산자가 존재합니다. Z-스태빌라이저에 대해서 $h_i=1$ 이면 $Z_i$ 연산자가 존재합니다.\\n\",\n \"\\n\",\n \"따라서 스태빌라이저 낳음 연산자는:\\n\",\n \"\\n\",\n \"* **X 스태빌라이저** (Z 오류 검출) 는 $H$ 의 행으로부터 유도됩니다:\\n\",\n \" * $S_{X0} = X_0 X_1 X_2 X_3 = IIIXXXX$\\n\",\n \" * $S_{X1} = X_0 X_1 X_4 X_5 = IXXIIXX$\\n\",\n \" * $S_{X2} = X_0 X_2 X_4 X_6 = XIXIXIX$\\n\",\n \"\\n\",\n \"* **Z 스태빌라이저** (X 오류 검출) 또한 $H$ 의 행으로부터 유도됩니다:\\n\",\n \" * $S_{Z0} = Z_0 Z_1 Z_2 Z_3 = IIIZZZZ$\\n\",\n \" * $S_{Z1} = Z_0 Z_1 Z_4 Z_5 = IZZIIZZ$\\n\",\n \" * $S_{Z2} = Z_0 Z_2 Z_4 Z_6 = ZIZIZIZ$\\n\",\n \"\\n\",\n \"이 $m = n-k = 7-1=6$ 개의 연산자들은 스태빌라이저 군 $S$ 를 만듭니다. 이러한 X 스태빌라이저는 모든 Z 스태빌라이저와 교환 가능합니다. 이는 X 스태빌라이저를 정의하는 $H$ 의 임의의 행 $h_a$ 와 Z 스태빌라이저를 정의하는 $H$ 의 임의의 행 $h_b$ 에 대해, 두 행이 모두 '1' (공유하는 큐비트) 을 갖는 위치의 수가 항상 짝수이기 때문입니다. 이는 $S_{Xa}$ 와 $S_{Zb}$ 가 교환 가능함을 보장합니다.\\n\",\n \"\\n\",\n \"**오류 탐지와 정정:** \\n\",\n \"$j$ 번째 큐비트에서 일어나는 하나의 Z 오류 $Z_j$ 는 $\\\\{S_{X0}, S_{X1}, S_{X2}\\\\}$ 의 특정 부분 집합과 반교환 가능합니다. 이러한 X-스태빌라이저를 측정하여 얻어진 3-비트 징후은 $j$ 를 유일하게 특정합니다. 예를 들어, $Z_0$ 오류는 $S_{X0}, S_{X1}, S_{X2}$ 와 반교환 가능하고, 이는 $(1,1,1)$ 이라는 X-징후 (고유값 반전) 를 줍니다. (1은 반전을 의미) $Z_3$ 오류는 오직 $S_{X0}$ 와 반교환 가능하므로, 징후 $(1,0,0)$ 를 줍니다. $H$ 의 각 열은 해당하는 큐비트에서 일어나는 오류에 대한 징후 패턴을 나타냅니다.\\n\",\n \"\\n\",\n \"비슷하게, $j$ 번째 큐비트에서 일어나는 하나의 X 오류 $X_j$ 는 $\\\\{S_{Z0}, S_{Z1}, S_{Z2}\\\\}$ 의 특정 부분 집합과 반교환 가능하고, 이러한 Z-스태빌라이저를 측정하여 얻어진 3-비트 징후은 $j$ 를 유일하게 특정합니다. \\n\",\n \"\\n\",\n \"$Y_j$ 오류는 두 징후의 집합에서 모두 검출될 것입니다.\\n\",\n \"\\n\",\n \"**논리 연산자:** \\n\",\n \"모든 스태빌라이저와 교환 가능하지만 그들 자신은 스태빌라이저가 아닌 논리 연산자를 위한 전형적인 선택은:\\n\",\n \" * $\\\\bar{X} = X_0 X_1 X_2 X_3 X_4 X_5 X_6$ (모든 7개의 큐비트에 대한 $X$ 의 곱)\\n\",\n \"* $\\\\bar{Z} = Z_0 Z_1 Z_2 Z_3 Z_4 Z_5 Z_6$ (모든 7개의 큐비트에 대한 $Z$ 의 곱)\\n\",\n \"입니다.\\n\",\n \"\\n\",\n \"#### Steane 코드의 최소 거리 $d$와 오류 탐지 능력\\n\",\n \"\\n\",\n \"이제 왜 $[[7,1,3]]$ 비트 반전 코드의 거리 $d$ 가 3인지 알아봅시다. 만약 이미 이 내용을 이해하고 있으시다면 건너뛰셔도 무방합니다:\\n\",\n \"\\n\",\n \"\\n\",\n \" 클릭하여 펼쳐보세요 \\n\",\n \"\\n\",\n \"### 가중치 1 연산자 (단일 큐비트 오류)\\n\",\n \"큐비트 $i$ 에 작용하는 어떠한 단일 큐비트 파울리 오류 $P_i \\\\in \\\\{X_i, Y_i, Z_i\\\\}$ 를 생각해봅시다.\\n\",\n \"* 만약 $P_i = X_i$ 또는 $Y_i$ 이면, 이는 큐비트 $i$ 에 $Z$ 가 있는 어떠한 Z 스태빌라이저 ($S_{Z0}, S_{Z1}, S_{Z2}$)와 반교환 가능할 것입니다. 예를 들어, $X_6$ 는 $S_{Z2} = Z_0 Z_2 Z_4 Z_6$ 와 반교환 가능합니다. $Y_6$ 또한 $S_{Z2}$ 와 반교환 가능합니다.\\n\",\n \"* 만약 $P_i = Z_i$ 또는 $Y_i$ 이면, 이는 큐비트 $i$ 에 $X$ 가 있는 어떠한 X 스태빌라이저 ($S_{X0}, S_{X1}, S_{X2}$)와 반교환 가능할 것입니다. 예를 들어, $Z_6$ 는 $S_{X2} = X_0 X_2 X_4 X_6$ 와 반교환 가능합니다. $Y_6$ 또한 $S_{X2}$ 와 반교환 가능합니다.\\n\",\n \"\\n\",\n \"$i \\\\in \\\\{0, \\\\dots, 6\\\\}$ 의 모든 큐비트 $i$ 에는 $\\\\{S_{X0}, \\\\dots, S_{Z2}\\\\}$ 에 있는 X와 Z 스태빌라이저 모두 비자명하게 작용하므로 **임의의** 단일 파울리 오류 $P_i$ 는 적어도 하나 이상의 스태빌라이저 낳음 연산자와 반교환 가능할 것입니다.\\n\",\n \"\\n\",\n \"결론: 모든 가중치 1 오류들이 **탐지 가능**하므로, $d > 1$ 입니다.\\n\",\n \"\\n\",\n \"### 가중치 2 연산자 (두 큐비트 오류)\\n\",\n \"\\n\",\n \"$i \\\\neq j$ 이고 $P, P' \\\\in \\\\{X, Y, Z\\\\}$ 인 두 큐비트 파울리 오류 $E_2 = P_i P'_j$ 를 고려해봅시다. 스태빌라이저 낳음 연산자를 생각해보면, $E_2$ 는 항상 적어도 하나 이상의 $S_k$ 와 반교환 가능해야함을 보일 수 있습니다.\\n\",\n \"* 예를 들어, $X_5 X_6$ 를 고려해봅시다.\\n\",\n \" * $X_5$ 는 $S_{Z1} (=Z_0Z_1Z_4Z_5)$, $S_{Z2} (=Z_0Z_2Z_4Z_6)$ 와 반교환 가능합니다. (조금 생각해보면 $X_5$ 는 $Z_5$ 를 포함하는 Z-스태빌라이저 (즉 $S_{Z1}$) 와 반교환 가능함을 알 수 있습니다.)\\n\",\n \" * $X_6$ 는 $S_{Z2} (=Z_0Z_2Z_4Z_6)$ 와 반교환 가능합니다:\\n\",\n \" * $S_{Z0} = Z_0Z_1Z_2Z_3$: 교환 가능\\n\",\n \" * $S_{Z1} = Z_0Z_1Z_4Z_5$: $X_5$ 와 $Z_5$ 때문에 반교환 가능\\n\",\n \" * $S_{Z2} = Z_0Z_2Z_4Z_6$: $X_6$ 와 $Z_6$ 때문에 반교환 가능\\n\",\n \" 따라서 $Z_1Z_2$ 는 $S_{X1}$, $S_{X2}$ 와 반교환 가능합니다.\\n\",\n \"* $Z_1 Z_2$ 인 다른 예시를 생각해봅시다.\\n\",\n \" * $Z_1$ 는 $S_{X0} (=X_0X_1X_2X_3)$, $S_{X1} (=X_0X_1X_4X_5)$ 와 반교환 가능합니다.\\n\",\n \" * $Z_2$ 는 $S_{X0} (=X_0X_1X_2X_3)$, $S_{X2} (=X_0X_2X_4X_6)$ 와 반교환 가능합니다.\\n\",\n \" * $Z_1 Z_2$ 이 X 스태빌라이저와 교환 가능한지 확인해봅시다:\\n\",\n \" * $S_{X0} = X_0X_1X_2X_3$: $Z_1$ 과 $X_1$, 그리고 $Z_2$ 와 $X_2$ 이 각각 반교환 관계이므로 전체적으로는 교환 가능합니다.\\n\",\n \" * $S_{X1} = X_0X_1X_4X_5$: $Z_1$ 과 $X_1$ 이 반교환 관계이므로 반교환 가능합니다.\\n\",\n \" * $S_{X2} = X_0X_2X_4X_6$: $Z_2$ 와 $X_2$ 가 반교환 관계이므로 반교환 가능합니다.\\n\",\n \" 따라서 $Z_1Z_2$ 은 $S_{X1}$, $S_{X2}$ 와 반교환 가능합니다.\\n\",\n \"\\n\",\n \"체계적인 확인 과정을 통해서 우리는 **모든** 가중치 2 파울리 오류가 적어도 하나 이상의 스태빌라이저 낳음 연산자 $S_k$ 와 반교환 가능함을 보였습니다.\\n\",\n \"결론: 모든 가중치 2 오류를 **탐지 가능**하므로, $d > 2$ 입니다.\\n\",\n \"\\n\",\n \"### 가중치 3 연산자 (세 큐비트 오류)\\n\",\n \"\\n\",\n \"이제 가중치 3의 파울리 오류 $E_3$ 를 고려해봅시다. $L_X = X_4X_5X_6$ 을 모든 Z 스태빌라이저에 대해 검사해봅시다:\\n\",\n \"* **vs $S_{Z0} = Z_0Z_1Z_2Z_3$**:\\n\",\n \" $X_4, X_5, X_6$ 모두 $S_{Z0}$ 에 속하지 않는 큐비트들에 작용하므로 $L_X$ 는 $S_{Z0}$ 와 교환 가능합니다.\\n\",\n \"* **vs $S_{Z1} = Z_0Z_1Z_4Z_5$**:\\n\",\n \" $L_X$ 에서의 $X_4$ 는 $S_{Z1}$ 에서의 $Z_4$ 와 반교환 가능합니다.\\n\",\n \" $L_X$ 에서의 $X_5$ 는 $S_{Z1}$ 에서의 $Z_5$ 와 반교환 가능합니다.\\n\",\n \" $L_X$ 에서의 $X_6$ 는 $S_{Z1}$ 과 교환 가능합니다. 이는 $S_{Z1}$ 에 $Z_6$ 가 없기 때문입니다.\\n\",\n \" 모든 것을 고려해봤을 때, $X_4$ 와 $Z_4$, $X_5$ 와 $Z_5$ 두 개의 반교환이 일어나므로 전체적으로 $L_X$ 연산자와 $S_{Z1}$ 는 교환 가능합니다.\\n\",\n \"\\n\",\n \"* **vs $S_{Z2} = Z_0Z_2Z_4Z_6$**:\\n\",\n \" $L_X$ 에서의 $X_4$ 는 $S_{Z2}$ 에서의 $Z_4$ 와 반교환 가능합니다.\\n\",\n \" $L_X$ 에서의 $X_5$ 는 $S_{Z2}$ 과 교환 가능합니다. 이는 $S_{Z2}$ 에 $Z_5$ 가 없기 때문입니다.\\n\",\n \" $L_X$ 에서의 $X_6$ 는 $S_{Z2}$ 에서의 $Z_6$ 와 반교환 가능합니다.\\n\",\n \" 모든 것을 고려해봤을 때, $X_4$ 와 $Z_4$, $X_6$ 와 $Z_6$ 두 개의 반교환이 일어나므로 전체적으로 $L_X$ 연산자와 $S_{Z2}$ 는 교환 가능합니다.\\n\",\n \"\\n\",\n \"따라서, $L_X = X_4X_5X_6$ 는 모든 스태빌라이저 낳음 연산자 $S_{X0}, \\\\dots, S_{Z2}$ 와 교환 가능합니다. 가중치 3인 $X_4X_5X_6$ 이 모든 $S_i$ 와 교환 가능하므로, 이는 자명한 징후 $(0,0,0,0,0,0)$ 를 야기하고, 이는 곧 오류가 스태빌라이저 측정을 해도 **탐지 불가능**하다는 것을 의미합니다. 이러한 연산자 $L_X$ 는 인코딩된 논리 상태를 바꾸게 될 것입니다.\\n\",\n \"\\n\",\n \"**따라서 [[7, 1, 3]] Steane 코드의 최소 거리 $d=3$ 입니다.**\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"71c750a1\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"연습문제 2: [[7, 1, 3]] Steane 코드의 lookup table \\n\",\n \"\\n\",\n \"이 문제의 목적은 가능한 모든 6-비트 징후를 단일 큐비트 오류 코드와 연결시키는 파이썬 딕셔너리를 만드는 것에 있습니다. 예를 들어 X0은 0번째 큐비트에 X 오류가 발생했다는 것을 의미합니다. 만약 우리가 각 징후의 측정 결과가 `s0`...`s5` = $S_{X0},...,S_{Z2}$ 이라고 가정하면, 이는 0번째 큐비트에 X 오류가 발생했다는 것을 의미하고, 이에 해당하는 딕셔너리의 key는 \\\"111000\\\" ((s5, s4, s3, s2, s1, s0)라는 징후 표기 관습을 따라서 표기할 때)이 될 것이고, value 값은 \\\"X0\\\"이 될 것입니다. 딕셔너리의 각 value는 \\\"Pq\\\"라는 표기 관습을 따를 것입니다. 여기서 P는 파울리 연산자를 의미하고 (X, Y 또는 Z) q는 0부터 6 사이의 큐비트의 번지수를 의미합니다. 만약 오류가 없다면 징후는 모두 0이 될 것이고, 정정 연산은 항등 연산 (아무 것도 하지 않는)을 의미하는 \\\"I\\\"가 될 것입니다.\\n\",\n \"\\n\",\n \"여러분의 역할은 X, Y, Z 오류에 의해 만들어진 징후를 결정하고 모든 단일 큐비트 파울리 오류에 대해 이러한 연결을 마무리 짓고, 딕셔너리에 정확한 라벨을 지정하는 것에 있습니다. 해당 연습을 끝마치시면, 여러분의 디코더는 단일 오류로 인해 생성된 임의의 6-비트 징후를 입력값으로 받아서 정정 연산을 수행할 수 있게 될 것입니다.\\n\",\n \"\\n\",\n \"(**팁**) X 오류에 대해 아래의 교환 가능/반교환 가능 테이블을 채워보세요! 교환 가능성에는 0, 반교환 가능성에는 1을 적어보세요. \\n\",\n \"\\n\",\n \"| 오류 코드 | 오류 파울리 문자열 |S5(ZIZIZIZ) | S4(IZZIIZZ) | S3 (IIIZZZZ) | S2 (XIXIXIX) | S1 (IXXIIXX) | S0 (IIIXXXX) |\\n\",\n \"|---|---|---|---|---|---|---|---|\\n\",\n \"| $X_0$ | IIIIIIX | 1(반교환) | 1(반교환) | 1(반교환) | 0(교환) | 0(교환) | 0(교환) |\\n\",\n \"| $X_1$ | IIIIIXI | | | | | | |\\n\",\n \"| $X_2$ | IIIIXII | | | | | | |\\n\",\n \"| $X_3$ | IIIXIII | | | | | | |\\n\",\n \"| $X_4$ | IIXIIII | | | | | | |\\n\",\n \"| $X_5$ | IXIIIII | | | | | | |\\n\",\n \"| $X_6$ | XIIIIII | | | | | | |\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 3: Lookup table을 사용한 오류 탐지\\n\",\n \"\\n\",\n \"이제 오류를 찾아내기 위해 여러분이 만드신 lookup table을 이용해봅시다!\\n\",\n \"\\n\",\n \"Grader로부터 양자 상태를 다운로드 받기 위해 아래의 셀을 실행하십시오. 이 양자 상태는 Steane 코드의 논리 0 상태이고, 어떠한 오류가 발생하였습니다.\\n\",\n \"\\n\",\n \"당신의 임무는:\\n\",\n \"1. 양자 회로를 구성하세요.\\n\",\n \"2. 받은 양자 상태를 `qr_data` 큐비트를 이용해 초기화하세요.\\n\",\n \"3. `measure_steane_syndrome` 함수를 완성하세요. 완성된 S0를 예시로 남겨뒀습니다.\\n\",\n \"4. 어떠한 오류가 큐비트에 일어났는지 확인하기 위해 징후 측정 결과를 당신의 lookup table과 비교하세요.\\n\",\n \"5. 확인을 위해 grader에 여러분의 답을 제출하세요.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"먼저 Steane 코드의 모든 스태빌라이저를 준비하기 위해 `measure_steane_syndrome`를 완성하세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a4a8f077\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def measure_steane_syndrome(qc, q_data, q_anc, c_reg):\\n\",\n \"\\n\",\n \" # X 스태빌라이저를 측정하세요 (S0, S1, S2 -> q_anc[0], q_anc[1], q_anc[2])\\n\",\n \" # 모든 정보 큐비트에 H 연산을 하고, CNOT 연산을 한 뒤에, 정보 큐비트에 H 연산을 하고, 보조 큐비트를 측정하세요\\n\",\n \" qc.h(q_data) # 정보 큐비트에 H 연산\\n\",\n \"\\n\",\n \" #S0: IIIXXXX (X_3 X_2 X_1 X_0)\\n\",\n \" qc.cx(q_data[0], q_anc[0])\\n\",\n \" qc.cx(q_data[1], q_anc[0])\\n\",\n \" qc.cx(q_data[2], q_anc[0])\\n\",\n \" qc.cx(q_data[3], q_anc[0])\\n\",\n \"\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" # S1 과 S2 에 해당하는 코드를 작성해주세요\\n\",\n \" #S1\\n\",\n \"\\n\",\n \" #S2\\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \"\\n\",\n \" qc.h(q_data) # 정보 큐비트에 다시 H 연산\\n\",\n \" qc.measure(q_anc[0:3], c_reg[0:3]) # X 징후 (s1, s2, s3) 측정\\n\",\n \" qc.barrier()\\n\",\n \"\\n\",\n \"\\n\",\n \" # --- 아래에 코드를 작성해주세요 ---\\n\",\n \" # Z 스태빌라이저를 측정하세요 (S3, S4, S5 -> q_anc[3], q_anc[4], q_anc[5])\\n\",\n \" # CNOT을 하고 보조 큐비트를 측정하세요\\n\",\n \" # S3, S4와 S5을 위해 필요한 코드를 작성해주세요\\n\",\n \"\\n\",\n \" #S3\\n\",\n \"\\n\",\n \" #S4\\n\",\n \"\\n\",\n \" #S5\\n\",\n \" # --- 코드 작성이 완료되었습니다 ---\\n\",\n \" \\n\",\n \" qc.measure(q_anc[3:6], c_reg[3:6]) # Z 징후 (s3, s4, s5) 측정\\n\",\n \" qc.barrier()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fed4a432\",\n \"metadata\": {},\n \"source\": [\n \"`QuantumCircuit`의 [initialize](https://docs.quantum.ibm.com/api/qiskit/qiskit.circuit.QuantumCircuit#initialize)를 이용해 `qr_data`를 제공된 상태 벡터 (`State`)로 초기화 시켜봅시다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9a55ec14\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"state = bring_states()\\n\",\n \"\\n\",\n \"# 논리 큐비트 (7개의 정보 큐비트)\\n\",\n \"qr_data = QuantumRegister(7, name='q')\\n\",\n \"# 징후 측정을 위한 보조 큐비트 (6)\\n\",\n \"qr_anc = QuantumRegister(6, name='anc')\\n\",\n \"# 징후 측정을 위한 고전 레지스터 (초기화 및 검사)\\n\",\n \"cr_initial_syn = ClassicalRegister(6, name='c_initial_syn')\\n\",\n \"cr_final_syn = ClassicalRegister(6, name='c_final_syn')\\n\",\n \"\\n\",\n \"# 전체 회로 (13개의 큐비트, 12개의 고전 비트)\\n\",\n \"qc = QuantumCircuit(qr_data, qr_anc, cr_initial_syn, cr_final_syn)\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"# 원하는 상태로 qc를 초기화 시키세요\\n\",\n \"\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"92a1a966\",\n \"metadata\": {},\n \"source\": [\n \"이제 아래의 셀을 실행하여 징후 측정 가하고 양자 회로를 그려서 확인해보세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0f4f071c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- 징후 측정을 가합니다 ---\\n\",\n \"\\n\",\n \"measure_steane_syndrome(qc, qr_data, qr_anc, cr_initial_syn)\\n\",\n \"\\n\",\n \"qc.draw('mpl', fold=-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1cee23bf\",\n \"metadata\": {},\n \"source\": [\n \"이제 만들어진 양자 회로를 실행하여 논리 $∣0\\\\rangle$ 상태에 어떠한 오류가 발생하였는지 확인해봅시다. 이 임무를 마치기 위해서 여러분은 `counts` 딕셔너리의 key (측정된 문자열) 가 필요할 것입니다. 만약 스태빌라이저 측정이 제대로 구현되었다면, 여러분은 시뮬레이션 결과에서 100%의 확률로 하나의 상태를 보게 될 것입니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"정확한 답을 얻기 위하여\\n\",\n \"\\n\",\n \"만약 여러분이 실제 백엔드에서 아래의 회로를 돌리게 되면, 각 큐비트들에서 발생하는 다양한 종류의 오류 때문에 정확한 오류 코드를 얻지 못하는 경우가 발생합니다. 따라서, 다음의 코드는 시뮬레이션 백엔드에서 돌려주시기 바랍니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b4eca236\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- AerSimulator를 이용하여 시뮬레이션을 실행합니다\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"# 양자 회로를 사용하는 백엔드에 맞게 최적화합니다\\n\",\n \"pm = generate_preset_pass_manager( backend = backend, optimization_level=3)\\n\",\n \"qc_isa = pm.run(qc)\\n\",\n \"\\n\",\n \"# 코드를 실행하고 실행 결과 (count) 를 얻습니다\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots = 10000).result()[0].data.c_initial_syn.get_counts()\\n\",\n \"\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"# 시뮬레이션 결과에서 key를 찾아 \\\"X1\\\"과 같이 오류 코드로 변환하세요\\n\",\n \"\\n\",\n \"error_code = \\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0a3d5dba\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_histogram(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4ad1bbb7\",\n \"metadata\": {},\n \"source\": [\n \"Grader에 여러분의 오류 코드 문자열을 제출하여 여러분이 제대로 탐지했는지 확인해보세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0a72d019\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab4_ex3(error_code)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ee2cf0ca\",\n \"metadata\": {},\n \"source\": [\n \"축하드립니다! 여러분은 성공적으로 Steane 코드의 스태빌라이저 측정 회로를 구현하셨고 오류를 탐지하셨습니다.\\n\",\n \"\\n\",\n \"파울리 오류는 유니터리 행렬이며 스스로의 역연산이 되므로 같은 파울리 게이트를 두 번 가하면 항등 행렬을 가하는 것과 같으며 ($P \\\\cdot P=I$), 이는 오류를 정정하는 것과 근본적으로 같습니다. 다음의 선택 연습문제 (채점이 없습니다) 에서는 이러한 성질을 이용하여 여러분이 탐지한 오류를 정정해봅니다. 문제가 생긴 양자 상태에 여러분이 탐지한 오류 코드에 해당하는 게이트를 가하고 오류가 정정되었음을 확인하세요.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"65b5f314\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"채점이 없는 추가 문제: 오류를 고쳐보세요\\n\",\n \"\\n\",\n \"이제 적절한 게이트를 가하여 이 오류를 정정해봅시다.\\n\",\n \"\\n\",\n \"적절한 정정을 하는 아래의 셀을 완성하고 징후 측정 결과를 얻기 위해 다시 한 번 스태빌라이저 측정을 하세요: $000000$ 은 오류가 탐지되지 않았다는 뜻입니다. `qr_data`를 상태 State로 초기화하고 오류 정정 게이트를 가한 뒤에 스태빌라이저 측정을 하세요. 여러분이 이미 만든 `measure_steane_syndrome` 함수를 쓰셔도 됩니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"정확한 답을 얻기 위하여\\n\",\n \"\\n\",\n \"만약 여러분이 실제 백엔드에서 아래의 회로를 돌리게 되면, 각 큐비트들에서 발생하는 다양한 종류의 오류 때문에 정확한 오류 코드를 얻지 못하는 경우가 발생합니다. 따라서, 다음의 코드는 시뮬레이션 백엔드에서 돌려주시기 바랍니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"90d4a474\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qc_isa = pm.run(qc)\\n\",\n \"counts = sampler.run([qc_isa], shots = 10000).result()[0].data.c_final_syn.get_counts()\\n\",\n \"\\n\",\n \"plot_histogram(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2730a94a\",\n \"metadata\": {},\n \"source\": [\n \"징후 측정에서 `000000`을 100%의 확률로 얻으셨나요? 축하드립니다 🎉!\\n\",\n \"여러분은 이제 Steane 코드를 이해하셨고, 이 코드가 어떻게 오류를 탐지하고 정정하는지 알게 되셨습니다.\\n\",\n \"\\n\",\n \"지금까지 여러분은 고전과 양자 오류 정정 코드의 근본적인 개념을 탐구하셨습니다.\\n\",\n \"여러분은 두 근본적인 양자 오류 정정 코드가 어떻게 작동하는지 배우셨고, 오류 탐지 및 정정을 위한 lookup table을 만드셨으며, 이러한 원리를 실제로 Steane 코드에서 오류를 탐지하는데에 적용하셨습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ce09252b\",\n \"metadata\": {},\n \"source\": [\n \"# 챕터 3: 고급 양자 오류 정정 부호와 그 효율성 탐구\\n\",\n \"\\n\",\n \"Steane 코드와 같은 기본적인 양자 오류 정정 부호를 공부했으므로, 이 챕터에서는 더욱 발전된 주제에 대해 다룰 것입니다. 우리의 중요한 목표는 강건한 오류 내성을 달성하기 위한 다양한 전략을 이해하고 더욱 발전한 Gross 코드와 같은 QLDPC 구성이 어떻게 surface/toric 코드와 같은 유명한 코드에 비해 이점을 제공하는지에 대해 배우는 것입니다. 특히 이러한 이점은 양자 오류 정정 능력과 효율에 있습니다.\\n\",\n \"\\n\",\n \"우리는 먼저 특정한 구조에서 정의된 코드를 다룰 것입니다. 이 때의 기하학적 레이아웃과 큐비트의 연결성은 코드의 성질을 바꿉니다. 우리는 toric 코드의 예시를 살펴보고, 이를 Gross 코드와 비교할 것입니다. 이를 위해 우리는 그들의 패리티 검사 행렬과 물리 큐비트의 개수와 비교해 얼마나 큰 거리의 얼마나 많은 논리 큐비트를 인코딩할 수 있는지 살펴볼 것입니다. 이를 통해 우리는 오류 내성을 달성하는 다양한 전략을 살펴보고 최근의 양자 오류 정정 연구의 다양한 면면을 조명하게 될 것입니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" 코드 표현과 교육적인 접근에 관하여 \\n\",\n \"\\n\",\n \"여기서의 중요한 목표는 다양한 코드의 차이를 이해하는 것에 있다는 것을 말씀드립니다. 해당 챕터에서 사용되는 예시나 그림은 다양한 코드 종류의 차이 (예를 들어 Surface/Toric vs. Gross 류의 코드)를 나타내기 위한 **예시용 모델**입니다. 이러한 예시 모델은 성능의 차이를 이끌어내는 오류 정정 코드의 핵심적인 성질을 알기 위함이지, 코드를 구성하는 완전한 과정을 설명하는 것은 아닙니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"## 3.1 비교를 위한 기본 개념과 주요 QLDPC 아키텍쳐\\n\",\n \"\\n\",\n \"바로 toric/Gross 코드를 소개하기보다는 여기서는 먼저 *양자 저밀도 패리티 검사 코드 (Quantum Low-Density Parity-Check, QLDPC)*와 주요 QLDPC 아키텍쳐를 먼저 소개하겠습니다.\\n\",\n \"\\n\",\n \"저밀도 패리티 검사 (LDPC) 코드는 **희박한 (sparse)** 패리티 검사 행렬을 가진 것으로 잘 알려진 고전 오류 정정 코드입니다. 이때, 희박한 행렬은 그의 크기에 비해 0이 아닌 원소의 개수가 매우 적은 행렬을 의미합니다. 이러한 희박성은 굉장히 효율적인 디코딩 알고리즘을 가능케 하기 때문에 매우 중요하고, 이는 LDPC 코드가 고전 통신과 정보 저장에 있어 강력한 도구가 되게 했습니다. 양자 저밀도 패리티 검사 (QLDPC) 코드는 이러한 고전 LDPC 코드의 양자 버전입니다. QLDPC 코드에서는 **스태빌라이저 낳음 연산자가 희박**합니다; 각 스태빌라이저 낳음 연산자는 오직 작은 개수의 큐비트에 비자명하게 작용하고, 각 큐비트는 그러한 낳음 연산자들의 아주 작은 수에만 포함됩니다. 이러한 코드를 설계하는 데에 있어 우리가 원하는 것은, 희박성을 갖는 코드 뿐만 아니라 코드 거리 $d$ (양자 오류 정정의 능력을 잘 보여주는 지표) 를 얼마나 효율적으로 늘릴 수 있는지입니다. 그리고 가능하면 높은 **인코딩 비율** ($k/n$, 여기서 $k$ 는 논리 큐비트의 개수입니다) 을 얻고자 합니다.\\n\",\n \"\\n\",\n \"이제 우리가 기초를 다졌으니 두 가지 중요한 QLDPC 코드의 종류를 삺펴보고 비교해봅시다:\\n\",\n \"\\n\",\n \"* **Surface/Toric 코드**: 이 코드들은 2차원 격자 구조에서 정의된, 스태빌라이저들이 오직 기하학적으로 **국소적** (보통 가장 가까운 이웃 큐비트하고) 으로만 작용하는 것으로 잘 알려진 QLDPC 코드입니다. 2차원 하드웨어와의 호환성과 높은 오류 한계점을 지닌 것으로 잘 알려져 있지만, 이들의 코드 거리 $d$ 는 보통 $n$ 개의 물리 큐비트에 대해서 $O(\\\\sqrt{n})$ 로 늘어납니다. 이는 아주 큰 거리를 효율적으로 구현하는 데에 한계점을 보인다는 것을 의미합니다. 또한 surface (toric) 코드의 각 부분은 한 (두) 개의 논리 큐비트밖에 저장하지 못하는 한계가 있습니다.\\n\",\n \"\\n\",\n \"* **Bivariate Bicyle 코드 (및 관련된 QLDPC 종류)**: 이 더 넓은 QLDPC 아키텍쳐의 종류는 변수 $k, d, n$ 에 대한 훌륭한 점근적 확장을 위해 만들어졌습니다. 이들의 아키텍쳐는 보통 간단한 2차원 기하학적 국소성을 고려하지 않습니다. 이들의 Tanner 그래프는 희박성을 유지하며 종종 더 복잡하거나 **\\\"먼 거리\\\"** (2차원 기준) 의 연결성을 지녔습니다. 이렇게 다른 구조적 연결성은 더 휼륭한 성능의 원천으로 믿어지고 있습니다:\\n\",\n \" * 더 좋은 거리 확장성: 코드 거리 $d$ 가 $n$ 에 의해 더 빠르게 확장됩니다.\\n\",\n \" * 더 높은 인코딩 비율 ($k/n$): 비슷한 숫자의 물리 큐비트와 거리에서 더 많은 논리 큐비트를 인코딩할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2a5c405c\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0598739c\",\n \"metadata\": {},\n \"source\": [\n \"(https://arxiv.org/pdf/2308.07915 에서 가져온 이미지: Gross 코드의 Tanner 그래프 표현. 여기서 toroidal 격자에서 정의된 정보 큐비트는 동그라미, 스태빌라이저/검사 큐비트는 사각형으로 그려졌습니다.)\\n\",\n \"\\n\",\n \"## 3.2 연습에서 사용할 큐비트 레이아웃과 표기 원칙\\n\",\n \"\\n\",\n \"단순화된 모델을 이용하여 서로 다른 코드 구조가 성능에 어떤 영향을 끼치는지 비교하기 위해서, 우리는 위의 이미지를 이용해 똑같은 물리적 큐비트의 배치 상에서 코드를 구성할 것입니다. Patrick Rall이 \\\"Toward Fault-tolerant Quantum Computing with IBM qLDPC codes\\\" 강의에서 다룬 개념을 토대로 진행해보도록 하겠습니다.\\n\",\n \"\\n\",\n \"우리의 목표는 주기적인 경계 조건을 갖는 2차원 격자 (torus) 에서 정의된 두 가지 코드의 패리티 검사 행렬을 구성하는 데에 있습니다. 이는 제공된 Tanner 그래프에서 시각적으로 확인할 수 있습니다:\\n\",\n \"1. 국소적 연결성을 갖는 **Toric 코드**를 나타내는 그래프\\n\",\n \"2. 추가적인 장거리 연결성을 갖는 **Gross 코드**를 나타내는 그래프\\n\",\n \"\\n\",\n \"**우리의 모델에 있어서 중요한 원소들:**\\n\",\n \"\\n\",\n \"* **정보 큐비트 (원):** 양자 정보를 저장하는 역할을 합니다. 주어진 도표에서 이들은 파란색과 주황색 원으로 나타납니다. 여기에는 $12 \\\\times 6 = 72$ 개의 파란색 큐비트와 $12 \\\\times 6 = 72$ 개의 주황색 큐비트가 있어, 전체 정보 큐비트 숫자는 $n = 144$ 개 입니다.\\n\",\n \"\\n\",\n \"* **스태빌라이저/검사 큐비트 (사각):** 코드의 스태빌라이저 연산자를 정의합니다.\\n\",\n \" * **X 스태빌라이저** (파울리 X의 곱)은 **빨간색** 사각형으로 표기됩니다.\\n\",\n \" * **Y 스태빌라이저** (파울리 Z의 곱)은 **초록색** 사각형으로 표기됩니다.\\n\",\n \"* **CSS 코드 구조:** 여기서도 CSS 코드의 프레임워크를 그대로 차용합니다.\\n\",\n \"\\n\",\n \"명료한 **큐비트 표기** 원칙은 Tanner 그래프의 연결성을 패리티 검사 행렬로 바꾸는데에 있어 매우 핵심적입니다. 이 행렬의 행은 스태빌라이저에 해당할 것이고, 열은 정보 큐비트에 해당할 것입니다.\\n\",\n \"\\n\",\n \"**큐비트 표기 원칙 (총 144개의 정보 큐비트):**\\n\",\n \"\\n\",\n \"* **파란색** 데이터 큐비트 (인덱스 0-71): 왼쪽 열에서 오른쪽 열로 가며 표기되고, 그 뒤에 각 열 내에서 아래에서 위로 표기됩니다.\\n\",\n \" * 좌측 하단 파란색 큐비트는 `0`입니다; 그 뒤에 있는 큐비트는 `1`부터 시작하여 `5`까지 갑니다.\\n\",\n \" * 그 다음 행의 파란색 큐비트는 `6`부터 `11`까지 있고, 이러한 패턴으로 이어집니다.\\n\",\n \"* **주황색** 데이터 큐비트 (인덱스 72-143): 똑같은 표기를 이어가고, 좌측 하단 주황색 큐비트 `72`부터 시작합니다.\\n\",\n \"\\n\",\n \"**(이번 랩을 위한) 패리티 검사 행렬 표기 원칙:**\\n\",\n \"\\n\",\n \"우리는 이제 두 개의 이진 패리티 검사 행렬을 정의할 것입니다:\\n\",\n \"\\n\",\n \"* **X 스태빌라이저 $H_X$:**\\n\",\n \" * 각 행은 **빨간색 사각형**으로부터 유도된 X 스태빌라이저를 의미합니다.\\n\",\n \" * 이 행렬 $H_X$ 는 **Z 오류**를 탐지하는데 쓰입니다. (징후: $s_x$)\\n\",\n \"* **Z 스태빌라이저 $H_Z$:**\\n\",\n \" * 각 행은 **초록색 사각형**으로부터 유도된 Z 스태빌라이저를 의미합니다.\\n\",\n \" * 이 행렬 $H_Z$ 는 **X 오류**를 탐지하는데 쓰입니다. (징후: $s_z$)\\n\",\n \"\\n\",\n \"위와 같은 레이아웃과 표기 원칙을 사용하여, 우리는 이제 연습을 위해 *같은* 물리 큐비트 레이아웃에서 정의된 두 개의 서로 다른 코드 Tanner 그래프의 연결성에 따라 스태빌라이저를 정의하겠습니다.\\n\",\n \"\\n\",\n \"1. **Toric 코드:** 이 버전은 주기적인 경계 조건을 갖는 격자에서의 표준적인 toric 코드 모델을 따라 스태빌라이저 (사각형) 과 이들이 작용하는 정보 큐비트 (원형) 사이의 국소적이고 가장 가까운 이웃과의 연결성만을 이용해 구성됩니다. 참고: Toric 코드와 surface 코드는 매우 비슷하지만, toric 코드는 $x$ 와 $y$ 방향에서의 주기적 경계 조건이 있는 torus에서 정의가 됩니다. 이는 이러한 경계 조건이 없는 surface 코드에 비해 하나의 추가적인 논리 큐비트를 더 인코딩할 수 있게 합니다.\\n\",\n \"\\n\",\n \"2. **Gross 코드:** 이 버전은 toric 코드의 국소적 연결에 더해 \\\"장거리\\\" 연결을 이용할 것입니다. 이러한 장거리 연결은 위의 그래프에서 나뭇잎처럼 생긴 모양의 연결입니다. 이러한 추가적인 연결은 스태빌라이저 낳음 연산자의 정의를 바꿀 것이고, 이는 같은 큐비트 레이아웃에서 정의된 표준적인 toric 코드와는 다른 파라미터와 성질을 갖는 코드를 만들어 냅니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c827399a\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"610ae37c\",\n \"metadata\": {},\n \"source\": [\n \"## 3.3 Toric 코드 연습\\n\",\n \"\\n\",\n \"### HX\\n\",\n \"\\n\",\n \"우리는 이제 torus 상에서 가장 가까운 이웃끼리의 연결만을 고려하는 toric 코드의 (빨간색 사각형들과 연관된) **X 스태빌라이저**를 이진수로 표현할 것입니다. 이는 여러분이 나중에 구성하게 될 (연습 문제의 출력 변수 $H_X$ 로 표기된) 행렬의 행이 될 것입니다.\\n\",\n \"\\n\",\n \"도표의 좌측 하단의 빨간색 사각형을 고려해봅시다. 이 X 스태빌라이저는 이웃한 네 개의 정보 큐비트에 작용합니다:\\n\",\n \"1. 아래에 있는 파란색 정보 큐비트: 이 파란색 큐비트는 0번째 열과 0번째 행에 있으므로 `0`입니다.\\n\",\n \"2. 위에 있는 파란색 정보 큐비트: 이 파란색 큐비트는 0번째 열과 1번째 행에 있으므로 `1`입니다.\\n\",\n \"3. 오른쪽에 있는 주황색 정보 큐비트: 이는 좌측 하단의 주황색 큐비트이므로 `72`입니다.\\n\",\n \"4. 주기적 경계를 건너 왼쪽에 있는 주황색 정보 큐비트: 이는 우측 하단 주황색 큐비트입니다. 주황색 큐비트들은 72-143까지 표기되고, 주황색 큐비트는 12개의 열이 있고, 각 열에는 6개의 큐비트가 있습니다. 이 마지막 주황색 열 (11번째 열)은 큐비트 $72 + 11 \\\\times 6 + $ row_idx 에 해당합니다. $72 + 11 \\\\times 6 + 0 = 72 + 66 = 138$ 이므로, 우측 하단에 있는 큐비트는 주황색 큐비트 `138`입니다.\\n\",\n \"\\n\",\n \"따라서 이 첫 번째 X 스태빌라이저는 정보 큐비트 `0`, `1`, `72`와 `138`에 작용합니다. (이 연습에 있어서 $H_X$ 의 행이 될 것이고 표준 표기법에서는 $H_Z$ 가 되는) 이진 행렬의 행은 열 0, 1, 72와 138에 1이 있고, 나머지는 모두 0으로 채워집니다. 이러한 144개의 원소가 있는 행 벡터는 다음과 같습니다:\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"문제에서 \\\"패리티 검사 행렬 $H_X$\\\"의 첫 6개의 행을 찾는 (첫 6개의 빨간색 사각형에 해당하는) 예시는 같은 논리가 적용됩니다:\\n\",\n \"\\n\",\n \"```\\n\",\n \"[[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0],\\n\",\n \"\\n\",\n \" [0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0],\\n\",\n \"\\n\",\n \" [0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0],\\n\",\n \"\\n\",\n \" [0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0],\\n\",\n \"\\n\",\n \" [0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0],\\n\",\n \"\\n\",\n \" [1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]]\\n\",\n \"```\\n\",\n \"\\n\",\n \"### HZ\\n\",\n \"\\n\",\n \"HZ는 HX와 같은 기본 규칙에 따라 가장 가까운 이웃을 찾습니다. 차이점은 HZ가 초록색 사각형으로 표기되며, 전체 격자의 왼쪽에서 두 번째 열의 최하단에서 시작한다는 것입니다.\\n\",\n \"\\n\",\n \"도표에서 좌측 하단의 초록색 사각형을 고려해봅시다. 이 Z 스태빌라이저는 네 개의 이웃한 정보 큐비트에 작용합니다:\\n\",\n \"1. 왼쪽에 있는 파란색 정보 큐비트: 이는 파란색 큐비트 `0`입니다. (파란색 0 행, 0 열).\\n\",\n \"2. 오른쪽에 있는 파란색 정보 큐비트: 이는 파란색 큐비트 `6`입니다. (파란색 0 행, 1 열).\\n\",\n \"3. 위에 있는 주황색 정보 큐비트: 이는 좌측 하단 주황색 큐비트이고, 이는 주황색 큐비트 `72`입니다. (주황색 0 행, 0 열).\\n\",\n \"4. 경계 조건 넘어 아래에 있는 주황색 정보 큐비트: 이는 좌측 상단 주황색 큐비트이므로 이는 주황색 큐비트 `77`입니다. 이 큐비트는 주황색 큐비트 `72`보다 5행 아래에 있습니다. (주황색 5 행, 0 열)\\n\",\n \"\\n\",\n \"따라서 첫 Z 스태빌라이저는 정보 큐비트 `0`, `6`, `72`와 `77`에 작용합니다. (이 연습에 있어서 $H_Z$ 의 행이 될 것이고 표준 표기법에서는 $H_X$ 가 되는) 이진 행렬의 행은 열 0, 6, 72와 77에 1이 있고, 나머지는 모두 0으로 채워집니다. 이러한 144개의 원소가 있는 행 벡터는 다음과 같습니다:\\n\",\n \"```\\n\",\n \" [1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 4: Toric 코드의 패리티 검사 행렬 찾기\\n\",\n \"\\n\",\n \"큐비트 표기 원칙과 도표에서의 스태빌라이저의 정의를 확인했으므로 이제 (오직 가장 가까운 이웃과의 연결만을 갖는) **toric 코드**의 패리티 검사 행렬을 찾아봅시다.\\n\",\n \"\\n\",\n \"$H_{X}$ 로 표기된 NumPy array에 **빨간색 사각형으로 표기된 X 스태빌라이저**의 결과를, 비슷하게 $H_{Z}$ 로 표기된 NumPy array에 **초록색 사각형으로 표기된 Z 스태빌라이저**의 결과를 적으세요. (더 명확하게 하기 위해 첨언하자면, 빨간색 사각형에서 여러분이 구하는 행렬 $H_X$ 는 Z 오류를 탐지하기 위해 사용될 것이고, $H_Z$ 는 X 오류를 탐지하기 위해 쓰일 것입니다.)\\n\",\n \"\\n\",\n \"NumPy array $H_{X}$ 와 $H_{Z}$ 가 정해진 차원과 구조(`np.zeros((72,144),dtype=int)`)를 갖는지 꼭 확인 부탁드립니다. 이러한 행렬의 크기나 구조를 정해진 값에서 바꾸면 grader가 제대로 채점을 하지 못하는 상황을 일으킬 수 있습니다.\\n\",\n \"\\n\",\n \"**힌트**: 예시에서의 주기적인 구조를 살펴보세요. 이러한 주기적인 구조를 활용해 좀 더 효율적으로 행렬을 만들 수는 없을까요?\\n\",\n \"
\\n\",\n \" \\n\",\n \" 도움 함수를 이용해 연결성을 확인하세요! \\n\",\n \"\\n\",\n \"패리티 검사 행렬이 완성되면, 주어진 코드를 이용하여 `HXtc`의 5번째 스태빌라이저와 `HZtc`의 66번째 스태빌라이저의 연결성을 확인해보세요. 경계 조건을 유심히 살펴보세요. 다음 예시와 같은 그래프를 얻었을 때 성공하신 것입니다.\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXtc, HZtc)\\n\",\n \"\\n\",\n \"```\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"**감사의 말**: [Daniel Sierra-Sosa 교수님](http://www.linkedin.com/in/dsierrasosa) (Qiskit Advocate 및 QGSS 2025의 멘토) 께서 도움 함수를 만드는 데에 많은 도움을 주셨습니다.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"497d1eb1-9a0b-4fc0-9486-9d20a7e1260d\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5dde8af2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 연습문제 4를 시작하기 위한 도움말\\n\",\n \"\\n\",\n \"\\n\",\n \"# Toric 코드의 패리티 검사 행렬을 정의합니다\\n\",\n \"HXtc = np.zeros((72, 144),dtype=int) # 행렬을 먼저 초기화합니다\\n\",\n \"HZtc = np.zeros((72, 144),dtype=int)\\n\",\n \"\\n\",\n \"# 우리는 여러분에게 알맞은 곳에 1을 더함으로써 행렬을 수정하도록 요청할 것입니다\\n\",\n \"# 예시를 위해 같이 toric 코드의 첫 몇 개의 행을 채워보도록 하겠습니다\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"# HXtc와 HZtc를 계산하기 위한 코드를 작성하세요\\n\",\n \"\\n\",\n \"# 0번째 스태빌라이저\\n\",\n \"HXtc[0][0] = 1\\n\",\n \"HXtc[0][1] = 1\\n\",\n \"HXtc[0][72] = 1\\n\",\n \"HXtc[0][138] = 1\\n\",\n \"\\n\",\n \"# 1번째 스태빌라이저\\n\",\n \"HXtc[1][1] = 1\\n\",\n \"HXtc[1][2] = 1\\n\",\n \"HXtc[1][73] = 1\\n\",\n \"HXtc[1][139] = 1\\n\",\n \"\\n\",\n \"# 2번째 스태빌라이저\\n\",\n \"HXtc[2][2] = 1\\n\",\n \"HXtc[2][3] = 1\\n\",\n \"HXtc[2][74] = 1\\n\",\n \"HXtc[2][140] = 1\\n\",\n \"\\n\",\n \"# 3번째 스태빌라이저\\n\",\n \"HXtc[3][3] = 1\\n\",\n \"HXtc[3][4] = 1\\n\",\n \"HXtc[3][75] = 1\\n\",\n \"HXtc[3][141] = 1\\n\",\n \"\\n\",\n \"# 4번째 스태빌라이저\\n\",\n \"HXtc[4][4] = 1\\n\",\n \"HXtc[4][5] = 1\\n\",\n \"HXtc[4][76] = 1\\n\",\n \"HXtc[4][142] = 1\\n\",\n \"\\n\",\n \"# 5번째 스태빌라이저\\n\",\n \"HXtc[5][5] = 1\\n\",\n \"HXtc[5][np.mod(6,6)] = 1\\n\",\n \"HXtc[5][72 + 5] = 1\\n\",\n \"HXtc[5][72 + 6*np.mod(-1,12) + 5] = 1\\n\",\n \"\\n\",\n \"# 마지막 정의는 어느 위치에 1을 두어야 하는지에 대한 일반적인 규칙을 제시합니다\\n\",\n \"# HXtc[j][j] = 1\\n\",\n \"# HXtc[j][6*np.floor(j/6) + np.mod(j+1,6)]\\n\",\n \"# HXtc[j][j+72] = 1\\n\",\n \"# HXtc[j][72 + 6*np.mod(np.floor(j/6)-1,12) + np.mod(j,6)]\\n\",\n \"\\n\",\n \"# 0부터 143까지의 j에 대한 반복문을 작성하여 모든 열을 채워넣어 보세요\\n\",\n \"\\n\",\n \"# 위의 예시를 참고하여, Z 패리티 검사 행렬을 반복문을 사용하여 작성하고 HXtc와 HZtc를 완성하세요\\n\",\n \"\\n\",\n \"HZtc[0][0] = 1\\n\",\n \"HZtc[0][6] = 1\\n\",\n \"HZtc[0][72] = 1\\n\",\n \"HZtc[0][77] = 1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e7bd2d52-72a8-4b5b-99c9-b0e9a75b7dc3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# HXtc와 HZtc의 연결성을 확인해보세요\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXtc, HZtc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f81fb0f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab4_ex4(HXtc, HZtc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fc9be9fa\",\n \"metadata\": {},\n \"source\": [\n \"## 3.4 Gross 코드 연습\\n\",\n \"\\n\",\n \"이제 우리는 Gross 코드의 패리티 검사 행렬을 탐구해볼 것입니다. 이 코드는 도표에서 추상적으로 표현된 장거리 연결을 포함하여 만들어집니다. 이러한 장거리 연결은 나뭇잎처럼 표현되어 있지만, 정확한 규칙은 코드의 정의에 따르게 됩니다. 근본적인 큐비트 레이아웃과 표기 원칙은 toric 코드 때와 동일합니다.\\n\",\n \"\\n\",\n \"Gross 코드와 toric 코드의 중요한 차이점은 각각의 스태빌라이저 (사각형 검사) 에 네 개의 가장 가까운 이웃을 연결하는 연결에 더해 두 개의 장거리 연결이 추가된다는 것입니다. 이는 Gross 코드의 스태빌라이저가 총 $4+2=6$ 개의 정보 큐비트에 작용한다는 것을 의미합니다. 따라서 Gross 코드의 패리티 검사 행렬 ($H_X$ 와 $H_Z$) 의 각 행에는 이제 여섯 개의 '1'이 있게 됩니다.\\n\",\n \"\\n\",\n \"이러한 행렬을 구축하는 효과적인 방법은 toric 코드의 연결성 (가장 가까운 이웃하고의 연결) 에서 시작하여 각 스태빌라이저에 두 개의 장거리 연결을 더하는 것입니다.\\n\",\n \"\\n\",\n \"$H_X$ 의 첫 행을 채우면서 이를 적용해봅시다. 이 첫 행은 좌측 하단의 빨간색 사각형 ($c_s=0, r_s=0$)에 있는 X 스태빌라이저에 대응될 것입니다. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f7b4b32d\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2dffcaea\",\n \"metadata\": {},\n \"source\": [\n \"#### HX\\n\",\n \"\\n\",\n \"1. Toric 코드에서와 같은 가장 가까운 이웃과의 연결:\\n\",\n \"3.3 장에서 살펴보았듯이 좌측 하단의 빨간색 사각형은 다음 큐비트들과 가장 가까운 이웃으로 연결되어 있습니다:\\n\",\n \"* 인덱스 $\\\\mathbf{0}$에 있는 파란색 정보 큐비트 (아래에 존재하는, 파란색 0 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{1}$에 있는 파란색 정보 큐비트 (위에 존재하는, 파란색 1 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{72}$에 있는 주황색 정보 큐비트 (오른쪽에 존재하는, 주황색 0 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{138}$에 있는 주황색 정보 큐비트 (주기적 경계를 건너 왼쪽에 존재하는, 주황색 0 행, 11 열)\\n\",\n \"\\n\",\n \"2. Gross 코드만의 추가적인 장거리 연결:\\n\",\n \"현재 고려하고 있는 Gross 코드 구성에서는 좌측 하단 빨간색 사각형 ($c_s=0, r_s=0$)이 두 개의 추가적인 장거리 연결을 가질 것입니다:\\n\",\n \"\\n\",\n \"* 첫 번째 장거리 연결: 파란색 정보 큐비트 20 과의 연결.\\n\",\n \" * 이 연결은 세 행 위에 있고 여섯 열 오른쪽에 있는 파란색 큐비트와의 연결입니다. Gross 코드의 규칙은 이러한 상대적인 위치가 torus에서의 큐비트 인덱스와 어떻게 연결되는지 결정합니다. 이 스태빌라이저에서는 해당 규칙이 전역 인덱스 $\\\\mathbf{20}$ 을 갖는 파란색 큐비트와의 연결을 지정합니다.\\n\",\n \"\\n\",\n \"* 두 번째 장거리 연결: 주황색 정보 큐비트 135 와의 연결.\\n\",\n \" * 이 연결은 여섯 행 아래에 있고 세 열 왼쪽에 있는 주황색 큐비트와의 연결입니다. Gross 코드의 규칙은 이러한 상대적인 위치가 torus에서의 큐비트 인덱스와 어떻게 연결되는지 결정합니다. 이 스태빌라이저에서는 해당 규칙이 전역 인덱스 $\\\\mathbf{135}$ 를 갖는 주황색 큐비트와의 연결을 지정합니다.\\n\",\n \"\\n\",\n \"3. Grosss 코드 $H_X$ 의 첫 번째 행:\\n\",\n \"가장 가까운 이웃과의 연결과 방금 설명한 장거리 연결을 합쳐서 생각하면 이제 좌측 하단의 X 스태빌라이저는 다음 인덱스의 정보 큐비트에 작용합니다: `0, 1, 20, 72, 135, 138`.\\n\",\n \"\\n\",\n \"따라서 이 Gross 코드 $H_X$ 행렬의 첫 번째 행 (144개의 원소를 갖는 이진 벡터) 은 6개의 위치에 '1'을 갖고, 나머지 위치에는 '0'을 갖게 될 것입니다. 이를 다음 코드 조각에서 확인해봅시다:\\n\",\n \"\\n\",\n \"**코드 조각:**\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"이는 Gross 코드 $H_X$ 행렬에서 발췌한 첫 번째 행에, 앞에서 찾은 가장 가까운 이웃 및 장거리 연결을 조합한 전역 인덱스 0, 1, 20, 72, 135, 138에 정확히 6개의 1이 있음을 보여줍니다.\\n\",\n \"\\n\",\n \"#### HZ\\n\",\n \"\\n\",\n \"$H_Z$ 를 찾기 위해서는 Tanner 그래프로 돌아가 장거리 연결의 `leaf` 모양을 유심히 살펴봐야합니다.\\n\",\n \"\\n\",\n \"1. Toric 코드에서와 같은 가장 가까운 이웃과의 연결:\\n\",\n \"3.3 장에서 살펴보았듯이 좌측 하단의 초록색 사각형은 다음 큐비트들과 가장 가까운 이웃으로 연결되어 있습니다:\\n\",\n \"* 인덱스 $\\\\mathbf{77}$에 있는 주황색 정보 큐비트 (아래에 존재하는, 주황색 5 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{72}$에 있는 주황색 정보 큐비트 (위에 존재하는, 주황색 0 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{6}$에 있는 파랑색 정보 큐비트 (오른쪽에 존재하는, 파랑색 0 행, 1 열)\\n\",\n \"* 인덱스 $\\\\mathbf{0}$에 있는 파랑색 정보 큐비트 (왼쪽에 존재하는, 파랑색 0 행, 0 열)\\n\",\n \"\\n\",\n \"2. Gross 코드만의 추가적인 장거리 연결:\\n\",\n \"* 첫 번째 장거리 연결: 파란색 정보 큐비트 15 와의 연결.\\n\",\n \" * 이 연결은 여섯 행 위에 있고 세 열 오른쪽에 있는 파란색 큐비트와의 연결입니다. Gross 코드의 규칙은 이러한 상대적인 위치가 torus에서의 큐비트 인덱스와 어떻게 연결되는지 결정합니다. 이 스태빌라이저에서는 해당 규칙이 전역 인덱스 $\\\\mathbf{15}$ 를 갖는 파란색 큐비트와의 연결을 지정합니다.\\n\",\n \"\\n\",\n \"* 두 번째 장거리 연결: 주황색 정보 큐비트 130 과의 연결.\\n\",\n \" * 이 연결은 세 행 아래에 있고 여섯 열 왼쪽에 있는 주황색 큐비트와의 연결입니다. Gross 코드의 규칙은 이러한 상대적인 위치가 torus에서의 큐비트 인덱스와 어떻게 연결되는지 결정합니다. 이 스태빌라이저에서는 해당 규칙이 전역 인덱스 $\\\\mathbf{130}$ 을 갖는 주황색 큐비트와의 연결을 지정합니다.\\n\",\n \"\\n\",\n \"3. Grosss 코드 $H_Z$ 의 첫 번째 행:\\n\",\n \"가장 가까운 이웃과의 연결과 방금 설명한 장거리 연결을 합쳐서 생각하면 이제 좌측 하단의 Z 스태빌라이저는 다음 인덱스의 정보 큐비트에 작용합니다: `0, 6, 15, 72, 77, 130`.\\n\",\n \"\\n\",\n \"따라서 이 Gross 코드 $H_Z$ 행렬의 첫 번째 행 (144개의 원소를 갖는 이진 벡터) 은 6개의 위치에 '1'을 갖고, 나머지 위치에는 '0'을 갖게 될 것입니다. 이를 다음 코드 조각에서 확인해봅시다:\\n\",\n \"\\n\",\n \"\\n\",\n \"**코드 조각:**\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"이는 Gross 코드 $H_Z$ 행렬에서 발췌한 첫 번째 행에, 앞에서 찾은 가장 가까운 이웃 및 장거리 연결을 조합한 전역 인덱스 0, 6, 15, 72, 77, 130에 정확히 6개의 1이 있음을 보여줍니다.\\n\",\n \"\\n\",\n \"두 장거리 연결성을 더하는 이 방식이 *모든* 스태빌라이저 (빨간색 사각형의 X 스태빌라이저와 초록색 사각형의 Z 스태빌라이저 모두!) 에 대해 반복되면 toric 코드 행렬을 Gross 코드 행렬로 바꿀 수 있습니다. 이 격자에서 Gross 코드를 구성하며 정의한 패턴은 다른 스태빌라이저를 정의할 때도 똑같이 쓰입니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 5: Gross 코드의 패리티 검사 행렬 찾기\\n\",\n \"\\n\",\n \"위의 표기 원칙을 따라 장거리 연결을 포함한 Gross 코드의 패리티 검사 행렬을 구성하세요. $X$ 스태빌라이저의 결과는 NumPy array $H_{X}$ 에 저장하고 $Z$ 스태빌라이저의 결과는 NumPy array $H_{Z}$ 에 저장하세요.\\n\",\n \"\\n\",\n \"**힌트**: 예시에서의 주기적인 구조를 살펴보세요. 이러한 주기적인 구조를 활용해 좀 더 효율적으로 행렬을 만들 수는 없을까요? 각 행의 가중치는 6이 되어야 합니다. (즉 여섯 개의 1을 가져야 합니다.)\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" 도움 함수를 이용해 연결성을 확인해보세요! \\n\",\n \"\\n\",\n \"문제 4에서처럼 `HXgc`와 `HZgc`를 구성한 뒤에, 아래의 코드를 돌려 5번째 스태빌라이저와 66번째 스태빌라이저의 연결성을 검증해보세요. 경계 조건을 언제나 꼼꼼하게 확인하셔야 합니다. 다음 예시와 같은 그래프가 나와야 정확한 연결을 구성한 것입니다.\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXgc, HZgc)\\n\",\n \"\\n\",\n \"```\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ab1b4b43-4d81-4e75-aae0-47c24ba7906a\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e46c47ef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Gross 코드의 패리티 검사 행렬을 정의합니다\\n\",\n \"HXgc = np.zeros((72,144),dtype=int) # 행렬을 먼저 초기화합니다\\n\",\n \"HZgc = np.zeros((72,144),dtype=int)\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"# HXtc와 HZtc를 계산하기 위한 코드를 작성하세요\\n\",\n \"\\n\",\n \"# 0번째 X 스태빌라이저\\n\",\n \"HXgc[0][0] = 1\\n\",\n \"HXgc[0][1] = 1\\n\",\n \"HXgc[0][20] = 1\\n\",\n \"HXgc[0][72] = 1\\n\",\n \"HXgc[0][135] = 1\\n\",\n \"HXgc[0][138] = 1\\n\",\n \"\\n\",\n \"\\n\",\n \"# 0번째 Z 스태빌라이저\\n\",\n \"HZgc[0][0] = 1\\n\",\n \"HZgc[0][6] = 1\\n\",\n \"HZgc[0][15] = 1\\n\",\n \"HZgc[0][72] = 1\\n\",\n \"HZgc[0][77] = 1\\n\",\n \"HZgc[0][130] = 1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ac83de2d-bdb3-4bd4-bf20-a1cef7e6640a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 스태빌라이저를 확인해보세요\\n\",\n \"\\n\",\n \"generate_stabilizer_plots(HXgc, HZgc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a180a7cd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab4_ex5(HXgc, HZgc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0f37a761\",\n \"metadata\": {},\n \"source\": [\n \"## 3.5 논리 큐비트의 개수 찾기\\n\",\n \"\\n\",\n \"$H_X$ 와 $H_Z$ 로 표기되는 패리티 검사 행렬은 코드의 스태빌라이저로부터 구성되었습니다. 이 행렬들의 각 행은 스태빌라이저 군의 낳음 연산자를 나타냅니다. 하지만 이렇게 고른 낳음 연산자들은 독립적이지 않을 수도 있습니다; 하나의 스태빌라이저 낳음 연산자가 다른 것들의 곱일 수 있습니다. 이런 반복적인 낳음 연산자는 코드 공간에 새로운 제약을 걸지 못합니다.\\n\",\n \"\\n\",\n \"독립적인 스태빌라이저 낳음 연산자의 실제 개수를 찾기 위해서는, 우리는 패리티 검사 행렬의 **rank**를 계산해야 합니다. 우리는 큐비트와 파울리 연산자를 다루고 있기에 모든 rank 계산에 있어서 행렬 연산은 modulo 2로 계산되어야 합니다. Rank는 스태빌라이저에 의해 강제된 고유한 조건의 실제 개수를 알려줍니다.\\n\",\n \"\\n\",\n \"보통 양자 스태빌라이저 코드에서는:\\n\",\n \"코드에서 쓰이는 물리 정보 큐비트의 숫자가 $n$ 이고 독립적인 스태빌라이저 낳음 연산자의 총 숫자가 $r$ 이면 코드에 인코딩되는 논리 큐비트의 숫자 ($k$)는 다음과 같이 주어집니다:\\n\",\n \"$$k = n - r$$\\n\",\n \"각 독립적인 스태빌라이저 낳음 연산자는 힐베르트 공간을 양분하므로, 하나의 자유도를 고정합니다. 따라서 $r$ 개의 독립적인 스태빌라이저는 $n$ 물리 큐비트의 $2^n$ 차원 공간을 $2^{n-r}$ 차원 코드 공간으로 줄입니다. 이러한 코드 공간은 $k = n-r$ 개의 논리 큐비트를 인코딩할 수 있습니다.\\n\",\n \"\\n\",\n \"Toric과 Gross 코드와 같은 CSS 코드에서는 X 스태빌라이저와 Z 스태빌라이저는 두 개의 서로 다른 교환 가능한 군을 이룹니다. 이는 그들의 독립적인 낳음 연산자를 개별적으로 셀 수 있게 해줍니다.\\n\",\n \"만약 $r_X$ 가 개별적인 X 스태빌라이저의 숫자이고 $r_Z$ 가 개별적인 Z 스태빌라이저의 숫자이면 CSS 코드에 인코딩된 논리 큐비트의 숫자는 다음과 같습니다:\\n\",\n \"$$k = n - r_X - r_Z$$\\n\",\n \"$r_X$ ($r_Z$) 는 각 행이 X (Z) 스태빌라이저 낳음 연산자를 이루는 빨간색 (초록색) 사각형의 $H_X$ ($H_Z$) 행렬의 rank를 구함으로써 얻어집니다.\\n\",\n \"이 공식은 X-스태빌라이저와 Z-스태빌라이저가 서로에 대해 모두 교환 가능하도록 골라졌고, 코드 공간에 강제되는 독립적인 조건의 총 숫자가 독립적인 X와 Z 조건의 숫자들의 합과 같기 때문에 성립합니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"연습문제 6: Toric과 Gross 코드에서의 논리 큐비트 개수 세기\\n\",\n \"\\n\",\n \"여러분은 이제 문제 4와 5에서 만든 Toric과 Gross 코드에서의 논리 큐비트의 개수를 결정하기 위해 `matrixRank` 함수를 사용할 것입니다.\\n\",\n \"\\n\",\n \"1. 함수를 불러오세요: `matrixRank` 함수를 다음의 명령어를 통해 불러오는 것으로 시작합시다:\\n\",\n \" `from lab4_util import matrixRank`\\n\",\n \" 이 함수는 `HXtc`, `HZtc`, `HXgc`와 `HZgc`와 같은 이진 행렬의 rank를 계산할 것이고, 이것이 바로 우리가 패리티 검사 행렬을 통해 얻고 싶은 것입니다.\\n\",\n \"\\n\",\n \"2. Rank를 계산하세요:\\n\",\n \" * Toric 코드에 대해 `matrixRank`를 사용하여 문제 4에서 빨간색 사각형을 통해 얻어낸 X 스태빌라이저의 행렬 $H_X$ 의 rank를 찾으세요. 이를 통해 $r_{X, \\\\text{toric}}$ 를 구할 수 있습니다.\\n\",\n \" * 비슷하게 문제 4에서 초록색 사각형을 통해 얻어낸 Z 스태빌라이저의 행렬 $H_Z$ 의 rank를 찾으세요. 이를 통해 $r_{Z, \\\\text{toric}}$ 를 구할 수 있습니다.\\n\",\n \" * 이 과정을 문제 5에서 구한 $H_X$ 와 $H_Z$ Gross 코드 행렬에도 적용하여 $r_{X, \\\\text{gross}}$ 와 $r_{Z, \\\\text{gross}}$ 를 찾으세요.\\n\",\n \"\\n\",\n \"3. 논리 큐비트의 개수 ($k$)를 계산하세요:\\n\",\n \" * 총 정보 큐비트가 $n=144$ 개 있을 때, $k = n - r_X - r_Z$ 공식을 이용하여 Toric 코드에 인코딩된 논리 큐비트의 개수 $k_{\\\\text{toric}}$ 를 찾으세요.\\n\",\n \" * 똑같은 계산을 Gross 코드에도 반복하여 $k_{\\\\text{gross}}$ 를 찾으세요.\\n\",\n \"\\n\",\n \"**채점**: Toric 코드와 Gross 코드에서의 계산된 논리 큐비트의 개수 ($k$) `k_toric` 과 `k_gross`를 제출하세요. Toric 코드에서의 결과를 해당 코드가 $k=2$ 의 논리 큐비트를 인코딩한다는 사실과 비교해보세요.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b991f749\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"# k_toric과 k_gross를 계산하는 코드를 작성하세요\\n\",\n \"# 힌트: lab4_utils에서 불러온 matrixRank를 사용할 수 있습니다\\n\",\n \"\\n\",\n \"# toric 코드\\n\",\n \"rx_toric = \\n\",\n \"rz_toric = \\n\",\n \"k_toric = \\n\",\n \"\\n\",\n \"# gross 코드\\n\",\n \"rx_gross = \\n\",\n \"rz_gross = \\n\",\n \"k_gross = \\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"48f316d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab4_ex6(k_toric, k_gross)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"100d8c84\",\n \"metadata\": {},\n \"source\": [\n \"## 3.6 끝맺으며: 연결의 힘\\n\",\n \"\\n\",\n \"이 연습 문제들은 양자 오류 정정의 중요한 원리를 보여줍니다: 장거리 연결성을 더하는 것과 같이 전략적으로 코드의 구조를 바꾸는 것으로 해당 코드의 성능을 크게 향상시킬 수 있습니다.\\n\",\n \"\\n\",\n \"이번 랩에서는 동일한 $144$ 개의 정보 큐비트에서 toric과 gross 코드의 패리티 검사 행렬을 구성해봤습니다. 논리 큐비트의 개수 ($k$) 를 구하는 문제 6에서 여러분은 기본적인 $k_{\\\\text{toric}}$ 에 새로운 연결성을 추가하는 것으로 $k_{\\\\text{Gross}}$ 가 바뀌는 것을 보였습니다.\\n\",\n \"\\n\",\n \"그러나 Gross 코드의 가장 큰 이점은 그의 (오류 정정 능력의 지표인) **코드 거리**($d$)에 있습니다. 구체적인 거리까지 계산해보는 것은 이번 랩의 목표는 아니었지만, 위의 연습에서의 toric 코드의 거리는 6인 데 반해 (하나의 방향에 있어서 주기성은 정보 큐비트만을 셀 때 6만큼의 격자 공간이 있기에) gross 코드의 거리는 12입니다!\\n\",\n \"\\n\",\n \"장거리 연결을 더함으로써 얻어지는 이러한 오류 정정 능력의 향상은 특히나 오류 정정 능력을 포함한 코드 성능에 있어서 코드 구조의 영향이 얼마나 큰지 보여줍니다. 이는 발전된 양자 부호를 설계하는데 있어 중요한 전략이 될 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"94450084\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 현재의 진도 상황을 확인해보세요\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3a1b6840\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Sophy Shin, Tomas Jochym-O'Connor, Sebastian Brandhofer\\n\",\n \"\\n\",\n \"**Advised by:** Blake Johnson, Patrick Rall, Olivia Lanes\\n\",\n \"\\n\",\n \"**Reviewed by:** Henry Zou, Abby Cross\\n\",\n \"\\n\",\n \"**Translated by:** Jinwoong Philip Kim, Boseong Kim\\n\",\n \"\\n\",\n \"**Version:** 2.1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"af68b0e9\",\n \"metadata\": {},\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.5\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-4/lab4.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9c80575e\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# Lab 4: Quantum Error Correction: From Core Concepts to the Road to Fault-Tolerant Quantum Computing\\n\",\n \"\\n\",\n \"Welcome to the forth coding challenge of the Qiskit Global Summer School. This lab explores error-correcting codes, beginning with foundational classical error-correcting codes and key concepts. It then transitions to Quantum Error Correction (QEC) — crucial for fault-tolerant quantum computing — and covers the stabilizer formalism along with key examples. Subsequently, the notebook introduces advanced QEC architectures, including the QLDPC, Toric, and gross codes, with exercises. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"690bf7d6\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"- Chapter 1: Classical error-correcting code revisit\\n\",\n \" - 1.1 Classical [n, k, d] Codes\\n\",\n \" - 1.2 Hamming Distance\\n\",\n \" - 1.3 The [3, 1, 3] Repetition Code\\n\",\n \" - Practice: Lookup table-based Decoder of [3,1,3] Code\\n\",\n \"- Chapter 2: Quantum Error Correcting Codes [[n, k, d]]\\n\",\n \" - 2.1 Stabilizer Formalism\\n\",\n \" - 2.2 3-qubit bit-flip Code Practice\\n\",\n \" - Exercise 1: Lookup Table-based Decoder of 3-bit code\\n\",\n \" - 2.3 CSS Codes (Calderbank-Shor-Steane)\\n\",\n \" - 2.4 [[7,1,3]] Steane Code Practice\\n\",\n \" - Exercise 2: Lookup table of [[7,1,3]] Steane Code\\n\",\n \" - Exercise 3: Detect Error with a Lookup Table\\n\",\n \"- Chapter 3: Exploring Advanced Quantum Error Correction Codes & Their Efficiency\\n\",\n \" - 3.1 Foundational Concepts and Key QLDPC Architectures for Comparison\\n\",\n \" - 3.2 Qubit Layout and Conventions for Exercises\\n\",\n \" - 3.3 Toric Code Exercise\\n\",\n \" - Exercise 4: Find the parity check matrices of the toric code\\n\",\n \" - 3.4 Gross Code Exercise\\n\",\n \" - Exercise 5: Find the parity check matrices of the gross code\\n\",\n \" - 3.5 Counting the Number of Logical Qubits\\n\",\n \" - Exercise 6: Count the number of logicals for the Toric and Gross codes\\n\",\n \" - 3.6 Concluding remarks: The power of the connectivity\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fa09aff1\",\n \"metadata\": {},\n \"source\": [\n \"## Requirements\\n\",\n \"\\n\",\n \"Before starting this tutorial, please make sure that you have the following installed:\\n\",\n \"\\n\",\n \"* Qiskit SDK v2.0 or later with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime v0.40 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"558e90cb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"48794705\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1d9769d1\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.12`. If you see a lower version, you need to reinstall the grader and restart the kernel.\\n\",\n \"Also make sure you have set up everything according to lab 0 and test it with the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a60cc22d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9984274d\",\n \"metadata\": {},\n \"source\": [\n \"# Imports\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"776ad97c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import common packages first\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"# Import qiskit classes\\n\",\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"\\n\",\n \"# Import utils and cosystems\\n\",\n \"from lab4_util import hamming_distance, minimum_distance, bring_states, matrixRank\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"from qiskit_ibm_runtime import SamplerV2 as Sampler\\n\",\n \"\\n\",\n \"# Import grader\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab4_ex1, \\n\",\n \" grade_lab4_ex2, \\n\",\n \" grade_lab4_ex3,\\n\",\n \" grade_lab4_ex4,\\n\",\n \" grade_lab4_ex5,\\n\",\n \" grade_lab4_ex6\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2ac33837\",\n \"metadata\": {},\n \"source\": [\n \"# Chapter 1: Classical error-correcting code revisit\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b51a2f0f\",\n \"metadata\": {},\n \"source\": [\n \"## 1.1 Classical [n, k, d] Codes\\n\",\n \"\\n\",\n \"In coding theory, a classical binary linear block code is often denoted as an $[n, k, d]$ code for messages composed of binary (bit) strings, that is, a series of 0s and 1s. These parameters represent:\\n\",\n \"\\n\",\n \"* **$n$**: The **codeword length**. Each codeword in the code is a binary string of length $n$.\\n\",\n \"* **$k$**: The **message length**. It represents the number of information bits encoded into a codeword. There are $2^k$ distinct messages and thus $2^k$ unique codewords in the code.\\n\",\n \"* **$d$**: The **minimum Hamming distance** of the code. This is the smallest Hamming distance between any pair of *distinct* codewords in the code. The minimum distance $d$ determines the code's error detection and correction capabilities.\\n\",\n \"Only a subset of the $2^n$ possible binary strings represent valid codewords that each carry $k$ bits of information.\\n\",\n \"\\n\",\n \"## 1.2 Hamming Distance\\n\",\n \"\\n\",\n \"The **Hamming distance** between two strings (or vectors) of equal length is the number of positions at which the corresponding symbols are different. For binary strings (composed of 0s and 1s), it's simply the count of positions where one string has a '0' and the other has a '1'.\\n\",\n \"\\n\",\n \"Mathematically, for two binary vectors $x = (x_1, x_2, ..., x_n)$ and $y = (y_1, y_2, ..., y_n)$, the Hamming distance $d_H(x, y)$ is:\\n\",\n \"$$ d_H(x, y) = \\\\sum_{i=1}^{n} (x_i \\\\oplus y_i) $$\\n\",\n \"where $\\\\oplus$ denotes the XOR operation (addition modulo 2), which is equivalent to counting the number of '1's in the result of the bitwise XOR operation between $x$ and $y$. This count is also known as the **Hamming weight** of the XOR result.\\n\",\n \"\\n\",\n \"**Important**\\n\",\n \"\\n\",\n \"The Hamming distance is crucial for understanding error correction codes:\\n\",\n \"1. **Error Detection**: A code with minimum distance $d$ can detect any error pattern affecting up to $d-1$ bits. If fewer than $d$ bits are flipped during the transmission of a codeword, the resulting string cannot be another valid codeword, so the error is detected.\\n\",\n \"2. **Error Correction**: A code with minimum distance $d$ can correct any error pattern affecting up to $t$ bits in a codeword, where $t = \\\\lfloor \\\\frac{d-1}{2} \\\\rfloor$. This is because if at most $t$ errors occur, the received word is still closer (in Hamming distance) to the original codeword than to any other valid codeword.\\n\",\n \"\\n\",\n \"Here's a Python function to calculate the Hamming distance between two binary strings:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ca1e251c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Example usage:\\n\",\n \"str1 = \\\"10110\\\"\\n\",\n \"str2 = \\\"11100\\\"\\n\",\n \"dist = hamming_distance(str1, str2)\\n\",\n \"print(f\\\"Hamming distance between '{str1}' and '{str2}' is: {dist}\\\") # Output: 2\\n\",\n \"\\n\",\n \"vec1 = [1, 0, 0, 1]\\n\",\n \"vec2 = [0, 0, 1, 1]\\n\",\n \"dist_vec = hamming_distance(vec1, vec2)\\n\",\n \"print(f\\\"Hamming distance between {vec1} and {vec2} is: {dist_vec}\\\") # Output: 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3ee79d97\",\n \"metadata\": {},\n \"source\": [\n \"### Minimum Distance of a Code ($d$)\\n\",\n \"\\n\",\n \"The minimum Hamming distance $d$ of a code $C$ is the smallest Hamming distance found between any pair of distinct codewords in $C \\\\subset \\\\{0, 1\\\\}^n$.\\n\",\n \"$$ d = \\\\min { d_H(c_1, c_2) \\\\mid c_1, c_2 \\\\in C, c_1 \\\\neq c_2 }. $$\\n\",\n \"For linear codes, where $c_1 \\\\in C$ and $c_2 \\\\in C$ implies $(c_1 +c_2) \\\\in C$, the minimum distance is also equal to the minimum Hamming weight of all non-zero codewords. The Hamming weight of a codeword is its distance from the all-zero codeword.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4fd8d205\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- Example: A Simple [4, 3, 2] Parity Check Code ---\\n\",\n \"# This code takes 3 message bits (k=3) and adds an even parity bit\\n\",\n \"# to make the total codeword length n=4.\\n\",\n \"# Messages: 000, 001, 010, 011, 100, 101, 110, 111\\n\",\n \"# Codewords (adding even parity bit):\\n\",\n \"parity_code_4_3 = [\\n\",\n \" \\\"0000\\\", # 000 + 0 (even parity)\\n\",\n \" \\\"0011\\\", # 001 + 1\\n\",\n \" \\\"0101\\\", # 010 + 1\\n\",\n \" \\\"0110\\\", # 011 + 0\\n\",\n \" \\\"1001\\\", # 100 + 1\\n\",\n \" \\\"1010\\\", # 101 + 0\\n\",\n \" \\\"1100\\\", # 110 + 0\\n\",\n \" \\\"1111\\\" # 111 + 1\\n\",\n \"]\\n\",\n \"\\n\",\n \"# Calculate the minimum distance d\\n\",\n \"d_parity = minimum_distance(parity_code_4_3)\\n\",\n \"print(f\\\"Codewords: {parity_code_4_3}\\\")\\n\",\n \"print(f\\\"Calculated minimum distance d = {d_parity}\\\") # Output: 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0c11207c\",\n \"metadata\": {},\n \"source\": [\n \"This is therefore a [4, 3, 2] code.\\n\",\n \"\\n\",\n \"For our example code parity_code_4_3, we found $d=2$.\\n\",\n \"\\n\",\n \"- Error Detection: $t_{detect} = d - 1 = 2 - 1 = 1$. This code can detect any single-bit error.\\n\",\n \"- Error Correction: $t_{correct} = \\\\lfloor \\\\frac{d-1}{2} \\\\rfloor = \\\\lfloor \\\\frac{2-1}{2} \\\\rfloor = \\\\lfloor 0.5 \\\\rfloor = 0$.\\n\",\n \"\\n\",\n \"In particular, because any single error will result in a string that is not in the codespace, we know an error occurred and, as such, is detected!\\n\",\n \"\\n\",\n \"While this code can identify errors, it lacks the capability to correct them in all cases. Consider an error where the codeword `0000` is altered to `0001`. The error is detectable as `0001` is not a valid codeword. However, correction is not possible because `0001` has an identical Hamming distance to four different codewords (`0000`, `0011`, `0101`, and `1001`).\\n\",\n \"\\n\",\n \"Hence, in a [4, 3, 2] code, a single bit-flip error can yield a binary string that is not a codeword - but one cannot reliably correct such an error, as there is no unique closest codeword for correction - thus, it is impossible to know which of the original codewords was sent.\\n\",\n \"\\n\",\n \"In general, the reason a code can detect up to $d-1$ errors is that if any such set of bit-flips were to occur and corrupt the sent message, then one could identify that the transformed message is no longer a codeword and, as such, detect that some set of errors occurred. While correcting such errors is more involved, any error of weight at most $\\\\lfloor \\\\frac{d-1}{2} \\\\rfloor$ can be corrected, as one can consider the corrupted codeword and look at all possible codewords within Hamming distance $\\\\lfloor \\\\frac{d-1}{2} \\\\rfloor$ - there will only be one such possibility. Doing this task efficiently, also known as decoding, is in general a non-trivial task that is an active area of research, depending on the code.\\n\",\n \"\\n\",\n \"To investigate this further, let's recalculate for a different code, e.g., the [3, 1, 3] repetition code $C = { 000, 111 }$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e79adbe9\",\n \"metadata\": {},\n \"source\": [\n \"## 1.3 The [3, 1, 3] Repetition Code\\n\",\n \"\\n\",\n \"The simplest error-correcting code is the repetition code.\\n\",\n \"To send a single bit (k=1), we repeat it `n` times. For the `[3, 1, 3]` code, we repeat the bit 3 times.\\n\",\n \"- Message `0` is encoded as `000`.\\n\",\n \"- Message `1` is encoded as `111`.\\n\",\n \"\\n\",\n \"Here, n=3 and k=1.\\n\",\n \"The codewords are {000, 111}.\\n\",\n \"The Hamming distance between 000 and 111 is 3. So, $d=3$.\\n\",\n \"This code can detect up to $d-1 = 2$ errors.\\n\",\n \"It can correct up to $\\\\lfloor((d-1)/2)\\\\rfloor = \\\\lfloor((3-1)/2)\\\\rfloor$ = 1 error.\\n\",\n \"\\n\",\n \"**Encoding:**\\n\",\n \"- If the message bit is `m`, codeword `c = mmm`.\\n\",\n \"\\n\",\n \"**Error & Decoding (Majority Vote):**\\n\",\n \"Suppose a codeword `c` is transmitted, and `c'` is received.\\n\",\n \"If at most one bit is flipped, the original message can be recovered by majority voting.\\n\",\n \"- Receive `000` -> Decode `0`\\n\",\n \"- Receive `001` -> Decode `0` (1 error)\\n\",\n \"- Receive `010` -> Decode `0` (1 error)\\n\",\n \"- Receive `100` -> Decode `0` (1 error)\\n\",\n \"- Receive `111` -> Decode `1`\\n\",\n \"- Receive `110` -> Decode `1` (1 error)\\n\",\n \"- Receive `101` -> Decode `1` (1 error)\\n\",\n \"- Receive `011` -> Decode `1` (1 error)\\n\",\n \"\\n\",\n \"If two bits are flipped (e.g., `000` -> `011`), majority voting decodes to the wrong message (`1` in this case). The code can detect this as a 2-bit error but cannot correct it.\\n\",\n \"\\n\",\n \"Let's first check the minimum distance of this code with both error detection and error correction capabilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3d9af05b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- Example: [3, 1, 3] Repetition Code ---\\n\",\n \"repetition_code_3_1 = [\\\"000\\\", \\\"111\\\"]\\n\",\n \"d_repetition = minimum_distance(repetition_code_3_1)\\n\",\n \"print(f\\\"Calculated minimum distance d = {d_repetition}\\\") # Output: 3\\n\",\n \"\\n\",\n \"# Capabilities for d=3:\\n\",\n \"t_detect = d_repetition - 1\\n\",\n \"t_correct = int((d_repetition - 1) / 2) // 1\\n\",\n \"print(f\\\"Error Detection Capability (t_detect = d-1): {t_detect}\\\") # Output: 2\\n\",\n \"print(f\\\"Error Correction Capability (t_correct = floor((d-1)/2)): {t_correct}\\\") # Output: 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f32e68b\",\n \"metadata\": {},\n \"source\": [\n \"### Practice: Lookup table-based Decoder ofthe [3, 1, 3] Code\\n\",\n \"\\n\",\n \"Since this code can correct errors, we can now create a lookup table-based decoder in the form of a dictionary to perform error correction. A lookup table-based decoder maps every possible received 3-bit string to the most-likely transmitted 1-bit message, with regard to the corrected message. Here we will use a helper function `hamming_distance` to see how the Hamming distance affects correcting errors. First let's do a simple test with one single case.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9dc8d7e7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"test_str = \\\"010\\\"\\n\",\n \"\\n\",\n \"print(\\\"Hamming distance between 010 and 000 is\\\", hamming_distance(test_str, \\\"000\\\"))\\n\",\n \"print(\\\"Hamming distance between 010 and 111 is\\\", hamming_distance(test_str, \\\"111\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"96a83fff\",\n \"metadata\": {},\n \"source\": [\n \"\\\\\\\"000\\\\\\\" is a logical `0` and \\\\\\\"111\\\\\\\" is a logical `1`, so this `test_str` will be corrected as `0`. Try to complete the dictionary below to complete the whole lookup table-based decoder of the [3, 1, 3] classical error correcting code, by mapping the final corrected string to the received string.\\n\",\n \"\\n\",\n \"Before checking the solution table below, it's helpful to understand its columns: 'Received text' is the 3-bit string that was observed after potential errors; 'Hamming distance with \\\"000\\\"' and 'Hamming distance with \\\"111\\\"' shows how many bits differ from the two valid logical codewords (000 and 111, respectively); and 'Corrected str' is the single bit (0 or 1) that the decoder determines was the original message.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5bad9fd5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hardcode_decoder_3_1_3 ={\\n\",\n \" '000': '0',\\n\",\n \" '001': '',\\n\",\n \" '010': '',\\n\",\n \" '011': '',\\n\",\n \" '100': '',\\n\",\n \" '101': '',\\n\",\n \" '110': '',\\n\",\n \" '111': '1'}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3f8018cb\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \" Check your decoder with this! \\n\",\n \"\\n\",\n \"| Received text | Hamming distance with \\\"000\\\" | Hamming distance with \\\"111\\\" | Corrected str |\\n\",\n \"|---|---|---|---|\\n\",\n \"|000|0|3|0|\\n\",\n \"|001|1|2|0|\\n\",\n \"|010|1|2|0|\\n\",\n \"|011|2|1|1|\\n\",\n \"|100|1|2|0|\\n\",\n \"|101|2|1|1|\\n\",\n \"|110|2|1|1|\\n\",\n \"|111|3|0|1|\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fea7ef37\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Chapter 2. Quantum Error Correcting Codes [[n, k, d]]\\n\",\n \"\\n\",\n \"Quantum information is much more fragile than classical information. Qubits are susceptible to various types of errors, not just bit-flips ($X$ errors) but also phase-flips ($Z$ errors) and combinations of both ($Y$ errors). Quantum error correction aims to protect quantum states from these errors.\\n\",\n \"\\n\",\n \"A quantum error correcting code is often denoted by `[[n, k, d]]`, where:\\n\",\n \"\\n\",\n \"* **`n`**: The number of **data qubits** used to construct the code.\\n\",\n \"* **`k`**: The number of **logical qubits** encoded by the code. This means the code protects a $2^k$-dimensional logical state space (codespace).\\n\",\n \"* **`d`**: The **minimum distance** of the code. This is the minimum number of qubits an operator needs to act on to perform a non-identity operation on a logical qubit. In the case of stabilizer codes, it is the minimum number of qubits a Pauli operator must act on in order to apply a logical Pauli operator. \\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Note on resource overhead:\\n\",\n \" \\n\",\n \"It's important to note that the `n` in the `[[n, k, d]]` notation typically refers to the data qubits involved in the code block. This count usually *omits* any ancilla or syndrome qubits required for tasks like error detection and correction. Therefore, the total resource overhead is not simply `n - k` data qubits, but rather `(n - k)` (the number of redundant data qubits) *plus* the number of ancilla qubits needed for syndrome measurements. In many stabilizer codes, the number of independent stabilizers (and thus often the number of ancilla qubits) is `n - k`, but this can vary, and in some schemes, the ancilla count could be significant, potentially as large as `n`.\\n\",\n \"
\\n\",\n \"\\n\",\n \"Similar to classical codes, the minimum distance $d$ determines the code's error correction capability. An `[[n, k, d]]` code can detect up to `d-1` errors and can potentially correct up to `floor((d-1)/2)` arbitrary single-qubit errors.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9895c32e\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"## 2.1 Stabilizer Formalism\\n\",\n \"\\n\",\n \"\\n\",\n \"The stabilizer formalism is a powerful framework for constructing and understanding many QEC codes.\\n\",\n \"\\n\",\n \"- The **Pauli group** $P_n$ on $n$ qubits consists of all $n$-fold tensor products of Pauli matrices $\\\\{I, X, Y, Z\\\\}$, multiplied by overall factors $\\\\{\\\\pm 1, \\\\pm i\\\\}$.\\n\",\n \"- A **stabilizer code** is defined by its **stabilizer group S**, which is an abelian subgroup of $P_n$ such that $-I \\\\notin S$.\\n\",\n \"- The **codespace C(S)** is the subspace of $(\\\\mathbb{C}^2)^{\\\\otimes n}$ simultaneously stabilized by all elements of $S$. That is, for any state $|\\\\psi\\\\rangle \\\\in C(S)$ and any $g \\\\in S$, $g|\\\\psi\\\\rangle = |\\\\psi\\\\rangle$. So, states in the codespace are +1 eigenstates of all stabilizer operators.\\n\",\n \"- Typically, $S$ is specified by a set of $m = n-k$ independent and commuting generators $S = \\\\langle g_1, g_2, ..., g_m \\\\rangle$.\\n\",\n \"\\n\",\n \"A comprehensive resource is the IBM Quantum Learning course by John Watrous: [Foundations of Quantum Error Correction](https://quantum.cloud.ibm.com/learning/courses/foundations-of-quantum-error-correction). If you wish to delve deeper into the mathematical details and further examples, we recommend exploring this resource. Click `Stabilizer Formalism Summary` to see the summary, or you can skip this.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" Stabilizer Formalism Summary\\n\",\n \"\\n\",\n \" Many powerful Quantum Error Correcting Codes (QECCs), including the 3-qubit repetition code discussed below, as well as the famous Steane (`[[7, 1, 3]]`) and Shor (`[[9, 1, 3]]`) codes, belong to the family of **stabilizer codes**. The stabilizer formalism provides a powerful framework for defining quantum codes and designing error detection and correction procedures.\\n\",\n \"\\n\",\n \"**1. Stabilizing States and Subspaces:**\\n\",\n \"\\n\",\n \"* An operator $S$ is said to **stabilize** a quantum state $|\\\\psi\\\\rangle$ if $|\\\\psi\\\\rangle$ is an eigenstate of $S$ with eigenvalue +1. That is, $S|\\\\psi\\\\rangle = |\\\\psi\\\\rangle$.\\n\",\n \"* An operator $S$ stabilizes a subspace (the **codespace**, denoted $\\\\mathcal{C}$) if it stabilizes *every* state $|\\\\psi\\\\rangle$ within that subspace: $S|\\\\psi\\\\rangle = |\\\\psi\\\\rangle$ for all $|\\\\psi\\\\rangle \\\\in \\\\mathcal{C}$.\\n\",\n \"\\n\",\n \"**2. The Stabilizer Group ($\\\\mathcal{S}$):**\\n\",\n \"\\n\",\n \"* For a given stabilizer code, the set of all operators that stabilize the codespace $\\\\mathcal{C}$ forms a mathematical group called the **stabilizer group**, denoted $\\\\mathcal{S}$.\\n\",\n \"* **Key Properties:**\\n\",\n \" * All operators $S_i, S_j$ in the group $\\\\mathcal{S}$ must **commute** with each other: $S_i S_j = S_j S_i$.\\n\",\n \" * Stabilizer operators are typically constructed from tensor products of **Pauli operators** ($I, X, Y, Z$) acting on $n$ qubits. For example, $X \\\\otimes Z \\\\otimes I \\\\otimes X$ could be a stabilizer element for $n=4$.\\n\",\n \" * The identity operator $I^{\\\\otimes n}$ is always an element of $\\\\mathcal{S}$. By convention, $-I^{\\\\otimes n}$ is usually excluded.\\n\",\n \"\\n\",\n \"**3. Generators ($\\\\{g_i\\\\}$):**\\n\",\n \"\\n\",\n \"* Instead of listing all elements of the potentially large group $\\\\mathcal{S}$, we define it using a smaller set of **generators**, $g_1, g_2, ..., g_{n-k}$.\\n\",\n \"* Any element $S \\\\in \\\\mathcal{S}$ can be created by taking products of these generators (e.g., $g_1 g_3$, $g_2 g_5 g_1$, etc.).\\n\",\n \"* **Key Properties of Generators:**\\n\",\n \" * They must **commute** with each other: $[g_i, g_j] = 0$ for all $i, j$.\\n\",\n \" * They must be **independent**: no generator can be formed by multiplying the others in the set.\\n\",\n \" * There are $n-k$ independent generators for an `[[n, k, d]]` code.\\n\",\n \"\\n\",\n \"**4. Defining the Codespace ($\\\\mathcal{C}$):**\\n\",\n \"\\n\",\n \"* The codespace $\\\\mathcal{C}$ of a stabilizer code is the **simultaneous +1 eigenspace** of all its generators (and thus all elements in $\\\\mathcal{S}$).\\n\",\n \"* If a state $|\\\\psi\\\\rangle$ is in the codespace, it must satisfy $g_i |\\\\psi\\\\rangle = |\\\\psi\\\\rangle$ for all generators $i=1, ..., n-k$.\\n\",\n \"* Starting with the full $2^n$-dimensional Hilbert space of $n$ physical qubits, each independent generator effectively halves the dimension of the subspace it stabilizes. Therefore, $n-k$ independent generators define a $2^{n-(n-k)} = 2^k$-dimensional codespace, which is exactly the space needed to encode $k$ logical qubits.\\n\",\n \"\\n\",\n \"**5. Error Detection using Stabilizers:**\\n\",\n \"\\n\",\n \"* The primary function of stabilizers in QEC is **error detection**. This works by measuring the eigenvalues of the stabilizer generators.\\n\",\n \"* **No Error:** If the system is in a valid codespace state $|\\\\psi\\\\rangle \\\\in \\\\mathcal{C}$ and no error occurs, measuring any generator $g_i$ will yield the outcome +1 with certainty (because $g_i|\\\\psi\\\\rangle=|\\\\psi\\\\rangle$).\\n\",\n \"* **Error Occurs:** Suppose an error $E$ (a Pauli operator product) occurs, transforming the state to $E|\\\\psi\\\\rangle$. Now, measure a generator $g_i$.\\n\",\n \" * If $g_i$ **commutes** with the error $E$ (i.e., $g_i E = E g_i$):\\n\",\n \" - $g_i (E |\\\\psi\\\\rangle) = E g_i |\\\\psi\\\\rangle = E |\\\\psi\\\\rangle$. The measurement outcome is still +1. The error $E$ is *not detected* by $g_i$.\\n\",\n \" * If $g_i$ **anti-commutes** with the error $E$ (i.e., $g_i E = -E g_i$):\\n\",\n \" - $g_i (E |\\\\psi\\\\rangle) = -E g_i |\\\\psi\\\\rangle = -E |\\\\psi\\\\rangle$. The measurement outcome is -1. The error $E$ *is detected* by $g_i$.\\n\",\n \"* **Error Syndrome:** The set of measurement outcomes (+1 or -1, often mapped to 0 or 1, respectively) for all the generators $\\\\{g_1, ..., g_{n-k}\\\\}$ constitutes the **error syndrome**.\\n\",\n \" * A syndrome of all +1s (or all 0s) indicates either no error occurred, or an error occurred that commutes with all stabilizers (which might be a logical operator or an undetectable error related to the code's distance $d$).\\n\",\n \" * A non-trivial syndrome (containing at least one -1 or 1) indicates that a detectable error has occurred. The specific pattern of the syndrome provides information about the likely error $E$ that occurred, which can then be used for correction.\\n\",\n \"\\n\",\n \"**Connection to `[[n, k, d]]`:**\\n\",\n \"\\n\",\n \"* `n` is the number of physical qubits the stabilizers act on.\\n\",\n \"* `k` is the number of encoded logical qubits, determined by $k = n - (\\\\text{number of independent generators})$.\\n\",\n \"* `d` is the minimum weight of a Pauli error $E$ that commutes with all stabilizers ($[E, g_i]=0$ for all $i$) but is *not* itself a product of stabilizers (i.e., $E \\\\notin \\\\mathcal{S}$). Such an operator is a **logical operator** (acting non-trivially on the encoded information) or an undetectable error. The distance $d$ quantifies the code's ability to distinguish between the effects of low-weight errors.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Stabilizer formalism provides the foundation for understanding how codes like the `[[7, 1, 3]]` example (below) are constructed and how they detect errors of any type.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88f289c0\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2 3-qubit bit-flip Code Practice\\n\",\n \"\\n\",\n \"(Note: Throughout this explanation, we use Qiskit's little-endian convention, where the rightmost character in a Pauli string corresponds to qubit 0, e.g., `IZZ` acts as $I_2 \\\\otimes Z_1 \\\\otimes Z_0$.)\\n\",\n \"\\n\",\n \"This code is the quantum analog of the classical [3, 1, 3] repetition code. It can correct a **single bit-flip (X) error** on any of the three physical qubits - however, it will not be able to correct for $Z$ phase errors.\\n\",\n \"- n=3 physical qubits, k=1 logical qubit. The goal of the code is to preserve the state of the logical qubit, despite errors on the physical qubits.\\n\",\n \"- Logical states (unnormalized):\\n\",\n \" - $|0_L\\\\rangle \\\\equiv |000\\\\rangle$\\n\",\n \" - $|1_L\\\\rangle \\\\equiv |111\\\\rangle$\\n\",\n \" - An arbitrary logical state is $\\\\alpha|0_L\\\\rangle + \\\\beta|1_L\\\\rangle = \\\\alpha|000\\\\rangle + \\\\beta|111\\\\rangle$.\\n\",\n \"\\n\",\n \"**Stabilizer Generators:**\\n\",\n \"This code is stabilized by:\\n\",\n \"- $S_0 = Z_2 Z_1 I_0$ (or simply $Z_1Z_2$)\\n\",\n \"- $S_1 = I_2 Z_1 Z_0$ (or simply $Z_0Z_1$)\\n\",\n \"These are Z-type stabilizers, which are used to detect X-errors (bit-flips).\\n\",\n \"\\n\",\n \"**Logical Operators:**\\n\",\n \"- Logical Z: $\\\\bar{Z} = Z_0$ (or $Z_1$ or $Z_2$, as $Z_0|0_L\\\\rangle = |0_L\\\\rangle, Z_0|1_L\\\\rangle = -|1_L\\\\rangle$). A common choice is $Z_2 Z_1 Z_0$.\\n\",\n \"- Logical X: $\\\\bar{X} = X_2 X_1 X_0$. $(\\\\bar{X}|0_L\\\\rangle = |1_L\\\\rangle, \\\\bar{X}|1_L\\\\rangle = |0_L\\\\rangle)$.\\n\",\n \"\\n\",\n \"**Error Detection (Syndrome Extraction):**\\n\",\n \"Let $s_0, s_1$ be the eigenvalues (-1 if flipped, +1 if not) for $S_0, S_1$ respectively. The syndrome is $(s_0, s_1)$.\\n\",\n \"- No error: $(+1, +1)$\\n\",\n \"- $X_0$ error (bit-flip on qubit 0): $S_0$ commutes, $S_1$ anti-commutes ($Z_0Z_1X_0 = -X_0Z_0Z_1$). Syndrome: $(s_1,s_0) = (-1, +1)$. Correction: Apply $X_0$.\\n\",\n \"- $X_1$ error (bit-flip on qubit 1): $S_0$ anti-commutes, $S_1$ anti-commutes. Syndrome: $(-1, -1)$. Correction: Apply $X_1$.\\n\",\n \"- $X_2$ error (bit-flip on qubit 2): $S_0$ anti-commutes, $S_1$ commutes. Syndrome: $(+1, -1)$. Correction: Apply $X_2$.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"**Vulnerability to Z-errors**\\n\",\n \" \\n\",\n \"Keep in mind that this code is primarily designed to protect against bit-flip (X-type) errors. It's crucial to note this code does undergo any Z-type (phase-flip) errors. A single Z-type error on any of the physical qubits would commute with the stabilizers that detect X-errors (e.g., $Z_i Z_j$) and would manifest as an uncorrectable logical Z error on the encoded qubit, altering its phase information without being detected.\\n\",\n \"
\\n\",\n \"Exercise 1: Lookup Table-based Decoder of 3-bit code\\n\",\n \"\\n\",\n \"As we introduced, the quantum bit-flip code, defined by two stabilizer generators $S_0=ZZI$ ($Z_2 \\\\otimes Z_1 \\\\otimes I_0$) and $S_1=IZZ$ ($I_2 \\\\otimes Z_1 \\\\otimes Z_0$) and be measured into two classical bit. So the syndrome outcome will be a 2-bit classical bit string. This bit strings serve as the input of a hardcord decoder, which is a lookup table that maps the syndrome to the specific correction operation needed.\\n\",\n \"\\n\",\n \"\\n\",\n \"Your task is to complete the dictionary that maps these measured syndrome bits to the corresponding error code (e.g., 'X0' for an X error on qubit 0, 'X1' for an X error on qubit 1, 'X2' for an X error on qubit 2, or 'I' for no error) for a single (Weight-1) bit-flip error.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2bae421e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hardcode_decoder_bit_flip_syndrome_map = {\\n\",\n \" # ---- TODO : Task 1 ---\\n\",\n \" # Fill in the other entries of the decoder (leave the \\\"\\\" in place).\\n\",\n \" #{\\\"s1s0\\\": \\\"Error Code\\\"}\\n\",\n \" '00': 'I',\\n\",\n \" '01': '', \\n\",\n \" '10': '', \\n\",\n \" '11': '' \\n\",\n \" # --- End of TODO --- \\n\",\n \"}\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"87281376\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab4_ex1(hardcode_decoder_bit_flip_syndrome_map )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"53623200\",\n \"metadata\": {},\n \"source\": [\n \"## 2.3 CSS Codes (Calderbank-Shor-Steane)\\n\",\n \"\\n\",\n \"CSS codes, named after Calderbank, Shor, and Steane, are a powerful family of quantum codes constructed from classical linear codes. \\n\",\n \"Here we provide a high-level introduction, and for a more detailed explanation of CSS codes, refer to the IBM Quantum Learning course material on [Quantum Code Constructions (CSS Codes)](https://quantum.cloud.ibm.com/learning/courses/foundations-of-quantum-error-correction/quantum-code-constructions/css-codes).\\n\",\n \"\\n\",\n \"**Construction:**\\n\",\n \"A CSS code is defined using two classical binary linear codes, $C_1 = [n, k_1, d_1]$ and $C_2 = [n, k_2, d_2]$, satisfying the condition $C_2^\\\\perp \\\\subseteq C_1$. Here, $C_2^\\\\perp$ is the dual code of $C_2$.\\n\",\n \"\\n\",\n \"* **Stabilizers:** The stabilizer group $S$ for the CSS code $[[n, k, d]]$ (where $k = k_1 + k_2 - n$) is generated by:\\n\",\n \" * **X-type stabilizers:** Derived from the parity check matrix $H_1$ of $C_1$. For each row $h \\\\in H_1$, create a stabilizer $\\\\bigotimes_{i: h_i=1} X_i$. These stabilizers consist only of Pauli X and I operators.\\n\",\n \" * **Z-type stabilizers:** Derived from the parity check matrix $H_2^\\\\perp$ of $C_2^\\\\perp$. For each row $h' \\\\in H_2^\\\\perp$, create a stabilizer $\\\\bigotimes_{i: h'_i=1} Z_i$. These stabilizers consist only of Pauli Z and I operators.\\n\",\n \" *(Note: Conventions might swap the roles of $C_1$ and $C_2^\\\\perp$ for X and Z stabilizers).*\\n\",\n \"\\n\",\n \"**Key Property and Decoding:**\\n\",\n \"The crucial property of CSS codes is the separation of stabilizer types:\\n\",\n \"* **X-stabilizers** commute with X errors but can anti-commute with Z errors on the qubits they act upon. They are used to detect **Z-type errors** (and the Z component of Y errors).\\n\",\n \"* **Z-stabilizers** commute with Z errors but can anti-commute with X errors on the qubits they act upon. They are used to detect **X-type errors** (and the X component of Y errors).\\n\",\n \"\\n\",\n \"This separation simplifies decoding:\\n\",\n \"1. Measure all stabilizers to get the full syndrome.\\n\",\n \"2. Extract the syndrome bits corresponding to the **Z-stabilizers**. Use these bits and a classical decoding algorithm associated with the code $C_1$ (whose checks define the X stabilizers) to identify and correct likely **X or Y errors**.\\n\",\n \"3. Extract the syndrome bits corresponding to the **X-stabilizers**. Use these bits and a classical decoding algorithm associated with the code $C_2^\\\\perp$ (whose checks define the Z stabilizers) to identify and correct likely **Z or Y errors**.\\n\",\n \"\\n\",\n \"This powerful CSS construction, which distinctly handles X and Z errors using classical code components, is exemplified by the renowned [[7, 1, 3]] Steane code. In the next section, we'll explore the specific stabilizers and practical aspects of this important code.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9da222c6\",\n \"metadata\": {},\n \"source\": [\n \"## 2.4 [[7,1,3]] Steane Code Practice\\n\",\n \"\\n\",\n \"The **[[7, 1, 3]] Steane code** is a quintessential CSS code. It encodes $k=1$ logical qubit into $n=7$ physical qubits and can correct any arbitrary single-qubit error (since $d=3$). It is constructed using the classical [7, 4, 3] Hamming code for both $C_1$ and $C_2$ (i.e., $H_1 = H_2 = H_{\\\\text{Hamming}}$).\\n\",\n \"\\n\",\n \"### Parity Check Matrix Primer\\n\",\n \"\\n\",\n \"A **parity check matrix ($H$)** for a classical linear code is a fundamental tool. If $c$ is a codeword, then the product $Hc^T = \\\\mathbf{0}$ (the zero vector) must hold. Each row of $H$ defines a parity check, meaning it specifies a subset of codeword bits that must sum to 0 (modulo 2). If $Hc^T \\\\neq \\\\mathbf{0}$, the result is called the **syndrome**, which indicates an error has occurred and can be used to identify it. For CSS codes, the rows of classical parity check matrices are used to define the X-type and Z-type stabilizer generators of the quantum code.\\n\",\n \"\\n\",\n \"### Stabilizer Generators from the Parity Check Matrix\\n\",\n \"\\n\",\n \"\\n\",\n \"The parity check matrix $H$ whose rows define stabilizer structures (for qubits $q_0, ..., q_6$) of the Steane code is:\\n\",\n \"$$ H = \\\\begin{pmatrix}\\n\",\n \"1 & 1 & 1 & 1 & 0 & 0 & 0 \\\\\\\\ % Corresponds to X0 X1 X2 X3 (and Z0 Z1 Z2 Z3)\\n\",\n \"1 & 1 & 0 & 0 & 1 & 1 & 0 \\\\\\\\ % Corresponds to X0 X1 X4 X5 (and Z0 Z1 Z4 Z5)\\n\",\n \"1 & 0 & 1 & 0 & 1 & 0 & 1 % Corresponds to X0 X2 X4 X6 (and Z0 Z2 X4 X6)\\n\",\n \"\\\\end{pmatrix} $$\\n\",\n \"Each row $(h_0, h_1, h_2, h_3, h_4, h_5, h_6)$ of this matrix $H$ corresponds to a stabilizer generator. For an X-stabilizer, an $X_i$ operator is present if $h_i=1$. For a Z-stabilizer, a $Z_i$ operator is present if $h_i=1$.\\n\",\n \"\\n\",\n \"So, the stabilizer generators are:\\n\",\n \"\\n\",\n \"* **X-type stabilizers** (these detect Z-errors), derived from the rows of $H$:\\n\",\n \" * $S_{X0} = X_0 X_1 X_2 X_3 = IIIXXXX$\\n\",\n \" * $S_{X1} = X_0 X_1 X_4 X_5 = IXXIIXX$\\n\",\n \" * $S_{X2} = X_0 X_2 X_4 X_6 = XIXIXIX$\\n\",\n \"\\n\",\n \"* **Z-type stabilizers** (these detect X-errors), also derived from the rows of $H$:\\n\",\n \" * $S_{Z0} = Z_0 Z_1 Z_2 Z_3 = IIIZZZZ$\\n\",\n \" * $S_{Z1} = Z_0 Z_1 Z_4 Z_5 = IZZIIZZ$\\n\",\n \" * $S_{Z2} = Z_0 Z_2 Z_4 Z_6 = ZIZIZIZ$\\n\",\n \"\\n\",\n \"\\n\",\n \"These $m = n-k = 7-1=6$ operators generate the stabilizer group $S$. The X-stabilizers commute with all Z-stabilizers. This is because for any row $h_a$ from $H$ defining an X-stabilizer and any row $h_b$ from $H$ defining a Z-stabilizer, the number of positions where both rows have a '1' (i.e., shared qubits) is even. This ensures that $S_{Xa}$ and $S_{Zb}$ commute.\\n\",\n \"\\n\",\n \"**Error Detection and Correction:**\\n\",\n \"A single Z-error on qubit $j$, $Z_j$, will anti-commute with a specific subset of $\\\\{S_{X0}, S_{X1}, S_{X2}\\\\}$. The 3-bit syndrome derived from measuring these X-stabilizers will uniquely identify $j$. For example, a $Z_0$ error anti-commutes with $S_{X0}, S_{X1}, S_{X2}$, giving an X-syndrome (eigenvalue flips) of $(1,1,1)$ (if 1 means flip). A $Z_3$ error anti-commutes only with $S_{X0}$, giving syndrome $(1,0,0)$. Each column of $H$ represents the syndrome pattern for an error on the corresponding qubit.\\n\",\n \"Similarly, a single X-error on qubit $j$, $X_j$, will anti-commute with a specific subset of $\\\\{S_{Z0}, S_{Z1}, S_{Z2}\\\\}$, and the 3-bit syndrome from these Z-stabilizers will identify $j$.\\n\",\n \"A $Y_j$ error will be caught by both sets of syndromes.\\n\",\n \"\\n\",\n \"**Logical Operators:**\\n\",\n \"A common choice for logical operators, which must commute with all stabilizers but are not themselves stabilizers, is:\\n\",\n \"* $\\\\bar{X} = X_0 X_1 X_2 X_3 X_4 X_5 X_6$ (product of $X$ on all 7 qubits)\\n\",\n \"* $\\\\bar{Z} = Z_0 Z_1 Z_2 Z_3 Z_4 Z_5 Z_6$ (product of $Z$ on all 7 qubits)\\n\",\n \"\\n\",\n \"#### Minimum distance and error detection of the Steane code\\n\",\n \"\\n\",\n \"Let's see why $d$ for the $[[7,1,3]]$ bit-flip code is 3. You can skip this if you already familiar with it.\\n\",\n \"\\n\",\n \"\\n\",\n \" Click to check \\n\",\n \"\\n\",\n \"\\n\",\n \"### Weight-1 Operators (Single-qubit errors)\\n\",\n \"\\n\",\n \"Consider any single-qubit Pauli error, $P_i \\\\in \\\\{X_i, Y_i, Z_i\\\\}$ acting on qubit $i$.\\n\",\n \"* If $P_i = X_i$ or $Y_i$, it will anti-commute with any Z-type stabilizer ($S_{Z0}, S_{Z1}, S_{Z2}$) that has a $Z$ on qubit $i$. For example, $X_6$ anti-commutes with $S_{Z2} = Z_0 Z_2 Z_4 Z_6$. $Y_6$ also anti-commutes with $S_{Z2}$.\\n\",\n \"* If $P_i = Z_i$ or $Y_i$, it will anti-commute with any X-type stabilizer ($S_{X0}, S_{X1}, S_{X2}$) that has an $X$ on qubit $i$. For example, $Z_6$ anti-commutes with $S_{X2} = X_0 X_2 X_4 X_6$. $Y_6$ also anti-commutes with $S_{X2}$.\\n\",\n \"\\n\",\n \"Since every qubit $i \\\\in \\\\{0, \\\\dots, 6\\\\}$ has both X-type and Z-type stabilizers acting non-trivially upon it within the set $\\\\{S_{X0}, \\\\dots, S_{Z2}\\\\}$, **any** single-qubit Pauli error $P_i$ will anti-commute with at least one stabilizer generator.\\n\",\n \"Conclusion: All weight-1 errors are **detectable**. Therefore, $d > 1$.\\n\",\n \"\\n\",\n \"### Weight-2 Operators (Two-qubit errors)\\n\",\n \"\\n\",\n \"Consider any two-qubit Pauli error $E_2 = P_i P'_j$ where $i \\\\neq j$ and $P, P' \\\\in \\\\{X, Y, Z\\\\}$. By examining the stabilizer generators, it can be shown that any such $E_2$ must anti-commute with at least one $S_k$.\\n\",\n \"* For example, take $X_5 X_6$.\\n\",\n \" * $X_5$ anti-commutes with $S_{Z1} (=Z_0Z_1Z_4Z_5)$ and $S_{Z2} (=Z_0Z_2Z_4Z_6)$ (no, $X_5$ anti-commutes with Z-stabilizers involving $Z_5$, i.e., $S_{Z1}$).\\n\",\n \" * $X_6$ anti-commutes with $S_{Z2} (=Z_0Z_2Z_4Z_6)$.\\n\",\n \" * Let's check $X_5 X_6$ commutation with the Z-stabilizers:\\n\",\n \" * $S_{Z0} = Z_0Z_1Z_2Z_3$: Commutes.\\n\",\n \" * $S_{Z1} = Z_0Z_1Z_4Z_5$: Anti-commutes (due to $X_5$ and $Z_5$).\\n\",\n \" * $S_{Z2} = Z_0Z_2Z_4Z_6$: Anti-commutes (due to $X_6$ and $Z_6$).\\n\",\n \" So, $X_5X_6$ anti-commutes with $S_{Z1}$ and $S_{Z2}$.\\n\",\n \"* As another example, take $Z_1 Z_2$.\\n\",\n \" * $Z_1$ anti-commutes with $S_{X0} (=X_0X_1X_2X_3)$ and $S_{X1} (=X_0X_1X_4X_5)$.\\n\",\n \" * $Z_2$ anti-commutes with $S_{X0} (=X_0X_1X_2X_3)$ and $S_{X2} (=X_0X_2X_4X_6)$.\\n\",\n \" * Let's check $Z_1 Z_2$ commutation with X-stabilizers:\\n\",\n \" * $S_{X0} = X_0X_1X_2X_3$: Anti-commutes (due to $Z_1$ with $X_1$, and $Z_2$ with $X_2$; an even number of anti-commutations means overall commutation for this product). No, $Z_1Z_2$ with $X_0X_1X_2X_3$: $Z_1$ flips sign with $X_1$, $Z_2$ flips sign with $X_2$. Total sign flip is $(-1) \\\\times (-1) = +1$. So it commutes.\\n\",\n \" * $S_{X1} = X_0X_1X_4X_5$: Anti-commutes (due to $Z_1$ with $X_1$).\\n\",\n \" * $S_{X2} = X_0X_2X_4X_6$: Anti-commutes (due to $Z_2$ with $X_2$).\\n\",\n \" So, $Z_1Z_2$ anti-commutes with $S_{X1}$ and $S_{X2}$.\\n\",\n \"\\n\",\n \"Through systematic check, it's found that **all** weight-2 Pauli errors anti-commute with at least one stabilizer generator $S_k$.\\n\",\n \"Conclusion: All weight-2 errors are **detectable**. Therefore, $d > 2$.\\n\",\n \"\\n\",\n \"### Weight-3 Operators (Three-qubit errors)\\n\",\n \"\\n\",\n \"Now consider weight-3 Pauli error $E_3$. Let's check $L_X = X_4X_5X_6$ against the Z-type stabilizers:\\n\",\n \"* **vs $S_{Z0} = Z_0Z_1Z_2Z_3$**:\\n\",\n \" $X_4, X_5, X_6$ all act on qubits not present in $S_{Z0}$. Thus, $L_X$ commutes with $S_{Z0}$.\\n\",\n \"* **vs $S_{Z1} = Z_0Z_1Z_4Z_5$**:\\n\",\n \" $X_4$ (from $L_X$) anti-commutes with $Z_4$ (from $S_{Z1}$).\\n\",\n \" $X_5$ (from $L_X$) anti-commutes with $Z_5$ (from $S_{Z1}$).\\n\",\n \" $X_6$ (from $L_X$) commutes with $S_{Z1}$ (no $Z_6$ in $S_{Z1}$).\\n\",\n \" The combined operation results from two anti-commutations ($X_4$ with $Z_4$, $X_5$ with $Z_5$). Since an even number (2) of components anti-commute, the overall operators $L_X$ and $S_{Z1}$ commute.\\n\",\n \"* **vs $S_{Z2} = Z_0Z_2Z_4Z_6$**:\\n\",\n \" $X_4$ (from $L_X$) anti-commutes with $Z_4$ (from $S_{Z2}$).\\n\",\n \" $X_5$ (from $L_X$) commutes with $S_{Z2}$ (no $Z_5$ in $S_{Z2}$).\\n\",\n \" $X_6$ (from $L_X$) anti-commutes with $Z_6$ (from $S_{Z2}$).\\n\",\n \" The combined operation results from two anti-commutations ($X_4$ with $Z_4$, $X_6$ with $Z_6$). Since an even number (2) of components anti-commute, $L_X$ and $S_{Z2}$ commute.\\n\",\n \"\\n\",\n \"So, $L_X = X_4X_5X_6$ commutes with all stabilizer generators $S_{X0}, \\\\dots, S_{Z2}$. Because $X_4X_5X_6$ (a weight-3 operator) commutes with all $S_i$, it produces a trivial syndrome $(0,0,0,0,0,0)$ and is therefore **undetectable** by stabilizer measurements in the same way that errors are detected. Such an operator changes the encoded logical state.\\n\",\n \"\\n\",\n \"**Therefore, the minimum distance of the [[7, 1, 3]] Steane code is $d=3$.**\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"71c750a1\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"Exercise 2: Lookup table of [[7, 1, 3]] Steane Code \\n\",\n \"\\n\",\n \"The goal is to create a Python dictionary that maps each possible 6-bit syndrome to a specific single-qubit error code (e.g., X0 is X error injected on the 0th qubit). If we assume syndrome measure results of each syndrome as `s0`...`s5` = $S_{X0},...,S_{Z2}$, the final error if the syndrome is (s5, s4, s3, s2, s1, s0), it indicates an X error on qubit 0, and the corresponding dictionary entry should be \\\"111000\\\": \\\"X0\\\". Each value in the dictionary follows the format \\\"Pq\\\", where P is the Pauli operator (X, Y, or Z) and q is the qubit index from 0 to 6. If there is no error, the syndrome will be all zeros, and the correction should be \\\"I\\\" to indicate the identity (i.e., do nothing).\\n\",\n \"\\n\",\n \"You are asked to complete this mapping for all possible single-qubit Pauli errors to determine the syndrome produced by X, Y, and Z errors, and assign the correct label in the dictionary. When you're done, your decoder will be able to take any 6-bit syndrome from a single error and output the appropriate corrective action.\\n\",\n \"\\n\",\n \"(**Tip**) Try to fill out the commute/anti-commute table below for the X errors! Mark 0 for commute and 1 for anti-commute.\\n\",\n \"\\n\",\n \"| Error Code | Error Pauli String |S5(ZIZIZIZ) | S4(IZZIIZZ) | S3 (IIIZZZZ) | S2 (XIXIXIX) | S1 (IXXIIXX) | S0 (IIIXXXX) |\\n\",\n \"|---|---|---|---|---|---|---|---|\\n\",\n \"| $X_0$ | IIIIIIX | 1(anti-commute) | 1(anti-commute) | 1(anti-commute) | 0(commute) | 0(commute) | 0(commute) |\\n\",\n \"| $X_1$ | IIIIIXI | | | | | | |\\n\",\n \"| $X_2$ | IIIIXII | | | | | | |\\n\",\n \"| $X_3$ | IIIXIII | | | | | | |\\n\",\n \"| $X_4$ | IIXIIII | | | | | | |\\n\",\n \"| $X_5$ | IXIIIII | | | | | | |\\n\",\n \"| $X_6$ | XIIIIII | | | | | | |\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 3: Detect Error with a Lookup Table\\n\",\n \"\\n\",\n \"Now, let's use the lookup table you've created to find errors.\\n\",\n \"\\n\",\n \"Run the cell below to download a quantum state from the grader. This quantum state is a logical zero state of the Steane code, into which a specific error has been injected.\\n\",\n \"\\n\",\n \"Your task is to:\\n\",\n \"1. Construct a quantum circuit.\\n\",\n \"2. Initialize the `qr_data` qubits to the provided quantum state.\\n\",\n \"3. Complete `measure_steane_syndrome` function. We left S0 as an example.\\n\",\n \"4. Compare the measurement results (the syndrome) with your lookup table to identify which qubit experienced what type of error.\\n\",\n \"5. Submit your findings to the grader for verification.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"First, complete `measure_steane_syndrome` by preparing all the stabilizers of the Steane code.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a4a8f077\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def measure_steane_syndrome(qc, q_data, q_anc, c_reg):\\n\",\n \"\\n\",\n \" # Measure X-type Stabilizers (S0, S1, S2 -> q_anc[0], q_anc[1], q_anc[2])\\n\",\n \" # Apply H to data qubits -> CNOT -> Apply H to data qubits -> Measure ancilla\\n\",\n \" qc.h(q_data) # H on data qubits\\n\",\n \"\\n\",\n \" #S0: IIIXXXX (X_3 X_2 X_1 X_0)\\n\",\n \" qc.cx(q_data[0], q_anc[0])\\n\",\n \" qc.cx(q_data[1], q_anc[0])\\n\",\n \" qc.cx(q_data[2], q_anc[0])\\n\",\n \" qc.cx(q_data[3], q_anc[0])\\n\",\n \"\\n\",\n \" # ---- TODO : Task 3 ---\\n\",\n \" # Fill in the required code for S1 and S2\\n\",\n \" #S1\\n\",\n \"\\n\",\n \" #S2\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" qc.h(q_data) # Restore H on data qubits\\n\",\n \" qc.measure(q_anc[0:3], c_reg[0:3]) # Measure X syndrome (s1, s2, s3)\\n\",\n \" qc.barrier()\\n\",\n \"\\n\",\n \"\\n\",\n \" # ---- TODO : Task 3 ---\\n\",\n \" # Measure Z-type Stabilizers (S3, S4, S5 -> q_anc[3], q_anc[4], q_anc[5])\\n\",\n \" # CNOT -> Measure ancilla \\n\",\n \" # Fill in the required code for S3, S4 and s5\\n\",\n \"\\n\",\n \" #S3\\n\",\n \"\\n\",\n \" #S4\\n\",\n \"\\n\",\n \" #S5\\n\",\n \" # --- End of TODO ---\\n\",\n \" \\n\",\n \" qc.measure(q_anc[3:6], c_reg[3:6]) # Measure Z syndrome (s3, s4, s5)\\n\",\n \" qc.barrier()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fed4a432\",\n \"metadata\": {},\n \"source\": [\n \"Let's initialize `qr_data` with the provided statevector (`State`) by using [initialize](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.QuantumCircuit#initialize) of `QuantumCircuit`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9a55ec14\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"state = bring_states()\\n\",\n \"\\n\",\n \"# Logical qubit (7 data qubits)\\n\",\n \"qr_data = QuantumRegister(7, name='q')\\n\",\n \"# Ancilla qubits for syndrome measurement (6)\\n\",\n \"qr_anc = QuantumRegister(6, name='anc')\\n\",\n \"# Classical registers for syndrome (initial & verify)\\n\",\n \"cr_initial_syn = ClassicalRegister(6, name='c_initial_syn')\\n\",\n \"cr_final_syn = ClassicalRegister(6, name='c_final_syn')\\n\",\n \"\\n\",\n \"# Total circuit (13 qubits, 12 classical bits)\\n\",\n \"qc = QuantumCircuit(qr_data, qr_anc, cr_initial_syn, cr_final_syn)\\n\",\n \"\\n\",\n \"\\n\",\n \"# ---- TODO : Task 3 ---\\n\",\n \"#initialize qc with the correctState\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"92a1a966\",\n \"metadata\": {},\n \"source\": [\n \"Next, add the syndrome measurement by executing the cell below and check your quantum circuit by drawing it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0f4f071c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- AddSyndrome Measurement ---\\n\",\n \"\\n\",\n \"measure_steane_syndrome(qc, qr_data, qr_anc, cr_initial_syn)\\n\",\n \"\\n\",\n \"qc.draw('mpl', fold=-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1cee23bf\",\n \"metadata\": {},\n \"source\": [\n \"Now, run this quantum circuit to find the error injected into the logical $∣0\\\\rangle$ state. To complete this task, you'll need the key (the measured bitstring) from the `counts` dictionary. If the stabilizer measurements are implemented correctly, you will observe a single state with 100% probability in the simulation results.\\n\",\n \"\\n\",\n \"
\\n\",\n \"To get a correct answer\\n\",\n \"\\n\",\n \"If you run the circuit below on a real backend, you might not be able to obtain the error codes accurately due to various errors injected into the qubits. Therefore, please execute the following code using a simulator backend.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b4eca236\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- Run the Simulation using AerSimulator\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"#make quantum circuit compatible to the backend\\n\",\n \"pm = generate_preset_pass_manager( backend = backend, optimization_level=3)\\n\",\n \"qc_isa = pm.run(qc)\\n\",\n \"\\n\",\n \"#run and get counts\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots = 10000).result()[0].data.c_initial_syn.get_counts()\\n\",\n \"\\n\",\n \"# ---- TODO : Task 3 ---\\n\",\n \"#get key of simulation result and find the error code, ex: X1\\n\",\n \"\\n\",\n \"error_code = \\n\",\n \"# --- End of TODO ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0a3d5dba\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_histogram(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4ad1bbb7\",\n \"metadata\": {},\n \"source\": [\n \"Submit your error code string to the grader to see if you detected correctly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0a72d019\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab4_ex3(error_code)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ee2cf0ca\",\n \"metadata\": {},\n \"source\": [\n \"Congratulations! You've successfully implemented the stabilizer measurement circuit for the Steane code and detected the error.\\n\",\n \"\\n\",\n \"Since Pauli errors are unitary operations and self-inverse, applying the same Pauli gate a second time acts as the identity operation ($P \\\\cdot P=I$), effectively canceling the error. For this optional exercise (non-graded), use this property to correct the error you detected. Apply the gate corresponding to your detected error code to the erroneous quantum state and verify that the error is corrected.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"65b5f314\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"No Grader Extra Exercise: Correct Error\\n\",\n \"\\n\",\n \"Now let's correct this error with a proper gate operation. \\n\",\n \"\\n\",\n \"Complete the cell below to add proper correction, and then do a stabilizer measurement one more time to get the syndrome measurement result: $000000$, which means no error detected. Start by initializing `qr_data` with State again and apply a correction gate, then do a stabilizer measurement. You can use the `measure_steane_syndrome` function you already made.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4f2219ff\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qc = QuantumCircuit(qr_data, qr_anc, cr_initial_syn, cr_final_syn)\\n\",\n \"\\n\",\n \"\\n\",\n \"# ---- TODO : ungraded task ---\\n\",\n \"#initialize qr_data with State and correct error by applying proper gate\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"measure_steane_syndrome(qc, qr_data, qr_anc, cr_final_syn)\\n\",\n \"\\n\",\n \"#qc.draw('mpl', fold=-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f90003a2\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"To get a correct answer\\n\",\n \"\\n\",\n \"If you run the circuit below on a real backend, you might not be able to obtain the error codes accurately due to various errors injected into the qubits. Therefore, please execute the following code using a simulator backend.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"90d4a474\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qc_isa = pm.run(qc)\\n\",\n \"counts = sampler.run([qc_isa], shots = 10000).result()[0].data.c_final_syn.get_counts()\\n\",\n \"\\n\",\n \"plot_histogram(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2730a94a\",\n \"metadata\": {},\n \"source\": [\n \"Did you obtain `000000` with 100% probability in your syndrome measurements? Congratulations 🎉! You now understand the Steane code and how to detect and correct errors.\\n\",\n \"\\n\",\n \"So far, you've explored the fundamental concepts of both classical and quantum error correction. You've delved into the operational mechanics of two foundational quantum error correction codes, constructed lookup tables for error detection and correction, and practically applied these principles by detecting errors in the Steane code.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ce09252b\",\n \"metadata\": {},\n \"source\": [\n \"# Chapter 3: Exploring Advanced Quantum Error Correction Codes & Their Efficiency\\n\",\n \"\\n\",\n \"Having explored basic quantum error correction codes like the Steane code, this chapter delves into more advanced topics. Our primary goal is to understand different strategies for achieving robust fault tolerance and to investigate what improvements advanced QLDPC constructions (such as the gross code and related families) offer over other well-known codes like the surface and toric codes, particularly in terms of error correction capabilities and efficiency.\\n\",\n \"\\n\",\n \"We will explore codes with particular topologies, where the geometric layout and connectivity of qubits influence the code's properties. We will analyze instances of the toric code and compare them with the gross code. For these, our focus will be on aspects like their parity check matrices and the number of logical qubits each can encode relative to physical qubits and distance. This exploration will highlight different strategies for achieving fault tolerance and shed light on the diverse landscape of contemporary QEC research.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Note on Code Representations and a Pedagogical Approach \\n\",\n \"\\n\",\n \"It's essential to reiterate that the primary objective here is to understand comparative principles. The specific examples or graphical representations used to illustrate differences between code families (e.g., surface/toric vs. gross-like codes) should be understood as **illustrative toy models**. They are designed to capture essential characteristics and distinguishing features that lead to different performance potentials, rather than being the exact process of building codes.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"## 3.1 Foundational Concepts and Key QLDPC Architectures for Comparison\\n\",\n \"\\n\",\n \"Before directly introducing the toric and gross codes, this section will give you a high-level introduction to the *Quantum Low-Density Parity-Check (QLDPC) codes* and introduce key QLDPC architectures.\\n\",\n \"\\n\",\n \"Low-Density Parity-Check (LDPC) codes are classical error-correcting codes known for having a sparse parity check matrix, meaning the matrix contains very few non-zero entries relative to its size. This sparsity is crucial because it allows for highly efficient decoding algorithms, making LDPC codes powerful tools for classical communication and data storage. Quantum Low-Density Parity-Check (QLDPC) codes are the quantum counterparts to classical LDPC codes. In QLDP codes, the **stabilizer generators are sparse**; each stabilizer generator acts non-trivially on only a small number of qubits, and each qubit is only a part of a small number of these generators. The aim is to design codes that not only feature this beneficial sparsity but also achieve good scaling of code distance $d$ (a measure of error correction capability) with the number of physical qubits $n$, and ideally offer high **encoding rates** ($k/n$, where $k$ is the number of logical qubits).\\n\",\n \"\\n\",\n \"Now that we've covered these basics, we'll look at and compare the two main types of QLDP codes:\\n\",\n \"\\n\",\n \"* **Surface/Toric Codes**: These are well-studied QLDP codes defined on a 2D lattice where stabilizers act geometrically **locally** (typically on nearest-neighbor qubits). While known for relatively high error thresholds and compatibility with 2D hardware, their code distance $d$ typically scales as $O(\\\\sqrt{n})$ for $n$ physical qubits, which can be a limitation for achieving very large distances efficiently - additionally, for each patch of surface (or toric) code, there is only 1 logical qubit (2 in the case of the toric code).\\n\",\n \"\\n\",\n \"* **Bivariate Bicycle Codes (and related QLDPC families)**: This broader class of QLDPC constructions aims for excellent asymptotic scaling of parameters $k, d, n$. Their construction does not necessarily restrict them to simple 2D geometric locality. The Tanner graphs of these codes might exhibit more complex or **\\\"long-range\\\"** (in a 2D sense) connections, while still maintaining overall sparsity. This different structural connectivity is believed to underpin their potential for superior performance, such as:\\n\",\n \" * Better distance scaling: Achieving a code distance $d$ that grows more favorably with $n$.\\n\",\n \" * Higher encoding rates ($k/n$): Encoding more logical qubits for a similar number of physical qubits and distance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2a5c405c\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0598739c\",\n \"metadata\": {},\n \"source\": [\n \"*(Image taken from https://arxiv.org/pdf/2308.07915: Tanner graph representation for the gross code, illustrating data qubits (circles) and stabilizers/checks (squares) on a toroidal grid.)*\\n\",\n \"\\n\",\n \"## 3.2 Qubit Layout and Conventions for Exercises\\n\",\n \"\\n\",\n \"To concretely compare how different code structures impact performance with simplified models, we will construct codes on the same physical arrangement of qubits by using above image. We will build upon concepts discussed in the lecture \\\"Toward Fault-tolerant Quantum Computing with IBM QLDPC codes\\\" by Patrick Rall.\\n\",\n \"\\n\",\n \"Our focus will be on constructing the parity check matrices for two illustrative code instances on a two-dimensional grid with periodic boundary conditions (a torus), as visualized in the provided Tanner graph:\\n\",\n \"1. One representing a **Toric code** with local connections.\\n\",\n \"2. Another representing a **gross code** that incorporates additional long-range connections.\\n\",\n \"\\n\",\n \"**Key Elements in Our Model:**\\n\",\n \"\\n\",\n \"* **Data Qubits (Circles)**: Store the quantum information. In the provided diagram, these are the blue and orange circles. There are $12 \\\\times 6 = 72$ blue qubits and $12 \\\\times 6 = 72$ orange qubits, totaling $n = 144$ data qubits.\\n\",\n \"* **Stabilizers/Checks (Squares)**: Define the code's stabilizer operators.\\n\",\n \" * **X-stabilizers** (products of Pauli-X) are associated with the **red** squares.\\n\",\n \" * **Z-stabilizers** (products of Pauli-Z) are associated with the **green** squares.\\n\",\n \"* **CSS Code Structure**: We are working within the Calderbank-Shor-Steane (CSS) framework.\\n\",\n \"\\n\",\n \"A clear **qubit labeling** convention is crucial for translating the Tanner graph's connectivity into parity check matrices. The rows of these matrices will correspond to stabilizers, and columns to data qubits.\\n\",\n \"\\n\",\n \"**Qubit Labeling Convention (144 total data qubits):**\\n\",\n \"\\n\",\n \"* **Blue** Data Qubits (Indices 0-71): Labeled column by column (left to right), then bottom-up within each column (0-indexed).\\n\",\n \" * The bottom-leftmost blue qubit is `0`; the one above it is `1`, up to `5`.\\n\",\n \" * The next column's blue qubits are `6` through `11`, and so on.\\n\",\n \"* **Orange** Data Qubits (Indices 72-143): Follow the same labeling scheme, starting with the bottom-leftmost orange qubit as `72`.\\n\",\n \"\\n\",\n \"**Parity Check Matrix Convention (for this lab):**\\n\",\n \"\\n\",\n \"We will define two binary parity check matrices:\\n\",\n \"\\n\",\n \"* **$H_X$ (for X-stabilizers)**:\\n\",\n \" * Each row represents an X-stabilizer (derived from a **red square**).\\n\",\n \" * This matrix $H_X$ is used to detect **Z-type errors**. (Syndrome: $s_x$)\\n\",\n \"* **$H_Z$ (for Z-stabilizers)**:\\n\",\n \" * Each row represents a Z-stabilizer (derived from a **green square**).\\n\",\n \" * This matrix $H_Z$ is used to detect **X-type errors**. (Syndrome: $s_z$)\\n\",\n \"\\n\",\n \"\\n\",\n \"Using the layout and conventions above, we will now define stabilizers based on the connectivity depicted in the provided Tanner graph for two distinct code versions on the *same* physical qubit layout as the exercises.\\n\",\n \"\\n\",\n \"1. **The toric code:** This version will be constructed using only local, nearest-neighbor connections between the stabilizers (squares) and the data qubits (circles) they act upon, following the standard toric code model on a grid with periodic boundaries. Note: the toric code is very similar to the surface code; however, it is embedded on a torus with periodic boundaries in both the $x$ and $y$ directions. In particular, it can encode one additional qubit when compared to the surface code without the periodic boundaries.\\n\",\n \"2. **The gross code:** This version will augment the local connections typical of the toric code by including specific \\\"long-range\\\" connections, the leaf-shaped connections in the above graph. These additional connections modify the definitions of the stabilizer generators, leading to a code with different parameters and properties compared to the standard toric code on the same qubit layout.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c827399a\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"610ae37c\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 3.3 Toric Code Exercise\\n\",\n \"\\n\",\n \"### HX\\n\",\n \"\\n\",\n \"We will now establish the binary representation for the **X-stabilizers** (associated with red squares) for the toric code, which considers only nearest-neighbor connections on the torus. These will form the rows of the matrix you'll be asked to construct (referred to as $H_X$ in the exercise prompt's output variable).\\n\",\n \"\\n\",\n \"Let's consider the bottom-leftmost red square in the diagram. This X-stabilizer acts on four neighboring data qubits:\\n\",\n \"1. The blue data qubit below it: This is blue qubit `0` (column 0, row 0).\\n\",\n \"2. The blue data qubit above it: This is blue qubit `1` (column 0, row 1).\\n\",\n \"3. The orange data qubit to its right: This is the bottom-leftmost orange qubit, which is orange qubit `72` (orange column 0, row 0).\\n\",\n \"4. The orange data qubit to its left (across the periodic boundary): This is the bottom-rightmost orange qubit. Orange qubits are indexed 72-143. There are 12 columns of orange qubits, 6 qubits per column. The last orange column (column 11) contains qubits $72 + 11 \\\\times 6 + $ row_idx. The bottom-rightmost is $72 + 11 \\\\times 6 + 0 = 72 + 66 = 138$. So, this is orange qubit `138`.\\n\",\n \"\\n\",\n \"Therefore, the first X-stabilizer acts on data qubits `0`, `1`, `72`, and `138`. Its corresponding row in the binary matrix (which will be a row in your $H_X$ for the exercise, or $H_Z$ in standard notation) will have 1s at columns 0, 1, 72, and 138, and 0s elsewhere. This 144-element row vector is:\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"The example given in the original problem for the first 6 rows of \\\"parity check matrix $H_X$\\\" (derived from the first 6 red squares) uses this logic:\\n\",\n \"\\n\",\n \"```\\n\",\n \"[[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0],\\n\",\n \"\\n\",\n \" [0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0],\\n\",\n \"\\n\",\n \" [0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0],\\n\",\n \"\\n\",\n \" [0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0],\\n\",\n \"\\n\",\n \" [0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0],\\n\",\n \"\\n\",\n \" [1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]]\\n\",\n \"```\\n\",\n \"\\n\",\n \"### HZ\\n\",\n \"\\n\",\n \"HZ follows the same basic rule as HX for connecting to its nearest neighbor. The only difference is that HZ is marked with a green square, and it starts at the very bottom of the second column from the left of the entire grid.\\n\",\n \"\\n\",\n \"Let's consider the bottom-leftmost green square in the diagram. This Z-stabilizer acts on four neighboring data qubits:\\n\",\n \"1. The blue data qubit to its left: This is blue qubit `0` (blue column 0, row 0).\\n\",\n \"2. The blue data qubit to its right: This is blue qubit `6` (blue column 1, row 0).\\n\",\n \"3. The orange data qubit above: This is the bottom-leftmost orange qubit, which is orange qubit `72` (orange column 0, row 0).\\n\",\n \"4. The orange data qubit below (across the periodic boundary): This is the top-leftmost orange qubit. So, this is orange qubit `77`, which is 5 rows above the orange qubit `72` (orange column 0, row 5)\\n\",\n \"\\n\",\n \"Therefore, the first Z-stabilizer acts on data qubits `0`, `6`, `72`, and `77`. Its corresponding row in the binary matrix. This 144-element row vector is:\\n\",\n \"```\\n\",\n \" [1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 4: Find the parity check matrices of the toric code\\n\",\n \"\\n\",\n \"Following the above convention for qubit labeling and the definition of stabilizers from the diagram, find the parity check matrices for the **toric code** (i.e., the code containing only nearest-neighbor connections).\\n\",\n \"\\n\",\n \"Output the result for the **X-stabilizers (red squares)** in a NumPy array labeled $H_{X}$ and similarly for the **Z-stabilizers (green squares)** in a NumPy array labeled $H_{Z}$.(As per our clarification note, the matrix $H_X$ you generate from red squares will be used to detect Z-errors, and $H_Z$ from green squares will detect X-errors).\\n\",\n \"\\n\",\n \"Please ensure that the NumPy arrays $H_{X}$ and $H_{Z}$ strictly adhere to the expected dimensions and structure (`np.zeros((72,144),dtype=int)`). Modifying the size or structure of these matrices from what is anticipated by the exercise might lead to a failure during grading.\\n\",\n \"\\n\",\n \"\\n\",\n \"**Hint**: Notice the periodic structure in the examples. Can this be used to your advantage to write an efficient piece of code to generate these matrices? \\n\",\n \"
\\n\",\n \" \\n\",\n \" Check the connectivity using a helper function! \\n\",\n \"\\n\",\n \"Once the parity check matrices are finalized, execute the provided code to verify the connectivity of the 5th stabilizer of HXtc and the 66th stabilizer of `HXtc` and `HZtc`, respectively. Please focus on carefully checking the boundary conditions. Success is indicated by generating a graph that matches the following example.\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXtc, HZtc)\\n\",\n \"\\n\",\n \"```\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"**Acknowledgement**: [Prof. Daniel Sierra-Sosa](http://www.linkedin.com/in/dsierrasosa), Qiskit Advocate and a Mentor of QGSS 2025), for the discussion and his contribution on making a helper function.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"497d1eb1-9a0b-4fc0-9486-9d20a7e1260d\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5dde8af2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Some helpful code to start Exercise 4.\\n\",\n \"\\n\",\n \"\\n\",\n \"# We will define the parity check matrices for the toric code\\n\",\n \"HXtc = np.zeros((72, 144),dtype=int) # initializing the matrices\\n\",\n \"HZtc = np.zeros((72, 144),dtype=int)\\n\",\n \"\\n\",\n \"# We will ask you to modify the matrices by adding 1s in appropriate places\\n\",\n \"# As an example, we will show how to do so for the first few rows of the toric code\\n\",\n \"\\n\",\n \"\\n\",\n \"# ---- TODO : Task 4 ---\\n\",\n \"# Write code to calculate HXtc and HZtc\\n\",\n \"\\n\",\n \"# 0-th stabilizer\\n\",\n \"HXtc[0][0] = 1\\n\",\n \"HXtc[0][1] = 1\\n\",\n \"HXtc[0][72] = 1\\n\",\n \"HXtc[0][138] = 1\\n\",\n \"\\n\",\n \"# 1-st stabilizer\\n\",\n \"HXtc[1][1] = 1\\n\",\n \"HXtc[1][2] = 1\\n\",\n \"HXtc[1][73] = 1\\n\",\n \"HXtc[1][139] = 1\\n\",\n \"\\n\",\n \"# 2-nd stabilizer\\n\",\n \"HXtc[2][2] = 1\\n\",\n \"HXtc[2][3] = 1\\n\",\n \"HXtc[2][74] = 1\\n\",\n \"HXtc[2][140] = 1\\n\",\n \"\\n\",\n \"# 3-rd stabilizer\\n\",\n \"HXtc[3][3] = 1\\n\",\n \"HXtc[3][4] = 1\\n\",\n \"HXtc[3][75] = 1\\n\",\n \"HXtc[3][141] = 1\\n\",\n \"\\n\",\n \"# 4-th stabilizer\\n\",\n \"HXtc[4][4] = 1\\n\",\n \"HXtc[4][5] = 1\\n\",\n \"HXtc[4][76] = 1\\n\",\n \"HXtc[4][142] = 1\\n\",\n \"\\n\",\n \"# 5-th stabilizer\\n\",\n \"HXtc[5][5] = 1\\n\",\n \"HXtc[5][np.mod(6,6)] = 1\\n\",\n \"HXtc[5][72 + 5] = 1\\n\",\n \"HXtc[5][72 + 6*np.mod(-1,12) + 5] = 1\\n\",\n \"\\n\",\n \"# The last definition suggested a general rule for finding the appropriate locations to place 1s\\n\",\n \"# HXtc[j][j] = 1\\n\",\n \"# HXtc[j][6*np.floor(j/6) + np.mod(j+1,6)]\\n\",\n \"# HXtc[j][j+72] = 1\\n\",\n \"# HXtc[j][72 + 6*np.mod(np.floor(j/6)-1,12) + np.mod(j,6)]\\n\",\n \"\\n\",\n \"# Write a loop over j from 0 to 143 to set all of the rows.\\n\",\n \"\\n\",\n \"# Inspired from this, write a similar loop for the Z parity check matrices and complete HXtc and HZtc\\n\",\n \"\\n\",\n \"HZtc[0][0] = 1\\n\",\n \"HZtc[0][6] = 1\\n\",\n \"HZtc[0][72] = 1\\n\",\n \"HZtc[0][77] = 1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- End of TODO ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e7bd2d52-72a8-4b5b-99c9-b0e9a75b7dc3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check the connectivity of HXtc and HZtc\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXtc, HZtc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f81fb0f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab4_ex4(HXtc, HZtc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fc9be9fa\",\n \"metadata\": {},\n \"source\": [\n \"## 3.4 Gross Code Exercise\\n\",\n \"\\n\",\n \"Now we consider the parity check matrices for the gross(-like) code. This code is generated by including the long-range connections shown abstractly in the figure (e.g., the leaf-like diagonal connections, though the exact rules are specific to the code's definition). The fundamental qubit layout and labeling convention remain the same as for the toric code.\\n\",\n \"\\n\",\n \"The key difference for the gross code is that each stabilizer (square check) will now have two additional long-range connections on top of its four nearest-neighbor connections. This means each stabilizer in the gross code will act on a total of $4+2=6$ data qubits. Consequently, each row in the parity check matrices ($H_X$ and $H_Z$) for the gross code will have six '1's.\\n\",\n \"\\n\",\n \"A practical way to construct these matrices is to start with the toric code's connectivity (nearest-neighbor connections) and then add the two specific long-range connections for each stabilizer.\\n\",\n \"\\n\",\n \"Let's illustrate this for the first row of $H_X$, which corresponds to the X-stabilizer at the bottom-leftmost red square (conceptually at $c_s=0, r_s=0$).\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f7b4b32d\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2dffcaea\",\n \"metadata\": {},\n \"source\": [\n \"#### HX\\n\",\n \"1. Nearest-Neighbor Connections (from Toric Code):\\n\",\n \"As established in Section 3.3, this bottom-leftmost red square has nearest-neighbor connections to:\\n\",\n \"* Blue data qubit at index $\\\\mathbf{0}$ (visualized as \\\"below\\\" or part of the square, blue column 0, row 0).\\n\",\n \"* Blue data qubit at index $\\\\mathbf{1}$ (visualized as \\\"above\\\" or part of the square, blue column 0, row 1).\\n\",\n \"* Orange data qubit at index $\\\\mathbf{72}$ (visualized as \\\"to the right\\\" or part of the square, orange column 0, row 0).\\n\",\n \"* Orange data qubit at index $\\\\mathbf{138}$ (visualized as \\\"to the left\\\" across periodic boundary, orange column 11, row 0).\\n\",\n \"\\n\",\n \"2. Additional Long-Range Connections (Specific to this Gross Code instance):\\n\",\n \"The problem states that for this particular gross code construction, the bottom-leftmost red square ($c_s=0, r_s=0$) will have two additional long-range connections:\\n\",\n \"\\n\",\n \"* First long-range connection: To the blue data qubit 20.\\n\",\n \" * The text describes this as \\\"the blue qubit three rows above and six columns to the right.\\\" The specific rules of the gross code define how these relative positions map to a precise qubit index on the torus. For this stabilizer, this rule results in a connection to blue data qubit with global index $\\\\mathbf{20}$.\\n\",\n \"\\n\",\n \"* Second long-range connection: To the orange data qubit 135.\\n\",\n \" * The text describes this as \\\"the orange qubit three columns to the left and six rows down (recall the periodic boundary conditions).\\\" Again, these relative descriptions are specific to the gross code's connection rules for this stabilizer, resulting in a connection to orange data qubit with global index $\\\\mathbf{135}$.\\n\",\n \"\\n\",\n \"3. Resulting First Row of $H_X$ for the Gross Code:\\n\",\n \"Combining both nearest-neighbor and the two specified long-range connections, the bottom-leftmost X-stabilizer now acts on data qubits with indices: `0, 1, 20, 72, 135, 138`.\\n\",\n \"\\n\",\n \"Therefore, the first row of the $H_X$ matrix for this gross code (a 144-element binary vector) will have '1's at these six positions and '0's elsewhere. Let's verify this against the provided snippet:\\n\",\n \"\\n\",\n \"**Snippet:**\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"This confirms that the snippet for the first row of $H_X$ for the gross code has exactly six '1's at the global data qubit indices 0, 1, 20, 72, 135, and 138, matching the combination of nearest-neighbor and the specified long-range connections.\\n\",\n \"\\n\",\n \"#### HZ\\n\",\n \"\\n\",\n \"To find the $H_Z$, please revisit the Tanner graph, and take a close look at the `leaf` shape of the long-range connection. \\n\",\n \"\\n\",\n \"1. Nearest-Neighbor Connections (from Toric Code):\\n\",\n \"As established in Section 3.3, this bottom-leftmost red square has nearest-neighbor connections to:\\n\",\n \"* Orange data qubit at index $\\\\mathbf{77}$ (visualized as \\\"to the below\\\" across periodic boundary, orange column 0, row 5).\\n\",\n \"* Orange data qubit at index $\\\\mathbf{72}$ (visualized as \\\"above\\\" or part of the square, orange column 0, row 0).\\n\",\n \"* Blue data qubit at index $\\\\mathbf{6}$ (visualized as \\\"to the right\\\" or part of the square, blue column 1, row 0).\\n\",\n \"* Blue data qubit at index $\\\\mathbf{0}$ (visualized as \\\"to the left\\\" or part of the square, blue column 0, row 0).\\n\",\n \"\\n\",\n \"2. Additional Long-Range Connections (Specific to this Gross Code instance):\\n\",\n \"\\n\",\n \"* First long-range connection: To the blue data qubit 15.\\n\",\n \" * The text describes this as \\\"the blue qubit six rows above and three columns to the right.\\\" The specific rules of the gross code define how these relative positions map to a precise qubit index on the torus. For this stabilizer, this rule results in a connection to blue data qubit with global index $\\\\mathbf{15}$.\\n\",\n \"\\n\",\n \"* Second long-range connection: To the orange data qubit 130.\\n\",\n \" * The text describes this as \\\"the orange qubit six columns to the left and three rows down (recall the periodic boundary conditions).\\\" Again, these relative descriptions are specific to the gross code's connection rules for this stabilizer, resulting in a connection to orange data qubit with global index $\\\\mathbf{130}$.\\n\",\n \"\\n\",\n \"3. Resulting First Row of $H_Z$ for the Gross Code:\\n\",\n \"Combining both nearest-neighbor and the two specified long-range connections, the bottom-leftmost X-stabilizer now acts on data qubits with indices: `0, 6, 15, 72, 77, 130`.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"**Snippet:**\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"This confirms that the snippet for the first row of $H_Z$ for the gross code has exactly six '1's at the global data qubit indices 0, 6, 15, 72, 77, and 130, matching the combination of nearest-neighbor and the specified long-range connections.\\n\",\n \"\\n\",\n \"This process of adding two specific long-range connections would be repeated for *every* stabilizer (both X-type from red squares and Z-type from green squares) to transform the toric code matrices into the gross code matrices. The exact rules for determining the long-range partners for other stabilizers would follow a consistent pattern defined by the gross code's construction on this lattice.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 5: Find the parity check matrices of the gross code \\n\",\n \"\\n\",\n \"Following the above labeling convention, construct the parity check matrix for the gross code, that is, including the long-range connections. Output the result for the $X$ stabilizers in numpy array labeled $H_{X}$ and similarly for the $Z$ stabilizers in an array labeled $H_{Z}$.\\n\",\n \"\\n\",\n \"**Hint**: Notice the periodic structure of the above examples. Can this be used to your advantage to make an efficient piece of code to generate the matrices? Each row should be weight-6 - that is, contain six 1s.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Check the connectivity using a helper function! \\n\",\n \"\\n\",\n \"Like exerciese 4 after finish compose `HXgc` and `HZgc`, execute the provided code to verify the connectivity of the 5th stabilizer of HXtc and the 66th stabilizer. Please focus on carefully checking the boundary conditions. Success is indicated by generating a graph that matches the following example.\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXgc, HZgc)\\n\",\n \"\\n\",\n \"```\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ab1b4b43-4d81-4e75-aae0-47c24ba7906a\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e46c47ef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We will define the parity check matrices for the gross code\\n\",\n \"HXgc = np.zeros((72,144),dtype=int) # initializing the matrices\\n\",\n \"HZgc = np.zeros((72,144),dtype=int)\\n\",\n \"# ---- TODO : Task 5 ---\\n\",\n \"# Write code to calculate HXgc and HZgc\\n\",\n \"\\n\",\n \"# 0-th X stabilizer\\n\",\n \"HXgc[0][0] = 1\\n\",\n \"HXgc[0][1] = 1\\n\",\n \"HXgc[0][20] = 1\\n\",\n \"HXgc[0][72] = 1\\n\",\n \"HXgc[0][135] = 1\\n\",\n \"HXgc[0][138] = 1\\n\",\n \"\\n\",\n \"\\n\",\n \"# 0-th Z stabilizer\\n\",\n \"HZgc[0][0] = 1\\n\",\n \"HZgc[0][6] = 1\\n\",\n \"HZgc[0][15] = 1\\n\",\n \"HZgc[0][72] = 1\\n\",\n \"HZgc[0][77] = 1\\n\",\n \"HZgc[0][130] = 1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- End of TODO ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ac83de2d-bdb3-4bd4-bf20-a1cef7e6640a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Check stabilizer\\n\",\n \"\\n\",\n \"generate_stabilizer_plots(HXgc, HZgc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a180a7cd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab4_ex5(HXgc, HZgc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0f37a761\",\n \"metadata\": {},\n \"source\": [\n \"## 3.5 Counting the Number of Logical Qubits\\n\",\n \"\\n\",\n \"The parity check matrices you've been working with, $H_X$ and $H_Z$, are constructed from the stabilizers of the code. Each row in these matrices represents a generator of the stabilizer group. However, it's possible that some of these chosen generators are not independent; one stabilizer generator might be a product of others. Redundant generators don't add new constraints to define the codespace.\\n\",\n \"\\n\",\n \"To find the actual number of independent stabilizer generators, we calculate the **rank** of these parity check matrices. Since we are dealing with qubits and Pauli operators (which square to identity and whose effects are often considered over GF(2) in the context of their binary representation), all matrix operations for rank calculation are performed modulo 2. The rank tells us the true number of unique conditions imposed by the stabilizers.\\n\",\n \"\\n\",\n \"In general, for a quantum stabilizer code:\\n\",\n \"If $n$ is the number of physical data qubits used in the code, and $r$ is the total number of independent stabilizer generators, then the number of logical qubits ($k$) encoded by the code is given by:\\n\",\n \"$$k = n - r$$\\n\",\n \"Each independent stabilizer generator effectively halves the dimension of the Hilbert space, \\\"fixing\\\" one degree of freedom. Thus, $r$ independent stabilizers reduce the $2^n$-dimensional space of $n$ physical qubits to a $2^{n-r}$-dimensional codespace, which can then encode $k = n-r$ logical qubits.\\n\",\n \"\\n\",\n \"For CSS codes, like the toric and gross codes we are considering, the X-stabilizers and Z-stabilizers form two distinct, commuting groups. This allows us to count their independent generators separately.\\n\",\n \"If $r_X$ is the number of independent X-stabilizers (obtained from the rank of the matrix whose rows are the X-stabilizer generators, which you've labeled $H_X$ from red squares) and $r_Z$ is the number of independent Z-stabilizers (obtained from the rank of the matrix whose rows are the Z-stabilizer generators, labeled $H_Z$ from green squares), then the number of logical qubits in the CSS code is:\\n\",\n \"$$k = n - r_X - r_Z$$\\n\",\n \"This formula holds because the X-stabilizers and Z-stabilizers are chosen such that they all commute with each other, and the total number of independent conditions imposed on the codespace is the sum of the independent X-type and Z-type conditions.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 6: Count the number of logicals for the toric and gross codes\\n\",\n \"\\n\",\n \"You will now use the `matrixRank` function to determine the number of logical qubits for both the toric code and the gross code you constructed in Exercises 4 and 5.\\n\",\n \"\\n\",\n \"1. Import the function: Start by importing the `matrixRank` function using the command:\\n\",\n \" `from lab4_util import matrixRank`\\n\",\n \" This function calculates the rank of a binary matrix (here `HXtc`, `HZtc`, `HXgc` and `HZgc`), which is exactly what we need for our parity check matrices.\\n\",\n \"\\n\",\n \"2. Calculate Ranks:\\n\",\n \" * For the toric code, use `matrixRank` to find the rank of $H_X$ (the matrix of X-stabilizers you generated from red squares in Exercise 4) – this will give you $r_{X, \\\\text{toric}}$.\\n\",\n \" * Similarly, find the rank of $H_Z$ (the matrix of Z-stabilizers from green squares in Exercise 4) – this will give you $r_{Z, \\\\text{toric}}$.\\n\",\n \" * Repeat this process for the gross code matrices ($H_X$ and $H_Z$) you generated in Exercise 5 to find $r_{X, \\\\text{gross}}$ and $r_{Z, \\\\text{gross}}$.\\n\",\n \"\\n\",\n \"3. Calculate Logical Qubits ($k$):\\n\",\n \" * Using the formula $k = n - r_X - r_Z$ (where $n=144$ is the total number of data qubits), calculate the number of logical qubits for the toric code ($k_{\\\\text{toric}}$).\\n\",\n \" * Perform the same calculation for the gross code ($k_{\\\\text{gross}}$).\\n\",\n \"\\n\",\n \"**Grading**: Submit the calculated number of logical qubits ($k$) for both the toric code and the gross code (`k_toric` and `k_gross`). You can verify your toric code result against the known fact that it encodes $k=2$ logical qubits.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b991f749\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO : Task 6 ---\\n\",\n \"# k_toric と k_gross を計算するコードを書いてください\\n\",\n \"# ヒント: lab4_utils からインポートした matrixRank を使用できます\\n\",\n \"\\n\",\n \"\\n\",\n \"# toric code\\n\",\n \"rx_toric =\\n\",\n \"rz_toric=\\n\",\n \"k_toric = \\n\",\n \"\\n\",\n \"# gross code\\n\",\n \"rx_gross=\\n\",\n \"rz_gross=\\n\",\n \"k_gross =\\n\",\n \"# --- End of TODO ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"48f316d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 次のコードを実行して、課題を提出してください\\n\",\n \"grade_lab4_ex6(k_toric, k_gross)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"100d8c84\",\n \"metadata\": {},\n \"source\": [\n \"## 3.6 まとめ：接続性の力\\n\",\n \"\\n\",\n \"演習を通じて、量子誤り訂正における重要な原則が示されました。すなわち、長距離接続の導入のように、符号の基盤構造に戦略的な修正を加えることで、その性能を大きく向上させることができるということです。\\n\",\n \"\\n\",\n \"Toric 風および Gross 風符号の両者について、同一の $144$ 個のデータ量子ビット構成上にパリティ検査行列を構築しました。演習6における論理量子ビット数 ($k$) の計算は、こうした異なる接続性が $k_{\\\\text{Gross}}$ にどのような影響を与えるかを、標準的な $k_{\\\\text{toric}}$ と比較して明確に示しています。\\n\",\n \"\\n\",\n \"しかし、Gross 符号のより顕著な利点は、**符号距離 ($d$)** にあります。これは符号の誤り訂正能力を決定づける重要なパラメータです。本演習では詳細な距離の計算は対象外ですが、上記のコード例において、Toric 符号は距離6（周期性がデータ量子ビット数で6格子間隔あるため）に対し、Gross 符号は距離12になるのです！\\n\",\n \"\\n\",\n \"このように、特定の長距離接続を追加することで誤り訂正能力が大幅に向上することは、構造的な修正が符号性能に与える影響、特に誤りからの保護を高めるという点において、極めて重要であることを示しています。これは、高度な量子符号を設計する上で非常に価値ある戦略であることを示唆しています。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"50af37af\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 以下のコードを実行して、提出状況を確認してください\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3a1b6840\",\n \"metadata\": {},\n \"source\": [\n \"# 追加情報\\n\",\n \"\\n\",\n \"**作成:** Sophy Shin, Tomas Jochym-O'Connor, Sebastian Brandhofer\\n\",\n \"\\n\",\n \"**監修:** Blake Johnson, Patrick Rall, Olivia Lanes\\n\",\n \"\\n\",\n \"**校正:** Henry Zou, Abby Cross\\n\",\n \"\\n\",\n \"**翻訳:** Daiki Murata\\n\",\n \"\\n\",\n \"**Version:** 2.1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5104e576\",\n \"metadata\": {},\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.3\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "lab-4/lab4_util-ko.py",
"content": "import math\n\nimport numpy as np\nimport itertools\nfrom qiskit.quantum_info import Pauli, PauliList\nfrom typing import List, Tuple, Dict, Optional, Set, Union\nimport sys\nfrom numpy.linalg import matrix_rank\nfrom numpy.linalg import matrix_power as m_power\n\nfrom qiskit.quantum_info import Statevector\n\ndef bring_states():\n state_list= [0, 0, 0, 0, 0, 0,\n 0, -1/(2*np.sqrt(2))*1j, 1/(2*np.sqrt(2))*1j,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,\n 0, -1/(2*np.sqrt(2))*1j, 0,0, 0, 0,\n 0, 0, 1/(2*np.sqrt(2))*1j,0, 0, 0,\n 0, 0, 0,0, 0, 0, 0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, -1/(2*np.sqrt(2))*1j, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,\n 1/(2*np.sqrt(2))*1j, 0, 0,0, -1/(2*np.sqrt(2))*1j, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 1/(2*np.sqrt(2))*1j,0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0]\n State = Statevector(state_list)\n return State\n\n\ndef hamming_distance(s1: Union[str, List[int], Tuple[int]],\n s2: Union[str, List[int], Tuple[int]]):\n distance = 0\n for i in range(len(s1)):\n # 입력값이 string이면 문자를 변환합니다\n bit1 = int(s1[i]) if isinstance(s1, str) else s1[i]\n bit2 = int(s2[i]) if isinstance(s2, str) else s2[i]\n if bit1 != bit2:\n distance += 1\n return distance\n\n\ndef minimum_distance(code: List[Union[str, List[int], Tuple[int]]]) -> int:\n \"\"\"\n 주어진 코드에서의 최소 Hamming 거리를 구합니다.\n\n Args:\n code: 각 원소가 코드어인 list입니다.\n (0과 1로 이루어진 string, list, 또는 tuple).\n 모든 코드어가 같은 길이로 이루어져 있다고 가정합니다.\n \n\n Returns:\n 코드의 최소 Hamming 거리 d\n 만약 코드가 2개의 코드어보다 더 적게 가지고 있으면 float('inf')를 반환합니다.\n \"\"\"\n num_codewords = len(code)\n if num_codewords < 2:\n # 최소 거리가 잘 정의되지 않았거나 무한대입니다\n return float('inf')\n\n # 첫 번째와 같이 모든 코드어가 같은 길이를 가지고 있다고 가정합니다\n codeword_length = len(code[0])\n min_dist = codeword_length + 1 # 그 어떠한 가능한 거리보다 더 큰 값으로 초기화합니다\n\n for i in range(num_codewords):\n for j in range(i + 1, num_codewords):\n dist = hamming_distance(code[i], code[j])\n if dist < min_dist:\n min_dist = dist\n\n return min_dist\n\n\n# GF(2) 위에서 정의된 행렬의 rank\ndef matrixRank(mat):\n M=mat.copy() # mat는 np.array이어야 합니다\n m=len(M) # 행의 개수\n pivots={} # pivot row --> pivot column로 연결해주는 dictionary\n # 행 소거\n for row in range(m):\n pos = next((index for index,value in enumerate(M[row]) if value != 0), -1) # 모두 0이면 -1을 출력하나, 첫 번째 0이 아닌 원소의 위치를 찾습니다\n if pos>-1:\n for row2 in range(m):\n if row2!=row and M[row2][pos]==1:\n M[row2]=((M[row2]+M[row]) % 2)\n pivots[row]=pos\n return len(pivots)\n\n\ndef generate_stabilizer_plots(hx_matrix, hz_matrix):\n \n def get_qubit_coords(label):\n if not 0 <= label <= 143: return None\n if label < 72:\n col_idx, row_idx = divmod(label, 6)\n return (col_idx * 2, row_idx * 2)\n else:\n normalized_label = label - 72\n col_idx, row_idx = divmod(normalized_label, 6)\n return (col_idx * 2 + 1, row_idx * 2 + 1)\n\n def get_stabilizer_coords(index, stabilizer_type):\n if not 0 <= index <= 71: return None\n col_idx, row_idx = divmod(index, 6)\n if stabilizer_type.upper() == 'X':\n return (col_idx * 2, row_idx * 2 + 1)\n elif stabilizer_type.upper() == 'Z':\n return (col_idx * 2 + 1, row_idx * 2)\n else:\n return None\n \n\n stabilizersZ_to_show = [\n {'index': 5, 'type': 'Z'},\n {'index': 66, 'type': 'Z'},\n ]\n\n stabilizersX_to_show = [\n {'index': 5, 'type': 'X'},\n {'index': 66, 'type': 'X'},\n ]\n\n def _create_plot(matrix, stabilizers, title):\n WIDTH, HEIGHT = 24, 12\n NEUTRAL_COLOR, GRID_COLOR = '#d3d3d3', '#e0e0e0'\n HIGHLIGHT_COLORS = ['gold', 'cyan', 'magenta', 'lime']\n \n fig, ax = plt.subplots(figsize=(18, 9))\n ax.set_aspect('equal')\n\n\n for j in range(HEIGHT):\n ax.plot([-0.5, WIDTH - 0.5], [j, j], color=GRID_COLOR, linestyle=':', linewidth=1, zorder=1)\n for i in range(WIDTH):\n ax.plot([i, i], [-0.5, HEIGHT - 0.5], color=GRID_COLOR, linestyle=':', linewidth=1, zorder=1)\n for i in range(WIDTH):\n for j in range(HEIGHT):\n if (i % 2) == (j % 2):\n ax.scatter(i, j, s=50, c=NEUTRAL_COLOR, marker='o', zorder=2)\n \n\n for i, stab_info in enumerate(stabilizers):\n stabilizer_index = stab_info['index']\n stabilizer_type = stab_info['type'].upper()\n color = HIGHLIGHT_COLORS[i % len(HIGHLIGHT_COLORS)] \n \n stab_coords = get_stabilizer_coords(stabilizer_index, stabilizer_type)\n if stab_coords:\n sx, sy = stab_coords\n ax.scatter(sx, sy, s=250, c=color, marker='o', zorder=3, label=f\"Stabilizer {stabilizer_type}{stabilizer_index}\")\n \n user_row = matrix[stabilizer_index]\n connected_qubit_labels = np.where(user_row == 1)[0]\n for label in connected_qubit_labels:\n coords = get_qubit_coords(label)\n if coords:\n cx, cy = coords\n ax.scatter(cx, cy, s=300, c=color, marker='o', zorder=4, alpha=0.9)\n \n ax.set_xlim(-1.5, WIDTH); ax.set_ylim(-1.5, HEIGHT)\n ax.set_xticks([]); ax.set_yticks([])\n ax.spines['top'].set_visible(False); ax.spines['right'].set_visible(False)\n ax.spines['bottom'].set_visible(False); ax.spines['left'].set_visible(False)\n fig.suptitle(title, fontsize=16)\n ax.legend()\n plt.show()\n \n ax.set_xlim(-1.5, WIDTH); ax.set_ylim(-1.5, HEIGHT)\n ax.set_xticks([]); ax.set_yticks([])\n ax.spines['top'].set_visible(False); ax.spines['right'].set_visible(False)\n ax.spines['bottom'].set_visible(False); ax.spines['left'].set_visible(False)\n fig.suptitle(title, fontsize=16)\n ax.legend()\n plt.show()\n\n _create_plot(hx_matrix, stabilizersX_to_show, \"X-Stabilizer Check - 5, 66\")\n _create_plot(hz_matrix, stabilizersZ_to_show, \"Z-Stabilizer Check - 5, 66\")\n"
},
{
"path": "lab-4/lab4_util.py",
"content": "import math\n\nimport numpy as np\nimport itertools\nfrom qiskit.quantum_info import Pauli, PauliList\nfrom typing import List, Tuple, Dict, Optional, Set, Union\nimport sys\nfrom numpy.linalg import matrix_rank\nfrom numpy.linalg import matrix_power as m_power\n\nfrom qiskit.quantum_info import Statevector\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef bring_states():\n state_list= [0, 0, 0, 0, 0, 0,\n 0, -1/(2*np.sqrt(2))*1j, 1/(2*np.sqrt(2))*1j,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,\n 0, -1/(2*np.sqrt(2))*1j, 0,0, 0, 0,\n 0, 0, 1/(2*np.sqrt(2))*1j,0, 0, 0,\n 0, 0, 0,0, 0, 0, 0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, -1/(2*np.sqrt(2))*1j, 0,0, 0, 0,\n 0, 0, 0,0, 0, 0,\n 1/(2*np.sqrt(2))*1j, 0, 0,0, -1/(2*np.sqrt(2))*1j, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 1/(2*np.sqrt(2))*1j,0, 0, 0,\n 0, 0, 0,0, 0, 0,0, 0, 0,\n 0, 0, 0,0, 0]\n State = Statevector(state_list)\n return State\n\n\ndef hamming_distance(s1: Union[str, List[int], Tuple[int]],\n s2: Union[str, List[int], Tuple[int]]):\n distance = 0\n for i in range(len(s1)):\n # Convert characters to integers if input is string\n bit1 = int(s1[i]) if isinstance(s1, str) else s1[i]\n bit2 = int(s2[i]) if isinstance(s2, str) else s2[i]\n if bit1 != bit2:\n distance += 1\n return distance\n\n\ndef minimum_distance(code: List[Union[str, List[int], Tuple[int]]]) -> int:\n \"\"\"\n Calculates the minimum Hamming distance for a given code.\n\n Args:\n code: A list where each element is a codeword\n (string, list, or tuple of 0s and 1s).\n Assumes all codewords have the same length.\n\n Returns:\n The minimum Hamming distance (d) of the code.\n Returns float('inf') if the code has less than 2 codewords.\n \"\"\"\n num_codewords = len(code)\n if num_codewords < 2:\n # Minimum distance is not well-defined or is infinite\n return float('inf')\n\n # Assuming all codewords have the same length as the first one\n codeword_length = len(code[0])\n min_dist = codeword_length + 1 # Initialize with a value larger than any possible distance\n\n for i in range(num_codewords):\n for j in range(i + 1, num_codewords):\n dist = hamming_distance(code[i], code[j])\n if dist < min_dist:\n min_dist = dist\n\n return min_dist\n\n\n# matrix rank over GF(2) \ndef matrixRank(mat):\n M=mat.copy() # mat should be an np.array\n m=len(M) # number of rows\n pivots={} # dictionary mapping pivot row --> pivot column\n # row reduction\n for row in range(m):\n pos = next((index for index,value in enumerate(M[row]) if value != 0), -1) #finds position of first nonzero element (or -1 if all 0s)\n if pos>-1:\n for row2 in range(m):\n if row2!=row and M[row2][pos]==1:\n M[row2]=((M[row2]+M[row]) % 2)\n pivots[row]=pos\n return len(pivots)\n\n\n\n\ndef generate_stabilizer_plots(hx_matrix, hz_matrix):\n \n def get_qubit_coords(label):\n if not 0 <= label <= 143: return None\n if label < 72:\n col_idx, row_idx = divmod(label, 6)\n return (col_idx * 2, row_idx * 2)\n else:\n normalized_label = label - 72\n col_idx, row_idx = divmod(normalized_label, 6)\n return (col_idx * 2 + 1, row_idx * 2 + 1)\n\n def get_stabilizer_coords(index, stabilizer_type):\n if not 0 <= index <= 71: return None\n col_idx, row_idx = divmod(index, 6)\n if stabilizer_type.upper() == 'X':\n return (col_idx * 2, row_idx * 2 + 1)\n elif stabilizer_type.upper() == 'Z':\n return (col_idx * 2 + 1, row_idx * 2)\n else:\n return None\n \n\n stabilizersZ_to_show = [\n {'index': 5, 'type': 'Z'},\n {'index': 66, 'type': 'Z'},\n ]\n\n stabilizersX_to_show = [\n {'index': 5, 'type': 'X'},\n {'index': 66, 'type': 'X'},\n ]\n\n def _create_plot(matrix, stabilizers, title):\n WIDTH, HEIGHT = 24, 12\n NEUTRAL_COLOR, GRID_COLOR = '#d3d3d3', '#e0e0e0'\n HIGHLIGHT_COLORS = ['gold', 'cyan', 'magenta', 'lime']\n \n fig, ax = plt.subplots(figsize=(18, 9))\n ax.set_aspect('equal')\n\n\n for j in range(HEIGHT):\n ax.plot([-0.5, WIDTH - 0.5], [j, j], color=GRID_COLOR, linestyle=':', linewidth=1, zorder=1)\n for i in range(WIDTH):\n ax.plot([i, i], [-0.5, HEIGHT - 0.5], color=GRID_COLOR, linestyle=':', linewidth=1, zorder=1)\n for i in range(WIDTH):\n for j in range(HEIGHT):\n if (i % 2) == (j % 2):\n ax.scatter(i, j, s=50, c=NEUTRAL_COLOR, marker='o', zorder=2)\n \n\n for i, stab_info in enumerate(stabilizers):\n stabilizer_index = stab_info['index']\n stabilizer_type = stab_info['type'].upper()\n color = HIGHLIGHT_COLORS[i % len(HIGHLIGHT_COLORS)] \n \n stab_coords = get_stabilizer_coords(stabilizer_index, stabilizer_type)\n if stab_coords:\n sx, sy = stab_coords\n ax.scatter(sx, sy, s=250, c=color, marker='o', zorder=3, label=f\"Stabilizer {stabilizer_type}{stabilizer_index}\")\n \n user_row = matrix[stabilizer_index]\n connected_qubit_labels = np.where(user_row == 1)[0]\n for label in connected_qubit_labels:\n coords = get_qubit_coords(label)\n if coords:\n cx, cy = coords\n ax.scatter(cx, cy, s=300, c=color, marker='o', zorder=4, alpha=0.9)\n \n ax.set_xlim(-1.5, WIDTH); ax.set_ylim(-1.5, HEIGHT)\n ax.set_xticks([]); ax.set_yticks([])\n ax.spines['top'].set_visible(False); ax.spines['right'].set_visible(False)\n ax.spines['bottom'].set_visible(False); ax.spines['left'].set_visible(False)\n fig.suptitle(title, fontsize=16)\n ax.legend()\n plt.show()\n \n ax.set_xlim(-1.5, WIDTH); ax.set_ylim(-1.5, HEIGHT)\n ax.set_xticks([]); ax.set_yticks([])\n ax.spines['top'].set_visible(False); ax.spines['right'].set_visible(False)\n ax.spines['bottom'].set_visible(False); ax.spines['left'].set_visible(False)\n fig.suptitle(title, fontsize=16)\n ax.legend()\n plt.show()\n\n _create_plot(hx_matrix, stabilizersX_to_show, \"X-Stabilizer Check - 5, 66\")\n _create_plot(hz_matrix, stabilizersZ_to_show, \"Z-Stabilizer Check - 5, 66\")\n"
},
{
"path": "lecture_supplementary/FermiHubbard-Example.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"419cc750-f1d6-47a7-8d1a-8e5bb67a3311\",\n \"metadata\": {},\n \"source\": [\n \"# 1-D, 3-Site Hubbard Model\\n\",\n \"\\n\",\n \"The Hubbard Hamiltonian for a 1-D chain of sites is:\\n\",\n \"\\n\",\n \"\\\\begin{equation} H = -t\\\\sum_{,\\\\sigma}(\\\\hat{c}_{i_\\\\sigma}\\\\hat{c}_{j_\\\\sigma} + \\\\hat{c}_{j_\\\\sigma}^\\\\dagger\\\\hat{c}_{i_\\\\sigma} ) + U\\\\sum_i\\\\hat{c}_{i_\\\\uparrow}^{\\\\dagger}\\\\hat{c}_{i_\\\\uparrow}\\\\hat{c}_{i_\\\\downarrow}^{\\\\dagger}\\\\hat{c}_{i_\\\\downarrow}\\n\",\n \"\\\\end{equation}\\n\",\n \"and under the Jordan-Wigner mapping, this Hamiltonian becomes:\\n\",\n \"\\n\",\n \"\\\\begin{equation}\\n\",\n \"H = -\\\\frac{t}{2}\\\\sum_{}Z_{i+1:j-1}(X_{i}X_{j} + Y_{i}Y_{j}) + \\\\frac{U}{4}\\\\sum_{ij}(I-Z_{i})(I-Z_{j})\\n\",\n \"\\\\end{equation}\\n\",\n \"where $Z_{i}$, $X_{i}$, and $Y_{i}$ are the corresponding Pauli matrices acting on the $i^{th}$ fermionic mode and for a chain with only 3-sites, there are no Pauli $Z$ strings in the hopping term of the Hamiltonian for a one-dimensional chain.\\n\",\n \"\\n\",\n \"## 3-Site Hamiltonian and Qubit Mapping\\n\",\n \"\\n\",\n \"Writing out the sum over three sites, the Hamiltonian becomes:\\n\",\n \"\\n\",\n \"\\\\begin{align}\\n\",\n \" H = &-\\\\frac{t}{2}(X_0X_1 + Y_0Y_1) - \\\\frac{t}{2}(X_1X_2 + Y_1Y_2) \\\\nonumber \\\\\\\\ \\n\",\n \"&-\\\\frac{t}{2}(X_3X_4 + Y_3Y_4) - \\\\frac{t}{2}(X_4X_5 + Y_4Y_5) \\\\nonumber\\\\\\\\\\n\",\n \"&+ \\\\frac{U}{4}(I-Z_0)(I-Z_3)+ \\\\frac{U}{4}(I-Z_1)(I-Z_4) + \\\\frac{U}{4}(I-Z_2)(I-Z_5) \\\\nonumber\\\\\\\\\\n\",\n \"= &H_{01} + H_{12} + H_{23} + H_{34} + H_{03} + H_{14} + H_{25}\\n\",\n \"\\\\end{align}\\n\",\n \"\\n\",\n \"### Qubit Mapping\\n\",\n \"\\n\",\n \"Each site in a 3-site chain is represented by two qubits, one for each spin, and the wavefunction is represented as\\n\",\n \"\\n\",\n \"$$ \\\\ket{\\\\psi} = \\\\ket{q_0}\\\\ket{q_1}\\\\ket{q_2}\\\\ket{q_3}\\\\ket{q_4}\\\\ket{q_5} $$\\n\",\n \"\\n\",\n \"where $\\\\ket{q_i} = \\\\{ \\\\ket{0}, \\\\ket{1} \\\\} $ represent unoccupied or occupied sites, $i=0,1,2$ are the spin up electron occupations and $i=3,4,5$ are the spin down electron occupations.\\n\",\n \"\\n\",\n \"\\n\",\n \"## Time Evolution\\n\",\n \"\\n\",\n \"We want to simulate the time evolution of $\\\\ket{\\\\psi}$ via \\n\",\n \"\\n\",\n \"$$ \\\\ket{\\\\psi(t+\\\\Delta t)} = e^{-iH\\\\Delta t}\\\\ket{\\\\psi(t)} $$\\n\",\n \"\\n\",\n \"\\n\",\n \"We can do this via the Suzuki-Trotter formula which states that, to first order in $\\\\Delta t$\\n\",\n \"\\n\",\n \"$$e^{iH\\\\Delta t} \\\\approx e^{iH_{10}^{\\\\uparrow}\\\\Delta t}e^{iH_{12}^{\\\\uparrow}\\\\Delta t}e^{iH_{10}^{\\\\downarrow}\\\\Delta t}e^{iH_{12}^{\\\\downarrow}\\\\Delta t}e^{iH_0\\\\Delta t}e^{iH_1\\\\Delta t}e^{iH_2\\\\Delta t}.$$\\n\",\n \"\\n\",\n \"This is the time evolution operator which evolves the system by a small amount of time $\\\\Delta t$. To then evolve an initial state for some reasonable (let's say for $t = 0\\\\rightarrow 2\\\\pi$) we will need to apply this operator several times in the same circuit.\\n\",\n \"\\n\",\n \"\\n\",\n \"So what do the gates look like for each of these terms?\\n\",\n \"\\n\",\n \"#### Hopping Terms\\n\",\n \"\\n\",\n \"For each pair of hopping terms we have\\n\",\n \"$$e^{-i\\\\Delta t(\\\\frac{-t}{2})(X_iX_j + Y_iY_j)} \\\\approx e^{\\\\frac{it\\\\Delta t}{2}X_iX_j} e^{\\\\frac{it\\\\Delta t}{2}Y_iY_j}.$$\\n\",\n \"\\n\",\n \"Expanding the first term on the right hand side\\n\",\n \"$$\\n\",\n \"\\\\begin{align}\\n\",\n \"e^{\\\\frac{it\\\\Delta t}{2}X_iX_j} = & \\\\sum_{k=0}^{\\\\infty} \\\\frac{1}{k!}\\\\left(\\\\frac{it\\\\Delta t}{2}X_iX_j\\\\right)^k \\\\nonumber \\\\\\\\\\n\",\n \" =& \\\\sum_{k, even}\\\\frac{i^k}{k!}\\\\left( \\\\frac{t\\\\Delta t}{2} \\\\right)^k I + \\\\sum_{k, odd}\\\\frac{i^k}{k!}\\\\left( \\\\frac{t\\\\Delta t}{2} \\\\right)X_i X_j \\\\nonumber \\\\\\\\ \\n\",\n \" = &\\\\cos\\\\left(\\\\frac{t\\\\Delta t}{2}\\\\right)I + i\\\\sin\\\\left( \\\\frac{t\\\\Delta t}{2}\\\\right)X_i X_j \\\\nonumber \\\\\\\\\\n\",\n \" = &\\\\begin{pmatrix}\\\\cos\\\\theta & 0 & 0 & i\\\\sin\\\\theta \\\\\\\\ 0 & \\\\cos\\\\theta & i\\\\sin\\\\theta & 0 \\\\\\\\ 0 & i\\\\sin\\\\theta & \\\\cos\\\\theta & 0 \\\\\\\\ i\\\\sin\\\\theta & 0 & 0 & \\\\cos\\\\theta\\\\end{pmatrix},\\n\",\n \"\\\\end{align} $$\\n\",\n \"with $\\\\theta=\\\\frac{t\\\\Delta t}{2}$, and written in the $\\\\ket{q_{i}q_{j}}$ basis.\\n\",\n \"\\n\",\n \"Similarly for the $Y_i Y_j$ terms\\n\",\n \"\\n\",\n \"\\\\begin{align}\\n\",\n \" e^{i\\\\frac{t\\\\Delta t}{2}Y_i Y_j} =& \\\\cos\\\\left(\\\\frac{t\\\\Delta t}{2}\\\\right)I + i\\\\sin\\\\left(\\\\frac{t\\\\Delta t}{2}\\\\right)Y_i Y_j \\\\nonumber \\\\\\\\\\n\",\n \"= & \\\\begin{pmatrix}\\\\cos\\\\theta & 0 & 0 & -i\\\\sin\\\\theta \\\\\\\\ 0 & \\\\cos\\\\theta & i\\\\sin\\\\theta & 0 \\\\\\\\ 0 & i\\\\sin\\\\theta & \\\\cos\\\\theta & 0 \\\\\\\\ -i\\\\sin\\\\theta & 0 & 0 & \\\\cos\\\\theta\\\\end{pmatrix}.\\n\",\n \"\\\\end{align}\\n\",\n \"\\n\",\n \"\\n\",\n \"Note also that these matrices are diagonal save for the 4x4 block corresponding to a gate acting on qubits $i$ and $j$.\\n\",\n \"\\n\",\n \"### On-Site Terms\\n\",\n \"\\n\",\n \"\\n\",\n \"Now we'll expand the on-site term, $e^{i\\\\frac{U\\\\Delta t}{4}(I-Z_i)(I-Z_j)}$. First we examine the powers of $(I-Z_i)(I-Z_j)$:\\n\",\n \"\\n\",\n \"\\n\",\n \"$$ \\n\",\n \"\\\\begin{align} (I-Z_i)^2(I-Z_j)^2 &= (I + I - 2Z_i)(I+I-2Z_j) = 4(I-Z_i)(I-Z_j) \\\\\\\\\\n\",\n \"(I-Z_i)^3(I-Z_j)^3& = (I-Z_i)(I-Z_j)(I-Z_i)^2(I-Z_j)^2 \\\\nonumber \\\\\\\\\\n\",\n \"&= 4(I-Z_i)^2(I-Z_j)^2 = 16(I-Z_i)(I-Z_j)\\\\\\\\\\n\",\n \"(I-Z_i)^4(I-Z_j)^4& = 16(I-Z_i)^2(I-Z_j)^2 = 4^3(I-Z_i)(I-Z_j)\\\\\\\\\\n\",\n \"\\\\implies (I-Z_i)^k(I-Z_j)^k& = 4^{k-1}(I-Z_i)(I-Z_j), \\\\end{align} $$\\n\",\n \"\\n\",\n \"then writing out the expansion of $e^{i\\\\frac{U\\\\Delta t}{4}(I-Z_i)(I-Z_j)}$ we get\\n\",\n \"\\n\",\n \"\\\\begin{align} e^{i\\\\frac{U\\\\Delta t}{4}(I-Z_i)(I-Z_j)} &= \\\\sum_k \\\\frac{1}{k!}\\\\left(\\\\frac{i\\\\Delta tU}{4}\\\\right)^k(I-Z_i)^k(I-Z_j)^k \\\\\\\\\\n\",\n \"&= I+(I-Z_i)(I-Z_j)\\\\sum_k \\\\frac{\\\\left(i\\\\Delta tU\\\\right)^k}{k!}\\\\frac{4^{k-1}}{4^k} - \\\\frac{1}{4}(I-Z_i)(I-Z_j) \\\\\\\\\\n\",\n \"& = I-\\\\frac{1}{4}(I-Z_i)(I-Z_j) + \\\\frac{1}{4}e^{iU\\\\Delta t}(I-Z_i)(I-Z_j) \\\\\\\\\\n\",\n \"& \\\\boxed{= I-(I-Z_i)(I-Z_j)\\\\left(1-e^{iU\\\\Delta t} \\\\right) } \\\\\\\\\\n\",\n \"&= \\\\begin{pmatrix}1 & 0 & 0 & 0\\\\\\\\ 0 & 1& 0 & 0 \\\\\\\\ 0 & 0 & 1 & 0 \\\\\\\\ 0 & 0 & 0 & e^{iU\\\\Delta t} \\\\end{pmatrix} .\\n\",\n \"\\\\end{align}\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"4d35a2f1-1217-42ca-9b7c-7a5eabac52a2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\n\",\n \"from qiskit.transpiler import generate_preset_pass_manager\\n\",\n \"from qiskit.quantum_info import SparsePauliOp\\n\",\n \"from qiskit.circuit import Parameter\\n\",\n \"from qiskit.circuit.library import evolved_operator_ansatz\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2, EstimatorV2, Batch\\n\",\n \"from qiskit_ibm_runtime.fake_provider import FakeManilaV2, FakeMelbourneV2\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.colors as mcolors\\n\",\n \"\\n\",\n \"\\n\",\n \"from qiskit.primitives import StatevectorSampler\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3f83a968-e805-4b1e-b63e-616f09f96c51\",\n \"metadata\": {},\n \"source\": [\n \"### Initial Functions\\n\",\n \"\\n\",\n \"The code below creates two functions, `construct_hamiltonian()` creates the Hamiltonian in terms of `SparsePauliOp` objects and `construct_evolved_circuit()` returns a circuit which executes the approximated time evolution operator on an initial state.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"c2d143ea-4a76-4038-b3d8-94afe8a6dad1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Construct the Hubbard Hamiltonian after JW transform\\n\",\n \"def construct_hamiltonian(hopping, repulsion, num_sites):\\n\",\n \" num_orbitals = 2*num_sites\\n\",\n \" hopping_operators = []\\n\",\n \" repulsion_operators = []\\n\",\n \" for site in range(0, num_sites-1):\\n\",\n \" hopping_operators.append(hopping*SparsePauliOp.from_sparse_list(\\n\",\n \" [(\\\"XX\\\", [site, site+1], 1),\\n\",\n \" (\\\"YY\\\", [site, site+1], 1),\\n\",\n \" (\\\"XX\\\", [num_sites+site, num_sites+site+1], 1),\\n\",\n \" (\\\"YY\\\", [num_sites+site, num_sites+site+1], 1)], \\n\",\n \" num_qubits=num_orbitals))\\n\",\n \" \\n\",\n \" number_op_up = SparsePauliOp.from_sparse_list([(\\\"I\\\", [site], 1)], num_qubits=num_orbitals) - SparsePauliOp.from_sparse_list([(\\\"Z\\\", [site], 1)], num_qubits=num_orbitals)\\n\",\n \" number_op_down = SparsePauliOp.from_sparse_list([(\\\"I\\\", [site+num_sites], 1)], num_qubits=num_orbitals) - SparsePauliOp.from_sparse_list([(\\\"Z\\\", [site+num_sites], 1)], num_qubits=num_orbitals)\\n\",\n \" repulsion_operators.append(repulsion*(number_op_up @ number_op_down))\\n\",\n \" hamiltonian = sum(hopping_operators) + sum(repulsion_operators)\\n\",\n \" return hamiltonian\\n\",\n \"\\n\",\n \"# Construct a circuit which evolves a fixed initial state in time\\n\",\n \"def construct_evolved_circuit(hamiltonian, dt, num_trotter_steps, num_time_steps):\\n\",\n \" timestep_circuit = evolved_operator_ansatz(hamiltonian, reps=num_trotter_steps)\\n\",\n \" evolved_state = QuantumCircuit(timestep_circuit.num_qubits)\\n\",\n \" \\n\",\n \" # Initial state\\n\",\n \" evolved_state.x(0)\\n\",\n \" \\n\",\n \" for step in range(num_time_steps):\\n\",\n \" evolved_state.compose(timestep_circuit, inplace=True)\\n\",\n \" return evolved_state\\n\",\n \" \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cba5aba7-50e3-41ac-ac10-c27d83f16d71\",\n \"metadata\": {},\n \"source\": [\n \"### System Parameters\\n\",\n \"The cell below defines our system parameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"3012360c-98d4-4680-b813-126946b3dc1b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"SparsePauliOp(['IIIIIIIIXX', 'IIIIIIIIYY', 'IIIXXIIIII', 'IIIYYIIIII', 'IIIIIIIXXI', 'IIIIIIIYYI', 'IIXXIIIIII', 'IIYYIIIIII', 'IIIIIIXXII', 'IIIIIIYYII', 'IXXIIIIIII', 'IYYIIIIIII', 'IIIIIXXIII', 'IIIIIYYIII', 'XXIIIIIIII', 'YYIIIIIIII', 'IIIIIIIIII', 'IIIIZIIIII', 'IIIIIIIIIZ', 'IIIIZIIIIZ', 'IIIIIIIIII', 'IIIZIIIIII', 'IIIIIIIIZI', 'IIIZIIIIZI', 'IIIIIIIIII', 'IIZIIIIIII', 'IIIIIIIZII', 'IIZIIIIZII', 'IIIIIIIIII', 'IZIIIIIIII', 'IIIIIIZIII', 'IZIIIIZIII'],\\n\",\n \" coeffs=[ 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\\n\",\n \" 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\\n\",\n \" 2.+0.j, -2.+0.j, -2.+0.j, 2.+0.j, 2.+0.j, -2.+0.j, -2.+0.j, 2.+0.j,\\n\",\n \" 2.+0.j, -2.+0.j, -2.+0.j, 2.+0.j, 2.+0.j, -2.+0.j, -2.+0.j, 2.+0.j])\\n\"\n ]\n }\n ],\n \"source\": [\n \"hopping_energy = 1.0\\n\",\n \"repulsion_energy = 2.0\\n\",\n \"num_sites = 5\\n\",\n \"num_orbitals = num_sites*2\\n\",\n \"dt = 0.2\\n\",\n \"time_steps = 30\\n\",\n \"trotter_steps = 5\\n\",\n \"\\n\",\n \"hamiltonian = construct_hamiltonian(hopping_energy, repulsion_energy, num_sites)\\n\",\n \"print(hamiltonian)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"23a48b16-98ea-4ee1-847c-bc87aab2a891\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Here is what the circuit looks like for a single application of our approximated time evolution operator\\n\",\n \"evolution_circuit = construct_evolved_circuit(hamiltonian, dt, trotter_steps,1)\\n\",\n \"evolution_circuit.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"27cf8a56-b5e7-4782-81ca-5dabd8a03913\",\n \"metadata\": {},\n \"source\": [\n \"## Construct circuits and execute\\n\",\n \"\\n\",\n \"The first cell below constructs `time_steps` number time evolution circuits, each of which evolves the initial state by `dt * n` times. (If we were running this on a real QPU this would also be where we transpile the circuits.)\\n\",\n \"\\n\",\n \"The second cell will execute all of these circuits and store the results in a list called `jobs`. For this example we just use the `StatevectorSampler()`, but you can play around with executing this on a real QPU. Be mindful these circuits end up being quite deep and you will need to apply error mitigation in order to refine the noise (doing so also requires a lot QPU time).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"fe1988cd-332b-4cfb-a149-abc4361e2d02\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Construct transpiled circuits\\n\",\n \"circuits = []\\n\",\n \"backend = FakeMelbourneV2()\\n\",\n \"service = QiskitRuntimeService()\\n\",\n \"#backend = service.least_busy(min_num_qubits=num_orbitals, use_fractional_gates=True)\\n\",\n \"pm = generate_preset_pass_manager(optimization_level=3, backend=backend)\\n\",\n \"for step in range(time_steps):\\n\",\n \" evolved_state = construct_evolved_circuit(hamiltonian, dt, trotter_steps, step)\\n\",\n \" evolved_state.measure_all()\\n\",\n \" circuits.append(evolved_state)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"61da54b9-d771-47c0-bc51-65e49760d1a1\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Running job: de4a2562-285c-4f52-b13a-1ceb6b61af11\\n\",\n \"Running job: ad1c1fbe-827b-412f-b8ea-9b0edf997134\\n\",\n \"Running job: 6f8e6854-587e-4e5a-bc3b-d8e0d87d7f45\\n\",\n \"Running job: 00569f79-fd81-4992-b21c-d7fe65b064bd\\n\",\n \"Running job: 4381b387-f9ed-4869-8111-5b0f52836f95\\n\",\n \"Running job: 73308956-ac66-48fb-b62a-dea0754b6f6e\\n\",\n \"Running job: 52c1ec1c-96ae-4c3e-b437-f0a521da0d7e\\n\",\n \"Running job: ecc54cc0-cd2a-4533-a2a3-580ab805d369\\n\",\n \"Running job: 4fa83ef5-1cd2-4e86-a97d-8e299b8afb7f\\n\",\n \"Running job: 5124487b-7f2d-4c6e-96d1-0c1d60822d7f\\n\",\n \"Running job: c8d7dc46-0c58-41ed-94d2-33e93611c99f\\n\",\n \"Running job: 8e14940d-8efc-4059-87c5-27556aba5fe2\\n\",\n \"Running job: 59f4a2d2-8df5-4341-8efc-b37bb749ee94\\n\",\n \"Running job: aec7a21d-5d7d-4449-98be-5bcecf73fb05\\n\",\n \"Running job: 38c2ed1c-fa52-4290-a8fc-1cb0a31ca84e\\n\",\n \"Running job: a243c4dc-e87c-4ddb-9af0-295c28112750\\n\",\n \"Running job: 2aec82fb-c4b6-43aa-84bf-35e4599f827c\\n\",\n \"Running job: 7fd1bc6a-3214-4e01-b981-c40d11830892\\n\",\n \"Running job: bb358f2b-ca9d-482e-aa77-d6e4685db286\\n\",\n \"Running job: 7d217557-e223-4ab1-9c7d-b8040fc84a6f\\n\",\n \"Running job: 726e4b32-df33-4e80-9afe-103ff67a2fdf\\n\",\n \"Running job: aa3fad45-14a2-486c-9883-7096d08b6901\\n\",\n \"Running job: 8289f31c-2f3b-4fc2-b46e-3cf3d2cebc04\\n\",\n \"Running job: 8ccd6adc-01f3-46b5-809f-8ba1aacae0ab\\n\",\n \"Running job: 7ebab2f5-1f1b-442a-9b0d-54a2fe942013\\n\",\n \"Running job: 379d283c-80ca-406d-be2f-31e8bb82d0bd\\n\",\n \"Running job: 95bbcf1f-89d0-4c5e-865e-94eb5112027e\\n\",\n \"Running job: edaede9b-c6b2-4bf2-b8f8-6a602a416264\\n\",\n \"Running job: 4a04a080-56ae-442f-8bb4-c5c21467f05a\\n\",\n \"Running job: db065f52-fe00-4df6-9030-1d8ff541e296\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Execute circuits on backend\\n\",\n \"shots = 2048\\n\",\n \"jobs = []\\n\",\n \"# Execute circuits all at once\\n\",\n \"with Batch(backend):\\n\",\n \" #sampler = SamplerV2(mode=backend)\\n\",\n \" sampler = StatevectorSampler()\\n\",\n \" for circuit in circuits:\\n\",\n \" job = sampler.run([(circuit, [dt/(2*trotter_steps)]*len(circuit.parameters))], shots=shots)\\n\",\n \" jobs.append(job)\\n\",\n \" print(f'Running job: {job.job_id()}')\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"cd8efea4-71c7-4875-84fe-6720ba9888af\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Grab the result data\\n\",\n \"all_counts = []\\n\",\n \"for job in jobs:\\n\",\n \" counts = job.result()[0].data.meas.get_counts()\\n\",\n \" all_counts.append(counts)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"33bda7cc-e593-421b-b43d-b465271a3668\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'0000000100': 724,\\n\",\n \" '0000000001': 858,\\n\",\n \" '0000000010': 349,\\n\",\n \" '0000010000': 116,\\n\",\n \" '0000001000': 1}\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"job.result()[0].data.meas.get_counts()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f6e2048-f738-452a-8a08-c0b95c4b0834\",\n \"metadata\": {},\n \"source\": [\n \"## Post Process\\n\",\n \"\\n\",\n \"Here we go through the results and create a list of occupation probability for each site. The loop includes a post-selection of count data to throw out unphysical results, which would be needed if we were getting data back from a noisy QPU.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"23202fce-9c2f-4ce1-8f01-a611ae7275b8\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Post process the results\\n\",\n \"def post_process_results(all_count_data, num_modes):\\n\",\n \" keys = []\\n\",\n \" occupancy_time_evolution = {}\\n\",\n \" for orbital in range(1, num_modes+1):\\n\",\n \" key = '0'*num_modes\\n\",\n \" key = '0'*(orbital-1) + '1' + '0'*(num_modes-orbital)\\n\",\n \" occupancy_time_evolution[key] = []\\n\",\n \" keys.append(key)\\n\",\n \" \\n\",\n \" for count_data in all_count_data:\\n\",\n \" post_selected_counts = {}\\n\",\n \" # Grab post selected count data\\n\",\n \" norm = 0\\n\",\n \" for key, val in count_data.items():\\n\",\n \" if key.count('1') == 1:\\n\",\n \" post_selected_counts[key] = val\\n\",\n \" norm+=val\\n\",\n \" \\n\",\n \" # Check for no measurements of state\\n\",\n \" for key in keys:\\n\",\n \" if key not in post_selected_counts.keys():\\n\",\n \" occupancy_time_evolution[key].append(0)\\n\",\n \" # Compute probabilities on post-selected counts\\n\",\n \" for key, val in post_selected_counts.items():\\n\",\n \" occupancy_time_evolution[key].append(val/norm)\\n\",\n \" \\n\",\n \" for key, val in occupancy_time_evolution.items():\\n\",\n \" if len(val) == 0:\\n\",\n \" occupancy_time_evolution[key] = np.zeros(time_steps)\\n\",\n \" \\n\",\n \" return occupancy_time_evolution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"1ab38578-9514-4f0b-acd1-f48c0b0679ef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"occupancy_data = post_process_results(all_counts, num_orbitals)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"101ff1b3-0d50-4097-b001-40ed8da9ac0b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Create plots of the processed data\\n\",\n \"fig2, ax2 = plt.subplots(figsize=(20,10))\\n\",\n \"colors = list(mcolors.TABLEAU_COLORS.keys())\\n\",\n \"times = np.arange(0.,time_steps*dt, dt)\\n\",\n \"\\n\",\n \"\\n\",\n \"for i, key in enumerate(occupancy_data.keys()):\\n\",\n \" ax2.plot(times, occupancy_data[key], marker='^', color=str(colors[i]), label=f'{key}')\\n\",\n \"\\n\",\n \"ax2.set_xlim(0, time_steps*dt+dt/2.)\\n\",\n \"ax2.tick_params(labelsize=16)\\n\",\n \"ax2.set_title('Time Evolution of One Dimensional Chain', fontsize=22)\\n\",\n \"ax2.set_xlabel('Time', fontsize=24)\\n\",\n \"ax2.set_ylabel('Probability', fontsize=24)\\n\",\n \"ax2.legend(fontsize=20)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"9a59f3ee-9a15-4b2b-9e78-861bf48fc971\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Time Steps: 30 Step Size: 0.2\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"\\n\",\n \"#Plot the raw data as a colormap\\n\",\n \"xticks = np.arange((num_sites*2))\\n\",\n \"xlabels = occupancy_data.keys()\\n\",\n \"print(\\\"Time Steps: \\\",time_steps, \\\" Step Size: \\\",dt)\\n\",\n \"\\n\",\n \"state_data = []\\n\",\n \"for label in xlabels:\\n\",\n \" state_data.append(occupancy_data[label])\\n\",\n \"\\n\",\n \"state_data = np.array(state_data)\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"fig, ax = plt.subplots(figsize=(20,10))\\n\",\n \"c = ax.pcolor(state_data, cmap='Blues')\\n\",\n \"ax.set_title('Time Evolution of 3 Site One Dimensional Chain', fontsize=22)\\n\",\n \"plt.yticks(xticks, xlabels, size=18)\\n\",\n \"ax.axhline(y=5, color='r', linewidth=2)\\n\",\n \"ax.set_xlabel('Time Step', fontsize=22)\\n\",\n \"ax.set_ylabel('State', fontsize=26)\\n\",\n \"plt.show()\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"59b19d5f-f0e8-411f-8052-9ace64dd854f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "solutions/lab-0/lab0-solution.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"551a947d\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 0: Hello Quantum World!\\n\",\n \"\\n\",\n \"# Table of Contents\\n\",\n \"\\n\",\n \"* [Welcome to the Qiskit Global Summer School 2025!](#welcome)\\n\",\n \" - [Lab 0 overview](#overview)\\n\",\n \" - [Install Qiskit](#install)\\n\",\n \" - [Troubleshooting](#troubleshooting)\\n\",\n \" - [Setting API token](#setting-ibm-cloud)\\n\",\n \" - [Imports](#imports)\\n\",\n \" - [Sanity check](#sanity-check)\\n\",\n \"* [Generate a three-qubit GHZ state using Qiskit patterns](#ghz) \\n\",\n \" - [Step 1. Map](#map)\\n\",\n \" - [Step 2. Optimize](#optimize)\\n\",\n \" - [Step 3. Execute](#execute)\\n\",\n \" - [Step 4. Post-process](#post-process)\\n\",\n \"* [Congratulations!](#congratulations)\\n\",\n \" - [Bonus challenge: Run GHZ on hardware](#bonus)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2144e6fe\",\n \"metadata\": {},\n \"source\": [\n \"# Welcome to the Qiskit Global Summer School 2025! \\n\",\n \"\\n\",\n \"We are thrilled to welcome you to another year of the Qiskit Global Summer School (QGSS). This two-week summer program combines theoretical lectures with hands-on exercises to expand the knowledge of quantum computing and quantum programming among the community of students, researchers, and professionals that use Qiskit in their everyday quantum journey. \\n\",\n \"\\n\",\n \"The hands-on component of this summer school consists of a series of Jupyter notebooks (\\\"labs\\\") designed to guide you through different topics of interest.\\n\",\n \"\\n\",\n \"Each lab complements the corresponding theoretical lectures and includes helpful links to documentation, tutorials, and references to the lectures. Furthermore, you can also find many useful resources in [IBM Quantum Learning](https://quantum.cloud.ibm.com/learning).\\n\",\n \"\\n\",\n \"## Lab 0 overview \\n\",\n \"\\n\",\n \"The main goal of this introductory lab is to show you how to use the challenge notebooks, grade your solutions, and verify that your computer is correctly set up for executing the quantum codes that you will be asked to complete.\\n\",\n \"\\n\",\n \"To complete the different labs of the QGSS, you need to know that every notebook will contain some predefined cells that you should not modify, and some challenge code blocks that you will need to fill with your own code. In particular, you will need to write the required code below each line that has the `### WRITE YOUR CODE BELOW HERE ###` comment. Make sure that every time you restart the kernel, you execute every cell in order, so that the challenge notebooks execute and are graded properly. This will guarantee that all the code is updated and everything runs smoothly. There may be a few exceptions, such as installation cells or cells where you save your account credentials. These will likely only need to be run once.\\n\",\n \"\\n\",\n \"The content of this lab focuses on using Qiskit patterns to produce a GHZ state, emphasizing the importance of designing optimized quantum circuits. At the end is a bonus exercise where you can execute your code on a real quantum computer.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b3df2ea\",\n \"metadata\": {},\n \"source\": [\n \"## Install Qiskit \\n\",\n \"Quantum computers stand as a technology that promises to disrupt how some calculations are done - from breaking encryption with Shor's algorithm, to enabling faster searches with Grover's algorithm, to designing better batteries with quantum phase estimation. A first step is choosing a software tool for designing and executing such algorithms. In these challenges you will use Qiskit for the design and implementation of quantum circuits in simulators and real hardware. The following will help you install Qiskit v2.0.\\n\",\n \"\\n\",\n \"First, check that the version of Python you are using in your environment is python>=3.10, to make sure that it is compatible with the latest Qiskit version we will use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a35543d4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Needed for grading now after summer school before other code is run.\\n\",\n \"%set_env QC_GRADING_ENDPOINT=https://qac-grading.quantum.ibm.com\\n\",\n \"%set_env QC_GRADE_ONLY=true\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4313f227\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from platform import python_version\\n\",\n \"\\n\",\n \"print(python_version())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47d097d3\",\n \"metadata\": {},\n \"source\": [\n \"If that is not the case, you can upgrade it using your preferred tool. If you are unsure how to do it, some recommended options are:\\n\",\n \"\\n\",\n \"- MacOS: [Homebrew](https://brew.sh/)\\n\",\n \"- Linux: `sudo apt-get update `\\n\",\n \"\\n\",\n \"A detailed guide on how to upgrade Python depending on your OS is detailed here: [How to update Python](https://4geeks.com/how-to/how-to-update-python-version)\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** You cannot run Lab 3 of the QGSS using Windows. Hence, if you are using Windows, we recommend you use [an online lab environment.](https://docs.quantum.ibm.com/guides/online-lab-environments) \\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"For more information take a look at the wiki: https://github.com/qiskit-community/qgss-2025/wiki/Jupyter-Notebook-Environment-(Local-and-Online)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74f76ade\",\n \"metadata\": {},\n \"source\": [\n \"Now let's install the grader that will install Qiskit 2.x and the necessary libraries we will need for the summer school.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b89c3cd1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1b216728\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d9f8ae4\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.9`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c34209af\",\n \"metadata\": {},\n \"source\": [\n \"## Troubleshooting \\n\",\n \"\\n\",\n \"If the previous cell raised an error, you can opt to install Qiskit in a virtual environment (two suggested methods follow). If you have no errors, you can ignore this cell and proceed to the next one.\\n\",\n \"\\n\",\n \"Here we propose two different methods to set up a virtual environment to install Qiskit.\\n\",\n \"1. Using [venv](https://docs.python.org/3/library/venv.html), as explained in the [Qiskit installation guide](https://docs.quantum.ibm.com/guides/install-qiskit). \\n\",\n \"2. Using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html), as explained in this video of [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e8e89851\",\n \"metadata\": {},\n \"source\": [\n \"## Set up your IBM Cloud account \\n\",\n \"\\n\",\n \"You must set up an IBM Cloud account in order to track progress with the grader, and to execute quantum circuits on real hardware.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03041cd7\",\n \"metadata\": {},\n \"source\": [\n \"Set up your account as follows:\\n\",\n \"\\n\",\n \"1. Go to the [upgraded IBM Quantum® Platform](https://quantum.cloud.ibm.com/).\\n\",\n \"2. Go to the top right corner (as shown in the above picture), create your API token, and copy it to a secure location.\\n\",\n \"3. In the next cell, replace `deleteThisAndPasteYourAPIKeyHere` with your API key.\\n\",\n \"4. Go to the bottom left corner (as shown in the above picture) and **create your instance**. Make sure to choose the open plan.\\n\",\n \"5. After the instance is created, copy its associated CRN code. You may need to refresh to see the instance.\\n\",\n \"6. In the cell below, replace `deleteThisAndPasteYourCRNHere` with your CRN code.\\n\",\n \"\\n\",\n \"See [this guide](https://quantum.cloud.ibm.com/docs/guides/cloud-setup) for more details on how to set up your IBM Cloud® account.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** Treat your API key as you would a secure password. See the [Cloud setup](https://quantum.cloud.ibm.com/docs/guides/cloud-setup#cloud-save) guide for more information about using your API key in both secure and untrusted environments.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"52cfa1be\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Save your API key to track your progress and have access to the quantum computers\\n\",\n \"\\n\",\n \"your_api_key = \\\"deleteThisAndPasteYourAPIKeyHere\\\"\\n\",\n \"your_crn = \\\"deleteThisAndPasteYourCRNHere\\\"\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"QiskitRuntimeService.save_account(\\n\",\n \" channel=\\\"ibm_quantum_platform\\\",\\n\",\n \" token=your_api_key,\\n\",\n \" instance=your_crn,\\n\",\n \" name=\\\"qgss-2025\\\",\\n\",\n \" overwrite=True,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"92766751\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c94c720c\",\n \"metadata\": {},\n \"source\": [\n \"## Imports \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3952087b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit, generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.quantum_info import SparsePauliOp\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler, EstimatorV2 as Estimator\\n\",\n \"\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_lab0_ex1, grade_lab0_ex2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"282b4699\",\n \"metadata\": {},\n \"source\": [\n \"## Sanity check \\n\",\n \"\\n\",\n \"Let's now create a very simple quantum circuit to check that everything is working as expected.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"840ba2c5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a new circuit with a single qubit\\n\",\n \"qc = QuantumCircuit(2)\\n\",\n \"# Add a H gate to qubit 0\\n\",\n \"qc.h(0)\\n\",\n \"# Add a CNOT gate to qubit 1\\n\",\n \"qc.cx(0, 1)\\n\",\n \"# Return a drawing of the circuit using MatPlotLib (\\\"mpl\\\").\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed117c17\",\n \"metadata\": {},\n \"source\": [\n \"The output you see represents a quantum circuit that produces a Bell state:\\n\",\n \"\\n\",\n \"$$|Bell\\\\rangle=\\\\frac{|00\\\\rangle+|11\\\\rangle}{\\\\sqrt{2}}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf861730\",\n \"metadata\": {},\n \"source\": [\n \"# Generate a three-qubit GHZ state using Qiskit patterns \\n\",\n \"\\n\",\n \"Now, we will follow this episode of [Coding with Qiskit](https://www.youtube.com/watch?v=93-zLTppFZw&list=PLOFEBzvs-VvrgHZt3exM_NNiNKtZlHvZi&index=4) to guide you through the process of generating a three-qubit GHZ state using [Qiskit patterns](https://quantum.cloud.ibm.com/docs/en/guides/intro-to-patterns). \\n\",\n \"\\n\",\n \"A Qiskit pattern is a general framework for breaking down domain-specific problems and contextualizing required capabilities in stages. This allows for the seamless composability of new capabilities developed by IBM Quantum researchers (and others) and enables a future in which quantum computing tasks are performed by powerful heterogenous (CPU/GPU/QPU) computing infrastructure. \\n\",\n \"\\n\",\n \"The four steps of a Qiskit pattern are as follows:\\n\",\n \"\\n\",\n \"1. **Map** problem to quantum circuits and operators\\n\",\n \"2. **Optimize** for target hardware\\n\",\n \"3. **Execute** on target hardware\\n\",\n \"4. **Post-process** results\\n\",\n \"\\n\",\n \"\\n\",\n \"## Step 1. Map \\n\",\n \"\\n\",\n \"The Greenberger–Horne–Zeilinger (GHZ) state is the extension to three (or more) qubits to the maximally entangled state characteristic of the Bell state depicted above. That means that the GHZ state is:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"|GHZ\\\\rangle = \\\\frac{|000\\\\rangle+|111\\\\rangle}{\\\\sqrt{2}}.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"One of the interesting features of the GHZ state is that there are different and equivalent ways to build it using a quantum circuit. In Exercise 1 you will do it in one of the most common ways.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ca23b8d3\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1: Design a GHZ state \\n\",\n \"\\n\",\n \"This is your first exercise of the Summer School! Exciting, huh?\\n\",\n \"\\n\",\n \"In this exercise, you will design a GHZ state following the steps below:\\n\",\n \"\\n\",\n \"1. Apply a Hadamard gate to qubit 0, putting it into a superposition. \\n\",\n \"2. Apply a CNOT gate between qubits 0 and 1.\\n\",\n \"3. Apply a CNOT gate between qubits 1 and 2.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1c389cbe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a new circuit with three qubits\\n\",\n \"qc = QuantumCircuit(3)\\n\",\n \"\\n\",\n \"### WRITE YOUR CODE BELOW HERE ###\\n\",\n \"# Add a H gate to qubit 0\\n\",\n \"qc.h(0)\\n\",\n \"# Add a CNOT gate to qubits 0 and 1\\n\",\n \"qc.cx(0, 1)\\n\",\n \"# Add a CNOT gate to qubits 1 and 2\\n\",\n \"qc.cx(1, 2)\\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"\\n\",\n \"# Return a drawing of the circuit using MatPlotLib (\\\"mpl\\\").\\n\",\n \"qc.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a29c6fe7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab0_ex1(qc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"66a3ed14\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2. Optimize \\n\",\n \"\\n\",\n \"Well done designing the circuit!\\n\",\n \"\\n\",\n \"In this case, the circuit is very shallow, and it is not possible to further simplify it or reduce the required number of gates that are needed to build the GHZ state. However, there are other scenarios in which optimizing the circuit is key.\\n\",\n \"\\n\",\n \"There may be situations where you face restrictions in how your quantum circuit can be designed. For example, when running circuits on quantum hardware, you might find connectivity constraints between qubits. This means that some qubits may not be physically connected, so you will need to think of a smart way to implement gates that produce the desired quantum state. Luckily for us, this is where the Qiskit pass manager comes to the rescue! You can provide the desired circuit along with the device's constraints to the pass manager and it will transpile the circuit and handle the optimization for you.\\n\",\n \"\\n\",\n \"Let us consider, for the GHZ state, a situation in which we are limited to interactions only between qubits 0 and 1 and qubits 0 and 2, but not between qubits 1 and 2. We can introduce these constraints to the paass manager using the `generate_preset_pass_manager` function.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"38782ae6\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2: Transpile a GHZ state \\n\",\n \"\\n\",\n \"In this second exercise you are asked to transpile the previous GHZ state with the mentioned connectivity constraints:\\n\",\n \"\\n\",\n \"- Qubit 0 <---> Qubit 1\\n\",\n \"- Qubit 0 <---> Qubit 2\\n\",\n \"- ~~Qubit 1 <---> Qubit 2~~\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"222b26f8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### WRITE YOUR CODE BELOW HERE ###\\n\",\n \"# Write the coupling map of connections between qubits 0 and 1 and 0 and 2 as a list of pairs: [[0,1],...]\\n\",\n \"coupling_map = [[0, 1], [1, 0], [0, 2], [2, 0]]\\n\",\n \"# Transpile the quantum circuit `qc` using the `generate_preset_pass_manager` function and the `coupling_map` list\\n\",\n \"pm = generate_preset_pass_manager(coupling_map=coupling_map)\\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"qc_transpiled = pm.run(qc)\\n\",\n \"\\n\",\n \"qc_transpiled.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5d860929\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab0_ex2(qc_transpiled)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e2aa24a5\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3. Execute \\n\",\n \"\\n\",\n \"The next step is exciting - we are going to run the quantum circuit using Qiskit Runtime! \\n\",\n \"\\n\",\n \"We will do that using the two [Qiskit primitives](https://quantum.cloud.ibm.com/docs/guides/primitives):\\n\",\n \"1. **Sampler** samples the output register from the execution of one or more quantum circuits. Its output is counts on per-shot measurements. \\n\",\n \"2. **Estimator** computes the expectation value of one or more observables with respect to the states generated by the quantum circuit. Its output consist of the expectation values along with their standard errors.\\n\",\n \"\\n\",\n \"First, we execute our circuit using the Sampler, then save the results as the variable `results_sampler`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"345c5084\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Add measurement operations\\n\",\n \"qc.measure_all()\\n\",\n \"\\n\",\n \"# Set up the backend\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"# Set up the sampler\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"\\n\",\n \"# Submit the circuit to Sampler\\n\",\n \"pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\\n\",\n \"job = sampler.run(pm.run([qc]))\\n\",\n \"\\n\",\n \"# Get the results\\n\",\n \"results_sampler = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1a8e7991\",\n \"metadata\": {},\n \"source\": [\n \"Now, we run our circuit using the Estimator primitive, then save the results as the variable `results_estimator`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f2354c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set up the Estimator\\n\",\n \"estimator = Estimator(mode=backend)\\n\",\n \"\\n\",\n \"# Define some observables\\n\",\n \"ZZZ = SparsePauliOp(\\\"ZZZ\\\")\\n\",\n \"ZZX = SparsePauliOp(\\\"ZZX\\\")\\n\",\n \"ZII = SparsePauliOp(\\\"ZII\\\")\\n\",\n \"XXI = SparsePauliOp(\\\"XXI\\\")\\n\",\n \"ZZI = SparsePauliOp(\\\"ZZI\\\")\\n\",\n \"III = SparsePauliOp(\\\"III\\\")\\n\",\n \"observables = [ZZZ, ZZX, ZII, XXI, ZZI, III]\\n\",\n \"\\n\",\n \"# Submit the circuit to Estimator\\n\",\n \"pub = (qc, observables)\\n\",\n \"job = estimator.run(pubs=[pub])\\n\",\n \"\\n\",\n \"# Get the results\\n\",\n \"results_estimator = job.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"08df17b9\",\n \"metadata\": {},\n \"source\": [\n \"Next is the final step of Qiskit patterns, where we will visualize our results.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03ccb1ce\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4. Post-process \\n\",\n \"\\n\",\n \"Finally, the last step of Qiskit patterns is to post-process the information we have gathered from the execution of the quantum circuit.\\n\",\n \"\\n\",\n \"First we visualize the results from the Sampler. We can visualize the counts with a histogram plot and quickly see how the two possible quantum states are measured with a probability of 50%.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2cb9ba4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"counts_list = results_sampler[0].data.meas.get_counts()\\n\",\n \"print(f\\\" Outcomes : {counts_list}\\\")\\n\",\n \"display(plot_histogram(counts_list, title=\\\"GHZ state\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86748b30\",\n \"metadata\": {},\n \"source\": [\n \"Now we can visualize the results of the Estimator.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"983608f7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"exp_values = results_estimator[0].data.evs\\n\",\n \"observables_list = [\\\"ZZZ\\\", \\\"ZZX\\\", \\\"ZII\\\", \\\"XXI\\\", \\\"ZZI\\\", \\\"III\\\"]\\n\",\n \"print(f\\\"Expectation values: {list(zip(observables_list, exp_values))}\\\")\\n\",\n \"\\n\",\n \"# Set up our plot\\n\",\n \"container = plt.bar(observables_list, exp_values, width=0.8)\\n\",\n \"# Label each axis\\n\",\n \"plt.xlabel(\\\"Observables\\\")\\n\",\n \"plt.ylabel(\\\"Values\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e5b49810\",\n \"metadata\": {},\n \"source\": [\n \"We see that the observables $ZZI$ and $III$ have an expectation value of 1, since $ZZI$ introduces two minus signs that cancel out, and $III$ acts as the identity, leaving the GHZ state unchanged. The rest of the observables have an expectation value of 0, since their $Z$ operators introduce an odd number of minus signs, or the $X$ operators flip a number of qubits that make the overlapping states orthogonal.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e905aea1\",\n \"metadata\": {},\n \"source\": [\n \"# Congratulations! \\n\",\n \"\\n\",\n \"You have successfully completed Lab 0 and are now set to start the Quantum Global Summer School 2025!\\n\",\n \"\\n\",\n \"In this lab you have successfully set up your machine to execute Qiskit v2.x, and saved your IBM Cloud token to track your progress during the Summer School and execute quantum circuits in real hardware. You have also learned about Qiskit patterns by implementing a specific example of the GHZ circuit and designing an optimized quantum circuit. Finally, you have executed a GHZ circuit on a quantum simulator, and observed how the measurements of the Sampler are in good agreement with the theoretical probabilities of measuring the states $|000\\\\rangle$ and $|111\\\\rangle$ with 50% probability.\\n\",\n \"\\n\",\n \"You can check your progress with the labs using the cell below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"677398c7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"83692cb4\",\n \"metadata\": {},\n \"source\": [\n \"As a bonus exercise, it would be interesting to perform the same experiment on real hardware to see how the noise affects the outcomes, then compare the results to show that we will not have a perfect agreement between the theoretical probabilities and the outcomes. \\n\",\n \"\\n\",\n \"Are you ready? Let's dig in!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c5e04fb1\",\n \"metadata\": {},\n \"source\": [\n \"## Bonus challenge: Run GHZ on hardware \\n\",\n \"\\n\",\n \"To execute a quantum circuit on a quantum computer using Qiskit, we first need to define the quantum backend. We could manually select a specific quantum computer we want to use out of the ones available from IBM Quantum. However, sometimes it is more convenient to select the machine that is least busy at the moment to ensure a fast execution. That's where the method `least_busy` is helpful.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ef101f81\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define the service. This allows you to access IBM QPUs.\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"\\n\",\n \"# Get a backend\\n\",\n \"backend = service.least_busy(operational=True, simulator=False)\\n\",\n \"print(f\\\"We are using the {backend.name} quantum computer\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b94e6ec7\",\n \"metadata\": {},\n \"source\": [\n \"The next call illustrates how easy it is to execute quantum circuits on hardware with `QiskitRuntimeService`. Once we have selected the backend in the cell above, we can simply copy and paste the same lines of code that we wrote for the Sampler simulator, including post-processing and visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c50b3aa1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### WRITE YOUR CODE BELOW HERE ###\\n\",\n \"# Step 1. Map\\n\",\n \"# You should have created a GHZ circuit above and assigned with variable `qc`\\n\",\n \"\\n\",\n \"# Step 2. Optimize\\n\",\n \"pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b90f52a1\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Warning: Queue time and 10 minute limit\\n\",\n \"\\n\",\n \"This will roughly take 10s of calculation time on the real hardware, however, running this on the real hardware can lead to long queue times and will take a while, \\n\",\n \"and will block the jupyter notebook in the meantime. \\n\",\n \"\\n\",\n \"Please note that the open plan only includes 10 minutes time on the real hardware, and since we will need these minutes in the later labs, please make sure to save most of your time for these labs. \\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2ba1eb48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 3. Execute\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"job = sampler.run(pm.run([qc]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fb229e5e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Step 4. Post-process\\n\",\n \"results = job.result()\\n\",\n \"counts_list = results[0].data.meas.get_counts()\\n\",\n \"### YOUR CODE FINISHES HERE ###\\n\",\n \"\\n\",\n \"print(f\\\"Outcomes : {counts_list}\\\")\\n\",\n \"plot_histogram(counts_list, title=\\\"GHZ state\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d830731e\",\n \"metadata\": {},\n \"source\": [\n \"Awesome! \\n\",\n \"\\n\",\n \"You have managed to run a circuit on a real quantum computer, and the results are very good! The most repeated states are $|000\\\\rangle$ and $|111\\\\rangle$, and they accumulate a probability just below 50%. However, in this case we observe that, due to noise from the quantum computer, some quantum states are measured, even if the theoretical probability of measuring is 0. This is indeed expected, and we will see in the next labs how we can try to correct or mitigate the errors that are introduced by the noisy nature of quantum computers.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bafe6e0d\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Jorge Martínez de Lejarza\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Junye Huang\\n\",\n \"\\n\",\n \"**Version:** 1.0.0\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda3.13.2\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "solutions/lab-1/lab1-solution.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4b5d9b98-ac5b-4121-915a-893bd64960f3\",\n \"metadata\": {},\n \"source\": [\n \"# 0. Setup: Gathering Our Tools\\n\",\n \"\\n\",\n \"Just like any good experiment, we need to prepare our equipment. In this case, it's the Python libraries we'll use, especially Qiskit, our quantum computing toolkit. You should be all set if you finished lab 0. If not, you can uncomment the follow cell to install the grader which will install Qiskit 2.x and the necessary libraries we will need for the summer school.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"128f4014\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Needed for grading now after summer school before other code is run.\\n\",\n \"%set_env QC_GRADING_ENDPOINT=https://qac-grading.quantum.ibm.com\\n\",\n \"%set_env QC_GRADE_ONLY=true\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"519fb94e-18f2-4ade-a40f-58c1078424e5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0e8bb120\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3fee413b\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.0`. If you see a lower version, you need to restart your kernel and reinstall the grader again.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"51102304\",\n \"metadata\": {},\n \"source\": [\n \"## Imports\\n\",\n \"\\n\",\n \"The cell below imports these necessary libraries and prints your Qiskit version.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8ae038b9-2181-4c10-8018-833b52ff17a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Essential libraries\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import ipywidgets as widgets\\n\",\n \"from IPython.display import display\\n\",\n \"from PIL import Image\\n\",\n \"import io\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.circuit import Parameter\\n\",\n \"from qiskit.visualization import plot_histogram, plot_distribution\\n\",\n \"from qiskit_ibm_runtime import Options, Session, SamplerV2 as Sampler\\n\",\n \"from qiskit.result import marginal_distribution\\n\",\n \"\\n\",\n \"from qiskit.transpiler import generate_preset_pass_manager\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab1_ex1_1, \\n\",\n \" grade_lab1_ex1_2, \\n\",\n \" grade_lab1_ex1_3, \\n\",\n \" grade_lab1_ex1_4, \\n\",\n \" grade_lab1_ex2, \\n\",\n \" grade_lab1_ex3,\\n\",\n \" grade_lab1_ex4,\\n\",\n \" grade_lab1_ex5,\\n\",\n \" grade_lab1_ex6\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"39f36676-bdd1-4ba2-8486-2b4796766eea\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Table of Contents\\n\",\n \"\\n\",\n \"1. Superposition, Interference and Measurement\\n\",\n \" 1. Double Slit Experiment - Superposition, Interference\\n\",\n \" 2. Schrödinger’s Cat and Double Slit Revisit\\n\",\n \"2. Entanglement\\n\",\n \" 1. The Curious Case of Alice, Bob, and the Unbeatable Game - CHSH Game\\n\",\n \" 2. Quantum Teleportation - Sending Secrets with Spooky Action\\n\",\n \" 1. Teleportation on a Simulator\\n\",\n \"3. (Bonus Challenge) Teleportation on Real Quantum Hardware\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d36937e1-9cc0-4c1b-8240-f87504825196\",\n \"metadata\": {},\n \"source\": [\n \"Welcome to the very first QGSS Lab of 2025, the International Year of Quantum! In the first lab, we’ll explore four of the most important concepts of the quantum world—superposition, interference, measurement, and entanglement—through hands-on experiments using Qiskit.\\n\",\n \"\\n\",\n \"# Chapter 1: Superposition, Interference and Measurement\\n\",\n \"\\n\",\n \"To kick off your amazing journey with the coolest start, we’ll begin by diving into superposition, interference, and quantum measurement through the famous double slit experiment. **Superposition** means a quantum particle—like an electron or a photon—can exist in multiple states at once, like being in two places or having two energies at the same time. It's like flipping a coin and having it be heads and tails at the same time... until you look! **Interference** describes what happens when these quantum possibilities, behaving like waves, overlap. If they are in phase, they reinforce each other (constructive interference), and if out of phase, they can weaken or cancel each other out (destructive interference), a key effect seen in the double-slit experiment.\\n\",\n \"\\n\",\n \"One of the most famous experiments showing this is the **double slit experiment**, first done by Thomas Young in 1801 and later repeated with individual particles like electrons. When no one watches which slit the particle goes through, you get a beautiful interference pattern, like waves overlapping. But the moment you **measure** which slit it goes through, the pattern disappears. It’s as if the particle knows it’s being watched!\\n\",\n \"\\n\",\n \"This strange idea was taken to the next level by Erwin Schrödinger in 1935 with the famous thought experiment known as **Schrödinger's Cat**, where a cat can be both alive and dead—until someone opens the box to check. Measurement in quantum mechanics doesn’t just observe reality, it shapes it.\\n\",\n \"\\n\",\n \"Ready to experience quantum weirdness yourself? With Qiskit, you can do the double slit experiment—with and without measurement—and see how superposition and interference create beautiful patterns and measurement affects quantum systems. It’s a fun and eye-opening way to explore the mysteries of the quantum world!\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"309c6569-6875-447e-a514-cc049e7a37f0\",\n \"metadata\": {},\n \"source\": [\n \"## Through the Slits: Where Quantum Weirdness Begins\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\\n\",\n \" image from wikipedia\\n\",\n \"
\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3d9deeff-2450-4ead-b78a-5533addb84a7\",\n \"metadata\": {},\n \"source\": [\n \"In his paper \\\"Experiments and calculations relative to physical optics\\\" 1803, Thomas Young explains about his experiments like below:\\n\",\n \"> I made a small hole in a window-shutter, and covered it with a piece of thick paper, which I perforated with a fine needle. For greater convenience of observation, I placed a small looking glass without the window-shutter, in such a positioned as to reflect the sun's light, in a direction nearly horizontal, upon the opposite wall, and to cause the cone of diverging light to pass over a table, on which were several little screens of card-paper. [1](https://royalsocietypublishing.org/doi/pdf/10.1098/rstl.1804.0001)\\n\",\n \"\\n\",\n \"With this experiment, he could observe beautiful fringe patterns by light and explained this comes from the differences in the lengths of the paths of two rays. George Paget Thomson later reproduced a similar experiment using electrons (electron diffraction experiment), and showed that even particles like electrons exhibit wave-like behavior, as demonstrated in his 1927 paper. [2](https://royalsocietypublishing.org/doi/10.1098/rspa.1927.0130) Later in 1965, Feynman explained the double-slit experiment in his \\\"Lectures on Physics, Vol. 3, Chapter 1\\\", and explained this comes from the superposition and interference of quantum mechanics.\\n\",\n \"\\n\",\n \"Now we can reproduce this fascinating quantum phenomenon using Qiskit, without complex physical equipment. Let's see how superposition and interference are implemented in quantum circuits.\\n\",\n \"\\n\",\n \"First step is mapping the physical double slit experiment to a quantum circuit, and this is your first exercise.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f79b538f-82c1-4297-a550-4360e3ab0082\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 1: Build a quantum circuit for the double slit experiment\\n\",\n \"\\n\",\n \"- Exercise 1-1: Make a slit\\n\",\n \"\\n\",\n \"**Your Goal:** Create a quantum circuit that models a particle (qubit) passing through two slits, achieving a superposition of being in the $|0\\\\rangle$ (lower slit) and $|1\\\\rangle$ (upper slit) states.\\n\",\n \"\\n\",\n \"Let's define the upper slit as the \\n\",\n \"$|1\\\\rangle$ state of a qubit and the lower slit as the $|0\\\\rangle$ state. Implement a quantum circuit where a qubit, initially in $|0\\\\rangle$, passes through the slits in a superposition of $|0\\\\rangle$ and $|1\\\\rangle$ using the [Hadamard gate](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.HGate). Draw your circuit.\\n\",\n \"\\n\",\n \"**Hint**: Assigning specific names to `QuantumRegister` (e.g., `name='q'`) and `ClassicalRegister` (e.g., `name='c_screen'`) is beneficial for clarity when interpreting measurement data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"884333a6-3489-4520-912c-99d337eb00d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\n\",\n \"\\n\",\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit = QuantumCircuit(qr, cr)\\n\",\n \"# your code here\\n\",\n \"\\n\",\n \"double_slit.h(0)\\n\",\n \"\\n\",\n \"# end of your code\\n\",\n \"double_slit.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f5d3c41c-05fc-46ff-a41e-f7de68e96275\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab1_ex1_1(double_slit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e92915b5-7396-4d04-b0da-05b84dd7e8c3\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 1: Build a quantum circuit for the double slit experiment\\n\",\n \"\\n\",\n \"- Exercise 1-2: Make a screen\\n\",\n \"\\n\",\n \"**Your Goal:** Extend the previous circuit to model the screen where interference occurs. Specifically, implement the center of the screen where no phase difference is introduced, and then measure the qubit.\\n\",\n \"\\n\",\n \"Now, implement the screen where the two superposed quantum states create an interference pattern by adding a gate. First, let's implement the center of the screen, where the two beams combine with no phase difference (resulting in the $|0\\\\rangle$ state). Then measure the qubit from `qr` and store the result in `c_screen`.\\n\",\n \"\\n\",\n \"**Hint**: You can use the Hadamard gate again.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1828c3c0-7bb6-451d-891a-89f56835c6bc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# your code here\\n\",\n \"double_slit.h(0)\\n\",\n \"double_slit.measure(qr, cr)\\n\",\n \"# end of your code\\n\",\n \"double_slit.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1fcf565a-b547-43fb-8492-c327a9ff7a10\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab1_ex1_2(double_slit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88cc5027-9435-4eaf-a3c3-0e391cc997d3\",\n \"metadata\": {},\n \"source\": [\n \"Let's check the execution result. Assuming the screen detects the qubit in the $|0\\\\rangle$ state, the following code runs the circuit on an ideal simulator. A probability of 1 means 100%.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Note on Retrieving Measurement Results with SamplerV2 \\n\",\n \"\\n\",\n \"When using *SamplerV2* in Qiskit, how you retrieve measurement counts depends on how you've set up your classical registers and measurements:\\n\",\n \"\\n\",\n \"1. Named Classical Register in *QuantumCircuit*:\\n\",\n \" If you define your circuit with a named classical register, e.g., *cr = ClassicalRegister(1, name='my_results')* and *qc = QuantumCircuit(qr, cr)*, and then measure to it *qc.measure(qr[0], cr[0])*, you access the counts using that name:\\n\",\n \"\\n\",\n \" \\n\",\n \" ```python\\n\",\n \" # result = job.result()\\n\",\n \" # counts = result[0].data.my_results.get_counts()\\n\",\n \" ```\\n\",\n \" \\n\",\n \" In our double_slit circuit, we used *cr = ClassicalRegister(1, name='c_screen')*, so we will use *result[0].data.c_screen.get_counts()*.\\n\",\n \"\\n\",\n \"3. measure_all():\\n\",\n \" If you use *qc.measure_all()*, Qiskit automatically adds classical bits and names the output data field \\\"meas\\\":\\n\",\n \"\\n\",\n \" \\n\",\n \" ```python\\n\",\n \" # qc.measure_all()\\n\",\n \" # ...\\n\",\n \" # counts = result[0].data.meas.get_counts()\\n\",\n \" ```\\n\",\n \"\\n\",\n \"5. Implicit Classical Bits (No Named Register in *QuantumCircuit*):\\n\",\n \" If you create a circuit like *qc = QuantumCircuit(1, 1)* (1 qubit, 1 classical bit) or *qc = QuantumCircuit(QuantumRegister(1), ClassicalRegister(1))* without naming the classical register in its constructor, and then use *qc.measure(0, 0)*, the *SamplerV2* might store results under a default name (often *c*, or based on the classical bit indices like *c0*).\\n\",\n \" For *qc = QuantumCircuit(1,1)* and *qc.measure(0,0)*, the output might be accessed via:\\n\",\n \"\\n\",\n \" ```python \\n\",\n \" # counts = result[0].data.c0.get_counts() # If single bit named c0, the indice can vary. You can see the actual indices when you plot your circuit.\\n\",\n \" ```\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d9d6d49e-19bc-482c-a3e4-049a986323f1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# use simulator\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"# make quantum circuit compatible to the backend\\n\",\n \"pm = generate_preset_pass_manager(backend = backend, optimization_level=3)\\n\",\n \"qc_isa = pm.run(double_slit)\\n\",\n \"\\n\",\n \"# run and get counts\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots = 1000).result()[0].data.c_screen.get_counts()\\n\",\n \"\\n\",\n \"plot_distribution(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9d9d8b9d-6db1-47b0-a7b3-46d78a0c51cb\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 1: Build a quantum circuit for the double slit experiment\\n\",\n \"\\n\",\n \"- Exercise 1-3: Make a difference\\n\",\n \"\\n\",\n \"**Your Goal:** Modify the double-slit circuit to introduce a phase difference between the two paths (slits) and observe its effect on the measurement outcome at the screen.\\n\",\n \"\\n\",\n \"Now, let's implement another part of the screen. The path difference between the two beams is implemented as a phase difference between $|0\\\\rangle$ and $|1\\\\rangle$. Apply a phase of $\\\\pi/2$ only to the $|1\\\\rangle$ state of the superposed quantum state and observe the measurement results. (Hint: Use a gate that applies a phase to the $|1\\\\rangle$ state. The [P gate](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.PhaseGate) and the [S gate](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.SGate) are representative examples.)\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"50a651fe-236a-4c64-ad11-f00200c1cedf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit_with_difference = QuantumCircuit(qr, cr)\\n\",\n \"double_slit_with_difference.h(0)\\n\",\n \"\\n\",\n \"#your code here\\n\",\n \"\\n\",\n \"double_slit_with_difference.p(np.pi/2, 0)\\n\",\n \"#double_slit_with_difference.s(0)\\n\",\n \"\\n\",\n \"#end of your code\\n\",\n \"\\n\",\n \"double_slit_with_difference.h(0)\\n\",\n \"double_slit_with_difference.measure(qr, cr)\\n\",\n \"double_slit_with_difference.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"810339b8-ab70-4aa8-b89c-220e8a7521f5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab1_ex1_3(double_slit_with_difference)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c4e217cf-065f-45ee-8d7c-3e8c94f7fede\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qc_isa = pm.run(double_slit_with_difference)\\n\",\n \"\\n\",\n \"#run and get counts\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots=10000).result()[0].data.c_screen.get_counts()\\n\",\n \"plot_distribution(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4a08020a-6caa-4415-bec0-b5e4f53471ad\",\n \"metadata\": {},\n \"source\": [\n \"As you can see, the probability of measuring $|0\\\\rangle$ decreased to approximately 0.5 (50%), signifying reduced brightness at this point on the screen.\\n\",\n \" \\n\",\n \"\\n\",\n \"Before go further let's do a simple mathematics of this process in aspect of superposition, interference and the probability of measure quantum state. You can skip this part, if you are already familiar to this concept.\\n\",\n \"\\n\",\n \"\\n\",\n \"
Superposition, Interference and the Probability of Measurement
\\n\",\n \"\\n\",\n \"\\n\",\n \"In our quantum circuit implementation of the double-slit experiment, we used a sequence of Hadamard (H) gates and a phase gate (P). Let's break down the mathematical representation of the quantum state at each step and analyze the superposition, interference, and measurement probability.\\n\",\n \"\\n\",\n \"**1. Initialization:**\\n\",\n \"\\n\",\n \"We start with a single qubit initialized in the $|0\\\\rangle$ state, representing the electron starting at a source before the slits:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_0\\\\rangle = |0\\\\rangle = \\\\begin{pmatrix} 1 \\\\\\\\ 0 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"Here we will assume $ |\\\\psi_1\\\\rangle$ is another quantum state that can pass upper slit and this will be:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_1\\\\rangle = |1\\\\rangle = \\\\begin{pmatrix} 0 \\\\\\\\ 1 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"**2. Superposition (First Hadamard Gate):**\\n\",\n \"\\n\",\n \"The first Hadamard gate (H) is applied to the qubit. This operation creates a superposition of the $|0\\\\rangle$ and $|1\\\\rangle$ states, representing the electron passing through both slits simultaneously:\\n\",\n \"\\n\",\n \"$$ H = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"Applying H to $|\\\\psi_0\\\\rangle$:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_1\\\\rangle = H |\\\\psi_0\\\\rangle = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} \\\\begin{pmatrix} 1 \\\\\\\\ 0 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ 1 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} (|0\\\\rangle + |1\\\\rangle) $$\\n\",\n \"\\n\",\n \"This state $|\\\\psi_1\\\\rangle$ shows the qubit is in an equal superposition of the $|0\\\\rangle$ state (representing one slit) and the $|1\\\\rangle$ state (representing the other slit).\\n\",\n \"\\n\",\n \"**3. Phase Shift (P Gate):**\\n\",\n \"\\n\",\n \"The phase gate (P) introduces a relative phase $\\\\phi$ between the $|0\\\\rangle$ and $|1\\\\rangle$ components of the superposition. This phase difference corresponds to the path difference experienced by the electron waves passing through the two slits in the physical experiment. The P gate is defined as:\\n\",\n \"\\n\",\n \"$$ P(\\\\phi) = \\\\begin{pmatrix} 1 & 0 \\\\\\\\ 0 & e^{i\\\\phi} \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"Applying P($\\\\phi$) to $|\\\\psi_1\\\\rangle$:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_2\\\\rangle = P(\\\\phi) |\\\\psi_1\\\\rangle = \\\\begin{pmatrix} 1 & 0 \\\\\\\\ 0 & e^{i\\\\phi} \\\\end{pmatrix} \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ 1 \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ e^{i\\\\phi} \\\\end{pmatrix} = \\\\frac{1}{\\\\sqrt{2}} (|0\\\\rangle + e^{i\\\\phi}|1\\\\rangle) $$\\n\",\n \"\\n\",\n \"The state $|\\\\psi_2\\\\rangle$ now contains the relative phase information, which is crucial for interference.\\n\",\n \"\\n\",\n \"**4. Interference (Second Hadamard Gate):**\\n\",\n \"\\n\",\n \"The second Hadamard gate is applied to bring the superposed states back together, allowing them to interfere:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_3\\\\rangle = H |\\\\psi_2\\\\rangle = \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 & 1 \\\\\\\\ 1 & -1 \\\\end{pmatrix} \\\\frac{1}{\\\\sqrt{2}} \\\\begin{pmatrix} 1 \\\\\\\\ e^{i\\\\phi} \\\\end{pmatrix} = \\\\frac{1}{2} \\\\begin{pmatrix} 1 + e^{i\\\\phi} \\\\\\\\ 1 - e^{i\\\\phi} \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"Expanding $e^{i\\\\phi} = \\\\cos(\\\\phi) + i\\\\sin(\\\\phi)$:\\n\",\n \"\\n\",\n \"$$ |\\\\psi_3\\\\rangle = \\\\frac{1}{2} \\\\begin{pmatrix} 1 + \\\\cos(\\\\phi) + i\\\\sin(\\\\phi) \\\\\\\\ 1 - (\\\\cos(\\\\phi) + i\\\\sin(\\\\phi)) \\\\end{pmatrix} = \\\\frac{1}{2} \\\\begin{pmatrix} (1 + \\\\cos(\\\\phi)) + i\\\\sin(\\\\phi) \\\\\\\\ (1 - \\\\cos(\\\\phi)) - i\\\\sin(\\\\phi) \\\\end{pmatrix} $$\\n\",\n \"\\n\",\n \"This final state before measurement embodies the interference effect, where the probabilities of measuring $|0\\\\rangle$ or $|1\\\\rangle$ depend on the phase difference $\\\\phi$.\\n\",\n \"\\n\",\n \"**5. Probability of Measurement:**\\n\",\n \"\\n\",\n \"The probability of measuring the qubit in the $|0\\\\rangle$ state (corresponding to one part of the screen) is given by the squared magnitude of the amplitude of $|0\\\\rangle$ in $|\\\\psi_3\\\\rangle$:\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\left| \\\\frac{1}{2} (1 + e^{i\\\\phi}) \\\\right|^2 = \\\\left| \\\\frac{1}{2} (1 + \\\\cos(\\\\phi) + i\\\\sin(\\\\phi)) \\\\right|^2 $$\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\frac{1}{4} [(1 + \\\\cos(\\\\phi))^2 + (\\\\sin(\\\\phi))^2] = \\\\frac{1}{4} [1 + 2\\\\cos(\\\\phi) + \\\\cos^2(\\\\phi) + \\\\sin^2(\\\\phi)] $$\\n\",\n \"\\n\",\n \"Using the identity $\\\\cos^2(\\\\phi) + \\\\sin^2(\\\\phi) = 1$:\\n\",\n \"\\n\",\n \"$$ P(|0\\\\rangle) = \\\\frac{1}{4} [1 + 2\\\\cos(\\\\phi) + 1] = \\\\frac{1}{4} [2 + 2\\\\cos(\\\\phi)] = \\\\frac{1}{2} (1 + \\\\cos(\\\\phi)) $$\\n\",\n \"\\n\",\n \"In case of $\\\\phi = \\\\pi/2$, $P(|0\\\\rangle) = \\\\frac{1}{2} (1 + \\\\cos(\\\\pi/2)) = 0.5$, as we checked already.\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6476c1c7-c119-4e60-a0f6-821321401081\",\n \"metadata\": {},\n \"source\": [\n \"Let's conclude Exercise 1 by generating the fringe pattern using a range of $\\\\phi$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5d40a6e6-fa86-47af-b556-2849906d5396\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 1: Build a quantum circuit for the double slit experiment\\n\",\n \"\\n\",\n \"- Exercise 1-4: Beautiful Fringes\\n\",\n \"\\n\",\n \"**Your Goal:** Create a parameterized quantum circuit where the phase difference $\\\\phi$ can be varied, allowing you to simulate and visualize the complete interference fringe pattern.\\n\",\n \"\\n\",\n \"Qiskit allows [using Parameters in quantum circuits](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.Parameter). We'll use this with the `Sampler` to observe interference fringes.\\n\",\n \"\\n\",\n \"Define a parameterized quantum circuit for the double-slit experiment with a variable parameter $\\\\phi$. A common structure is H-gate, then $P(\\\\phi)$, then another H-gate, before measurement. Measure `q` and save to `c_screen`.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4a90de8b-f5fb-4adf-a6d3-466a46f325b8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ = Parameter('φ')\\n\",\n \"\\n\",\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr = ClassicalRegister(1, name='c_screen')\\n\",\n \"\\n\",\n \"double_slit_fringe = QuantumCircuit(qr, cr)\\n\",\n \"\\n\",\n \"#your code here\\n\",\n \"double_slit_fringe.h(0)\\n\",\n \"double_slit_fringe.p(φ,0)\\n\",\n \"double_slit_fringe.h(0)\\n\",\n \"double_slit_fringe.measure(qr, cr)\\n\",\n \"\\n\",\n \"#end of your code\\n\",\n \"\\n\",\n \"double_slit_fringe.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d2d987c4-d66b-4e1a-84f4-d5915ddde852\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab1_ex1_4(double_slit_fringe)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7a2c005f-dd42-4ee8-8073-b6b483045d76\",\n \"metadata\": {},\n \"source\": [\n \"Excellent! Now, let's use this parameterized circuit to plot the fringe pattern using matplotlib's heatmap. The code below generates 100 phase values, runs the circuits (1000 shots each) via `Sampler`, stores the probability of measuring $|0\\\\rangle$, and plots the heatmap.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Resource limit\\n\",\n \"\\n\",\n \"When running the code below on an actual backend, increasing the number of parameters or shots consumes more QPU time than expected. The current configuration (100 parameterized circuits + 1000 shots) uses less than 1 minute of QPU time, so please try to maintain these settings if possible.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2d447625-a7c9-46e5-9a8d-690419747ef1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"φ_lst = np.linspace(-3*np.pi, 3*np.pi, 100)\\n\",\n \"qc_isa = pm.run(double_slit_fringe)\\n\",\n \"\\n\",\n \"φ_hit = []\\n\",\n \"dist = sampler.run([(qc_isa, φ_lst)], shots=1000).result()[0].data.c_screen\\n\",\n \"\\n\",\n \"for i in range(len(φ_lst)):\\n\",\n \" result = dist[i].get_counts()\\n\",\n \" if '0' in result:\\n\",\n \" φ_hit.append(result['0']/1000)\\n\",\n \" else:\\n\",\n \" φ_hit.append(0)\\n\",\n \"\\n\",\n \"#plot heat map\\n\",\n \"φ_hit_2d = np.array(φ_hit).reshape(1, -1)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 2))\\n\",\n \"plt.imshow(φ_hit_2d, cmap='gray', aspect='auto', extent=[-3*np.pi, 3*np.pi, 0, 0.1])\\n\",\n \"\\n\",\n \"plt.xlabel('φ (Phase difference)', fontsize=14)\\n\",\n \"plt.colorbar(label='prob')\\n\",\n \"plt.xticks(ticks=[-3*np.pi, -2*np.pi, -np.pi, 0, np.pi, 2*np.pi, 3*np.pi],\\n\",\n \" labels=['-3π', '-2π', '-π', '0', 'π', '2π', '3π'])\\n\",\n \"plt.yticks([]) \\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0a6bc2c3-7c4a-46a2-9984-bc8a94666ead\",\n \"metadata\": {},\n \"source\": [\n \"Fantastic! You've reproduced the double-slit experiment's fringes using qubits. Next, we'll explore quantum measurement with Schrödinger's cat and then see how observation affects the double-slit outcome.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed7715c1-2169-4fef-9b7d-3ed61c4b2f41\",\n \"metadata\": {},\n \"source\": [\n \"## Schrödinger's Cat: Exploring Quantum Superposition and the Act of Observation\\n\",\n \"\\n\",\n \"This thought experiment by Erwin Schrödinger (1935, [\\\"Die gegenwärtige Situation in der Quantenmechanik\\\"](https://link.springer.com/article/10.1007/BF01491891)) illustrates quantum superposition and measurement's role in collapsing possibilities into a single outcome. A cat is sealed in a box with a radioactive atom. If the atom is in a superposition of decayed/not decayed, the cat, entangled with it, should be in a superposition of alive/dead until observed.\\n\",\n \"\\n\",\n \"Here, we'll use a rotational gate. Imagine a cat disliking the $|1\\\\rangle$ state. Let's see if the cat is happy or upset when we \\\"open the box\\\" (measure the qubit). Be careful! An angry quantum cat might scratch you.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5559255f-f8c1-4a49-bcf1-738d9730f92f\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Exercise 2: Is the cat happy or grumpy?\\n\",\n \"\\n\",\n \"**Your Goal:** Create a quantum circuit that prepares a qubit in a superposition state using an $R_X(\\\\theta)$ gate, then simulate a single measurement to determine if the \\\"cat\\\" (represented by the qubit state) is \\\"happy\\\" ($|0\\\\rangle$) or \\\"grumpy\\\" ($|1\\\\rangle$).\\n\",\n \"\\n\",\n \"Complete the Python code below using the [Rotational X gate](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.RXGate) with $\\\\theta \\\\in [0, 2\\\\pi]$ to prepare a qubit in various superposition states. Then, measure once to see if the cat is happy ($|0\\\\rangle$) or grumpy ($|1\\\\rangle$). $\\\\theta$ will be given to you by the slider.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6aa42755-b319-4aee-a6bc-cf6b34e00222\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def schrodingers_cat_experiment_theta(theta):\\n\",\n \" \\n\",\n \" qc = QuantumCircuit(1)\\n\",\n \"\\n\",\n \" #your code start here\\n\",\n \"\\n\",\n \" qc.rx(theta,0)\\n\",\n \"\\n\",\n \" #end of your code\\n\",\n \"\\n\",\n \" qc.measure_all()\\n\",\n \" \\n\",\n \" backend = AerSimulator()\\n\",\n \" pm = generate_preset_pass_manager( backend = backend, optimization_level=3)\\n\",\n \" qc_isa = pm.run(qc)\\n\",\n \"\\n\",\n \" # Circuit compile and run, shot = 1 \\n\",\n \" sampler = Sampler(mode=backend)\\n\",\n \" counts = sampler.run([qc_isa], shots = 1).result()[0].data.meas.get_counts()\\n\",\n \"\\n\",\n \" measured_state = list(counts.keys())[0] if counts else '0' # bring measured result\\n\",\n \"\\n\",\n \" if measured_state == '0':\\n\",\n \" cat_happy = True\\n\",\n \" else:\\n\",\n \" cat_happy = False\\n\",\n \"\\n\",\n \" return cat_happy, qc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"49747cda-70bf-44d7-816c-607dd601a95f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab1_ex2(schrodingers_cat_experiment_theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"18096b47-adb4-47dc-a41f-2273db2aacab\",\n \"metadata\": {},\n \"source\": [\n \"Now, use the widget below to check the cat's mood. Make sure `happy.png` and `grumpy.png` are in the same folder. (Image credit: James Weaver)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4aed0937-fa98-416e-a2c8-e571df1ed907\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"happy_img = Image.open('happy.png')\\n\",\n \"grumpy_img = Image.open('grumpy.png')\\n\",\n \"\\n\",\n \"out = widgets.Output()\\n\",\n \"\\n\",\n \"slider = widgets.FloatSlider(\\n\",\n \" value=0,\\n\",\n \" min=0,\\n\",\n \" max=2*np.pi,\\n\",\n \" step=0.01,\\n\",\n \" description='θ',\\n\",\n \" continuous_update=True\\n\",\n \")\\n\",\n \"\\n\",\n \"button = widgets.Button(\\n\",\n \" description='Open the Box',\\n\",\n \" button_style='success'\\n\",\n \")\\n\",\n \" \\n\",\n \"def on_button_click(b):\\n\",\n \" with out:\\n\",\n \" out.clear_output(wait=True) # clean output\\n\",\n \"\\n\",\n \" result = schrodingers_cat_experiment_theta(slider.value)[0]\\n\",\n \"\\n\",\n \" if result==True:\\n\",\n \" img = happy_img\\n\",\n \" txt = \\\"happy\\\"\\n\",\n \" else:\\n\",\n \" img = grumpy_img\\n\",\n \" txt = \\\"grumpy\\\"\\n\",\n \"\\n\",\n \" new_size = (400, 400)\\n\",\n \" resized_img = img.resize(new_size)\\n\",\n \" \\n\",\n \" buf = io.BytesIO()\\n\",\n \" resized_img.save(buf, format='PNG')\\n\",\n \" buf.seek(0)\\n\",\n \" probability = int(np.cos(slider.value/2)**2 * 100)\\n\",\n \"\\n\",\n \" display(f\\\"The probability of cat is happy: {probability}%\\\")\\n\",\n \" display(f\\\"The observed cat is : {txt}\\\")\\n\",\n \" display(widgets.Image(value=buf.read(), format='png'))\\n\",\n \"\\n\",\n \"button.on_click(on_button_click)\\n\",\n \"\\n\",\n \"display(slider, button, out)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7d8e5717-b4d3-4e0c-84d9-279210916258\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"Before we revisit the double-slit experiment, let's clarify what happens when we try to observe or \\\"measure\\\" a qubit, especially after applying a gate like $R_x(\\\\theta)$. If you are familiar to the quantum measurement, you can skip this part.\\n\",\n \"\\n\",\n \"\\n\",\n \"
Quantum Measurement details: Observing the Qubit
\\n\",\n \"\\n\",\n \"**1. Qubit State Before Measurement:**\\n\",\n \"After $R_x(\\\\theta)$ on $|0\\\\rangle$, the state is a superposition:\\n\",\n \"$$|\\\\psi\\\\rangle = R_x(\\\\theta)|0\\\\rangle = \\\\cos\\\\left(\\\\frac{\\\\theta}{2}\\\\right)|0\\\\rangle - i \\\\sin\\\\left(\\\\frac{\\\\theta}{2}\\\\right)|1\\\\rangle = \\\\alpha|0\\\\rangle + \\\\beta|1\\\\rangle$$\\n\",\n \"where $|\\\\alpha|^2 + |\\\\beta|^2 = 1$.\\n\",\n \"\\n\",\n \"**2. Act of Measurement:**\\n\",\n \"Measurement in the computational basis ($\\\\{|0\\\\rangle, |1\\\\rangle\\\\}$) forces the qubit to choose one state. The superposition is not directly observable.\\n\",\n \"\\n\",\n \"**3. Probabilistic Outcomes:**\\n\",\n \"Probability of measuring $|0\\\\rangle$: $P(0) = |\\\\alpha|^2 = \\\\cos^2\\\\left(\\\\frac{\\\\theta}{2}\\\\right)$\\n\",\n \"Probability of measuring $|1\\\\rangle$: $P(1) = |\\\\beta|^2 = \\\\sin^2\\\\left(\\\\frac{\\\\theta}{2}\\\\right)$\\n\",\n \"\\n\",\n \"**4. State Collapse:**\\n\",\n \"Post-measurement, the state collapses:\\n\",\n \"* If '0' is measured $\\\\rightarrow$ state becomes $|0\\\\rangle$.\\n\",\n \"* If '1' is measured $\\\\rightarrow$ state becomes $|1\\\\rangle$.\\n\",\n \"This \\\"collapse\\\" destroys the superposition.\\n\",\n \"\\n\",\n \"**Connection to Double-Slit:**\\n\",\n \"Detecting which slit a particle uses is a measurement. It collapses the particle's wave function to a definite path, destroying the superposition needed for interference fringes.\\n\",\n \"\\n\",\n \"\\n\",\n \"Let's revisit double slit experiments.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a759bf4a-f9ce-4d23-bd90-c72d15b0c87b\",\n \"metadata\": {},\n \"source\": [\n \"## Double Slit with Measurement\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 3: Double slit with a path detector\\n\",\n \"\\n\",\n \"**Your Goal:** Construct a double-slit circuit that includes an intermediate measurement acting as a \\\"which-path\\\" detector. This will allow you to observe how acquiring information about the particle's path affects the final interference pattern.\\n\",\n \"\\n\",\n \"Modify the double-slit circuit to include a \\\"which-path\\\" detector (a measurement after the first H-gate).\\n\",\n \"\\n\",\n \"Setup:\\n\",\n \"* `qr`: 1 qubit (`'q'`).\\n\",\n \"* `cr1`: 1 classical bit (`'c_detector'`) for path detection.\\n\",\n \"* `cr2`: 1 classical bit (`'c_screen'`) for final measurement.\\n\",\n \"* `φ`: A `Parameter` for the phase gate.\\n\",\n \"\\n\",\n \"**Hint**: Your circuit will have two measurements.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ba9f4861-356b-4b66-99d5-e06561e5f340\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qr = QuantumRegister(1, name='q')\\n\",\n \"cr1 = ClassicalRegister(1, name='c_detector')\\n\",\n \"cr2 = ClassicalRegister(1, name='c_screen')\\n\",\n \"double_slit_with_detector = QuantumCircuit(qr, cr1, cr2)\\n\",\n \"\\n\",\n \"φ = Parameter('φ')\\n\",\n \"\\n\",\n \"#your code here\\n\",\n \"\\n\",\n \"double_slit_with_detector.h(0)\\n\",\n \"double_slit_with_detector.measure(qr,cr1)\\n\",\n \"double_slit_with_detector.p(φ,0)\\n\",\n \"double_slit_with_detector.h(0)\\n\",\n \"double_slit_with_detector.measure(qr,cr2)\\n\",\n \"\\n\",\n \"#end of your code\\n\",\n \"\\n\",\n \"double_slit_with_detector.draw('mpl')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c6533c86-e7e6-4b0f-92ef-5179d75b7702\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab1_ex3(double_slit_with_detector)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"48ffc450-3cf9-423c-be89-808a03dd246a\",\n \"metadata\": {},\n \"source\": [\n \"Now, repeat the heatmap generation to see the new pattern with the detector.\\n\",\n \"\\n\",\n \"**Warning**: This can take minutes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1dbb4f19-9a18-4c2e-82e4-3365a2a87880\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Resource limit\\n\",\n \"\\n\",\n \"When running the code below on an actual backend, increasing the number of parameters or shots consumes more QPU time than expected. The current configuration (100 parameterized circuits + 1000 shots) uses less than 1 minute of QPU time, so please try to maintain these settings.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"1. **Initial State**: $|\\\\psi_0\\\\rangle = |0\\\\rangle$\\n\",\n \"2. **First H-gate**: $|\\\\psi_1\\\\rangle = \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle + |1\\\\rangle)$ (particle can pass through both slits)\\n\",\n \"3. **First Measurement (Detector)**: Collapses the superposition.\\n\",\n \" * Outcome 0 (prob 1/2) $\\\\rightarrow |\\\\psi_{2a}\\\\rangle = |0\\\\rangle$\\n\",\n \" * Outcome 1 (prob 1/2) $\\\\rightarrow |\\\\psi_{2b}\\\\rangle = |1\\\\rangle$\\n\",\n \" The qubit is now in a definite state.\\n\",\n \"4. **P-gate**:\\n\",\n \" * If outcome 0: $|\\\\psi_{3a}\\\\rangle = P(\\\\phi)|0\\\\rangle = |0\\\\rangle$\\n\",\n \" * If outcome 1: $|\\\\psi_{3b}\\\\rangle = P(\\\\phi)|1\\\\rangle = e^{i\\\\phi}|1\\\\rangle$\\n\",\n \" The phase $\\\\phi$ cannot act as a *relative* phase for interference due to the collapse.\\n\",\n \"5. **Second H-gate**:\\n\",\n \" * If outcome 0: $|\\\\psi_{4a}\\\\rangle = H|0\\\\rangle = \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle + |1\\\\rangle)$\\n\",\n \" * If outcome 1: $|\\\\psi_{4b}\\\\rangle = H(e^{i\\\\phi}|1\\\\rangle) = e^{i\\\\phi} \\\\frac{1}{\\\\sqrt{2}}(|0\\\\rangle - |1\\\\rangle)$\\n\",\n \"6. **Second Measurement (Screen)**:\\n\",\n \" * If first measurement was 0: Prob(final '0') = 1/2, Prob(final '1') = 1/2.\\n\",\n \" * If first measurement was 1: Prob(final '0') = 1/2, Prob(final '1') = 1/2.\\n\",\n \"\\n\",\n \"**Conclusion**: Regardless of the detector's outcome, the final measurement is always 50/50 for $|0\\\\rangle$ or $|1\\\\rangle$. The intermediate measurement destroyed the superposition, and thus the interference. This demonstrates that \\\"which-path\\\" information destroys interference.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"So far, we've explored superposition, interference, and measurement. Now, let's dive into another profound quantum phenomenon: **entanglement**.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8fdb70d8-229c-4e3b-abdc-ce9f60230e1a\",\n \"metadata\": {},\n \"source\": [\n \"# Chapter 2: Entanglement\\n\",\n \"\\n\",\n \"**Quantum entanglement** is a phenomenon where particles become interconnected, sharing a quantum state that links their properties, no matter how far apart they are. Measuring one particle's state instantly reveals information about the state of the other. Einstein called this “spooky action at a distance.”\\n\",\n \"\\n\",\n \"In 1964, John Bell proposed Bell’s inequality to test if entangled particles behave classically. The CHSH experiment provided a concrete test. Quantum mechanics passed, defying classical expectations[3]. In 1997, quantum teleportation—transferring a quantum state without moving the particle—was achieved using entanglement[4].\\n\",\n \"\\n\",\n \"With Qiskit, let's try the CHSH experiment and quantum teleportation!\\n\",\n \"\\n\",\n \"\\n\",\n \"## 2.1. The Curious Case of Alice, Bob, and the Unbeatable Game - CHSH Game\\n\",\n \"\\n\",\n \"Alice and Bob are far apart and cannot communicate. A Referee gives them a challenge:\\n\",\n \"1. Referee picks two bits (`x`, `y`), sends `x` to Alice, `y` to Bob.\\n\",\n \"2. Alice and Bob instantly reply with their bits (`a`, `b`).\\n\",\n \"3. **Win Condition**: `a XOR b == x AND y`.\\n\",\n \"\\n\",\n \"They want a pre-agreed strategy to maximize their average win rate.\\n\",\n \"\\n\",\n \"**Your Goal**: Implement the *quantum* strategy for the CHSH game using Qiskit, simulate its performance, and compare it to the classical limit to understand the advantage provided by quantum entanglement. \\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"For your in-depth study \\n\",\n \"\\n\",\n \"For a more in-depth explanation and exploration of entanglement in action, including the CHSH game, you can refer to the Qiskit learning resource: [Entanglement in Action](https://quantum.cloud.ibm.com/learning/en/courses/basics-of-quantum-information/entanglement-in-action/introduction).\\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91b4ee20-4f7a-4cdc-ae82-b8626a55f5fd\",\n \"metadata\": {},\n \"source\": [\n \"### The Classical Limit: Why 75% is the Barrier\\n\",\n \"\\n\",\n \"Let's first think classically. Since Alice and Bob can't communicate after receiving `x` and `y`, Alice's output `a` can only depend on `x`, and Bob's `b` only on `y`. They could pre-agree on a strategy, even a random one, but it boils down to local choices.\\n\",\n \"\\n\",\n \"Winning condition means:\\n\",\n \"* Inputs (0,0), (0,1), or (1,0): Win if `a == b`.\\n\",\n \"* Input (1,1): Win if `a != b`.\\n\",\n \"\\n\",\n \"Can you find a fixed strategy (like `a=0, b=0` or `a=x, b=y`) that wins more than 3 out of the 4 possible input cases? Try it! You'll find the maximum average win rate is stubbornly stuck at **75%**, a fundamental limit of local realism.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74227c42-4fd9-4acb-a55a-564b836bdc38\",\n \"metadata\": {},\n \"source\": [\n \"### The Quantum Strategy: Entanglement to the Rescue!\\n\",\n \"\\n\",\n \"What if Alice and Bob prepared something special *before* the game? What if they shared a pair of **entangled qubits**? Imagine entanglement like two \\\"magic\\\" coins that are mysteriously linked. Even miles apart, measuring one instantly influences the *correlations* you'll find when measuring the other. They don't send messages faster than light, but their fates are intertwined.\\n\",\n \"\\n\",\n \"Specifically, they share a [Bell state](https://en.wikipedia.org/wiki/Bell_state): $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt(2)$. If they both measure their qubit in the *same* way, they always get the same result (both 0 or both 1).\\n\",\n \"\\n\",\n \"The Quantum Strategy: Instead of outputting fixed bits, Alice and Bob use their input (`x` or `y`) to decide *how* to measure their shared qubit.\\n\",\n \"1. Shared Resource: Alice holds qubit 0, Bob holds qubit 1 of their entangled pair.\\n\",\n \"2. Measurement Choice (The Clever Part):\\n\",\n \" * Alice (input `x`): Measures along angle 0 (standard Z basis) if `x=0`, or angle π/2 (X basis) if `x=1`.\\n\",\n \" * Bob (input `y`): Measures along angle -π/4 if `y=0`, or angle π/4 if `y=1`.\\n\",\n \"3. Output: They output the result of their respective measurements as `a` and `b`.\\n\",\n \"\\n\",\n \"These specific angles are crucial! They exploit the unique correlations of the Bell state to maximize their chances of satisfying `a XOR b == x AND y` across all input pairs.\\n\",\n \"\\n\",\n \"Let's build the quantum circuit for this strategy.\\n\",\n \"\\n\",\n \"Alice and Bob share an entangled qubit pair in the Bell state: $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$. If measured the same way, they always get the same result.\\n\",\n \"\\n\",\n \"**Note**\\n\",\n \"\\n\",\n \"For detailed information on the quantum circuit's configuration, its operational flow, or underlying principles, please refer to [this document](https://quantum.cloud.ibm.com/learning/en/courses/basics-of-quantum-information/entanglement-in-action/chsh-game)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"aefc0aa2-ad6f-4a61-ae6f-6f92b558a489\",\n \"metadata\": {},\n \"source\": [\n \"### Qiskit Implementation: Building the Quantum Circuit\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 4: Quantum Circuit for CHSH Game\\n\",\n \"\\n\",\n \"**Your Goal:** Define a function `create_chsh_circuit(x, y)` by construct the quantum circuit that Alice and Bob will use for their quantum strategy in the CHSH game. This involves preparing an entangled Bell state and then applying measurement basis rotations based on their respective inputs.\\n\",\n \"\\n\",\n \"**Tasks:**\\n\",\n \"* **Task 1:** Create the Bell state $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$.\\n\",\n \"* **Task 2:** Implement Bob's rotation based on his input `y`.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a174b414-36d6-40f0-a1ac-df3350b5f80e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from numpy import pi as pi\\n\",\n \"\\n\",\n \"def create_chsh_circuit(x, y):\\n\",\n \" \\\"\\\"\\\"Builds Qiskit circuit for Alice & Bob's quantum strategy.\\\"\\\"\\\"\\n\",\n \" qc = QuantumCircuit(2, 2, name=f'CHSH_{x}{y}') # 2 qubits, 2 classical bits\\n\",\n \"\\n\",\n \" # ---- TODO : Task 1 ---\\n\",\n \" # Implement the gates to create the Bell state |Φ+> = (|00> + |11>)/sqrt(2).\\n\",\n \"\\n\",\n \" qc.h(0)\\n\",\n \" # Apply Gate 2 here\\n\",\n \" qc.cx(0, 1)\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \" qc.barrier()\\n\",\n \" # Step 2a: Alice's measurement basis (X if x=1, Z if x=0)\\n\",\n \" if x == 1:\\n\",\n \" qc.h(0) # H for X-basis measurement\\n\",\n \"\\n\",\n \" ## --- TODO: Task 2 ----\\n\",\n \" # Step 2b: Bob's measurement basis\\n\",\n \"\\n\",\n \" if y == 0:\\n\",\n \" # Apply the rotation for y=0 to Bob's qubit (index 1)\\n\",\n \" #pass # Replace 'pass' with your qc.ry(...) call\\n\",\n \" qc.ry(-np.pi/4, 1) #TODO REMOVE ANSWER\\n\",\n \"\\n\",\n \" else: # y == 1\\n\",\n \" # Apply the rotation for y=1 to Bob's qubit (index 1)\\n\",\n \" #pass # Replace 'pass' with your qc.ry(...) call\\n\",\n \" qc.ry(np.pi/4, 1) #TODO REMOVE ANSWER\\n\",\n \"\\n\",\n \" \\n\",\n \" # --- End of TODO ---\\n\",\n \" qc.barrier()\\n\",\n \" \\n\",\n \" # Step 3: Measure\\n\",\n \" qc.measure([0, 1], [0, 1]) # q0 to c0 (Alice), q1 to c1 (Bob) -> 'ba' format\\n\",\n \"\\n\",\n \" return qc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"27caf6cf-9497-4c02-b4ca-0f6d55a464dc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab1_ex4(create_chsh_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28c48bd5-eaeb-4360-981d-959a4ac913b6\",\n \"metadata\": {},\n \"source\": [\n \"### Create Circuits for All Input Pairs\\n\",\n \"\\n\",\n \"Now we use the function (which you hopefully completed!) to generate circuits for all four (x,y) scenarios.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7a440c15-4467-42d6-b0a1-80fd9bdd5dd5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"circuits = []\\n\",\n \"input_pairs = []\\n\",\n \"for x_in in [0, 1]:\\n\",\n \" for y_in in [0, 1]:\\n\",\n \" input_pairs.append((x_in, y_in))\\n\",\n \" circuits.append(create_chsh_circuit(x_in, y_in))\\n\",\n \"\\n\",\n \"print(\\\"Quantum circuit for inputs x=1, y=1 (Check your Exercises 1 & 2 implementation):\\\")\\n\",\n \"if len(circuits) == 4:\\n\",\n \" display(circuits[3].draw('mpl')) # (x,y) = (1,1)\\n\",\n \"else:\\n\",\n \" print(\\\"Circuits not generated. Run previous cell after completing Exercises 1 & 2.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b85480d-6172-457c-805c-6f168a86930f\",\n \"metadata\": {},\n \"source\": [\n \"### Simulation: Playing the Game Many Times\\n\",\n \"\\n\",\n \"Real quantum computers are noisy and sometimes hard to access. Thankfully, we can simulate their behavior quite accurately for small systems like this one. We'll use Qiskit's `AerSimulator` to run our four circuits many times (\\\"shots\\\") and collect statistics on Alice and Bob's outputs (`a` and `b`).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b2052339-c0ab-4051-8d22-1a77df74d56f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# AerSimulator (if not already defined)\\n\",\n \"# backend = AerSimulator()\\n\",\n \"# Pass manager (if not already defined)\\n\",\n \"# pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\\n\",\n \"\\n\",\n \"SHOTS = 1024\\n\",\n \"\\n\",\n \"print(\\\"Preparing circuits for the simulator...\\\")\\n\",\n \"isa_qc_chsh = pm.run(circuits)\\n\",\n \"\\n\",\n \"sampler_chsh = Sampler(mode=backend) # SamplerV2\\n\",\n \"job_chsh = sampler_chsh.run(isa_qc_chsh, shots=SHOTS)\\n\",\n \"results_chsh = job_chsh.result()\\n\",\n \"\\n\",\n \"# SamplerV2: results_chsh[i].data.c.get_counts() where 'c' is the default name of classical register\\n\",\n \"counts_list = [results_chsh[i].data.c.get_counts() for i in range(len(circuits))]\\n\",\n \"\\n\",\n \"print(\\\"\\\\n--- Simulation Results (Counts) ---\\\")\\n\",\n \"for i, (x, y) in enumerate(input_pairs):\\n\",\n \" print(f\\\"Inputs (x={x}, y={y}):\\\")\\n\",\n \" sorted_counts = dict(sorted(counts_list[i].items()))\\n\",\n \" print(f\\\" Outcomes (ba): {sorted_counts}\\\")\\n\",\n \"\\n\",\n \"print(\\\"\\\\nPlotting results...\\\")\\n\",\n \"display(plot_histogram(counts_list,\\n\",\n \" legend=[f'(x={x}, y={y})' for x, y in input_pairs],\\n\",\n \" title='CHSH Game Outcomes (ba format)'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3b84b66e-9503-4dbe-9023-0d08bbd584fd\",\n \"metadata\": {},\n \"source\": [\n \"### Analysis: Did They Beat the Classical Limit?\\n\",\n \"\\n\",\n \"Now, the moment of truth! We need to analyze the simulation results (`counts_list`) to calculate the average win probability.\\n\",\n \"\\n\",\n \"**Recap:**\\n\",\n \"* **Win Condition:** `a XOR b == x AND y`\\n\",\n \"* **Output Format:** Counts are for `'ba'` strings.\\n\",\n \" * `a XOR b = 0` for outcomes `'00'` (`b=0, a=0`) and `'11'` (`b=1, a=1`).\\n\",\n \" * `a XOR b = 1` for outcomes `'01'` (`b=0, a=1`) and `'10'` (`b=1, a=0`).\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 5: Analyze Circuit for CHSH Game\\n\",\n \"\\n\",\n \"**Your Goal:** Calculate the win probability for Alice and Bob for each input case (`x`, `y`) based on the simulation results, and then determine their overall average win probability using the quantum strategy.\\n\",\n \"\\n\",\n \"**Tasks:**\\n\",\n \"* **Task 1:** Determine the target `a XOR b` value for a win, given `x` and `y`.\\n\",\n \"* **Task 2:** Count shots (`wins_for_this_case`) satisfying the win condition for the current (`x`, `y`).\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"28f7b7ef-612a-4762-a522-d32d539b37f9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"win_probabilities = {}\\n\",\n \"print(\\\"--- Calculating Win Probabilities ---\\\")\\n\",\n \"\\n\",\n \"for i, (x, y) in enumerate(input_pairs):\\n\",\n \" counts = counts_list[i]\\n\",\n \"\\n\",\n \" # ---- TODO : Task 1 ---\\n\",\n \" # Target (a XOR b) value for winning\\n\",\n \" \\n\",\n \"\\n\",\n \" target_xor_result = x * y\\n\",\n \" # --- End of TODO --\\n\",\n \"\\n\",\n \" wins_for_this_case = 0\\n\",\n \"\\n\",\n \" # ---- TODO : Task 2 ---\\n\",\n \" # Calculate the total number of shots that satisfy the winning condition determined above. Check the 'target_xor_result'\\n\",\n \" \\n\",\n \" if target_xor_result == 0:\\n\",\n \" # They win if a XOR b == 0. Sum counts for '00' and '11'.\\n\",\n \" wins_for_this_case = counts.get('00', 0) + counts.get('11', 0)\\n\",\n \" else: # target_xor_result == 1\\n\",\n \" # They win if a XOR b == 1. Sum counts for '01' and '10'.\\n\",\n \" wins_for_this_case = counts.get('01', 0) + counts.get('10', 0)\\n\",\n \"\\n\",\n \" # --- End of TODO --\\n\",\n \"\\n\",\n \" prob = wins_for_this_case / SHOTS if SHOTS > 0 else 0\\n\",\n \" win_probabilities[(x, y)] = prob\\n\",\n \" print(f\\\"Inputs (x={x}, y={y}): Target (a XOR b) = {target_xor_result}. Win Probability = {prob:.4f}\\\")\\n\",\n \"\\n\",\n \"avg_win_prob = sum(win_probabilities.values()) / 4.0\\n\",\n \"P_win_quantum_theory = np.cos(np.pi / 8)**2 # ~0.8536\\n\",\n \"P_win_classical_limit = 0.75\\n\",\n \"\\n\",\n \"print(\\\"\\\\n--- Overall Performance ---\\\")\\n\",\n \"print(f\\\"Experimental Average Win Probability: {avg_win_prob:.4f}\\\")\\n\",\n \"print(f\\\"Theoretical Quantum Win Probability: {P_win_quantum_theory:.4f}\\\")\\n\",\n \"print(f\\\"Classical Limit Win Probability: {P_win_classical_limit:.4f}\\\")\\n\",\n \"\\n\",\n \"if avg_win_prob > P_win_classical_limit + 0.01: # Allow for small simulation variance\\n\",\n \" print(f\\\"\\\\nSuccess! Your result ({avg_win_prob:.4f}) clearly beats the classical 75% limit!\\\")\\n\",\n \" print(f\\\"It's likely close to the theoretical quantum prediction of {P_win_quantum_theory:.4f}.\\\")\\n\",\n \"elif avg_win_prob > P_win_classical_limit - 0.02 : # Could be noise or minor error\\n\",\n \" print(f\\\"\\\\nClose, but no cigar? Your result ({avg_win_prob:.4f}) is around the classical limit ({P_win_classical_limit:.4f}).\\\")\\n\",\n \" print(\\\"Check your solutions for Exercises 1-4 carefully, especially the win counting logic in Ex 4.\\\")\\n\",\n \"else:\\n\",\n \" print(f\\\"\\\\nHmm, the result ({avg_win_prob:.4f}) is unexpectedly low, even below the classical limit.\\\")\\n\",\n \" print(\\\"There might be an error in Exercises 1-4. Please review your circuit and analysis code.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a8842d57-eea6-4791-b5a9-0d4b37219c15\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab1_ex5(counts_list, avg_win_prob)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5ced3f21-6210-46fc-b9c8-e539e88f854e\",\n \"metadata\": {},\n \"source\": [\n \"### Conclusion: The Quantum Edge\\n\",\n \"\\n\",\n \"So, what did we find? If you successfully completed the exercises, your simulation should clearly show that Alice and Bob, using their shared entangled qubits and clever measurements, achieved an average win rate **significantly** higher than the 75% classical limit, likely approaching the theoretical quantum maximum of around **85.4%**.\\n\",\n \"\\n\",\n \"This isn't just a mathematical trick; it reflects how nature works at its most fundamental level. Entanglement allows for correlations between distant particles that are stronger than any classical theory can explain. This \\\"spooky action at a distance\\\" doesn't allow faster-than-light communication, but it underpins many quantum technologies.\\n\",\n \"\\n\",\n \"**Discussion Point:** Based on these results, what does the CHSH game demonstrate about the nature of reality compared to classical intuition? Can you think of any potential applications where these stronger-than-classical correlations might be useful?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"100cdba6-f257-4b0a-b905-380c1876839a\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2. Quantum Teleportation - Sending Secrets with Spooky Action\\n\",\n \"\\n\",\n \"Imagine Alice has a qubit (`q0`) in a specific, delicate quantum state (`|ψ⟩`). She wants to send this exact state to Bob, who is far away. She can't just physically send the qubit (maybe it's too fragile, or she needs the original particle). Can she somehow transfer *just the state* itself?\\n\",\n \"\\n\",\n \"Classically, this seems impossible. If Alice measures her qubit to see what state it's in, the measurement itself will likely collapse the superposition and destroy the original state!\\n\",\n \"\\n\",\n \"Enter **Quantum Teleportation**! It's a remarkable protocol that uses **quantum entanglement** and **classical communication** to transfer an unknown quantum state from one qubit to another.\\n\",\n \"\\n\",\n \"**Disclaimer:** We're teleporting the *state*, not the physical particle! No people are being beamed up here.\\n\",\n \"\\n\",\n \"**Your Goal:** In this lab, you will implement the quantum teleportation protocol using Qiskit. You'll complete the code to prepare the necessary quantum resources, perform the key steps, and verify that Bob successfully receives Alice's quantum state.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f1dbf7d4-97aa-4fdd-90ed-51f55c74007b\",\n \"metadata\": {},\n \"source\": [\n \"### The Teleportation Protocol: Step-by-Step\\n\",\n \"\\n\",\n \"The protocol requires three qubits and two classical bits:\\n\",\n \"* **`q0`:** Alice's qubit, initially in the state `|ψ⟩` we want to teleport.\\n\",\n \"* **`q1`:** Alice's half of a shared entangled pair.\\n\",\n \"* **`q2`:** Bob's half of the shared entangled pair.\\n\",\n \"* **`c0`, `c1`:** Classical bits to store Alice's measurement results.\\n\",\n \"\\n\",\n \"Here's the plan:\\n\",\n \"1. **(Optional) Prepare Alice's state `|ψ⟩` on `q0`.** (We'll create a specific state like `|+>` for verification).\\n\",\n \"2. **Create entanglement:** Generate a Bell pair between `q1` and `q2`.\\n\",\n \"3. **Alice's operations:** Alice performs a \\\"Bell measurement\\\" on her two qubits (`q0` and `q1`) and stores the classical results in `c0` and `c1`.\\n\",\n \"4. **Classical communication:** Alice sends her two classical bits (`c0`, `c1`) to Bob.\\n\",\n \"5. **Bob's corrections:** Bob applies specific quantum gates (X and/or Z) to his qubit (`q2`), conditioned on the values of `c0` and `c1` he received.\\n\",\n \"\\n\",\n \"If everything is done correctly, Bob's qubit `q2` will end up in the state `|ψ⟩`, the original state of Alice's `q0`!\\n\",\n \"\\n\",\n \"
\\n\",\n \"For your in-depth study \\n\",\n \"\\n\",\n \"For a more in-depth explanation and exploration of quantum teleportation, you can refer to the Qiskit learning resource: [Quantum Teleportation](https://quantum.cloud.ibm.com/learning/en/courses/basics-of-quantum-information/entanglement-in-action/quantum-teleportation) of [Entanglement in Action](https://quantum.cloud.ibm.com/learning/en/courses/basics-of-quantum-information/entanglement-in-action/quantum-teleportation) by John Watrous. This is a part of his [Basics of Quantum Information](https://quantum.cloud.ibm.com/learning/en/courses/basics-of-quantum-information) course.\\n\",\n \"
\\n\",\n \"\\n\",\n \"### Tool: Qiskit Dynamic Circuits\\n\",\n \"\\n\",\n \"For Bob's actions depending on Alice's measurements, we use dynamic circuits. This allows classical information from mid-circuit measurements to influence later quantum operations. This capability is crucial for protocols like teleportation and for creating efficient long-range entanglement on quantum hardware, as explored in recent research (e.g., Bäumer et al., PRX QUANTUM 5, 030339 (2024))\\n\",\n \"\\n\",\n \"\\n\",\n \"**Reference:**\\n\",\n \"* [Qiskit documentation on dynamic circuits and conditional operations](https://quantum.cloud.ibm.com/docs/en/guides/classical-feedforward-and-control-flow)\\n\",\n \"* [Efficient Long-Range Entanglement Using Dynamic Circuits](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.030339)\\n\",\n \"\\n\",\n \"**Key Concepts for Teleportation:**\\n\",\n \"\\n\",\n \"1. Mid-Circuit Measurement:\\n\",\n \" * In Qiskit, you can measure a qubit and store the result in a classical bit at any point in the circuit, not just at the end. This is crucial for Alice to measure her qubits (`q0` and `q1`) and obtain classical bits `c0` and `c1`.\\n\",\n \" * The `QuantumCircuit.measure(qubit, classical_bit)` instruction is used.\\n\",\n \"\\n\",\n \"2. Conditional Operations (if_test):\\n\",\n \" * After Alice's measurements, Bob needs to apply corrective gates to his qubit (`q2`) based on the classical bits (`c0`, `c1`) he receives.\\n\",\n \" * Qiskit allows gates to be applied conditionally based on the state of classical bits.\\n\",\n \" * The `QuantumCircuit.if_test((classical_bit, value), true_circuit, false_circuit=None)` method (in newer Qiskit versions) or the older `Instruction.c_if(classical_bit, value)` method can be used to apply gates only if a certain classical bit has a specific value (0 or 1).\\n\",\n \" * For teleportation, Bob will use these conditional operations:\\n\",\n \" * If `c1` is 1, apply an X gate to `q2`.\\n\",\n \" * If `c0` is 1, apply a Z gate to `q2`.\\n\",\n \"\\n\",\n \"These features enable the classical communication aspect of the teleportation protocol to be directly implemented and simulated within a single quantum circuit description.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a7505da2-a554-4e2e-9844-70c947059e78\",\n \"metadata\": {},\n \"source\": [\n \"### Building the Teleportation Circuit in Qiskit\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 6: Quantum Teleportation\\n\",\n \"\\n\",\n \"**Your Goal:** Construct the complete quantum circuit for teleporting an unknown quantum state from Alice's message qubit to Bob's qubit, utilizing an entangled Bell pair and mid-circuit measurements with conditional operation.\\n\",\n \"\\n\",\n \"**Tasks:**\\n\",\n \"* **Step 1:** Create the shared entangled Bell pair $(|00\\\\rangle + |11\\\\rangle) / \\\\sqrt{2}$ between `q1` and `q2`.\\n\",\n \"* **Step 2:** Implement Alice's Bell measurement gates (before actual measurement).\\n\",\n \"* **Step 3:** Implement Bob's conditional correction gates based on Alice's measurement results.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"af207afd-1a82-43c8-8254-6e0b422f613d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Essential libraries\\n\",\n \"import numpy as np\\n\",\n \"from numpy import pi, sqrt\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\n\",\n \"from qiskit_aer import AerSimulator, Aer\\n\",\n \"\\n\",\n \"# Import visualization tools\\n\",\n \"from qiskit.visualization import plot_histogram, plot_bloch_multivector, circuit_drawer\\n\",\n \"from qiskit import __version__ as qiskit_version\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit_version}\\\")\\n\",\n \"\\n\",\n \"# Define the quantum and classical registers\\n\",\n \"# q0: Alice's message qubit\\n\",\n \"# q1: Alice's entangled qubit\\n\",\n \"# q2: Bob's entangled qubit\\n\",\n \"qr = QuantumRegister(3, name='q')\\n\",\n \"# c0, c1: Classical bits for Alice's measurements\\n\",\n \"cr_alice = ClassicalRegister(2, name='c_alice') # A register to hold Alice's results\\n\",\n \"\\n\",\n \"# Create the quantum circuit\\n\",\n \"teleport_qc = QuantumCircuit(qr, cr_alice, name='Teleportation')\\n\",\n \"\\n\",\n \"# --- Step 1: Prepare Alice's message state |ψ> on q0 ---\\n\",\n \"# Let's prepare the state |+> = (1/sqrt(2))(|0> + |1>) for demonstration\\n\",\n \"# This is done by applying a Hadamard gate to q0 (which starts as |0>)\\n\",\n \"teleport_qc.h(qr[0])\\n\",\n \"teleport_qc.barrier() # Barrier for visual clarity\\n\",\n \"\\n\",\n \"# --- Step 2: Create entanglement between q1 and q2 ---\\n\",\n \"# They should form the Bell state |Φ+> = (|00> + |11>)/sqrt(2)\\n\",\n \"# ---\\n\",\n \"## TODO ##: Exercise 1\\n\",\n \"# Apply the necessary gates to qr[1] (Alice's) and qr[2] (Bob's) to create the Bell pair.\\n\",\n \"# Hint: Similar to the CHSH lab, use a Hadamard on the first qubit (q1)\\n\",\n \"# and a CNOT controlled by the first (q1) targeting the second (q2).\\n\",\n \"\\n\",\n \"# Apply Hadamard to qr[1]\\n\",\n \"teleport_qc.h(qr[1]) #TODO REMOVE ANSWER\\n\",\n \"# Apply CNOT from qr[1] to qr[2]\\n\",\n \"teleport_qc.cx(qr[1], qr[2]) #TODO REMOVE ANSWER\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# --- Step 3: Alice's Bell Measurement ---\\n\",\n \"# Alice interacts her message qubit (q0) with her entangled qubit (q1)\\n\",\n \"# ---\\n\",\n \"## TODO ##: Exercise 2\\n\",\n \"# Apply the gates for Alice's part of the Bell measurement.\\n\",\n \"# This involves two gates:\\n\",\n \"# 1. A CNOT gate controlled by the message qubit (q0) targeting Alice's entangled qubit (q1).\\n\",\n \"# 2. A Hadamard gate applied to the message qubit (q0).\\n\",\n \"\\n\",\n \"# Apply CNOT from q0 to q1\\n\",\n \"teleport_qc.cx(qr[0], qr[1]) #TODO REMOVE ANSWER\\n\",\n \"# Apply Hadamard to q0\\n\",\n \"teleport_qc.h(qr[0]) #TODO REMOVE ANSWER\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# Now Alice measures her two qubits (q0 and q1)\\n\",\n \"teleport_qc.measure(qr[0], cr_alice[0]) # Measure q0 -> store in c0 (bit 0 of cr_alice)\\n\",\n \"teleport_qc.measure(qr[1], cr_alice[1]) # Measure q1 -> store in c1 (bit 1 of cr_alice)\\n\",\n \"teleport_qc.barrier()\\n\",\n \"\\n\",\n \"# --- Step 4 & 5: Bob's Conditional Corrections ---\\n\",\n \"# Bob receives Alice's classical bits (c0, c1) and applies gates to his qubit (q2)\\n\",\n \"# ---\\n\",\n \"## TODO ##: Exercise 3\\n\",\n \"# Apply Bob's correction gates to his qubit (qr[2]).\\n\",\n \"# The gates depend on the values measured by Alice (stored in cr_alice).\\n\",\n \"# Rule 1: If Alice's second measurement (c1, stored in cr_alice[1]) is 1, Bob applies an X gate.\\n\",\n \"# Rule 2: If Alice's first measurement (c0, stored in cr_alice[0]) is 1, Bob applies a Z gate.\\n\",\n \"# Use the `with circuit.if_test((classical_bit, value)):` syntax.\\n\",\n \"\\n\",\n \"# Apply X gate to qr[2] IF cr_alice[1] is 1 (True)\\n\",\n \"# The condition checks if the specific classical bit is equal to the value (1 or True)\\n\",\n \"with teleport_qc.if_test((cr_alice[1], 1)): #TODO REMOVE ANSWER\\n\",\n \" teleport_qc.x(qr[2]) #TODO REMOVE ANSWER\\n\",\n \"\\n\",\n \"# Apply Z gate to qr[2] IF cr_alice[0] is 1 (True)\\n\",\n \"with teleport_qc.if_test((cr_alice[0], 1)): #TODO REMOVE ANSWER\\n\",\n \" teleport_qc.z(qr[2]) #TODO REMOVE ANSWER\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"# The protocol is complete! Bob's qubit qr[2] should now be in the state |+>\\n\",\n \"\\n\",\n \"\\n\",\n \"# Let's draw the circuit to check\\n\",\n \"print(\\\"Full Teleportation Circuit (Check your Exercises 1, 2, 3):\\\")\\n\",\n \"# Using 'mpl' requires matplotlib and pylatexenc\\n\",\n \"try:\\n\",\n \" display(teleport_qc.draw('mpl'))\\n\",\n \"except ImportError:\\n\",\n \" print(\\\"matplotlib or pylatexenc not found, using text drawer:\\\")\\n\",\n \" print(teleport_qc.draw('text'))\\n\",\n \"except Exception as e: # Catch potential rendering issues\\n\",\n \" print(f\\\"Matplotlib drawing failed: {e}. Using text drawer:\\\")\\n\",\n \" print(teleport_qc.draw('text'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4aa33305-02cd-424e-bd3d-4788e4cfcc71\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab1_ex6(teleport_qc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"57c81d2f-1067-42c7-a476-375d2faf4ed4\",\n \"metadata\": {},\n \"source\": [\n \"### Simulation and Verification\\n\",\n \"\\n\",\n \"How do we verify that the teleportation worked? We can't directly 'see' the state of Bob's qubit after the protocol. However, since we *prepared* Alice's initial state `|ψ⟩` (we chose `|+>`), we can use a special type of simulation to check if Bob's qubit `q2` ended up in that same state.\\n\",\n \"\\n\",\n \"We'll use `AerSimulator` with `save_statevector` to check if Bob's qubit `q2` ends up in Alice's original state (`|+>`). This simulator calculates the final quantum state vector.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 7 - No Grading: Analyze result of Quantum Teleportation\\n\",\n \"\\n\",\n \"**Your Goal:** Verify the successful teleportation of Alice's quantum state to Bob's qubit by simulating the circuit and visualizing the final state of Bob's qubit.\\n\",\n \"\\n\",\n \"**Task:**\\n\",\n \"* Extract the final statevector and use `plot_bloch_multivector` to visualize Bob's qubit (`q2`). Compare to Alice's initial state (`q0`).\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"85f73394-d361-4c0a-afd3-28dc2005c07a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"# Use Statevector Simulator\\n\",\n \"print(\\\"Using statevector simulator...\\\")\\n\",\n \"sv_simulator = AerSimulator(method='statevector') # Explicitly set method for clarity\\n\",\n \"teleport_qc_sv = teleport_qc.copy() # Work with a copy for statevector simulation\\n\",\n \"teleport_qc_sv.save_statevector() # Save statevector at the end\\n\",\n \"\\n\",\n \"print(\\\"Running statevector simulation...\\\")\\n\",\n \"job_sv = sv_simulator.run(teleport_qc_sv) # shots=1 is default for statevector\\n\",\n \"result_sv = job_sv.result()\\n\",\n \"\\n\",\n \"if result_sv.success:\\n\",\n \" print(\\\"Simulation successful.\\\")\\n\",\n \" final_statevector = result_sv.get_statevector()\\n\",\n \" print(\\\"Statevector retrieved successfully.\\\")\\n\",\n \" print(\\\"\\\\nVisualizing final qubit states (q2 should match initial q0 state |+>):\\\")\\n\",\n \" # q0 was |+> (points along +X). After teleportation, q2 should be |+>.\\n\",\n \" # q0 and q1 states are after Alice's measurement, so they'll be collapsed.\\n\",\n \" \\n\",\n \" display(plot_bloch_multivector(final_statevector)) #TODO REMOVE ANSWER\\n\",\n \" \\n\",\n \"else:\\n\",\n \" print(f\\\"Statevector simulation failed! Status: {result_sv.status}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"be79ba6b-c8d7-44e3-b7fa-c2043ea7cff0\",\n \"metadata\": {},\n \"source\": [\n \"### Interpreting the Results\\n\",\n \"\\n\",\n \"If Exercise 6 was correct, you should see:\\n\",\n \"* `q0` (Alice's original): Random state (collapsed by measurement).\\n\",\n \"* `q1` (Alice's entangled): Random state (collapsed by measurement).\\n\",\n \"* `q2` (Bob's entangled): Bloch sphere vector pointing along the **positive X-axis**. This is the `|+>` state, matching Alice's initial `q0`!\\n\",\n \"\\n\",\n \"**Success!** The state `|ψ⟩ = |+⟩` was teleported.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2c91c647-3843-4574-ad18-c283e16f8d16\",\n \"metadata\": {},\n \"source\": [\n \"### Conclusion: The Magic of Entanglement and Information\\n\",\n \"\\n\",\n \"We have successfully implemented the quantum teleportation protocol!\\n\",\n \"\\n\",\n \"Key Takeaways:\\n\",\n \"* Teleportation transfers an unknown quantum *state* using shared entanglement and classical communication.\\n\",\n \"* It doesn't transfer the physical particle itself.\\n\",\n \"* The original state on Alice's qubit is destroyed (consistent with the No-Cloning Theorem).\\n\",\n \"* Requires classical communication (Alice's measurement results to Bob), so no faster-than-light information transfer.\\n\",\n \"\\n\",\n \"Quantum teleportation is foundational for quantum communication and computation.\\n\",\n \"\\n\",\n \"**Discussion Point**: Why can't Alice just measure `q0` (e.g., in Z then X basis) and send results to Bob for reconstruction? What's different about teleportation?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f65c02e-5def-46aa-9546-ad9026669b4b\",\n \"metadata\": {},\n \"source\": [\n \"# (Bonus Challenge) Teleportation on Real Quantum Hardware\\n\",\n \"\\n\",\n \"We've seen teleportation work perfectly on a simulator. Now, let's try it on a **real IBM Quantum backend**! This is where the true challenges and excitement of quantum computing lie. Real devices have noise and limited qubit connectivity.\\n\",\n \"\\n\",\n \"### Dynamic Circuits: A Key for Real Hardware\\n\",\n \"\\n\",\n \"The teleportation protocol inherently requires **dynamic circuits**: Alice measures her qubits, and Bob *classically communicates* these results to decide which correction gates to apply to his qubit. Qiskit enables these \\\"mid-circuit measurements\\\" and \\\"classical feed-forward\\\" operations.\\n\",\n \"\\n\",\n \"Recent research, such as the work by Bäumer et al. in \\\"Efficient Long-Range Entanglement Using Dynamic Circuits\\\" (PRX QUANTUM 5, 030339 (2024)), demonstrates the power of dynamic circuits. They show that for tasks like CNOT gate teleportation and preparing GHZ states over long distances, dynamic circuits can outperform their purely unitary counterparts on large-scale devices by keeping the quantum circuit depth constant regardless of the distance.\\n\",\n \"\\n\",\n \"For our teleportation protocol, dynamic circuits allow Alice's measurement outcomes to directly control Bob's gate applications in real-time on the quantum processor. Accessing IBM Quantum backends is done through the **IBM Cloud**. You'll need an IBM Cloud account with Quantum services enabled.\\n\",\n \"\\n\",\n \"### The Challenge: – How Far Can You Send It?\\n\",\n \"\\n\",\n \"**Your Goal:** Successfully teleport a quantum state (e.g., $|+\\\\rangle$) from 0th qubit (Alice's message) to another qubit (Bob's) on a real IBM Quantum backend, ideally choosing qubits that are not directly connected on the chip by using Qiskit Dynamic Circuit. Observe and report the fidelity of the teleportation. Try to improve it!\\n\",\n \"\\n\",\n \"**Basic steps for a real backend run:**\\n\",\n \"\\n\",\n \"You can find more detailed explanation at lab0 but here we give you an abstracted intro.\\n\",\n \"\\n\",\n \"1. Set up IBM Quantum Access (via IBM Cloud):\\n\",\n \" * If you don't have one, create an IBM Cloud account and enable access to IBM Quantum services.\\n\",\n \" * Get your API token from the IBM Quantum dashboard on IBM Cloud.\\n\",\n \" * Use `QiskitRuntimeService` to save your account and list available backends.\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \" QiskitRuntimeService.save_account(channel=\\\"ibm_cloud\\\", token=\\\"YOUR_IBM_CLOUD_API_TOKEN\\\", instance=\\\"YOUR_CRN_INSTANCE_ID_OR_CLOUD_INSTANCE_NAME\\\", overwrite=True)\\n\",\n \" service = QiskitRuntimeService(channel=\\\"ibm_cloud\\\")\\n\",\n \" print(service.backends())\\n\",\n \" ```\\n\",\n \" \\n\",\n \"2. Choose a Backend:\\n\",\n \" * Select an available backend from the list. Select [least_busy](https://quantum.cloud.ibm.com/docs/en/api/qiskit-ibm-runtime/qiskit-runtime-service) QPU is also a good choice.\\n\",\n \" * Examine its coupling map to find suitable qubits. For this challenge, try to pick qubits for Alice's message (`q_A_msg`) and Bob's final qubit (`q_B_ent`) that are \\\"far apart\\\" or not directly connected. Alice's entangled qubit (`q_A_ent`) should ideally be connectable to both `q_A_msg` (for her Bell measurement) and `q_B_ent` (for creating the initial Bell pair).\\n\",\n \"\\n\",\n \" ```python\\n\",\n \" #backend_name = \\\"ibm_your_chosen_backend\\\" # e.g., \\\"ibm_brisbane\\\" or a simulator like \\\"ibmq_qasm_simulator\\\" to test flow\\n\",\n \" backend = service.least_busy()\\n\",\n \" # print(f\\\"Coupling map: {backend.configuration().coupling_map}\\\")\\n\",\n \" # print(f\\\"Number of qubits: {backend.configuration().n_qubits}\\\")\\n\",\n \" # from qiskit.visualization import plot_gate_map # if needed\\n\",\n \" # display(plot_gate_map(backend)) # Visualizes connectivity\\n\",\n \" \\n\",\n \" ```\\n\",\n \"You can keep most of the code the same. To use a real quantum processing unit (QPU) instead of a simulator, simply pass the real backend object to the `Sampler`. However, it's crucial to transpile your circuit using a `PassManager`, especially when targeting actual quantum hardware.\\\"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Tips\\n\",\n \"\\n\",\n \"* Technical Reference for Long-Range Entanglement: For a deeper technical dive into creating entanglement over distances on IBM hardware, including techniques that might inspire your qubit selection or circuit optimization, refer to IBM's tutorial: [Efficiently generating long-range entanglement](https://quantum.cloud.ibm.com/docs/en/tutorials/long-range-entanglement).\\n\",\n \" \\n\",\n \"* Simulating Larger Circuits (20+ Qubits):\\n\",\n \" * If you're designing or testing teleportation schemes that scale to 20 qubits or more for the total circuit size (including Alice's message, her ancilla, Bob's qubit, and any intermediary qubits for routing or advanced teleportation schemes), simulating the full statevector can become computationally intensive.\\n\",\n \" * For such larger circuits, if your teleportation circuit (or components of it, like Bell state creation and Bell measurements) can be constructed using only **Clifford** gates (H, S, X, Y, Z, CNOT, SWAP, and their combinations), you can use a more efficient simulator.\\n\",\n \" * Recommendation: Use `AerSimulator(method=\\\"stabilizer\\\")`. The stabilizer simulator is optimized for Clifford circuits and can handle much larger qubit counts efficiently.\\n\",\n \"
\\n\",\n \"\\n\",\n \"Good luck pushing the limits of quantum state transfer! Share your try and result with participants and discuss how you can send qubit more \\\"farther\\\".\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eee76bb9-11ea-45d3-8cd5-853b140e7e9f\",\n \"metadata\": {},\n \"source\": [\n \"## References\\n\",\n \"\\n\",\n \"- 1. Young Thomas 1804I. The Bakerian Lecture. Experiments and calculations relative to physical opticsPhil. Trans. R. Soc.941–16\\n\",\n \"- 2. Fage Arthur and Johansen F. C. 1927On the flow of air behind an inclined flat plate of infinite spanProc. R. Soc. Lond. A116170–197\\n\",\n \"- 3. Clauser, J. F., Horne, M. A., Shimony, A., & Holt, R. A. (1969). Proposed Experiment to Test Local Hidden-Variable Theories. Phys. Rev. Lett., 23(15), 880–884.\\n\",\n \" 4. Bouwmeester, D., Pan, J.-W., Mattle, K., Eibl, M., Weinfurter, H., & Zeilinger, A. (1997). Experimental quantum teleportation. Nature, 390(6660), 575–579.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae55945d-d627-42bd-8ce6-6f39b6c6d973\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Sophy Shin, James Weaver\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Jessie Yu, Meltem Tolunay, Junye Huang\\n\",\n \"\\n\",\n \"**Version:** 1.0.0\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.11\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "solutions/lab-2/lab2-solution.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Lab 2: Cutting through the noise\\n\",\n \"\\n\",\n \"In this lab we dive into the world of quantum noise, and explore the different techniques for navigating the challenges of noise in order to obtain good results with current quantum computers. In the context of a small-size Max-cut problem, we provide a step-by-step guide to reduce noise in order to execute a quantum algorithm on a quantum computer. We present different transpilation and error mitigation techniques to minimize the error and ensure that the results remain correct despite the noise. Finally, the lab concludes with a bonus exercise where all the discussed techniques are implemented on real quantum hardware.\\n\",\n \"\\n\",\n \"# Table of contents\\n\",\n \"\\n\",\n \"0. [Requirements](#requirements)\\n\",\n \"1. [Introduction](#intro) \\n\",\n \" - [Spirit of the Lab](#spirit)\\n\",\n \" - [What is quantum noise?](#quantum-noise) \\n\",\n \" - [Sources of noise](#sources)\\n\",\n \" * [Exercise 1: Find the qubits with longer T1, T2, higher gate fidelities, and smaller readout errors](#exercise_1)\\n\",\n \"2. [Max-cut problem](#max-cut)\\n\",\n \" - [The problem: Define the graph](#the-graph)\\n\",\n \" - [The mapping: a graph into a quantum computer](#the-mapping)\\n\",\n \" * [Exercise 2: Find the Ising Hamiltonian of the Max-cut problem](#exercise_2)\\n\",\n \" - [QAOA solution](#qaoa-solution)\\n\",\n \" - [Checking our solution](#checking)\\n\",\n \"3. [Noisy quantum simulator](#noisy-simulator)\\n\",\n \" - [Choosing backend](#choosing-backend)\\n\",\n \" * [Exercise 3: Counting errors](#exercise_3)\\n\",\n \" - [Estimating errors using NEAT](#neat)\\n\",\n \"4. [Transpiler](#transpiler)\\n\",\n \" - [Minimizing two-qubit gates](#min-two-qubit)\\n\",\n \" - [Find the optimal layout](#opt-layout)\\n\",\n \" * [Exercise 4: Find all the good mappings](#exercise_4)\\n\",\n \" * [Exercise 5: Use the transpiler to find the optimal layout](#exercise_5)\\n\",\n \"5. [Error mitigation](#em)\\n\",\n \" - [Zero Noise Extrapolation (ZNE)](#zne)\\n\",\n \" * [Exercise 6a: Implement global folding on quantum circuits](#exercise_6a)\\n\",\n \" * [Exercise 6b: Implement local folding on quantum circuits](#exercise_6b)\\n\",\n \" * [Exercise 7: Implement Local Zero Noise Extrapolation (ZNE)](#exercise_7)\\n\",\n \"6. [Conclusions](#conclusions)\\n\",\n \"7. [Bonus challenge: Scale it up!](#bonus)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 0. Requirements \\n\",\n \"\\n\",\n \"Before starting this tutorial, please make sure that you have the following libraries installed:\\n\",\n \"\\n\",\n \"* Qiskit SDK v2.0 with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"* Rustworkx (`pip install rustworkx`)\\n\",\n \"* Qiskit Aer v0.17\\n\",\n \"* Pylatexenc\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Needed for grading now after summer school before other code is run.\\n\",\n \"%set_env QC_GRADING_ENDPOINT=https://qac-grading.quantum.ibm.com\\n\",\n \"%set_env QC_GRADE_ONLY=true\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install -U qiskit \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Qiskit version: 2.1.1\\n\",\n \"Grader version: 0.22.12\\n\"\n ]\n }\n ],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.12`. If you see a lower version, restart your kernel and reinstall the grader.\\n\",\n \"Also make sure you have set up everything according to lab 0 and test it with the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Imports\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import rustworkx as rx\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from rustworkx.visualization import mpl_draw as draw_graph\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from scipy.optimize import minimize\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.providers.fake_provider import GenericBackendV2\\n\",\n \"from qiskit.quantum_info import SparsePauliOp, Statevector, DensityMatrix, Operator\\n\",\n \"from qiskit.circuit.library import QAOAAnsatz\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.transpiler import Layout\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import (\\n\",\n \" Session,\\n\",\n \" EstimatorV2 as Estimator,\\n\",\n \" SamplerV2 as Sampler,\\n\",\n \" EstimatorOptions,\\n\",\n \")\\n\",\n \"from qiskit_ibm_runtime.debug_tools import Neat\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from utils import zne_method, plot_zne, plot_backend_errors_and_counts\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab2_ex1,\\n\",\n \" grade_lab2_ex2,\\n\",\n \" grade_lab2_ex3,\\n\",\n \" grade_lab2_ex4,\\n\",\n \" grade_lab2_ex5,\\n\",\n \" grade_lab2_ex6a,\\n\",\n \" grade_lab2_ex6b,\\n\",\n \")\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Introduction \\n\",\n \"\\n\",\n \"### 1.1 Spirit of the lab \\n\",\n \"\\n\",\n \"\\n\",\n \"*Here we walk you through the process of tackling real-world problems by using the right transpilation and error mitigation techniques to maximize performance on IBM quantum hardware*\\n\",\n \"\\n\",\n \"### 1.2 What is quantum noise? \\n\",\n \"\\n\",\n \"Noise is a central topic in designing and using quantum computers, and it is one of the main characteristics of current devices. But what exactly does noise mean in quantum information? We usually refer to the term noise as any undesired transformation that disturbs the expected outcome of the measurement of a quantum state. We categorize errors in two groups:\\n\",\n \"\\n\",\n \"- Coherent errors: These errors are caused by small, consistent mistakes in how quantum operations (gates) are applied. These errors are unitary, meaning they don't destroy information and can, in theory, be reversed. A typical example is a gate that's supposed to rotate a qubit by an angle $\\\\theta$, but instead rotates it by $\\\\theta+\\\\varepsilon$ due to imperfect calibration. For instance, if a Hadamard gate is slightly miscalibrated, it might not create a perfect superposition, producing a biased measurement outcome.\\n\",\n \"- Incoherent errors: The origin of these errors is naturally quantum as it comes from the interaction of the quantum system with the environment. Incoherent errors are the reason why most of the current quantum computers need to be cooled down to a temperature of the order of mK, to reduce these unwanted interactions and preserve the system's coherence. These interactions create mixed states, which introduce randomness into the output and make these errors particularly problematic. A concrete example is $T_1$ relaxation time, which describes how a qubit in the excited state $|1\\\\rangle$ spontaneously decays to the ground state $∣0\\\\rangle$ over time. If a measurement is delayed, this decay can cause the qubit to flip states, leading to incorrect results.\\n\",\n \"\\n\",\n \"### 1.3 Sources of noise \\n\",\n \"\\n\",\n \"As mentioned above, some sources of noise come from different factors, including imperfect implementation of quantum gates as electromagnetic pulses, readout errors, decoherence of the quantum system, and so on.\\n\",\n \"\\n\",\n \"Among the different metrics that can be used to evaluate how good or noise-resilient a quantum computer (or a qubit) is against sources of noise, we can distinguish a few:\\n\",\n \"\\n\",\n \"* T1: relaxation time, is a decay constant that measures how quickly a qubit in state $|1\\\\rangle$ decays into state $|0\\\\rangle$.\\n\",\n \"* T2: dephasing time, is a decay constant that measures how quickly a qubit in state $|+\\\\rangle$ becomes an indistinguishable mixture of $|+\\\\rangle$ and $|-\\\\rangle$ states.\\n\",\n \"* Single-qubit gate fidelity quantifies how closely the actual operation $\\\\mathcal{E}$ performed by the hardware matches the ideal single-qubit unitary $U$. A fidelity of 1 means the gate behaves exactly as intended, with no deviation from the ideal case.\\n\",\n \"* Two-qubit gate fidelity quantifies how closely the actual operation $\\\\mathcal{E}_2$ performed by the hardware matches the ideal two-qubit unitary $U_2$. Since these gates are more complex, they tend to have lower fidelities. As in the single-qubit case, a fidelity of 1 represents a perfect match.\\n\",\n \"* Readout assignment error: probability that a qubit is measured incorrectly, implying that the measured classical bit does not match the actual quantum state of the qubit just before measurement.\\n\",\n \"\\n\",\n \"You can see information about specific IBM quantum devices on the [IBM Quantum Cloud Platform](https://quantum.cloud.ibm.com/computers). Click the device you want to analyze to see detailed characteristics of each qubit, along with a summary of the machine's overall properties. The image below shows an example of this summary for the device `ibm_brisbane`.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"You can also access this information about the devices directly through code.\\n\",\n \"\\n\",\n \"Let's see how it's done!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1: Find the best qubits \\n\",\n \"\\n\",\n \"**Your Goal:** Find the qubits with longer T1, T2, higher gate fidelities, and smaller readout errors.\\n\",\n \"\\n\",\n \"In this first exercise, you will provide the best value of each of the following metrics, as well as the qubit, or pair of qubits, that have this value:\\n\",\n \"\\n\",\n \"- Relaxation time: T1\\n\",\n \"- Dephasing time: T2\\n\",\n \"- Readout error\\n\",\n \"- Single-qubit gate error: 'PauliX' error\\n\",\n \"- Two-qubit gate error: 'ECR' error \\n\",\n \"\\n\",\n \"To make your solution robust, you should write a routine that works for any backend.\\n\",\n \"\\n\",\n \"Note 1: The ECR gate is the two-qubit gate specific to this backend and may differ on other quantum devices, which might have CZ or CNOT gates. You can check the basis gate of backend by using [backend.basis_gates](/docs/api/qiskit-ibm-runtime/ibm-backend).\\n\",\n \"\\n\",\n \"Note 2: Since the best-performing qubits and their values can change over time, we recommend finding these values across all qubits directly using Python's `min()` and `max()` functions for each run.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"The code below demonstrates how to retrieve and store properties for all qubits from the `ibm_brisbane` backend into an array. Use this as a reference when implementing your function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Execute to make arrays of properties\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"# We define a specific backend\\n\",\n \"brisbane_backend = service.backend(\\\"ibm_brisbane\\\")\\n\",\n \"# We obtain the system properties, number of qubits and coupling map\\n\",\n \"properties = brisbane_backend.properties()\\n\",\n \"num_qubits = brisbane_backend.num_qubits\\n\",\n \"coupling_map = brisbane_backend.coupling_map\\n\",\n \"\\n\",\n \"# We define various lists of metrics for all the qubits of the backend\\n\",\n \"t1, t2, gate_error_x, readout_error, gate_error_ecr = [], [], [], [], []\\n\",\n \"for i in range(num_qubits):\\n\",\n \" t1.append(properties.t1(i))\\n\",\n \" t2.append(properties.t2(i))\\n\",\n \" gate_error_x.append(properties.gate_error(gate=\\\"x\\\", qubits=i))\\n\",\n \" readout_error.append(properties.readout_error(i))\\n\",\n \"for pair in coupling_map:\\n\",\n \" gate_error_ecr.append(properties.gate_error(gate=\\\"ecr\\\", qubits=pair))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def find_best_metrics(backend: QiskitRuntimeService.backend) -> list[tuple[int or list, float]]:\\n\",\n \" \\\"\\\"\\\"Finds the best-performing qubits and qubit pair based on various hardware metrics.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # ---- TODO : Task 0 ---\\n\",\n \" # Define metrics lists for the backend\\n\",\n \" properties = backend.properties()\\n\",\n \" num_qubits = backend.num_qubits\\n\",\n \" coupling_map = backend.coupling_map\\n\",\n \" t1, t2, gate_error_x, readout_error, gate_error_ecr = [], [], [], [], []\\n\",\n \" for i in range(num_qubits):\\n\",\n \" t1.append(properties.t1(i))\\n\",\n \" t2.append(properties.t2(i))\\n\",\n \" gate_error_x.append(properties.gate_error(gate=\\\"x\\\", qubits=i))\\n\",\n \" readout_error.append(properties.readout_error(i))\\n\",\n \" for pair in coupling_map:\\n\",\n \" gate_error_ecr.append(properties.gate_error(gate=\\\"ecr\\\", qubits=pair))\\n\",\n \"\\n\",\n \" # ---- TODO : Task 1 ---\\n\",\n \" # Goal: Obtain the best value and the index or indices of the qubits of the following metrics:\\n\",\n \"\\n\",\n \" # find the best qubit (index_t1_max) with the longest T1 and its value (max_t1)\\n\",\n \" index_t1_max = t1.index(max(t1))\\n\",\n \" max_t1 = t1[index_t1_max]\\n\",\n \"\\n\",\n \" # find the best qubit (index_t2_max) with the longest T2 and its value (max_t2)\\n\",\n \" index_t2_max = t2.index(max(t2))\\n\",\n \" max_t2 = t2[index_t2_max]\\n\",\n \"\\n\",\n \" # find the best qubit (index_min_x_error) with the smallest x gate error and its value (min_x_error)\\n\",\n \" index_min_x_error = gate_error_x.index(min(gate_error_x))\\n\",\n \" min_x_error = gate_error_x[index_min_x_error]\\n\",\n \"\\n\",\n \" # find the best qubit (index_min_readout) with the smallest readout error and its value (min_readout)\\n\",\n \" index_min_readout = readout_error.index(min(readout_error))\\n\",\n \" min_readout = readout_error[index_min_readout]\\n\",\n \"\\n\",\n \" # find the best qubit pairs with minimum ecr error (min_ecr_pair) and its value (min_ecr_error)\\n\",\n \" index_min_ecr_error = gate_error_ecr.index(min(gate_error_ecr))\\n\",\n \" min_ecr_pair = coupling_map.get_edges()[index_min_ecr_error]\\n\",\n \" min_ecr_error = gate_error_ecr[index_min_ecr_error]\\n\",\n \" min_ecr_error = min(gate_error_ecr)\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" solutions = [\\n\",\n \" [int(index_t1_max), max_t1],\\n\",\n \" [int(index_t2_max), max_t2],\\n\",\n \" [int(index_min_x_error), min_x_error],\\n\",\n \" [int(index_min_readout), min_readout],\\n\",\n \" [list(min_ecr_pair), min_ecr_error],\\n\",\n \" ]\\n\",\n \" return solutions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations! 🎉 Your answer is correct.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex1(find_best_metrics)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Good job! You've learned how to access the device properties and search for specific characteristics among them. Perhaps at this point you might be wondering whether we cannot simply look for the qubits with the best metrics. Well, it is not that easy. \\n\",\n \"\\n\",\n \"If you print the indices of the qubits you've obtained, you can see that they aren't clustered in a specific region of the device. Instead, they're spread out across different areas. This highlights a key challenge: a qubit with long relaxation time might also have a large single-qubit gate error, or vice versa. These trade-offs mean that choosing qubits based only on one metric may not lead to optimal performance for certain quantum algorithms. Furthermore, you also need to take into account that not all the qubits are connected, as shown on the coupling map. These factors make the task of choosing the specific layout of qubits an intricate, but fascinating, challenge. Fortunately for us, in Qiskit there's a dedicated tool designed to handle this problem: **the transpiler**. \\n\",\n \"\\n\",\n \"We'll explore the transpiler in detail in a later section.\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2. The problem: Max-cut \\n\",\n \"\\n\",\n \"The maximum cut ([Max-cut](https://en.wikipedia.org/wiki/Maximum_cut)) problem is a graph problem that lies in the category of [NP-hard](https://en.wikipedia.org/wiki/NP-hardnes), which means no algorithm exists that can solve it in polynomial time. Max-cut is an optimization problem with a wide range of applications including clustering, network science, statistical physics, and machine learning. The goal of this problem is to divide the nodes of a graph into two sets using a single cut, in such a way that the number of edges traversed by this cut is maximized. \\n\",\n \"Let's visualize it with one example. \\n\",\n \"\\n\",\n \"In the picture below you see the max-cut solution of a five-node problem that exemplifies how the graph is divided, maximizing the number of edges cut. Another way to view this problem is to consider that finding the maximum cut in a graph means identifying a way to divide the nodes into two groups such that the number of edges connecting nodes from different groups is as large as possible.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"In this lab, we will solve a Max-cut problem using a quantum algorithm. We'll also study how quantum noise affects our solution and discuss strategies to reduce its impact, ensuring we can still obtain accurate results despite the noise.\\n\",\n \"\\n\",\n \"## 2.1 The problem: Define the graph \\n\",\n \"\\n\",\n \"Now that we have a little bit of context of the Max-cut problem, let's choose a specific graph for which we want to find the maximum cut. In particular, we will choose the graph of the coupling map of a hypothetical quantum computer with all-to-all connectivity $^*$.\\n\",\n \"\\n\",\n \"$^*$ *Note that the last qubit is not actually connected to the first one, to explicitly break the symmetry of the problem and reduce the number of solutions.*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# We define the seed\\n\",\n \"seed = 43\\n\",\n \"# We define the number of nodes:\\n\",\n \"n = 5\\n\",\n \"# We define the graph\\n\",\n \"graph = rx.PyGraph()\\n\",\n \"graph.add_nodes_from(np.arange(0, n, 1))\\n\",\n \"generic_backend = GenericBackendV2(n, seed=seed)\\n\",\n \"weights = 1\\n\",\n \"# We make it explicitly asymmetrical to have a smaller set of solutions\\n\",\n \"graph.add_edges_from([(edge[0], edge[1], weights) for edge in generic_backend.coupling_map][:-1])\\n\",\n \"draw_graph(graph, node_size=200, with_labels=True, width=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2 The mapping: a graph into a quantum computer \\n\",\n \"\\n\",\n \"To solve our graph problem with a quantum computer, we must first translate it into a language the computer understands. A common and powerful approach is to reframe the optimization problem in terms of a **Hamiltonian**, a mathematical object from quantum mechanics that describes the total energy of a system. The solution to our problem will then be encoded in the lowest energy state (the \\\"ground state\\\") of this Hamiltonian.\\n\",\n \"\\n\",\n \"There are two closely related languages for formulating such problems:\\n\",\n \"\\n\",\n \"1. **QUBO (Quadratic Unconstrained Binary Optimization):** This is a language often used in computer science and classical optimization. It uses binary variables **xi ∈ {0, 1}** and seeks to minimize an objective function of the form:\\n\",\n \" \\n\",\n \" $$ \\\\text{minimize} \\\\sum_{i} Q_{ii}x_{i} + \\\\sum_{ii ∈ {+1, -1}** and seeks to minimize an energy Hamiltonian of the form:\\n\",\n \"\\n\",\n \" $$ H = -\\\\sum_{i} h_{i}s_{i} - \\\\sum_{ii = 2xi - 1**.\\n\",\n \"\\n\",\n \"For this lab, we will map our graph problem directly to an **Ising Hamiltonian**. This is the most direct path for a quantum computer because the eigenvalues of the Pauli Z operator are **+1** and **-1**, which perfectly correspond to the spin variables **si** in the Ising model.\\n\",\n \"\\n\",\n \"### From Max-cut to an Ising Hamiltonian\\n\",\n \"\\n\",\n \"For the Max-cut problem on a graph $G = (V, E)$, we want to partition the vertices $V$ into two sets. The goal is to maximize the number of edges connecting vertices in different sets. We can express this objective using our spin variables **si**. If two connected nodes `i` and `j` are in different partitions (si ≠ sj), the term $s_i * s_j$ will be -1. If they are in the same partition (si = sj), the term will be +1.\\n\",\n \"\\n\",\n \"To *maximize* the cuts, we want to *minimize* the sum of $s_i * s_j$ over all connected edges. This gives us our cost Hamiltonian:\\n\",\n \"\\n\",\n \"$$ H_{cost} = \\\\sum_{(i,j) \\\\in E} Z_i \\\\otimes Z_j $$\\n\",\n \"\\n\",\n \"Here we've replaced the classical spin variables `s_i` with their quantum counterparts, the Pauli **Zi** operators, which act on the `i`-th qubit. The ground state of this Hamiltonian is the specific arrangement of qubit states (`|0⟩` or `|1⟩`) that minimizes this energy, directly giving us the solution to the Max-cut problem.\\n\",\n \"\\n\",\n \"Please refer this [tutorial](https://quantum.cloud.ibm.com/docs/tutorials/quantum-approximate-optimization-algorithm) for more details.\\n\",\n \"\\n\",\n \"That's exactly what the next exercise is all about!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2: From Graph to Hamiltonian \\n\",\n \"\\n\",\n \"**Your Goal:** Convert the 'graph' object we just created into an Ising Hamiltonian, which is the cost function for our Max-cut problem.\\n\",\n \"\\n\",\n \"In this second exercise you must find the way of mapping the graph problem you are given to a Hamiltonian using the identity and Pauli gates.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \" Hint 💡: (Click to expand)\\n\",\n \" The intuition of how to build this Hamiltonian consists of considering a Hilbert space of size n x n in which we add as many terms as edges in the graph, and in which the nodes connected by each edge are represented by Pauli-Z matrices, whereas the rest of the nodes are represented by the identity.
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Cost Function Hamiltonian: SparsePauliOp(['IIIZZ', 'IIIZZ', 'IIZIZ', 'IIZIZ', 'IZIIZ', 'IZIIZ', 'ZIIIZ', 'ZIIIZ', 'IIZZI', 'IIZZI', 'IZIZI', 'IZIZI', 'ZIIZI', 'ZIIZI', 'IZZII', 'IZZII', 'ZIZII', 'ZIZII', 'ZZIII'],\\n\",\n \" coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\\n\",\n \" 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,\\n\",\n \" 1.+0.j])\\n\"\n ]\n }\n ],\n \"source\": [\n \"def graph_to_Pauli(graph: rx.PyGraph) -> list[tuple[str, float]]:\\n\",\n \" \\\"\\\"\\\"Convert the graph to Pauli list.\\\"\\\"\\\"\\n\",\n \" pauli_list = []\\n\",\n \"\\n\",\n \" # ---- TODO : Task 2 ---\\n\",\n \" # Goal: Convert the graph into a list like: [['PauliWord_1', weight_1], ['PauliWord_2', weight_2],...]\\n\",\n \" num_nodes = len(graph.nodes())\\n\",\n \" # Loop over each edge in the graph\\n\",\n \" for edge in graph.edge_list():\\n\",\n \" pauli_string = \\\"\\\"\\n\",\n \" # Loop over each node in the graph to add Z or I\\n\",\n \" for i in range(num_nodes):\\n\",\n \" if i == edge[0] or i == edge[1]:\\n\",\n \" pauli_string += \\\"Z\\\"\\n\",\n \" else:\\n\",\n \" pauli_string += \\\"I\\\"\\n\",\n \" # We reverse the string assuming qubit 0 is on the right (standard convention)\\n\",\n \" pauli_string = pauli_string[::-1]\\n\",\n \" weight = graph.get_edge_data(edge[0], edge[1])\\n\",\n \" pauli_list.append((pauli_string, weight))\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" return pauli_list\\n\",\n \"\\n\",\n \"\\n\",\n \"max_cut_paulis = graph_to_Pauli(graph)\\n\",\n \"cost_hamiltonian = SparsePauliOp.from_list(max_cut_paulis)\\n\",\n \"print(\\\"Cost Function Hamiltonian:\\\", cost_hamiltonian)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations! 🎉 Your answer is correct.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex2(graph_to_Pauli)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2.3 QAOA solution \\n\",\n \"\\n\",\n \"Now that we have successfully mapped our Max-cut problem to an Ising Hamiltonian, we have translated a classical graph problem into a quantum ground state search problem. The solution to the original problem is encoded in the state with the lowest possible energy (the \\\"ground state\\\") of this Hamiltonian.\\n\",\n \"\\n\",\n \"Several quantum algorithms can be employed to find this ground state. One of the most prominent is the Quantum Approximate Optimization Algorithm ([QAOA](https://en.wikipedia.org/wiki/Quantum_optimization_algorithms)). QAOA is known for its adaptability, relatively shallow circuit depth, and strong performance across a range of optimization tasks.\\n\",\n \"\\n\",\n \"\\n\",\n \"If you want a full description of QAOA, we recommend this [tutorial](https://quantum.cloud.ibm.com/docs/tutorials/quantum-approximate-optimization-algorithm) as well as the [Utility-scale QAOA](https://quantum.cloud.ibm.com/learning/courses/quantum-computing-in-practice/utility-scale-qaoa) lesson from the Quantum computing in practice course. Here, we will briefly comment on the main idea behind it. \\n\",\n \"\\n\",\n \"QAOA is a variational quantum algorithm inspired by the adiabatic theorem, which states that a quantum system remains in its ground state if it evolves slowly enough. QAOA mimics this by starting in the ground state of a known Hamiltonian (the mixer) and evolving toward the ground state of a Hamiltonian that encodes our problem (the cost). This is done through alternating layers of the two Hamiltonians, with each step controlled by tunable parameters that guide the system toward the optimal solution.\\n\",\n \"\\n\",\n \"After this little explanation of the theory, let's get some hands-on practice using `QAOAAnsatz` from Qiskit to implement QAOA and solve our Max-cut problem.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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mUXC7Hxx9/DLlcLnQpRERkIgyURERERGQUBkoiIiIiMgoDJREREREZhYGSiCzKyckJI0eOhJOTk9ClEBGRiUiFLoCIHiy+vr544403hC6DiIhMiD2URGRRlZWVuHHjBiorK4UuhYiITISBkogsKjMzE3FxccjMzBS6FCIiMhFe8iYiIi1qtRpKpVLoMvQikUggEomELoPogcRASUREWpRKJbZv3y50GXqJi4uDVMpfa0RC4CVvIiIiIjIKAyURERERGYXXBojIosLDw3Hq1CmhyyAiIhNiDyURERERGYWBkogsKjs7G7Nnz0Z2drbQpRARkYkwUBKRRZWXl+PixYsoLy8XuhQiIjIRBkoiIiIiMgoDJREREREZhYGSiIiIiIzCQElEFuXt7Y1ly5bB29tb6FKIiMhEOA8lEVmUi4sLYmNjhS6DmqHCwkJUVFTwwwhRE8RASUQWVVhYiH379iEmJgaurq5Cl0NmlpeXh5SUFGRmZiIzMxNFRUVQKpWQSqVwd3dHYGAgAgICEBkZiVatWtW7n8LCQrz55psoLy/Ha6+9xlBJ1MQwUFqAXC7H6tWr8d1330Emk8HT0xOPPvooVqxYgRdeeAGffvopNm7ciHnz5gldKpHZFRQUYM2aNejcuTMDZQulVCpx5swZ/Prrr7h48WK97eRyOdLS0gAAEokEvXv3xvDhwxEREQGRSKRpVxsmc3NzAQAffvghXn/99TptiEhYDJRmdu7cOcTGxiI/Px8ODg7o0KEDcnNzsWHDBly7dg137twBAHTt2lXQOjs/Px7unQPhHhkIp/ZtUHrjJr7t/ZygNRFR85OdnY0PPvgAWVlZWutEIhFcXFwgkUhQVVWFkpISzTqlUonjx4/j+PHj6N69O5588km4ublphUkPDw/84x//YJgkamIYKM1ILpdjzJgxyM/Px4IFC7B06VI4OTkBAFavXo1FixZBKpVCJBIhMjJS0Fp7LJmGijsluHMhA9bO9oLWQkTNj0qlwo8//ohvv/0WSqVSs9zLywvR0dEICwuDv78/bG1tNeuKi4uRmZmJlJQUHDx4EMXFxQCAs2fPYuHChZgyZQp2795dJ0y+9tpraN26tWVPjogaxEBpRi+88AJkMhnmzZuHtWvX1lmXkJCArVu34vz58wgICICzs7NAVdb4ts9zKL1+EwAw7sDbsHKwbWALIqIaKpUKmzdvxsGDBzXL/Pz8MH36dERGRkIs1j2hiLOzM7p06YIuXbpg4sSJOH78OL766isUFRWhrKwMn3zyiaYtwyRR08Zpg8wkNTUV27Ztg4eHB1auXKmzTY8ePQAAXbp00SyrDaC9e/eGjY2NxS7r1IZJInOzt7dHnz59YG/PnvCWQK1W4+OPP9aESZFIhEceeQQrV65E165d6w2Tf2dlZYVBgwZh7dq16NmzZ511jo6ODJNETRx7KM0kMTERKpUK06ZNg6Ojo842dnZ2AOoGyqtXr2L79u3o1asXrK2tcfToUYvUS2Qp7dq1w8aNG4Uug0xk//792L9/P4CaG2uef/559O3b1+D9KRQKzSXuWqWlpSgqKmKgJGrC2ENpJrU/YKOjo+ttI5PJANQNlIMGDUJeXh527NiBmJgY8xZJJAClUonS0tI64+yoeZLL5fjPf/6jef3cc88ZFSb/fgNO7YduAPjggw9QVVVleLFEZFYMlGaSnZ0NAGjfvr3O9QqFQtP7+NdA2djLQ0TNVXp6OoYOHYr09HShSyEjffLJJygvLwcADBkyBP379zd4X7ru5l65ciWCgoIAALm5ufj++++NL5qIzIKXvM2krKwMADQ/bP9u27ZtkMvlcHJyQkBAgFlr6dmzJ27n3cRS9Dbrcah5CQ0JRbVIZfR+JkyYoFf7mzdrxuvu3r0bZ86cadQ248eP17uupmb8Ey/CwdEZefl58PPz03rd1FhbW9c7/hsAcnJy8PvvvwMA3NzcMHPmTIOPpStM1o6ZfPbZZ/Hyyy9DoVDg119/xSOPPAIbGxud+wkNDWUvJpERvLy8kJycbNC2DJRm4uXlhcLCQpw9exZRUVF11uXl5WHhwoUAgMjISLPfeJOfn49beflAG7MehpqZ3LxcVKmNv+xc++GpsWo/ZJWXlzd625ycHL3rampU/7vEr1IqkZOTo/W6qakvtNXau3ev5uvRo0cbfJPV/cIkUHO3eL9+/XD48GGUlZXh+PHjGDJkiM595ebmorKy0qA6iMg4DJRmEhMTg9TUVKxatQrDhw9HaGgoAOD06dOYMWMG5HI5AMtMaO7l5QUrtRgwvjOKWhAfbx+T9FA6ODjo1b42RNrZ2TV6W19fX73ramrEEonmb19fX63XTY21tXW965RKJQ4dOqRpN3jwYIOO0VCYrDV8+HAcPnwYAHDgwIF6A6WPjw97KImM4OXlZfC2DJRmUjvP5I0bN9CxY0eEh4ejoqICV69eRWxsLPz9/bFnz5464yfNJTk5GdX3KvBV0HSzH4uajyvpV2Blb/x8o6dPn9ar/eXLl5GYmIjY2FiEh4c3apv169cbUFnTsuK9r1BcWgZvL2/IZDKt102NQqHA9u3bda7LycnR9DR369ZN7w8VQOPDJAAEBwejTZs2KCgoQEZGBhQKBaRS7V9fV65c0bmciMyP33lm4ufnh6SkJCxcuBCHDh1CVlYWOnTogE2bNuGpp57SDDS3RKBsjMAJg+Do5wkAsHV3hthKisgX4wAApbJbyPj2sJDlUQsSHByMPXv2aJ4aRc1PRkaG5uvg4GC9t9cnTAI1c1sGBQWhoKAA1dXVkMlk8Pf3N6h2IjIPBkozioiIwM6dO7WWl5aWIisrC2KxGJ06dRKgMm2hU4fBq1/HOsu6L5oKAMg/lsJASSYjlUrh6uoqdBlkhOvXr2u+1jfY6RsmawUGBuLYsWMAambRYKAkaloYKAWQkpICtVqN0NBQnQPZv/32WwDApUuX6rz29/fXeoKEqfwSt9Qs+yX6O5lMhnXr1mH+/PlN8u5mati9e/c0X7dq1arR2xkaJgHAxcVF83V9s2cQkXAYKAVw4cIFAPVf7p44caLO17NmzcLnn39u1tqIzK20tBRJSUl46qmnhC6FDDRz5kxMmDABVVVV8PT0bPR2crkct2/fBqD/s7l79OiBdevWwdraut6njxGRcBgoBdBQoFSr1ZYsh4hIL/b29gZNExQSEoLFixfjk08+wcKFC/V6lKKhxyQiy2CgFEBDgZKIqKUKDw/HqlWr+FQwohaGgVIAtc/5JiJ6EDFMErU8/K4mIovy9PREfHy8XmPviIioaWMPJRFZlLu7O6ZNmyZ0GUREZELsoSQiiyouLsa+fftQXFwsdClERGQiDJREZFG5ublYsmSJZi5CIiJq/hgoiYiIiMgoDJREREREZBQGSiIiIiIyCgMlEVmUjY0NwsLCYGNjI3QpRERkIpw2iIgsKiAgAFu2bBG6DCIiMiH2UBIRERGRURgoicii0tLS0L9/f6SlpQldChERmQgDJRFZlFqtRnV1NdRqtdClEBGRiXAMJRERaZFIJIiLizPZ/tZs2oaSsjI4OThg4TOTtV6bgkQiMcl+iEh/DJRERKRFJBJBKjXdrwg1AJW65m+pVKr1moiaN17yJiIiIiKj8GMhEVmUv78/EhMT4evrK3QpRERkIgyURGRRtra2CAoKEroMIiIyIV7yJiKLysvLw/Lly5GXlyd0KUREZCIMlERkUUVFRdixYweKioqELoWIiEyEgZKIiIiIjMJASURERERGYaAkIiIiIqMwUBKRRYnFYnTr1g1iMX/8EBG1FPyJTkQWpVKp8Pvvv0OlUgldChERmQgDJREREREZhYGSiIiIiIzCQElERERERmGgJCKLcnJywsiRI+Hk5CR0KUREZCJ8ljcRWZSvry/eeOMNocsgIiITYg8lEVlUZWUlbty4gcrKSqFLISIiE2GgJCKLyszMRFxcHDIzM4UuhYiITISXvImIiHRQq9VQKpVCl6EXiUQCkUgkdBn0AGKgJCIi0kGpVGL79u1Cl6GXuLg4SKX81U6Wx0veRERERGQUBkoiIiIiMgr7xYnIosLDw3Hq1CmhyyAiIhNiDyURERERGYWBkogsKjs7G7Nnz0Z2drbQpRARkYkwUBKRRZWXl+PixYsoLy8XuhQiIjIRBkoiIiIiMgoDJREREREZhYGSiIiIiIzCQElEFuXt7Y1ly5bB29tb6FKIiMhEOA8lEVmUi4sLYmNjhS6DqFlSqVQQi9kXRE0PAyURWVRhYSH27duHmJgYuLq6Cl0OkdmpVCoUFBQgMzMTeXl5qK6uhlgshoODA9q3b4+AgAA4ODg0uJ+0tDRs2rQJL730Enx8fCxQOVHjMVASkUUVFBRgzZo16Ny5MwMltWiZmZn49ddfcfLkSdy7d+++bf38/DB06FAMHjxYZ7hMS0vDypUrUVFRgTfffBPLli1D69atzVU6kd7Yb24hcrkcCQkJCA4Ohq2tLdq2bYv4+HiUlZVhzpw5EIlEePfddwWrzznQG10XTsaonSsw5eInmJa+BWP3rkFk/KOQ2tkIVhcRUXNz7do1vPrqq1i8eDEOHDjQYJgEAJlMhi+//BLPPfccvvzyS1RUVGjW/TVMAjXhs1WrVuYqn8gg7KG0gHPnziE2Nhb5+flwcHBAhw4dkJubiw0bNuDatWu4c+cOAKBr166C1RgyZSjCnxiJ678m49p3SVArlPDq1xHdX34M/mP6YdfoJVBWVAlWHxFRU1ddXY3t27djx44dUKlUmuV2dnbo0KEDAgMD0a5dO9ja2kKlUqGwsBAZGRm4evUqMjIyAACVlZX4+eefcebMGcydOxdisbhOmOzcuTMWLlwIa2trQc6RqD4MlGYml8sxZswY5OfnY8GCBVi6dCmcnJwAAKtXr8aiRYsglUohEokQGRkpWJ1Zu07gj43fo7rkz0/SaV/+iuLMPHR5cQJCpg7F5c9+Eaw+IqKmrLS0FKtWrUJ6erpmmZ+fH2JjY9G/f3/Y2trq3G7IkCEAanoo9+7diwMHDqCqqgoFBQVYtmwZpFIpFAoFAIZJatp4ydvMXnjhBchkMsybNw9r167VhEkASEhIQJcuXaBQKODv7w9nZ2fB6rx9/lqdMFkr88djAADX8HaWLolaKHt7e/Tp0wf29vZCl0JkEmVlZVi+fLkmTEokEkycOBFvvfUWhg0bVm+Y/Cs/Pz888cQTWLVqFcLCwjTLGSapuWCgNKPU1FRs27YNHh4eWLlypc42PXr0AAB06dJFs+zbb79FXFwc2rdvD3t7e4SHh+OVV15BaWmpRer+KwcfdwBA+a27Fj82tUzt2rXDxo0b0a4dP6RQ86dSqfD2228jKysLQM20WMuXL0dcXBykUv0vAnp7e2Pq1KmQSCR1lg8ZMoRhkpo0BkozSkxMhEqlwrRp0+Do6KizjZ2dHYC6gXLt2rWQSCRYsWIFdu/ejWeffRYffPABRo4cWWdcjrmJxGJ0eXECVNUKZHx/xGLHpZZNqVSitLQUSqVS6FKIjLZnzx6kpKQAAJycnPDaa68hICDA4P2lpaXhrbfe0vr++Pzzz1FUVGRUrUTmxDGUZrR//34AQHR0dL1tZDIZgLqB8qeffoKnp6fm9eDBg+Hp6Ylp06bhyJEjGDRokJkqrqv3G4+jda8wnFnxFYqv5VrkmNTypaenY+bMmfjyyy8RHh4udDlEBsvPz0diYqLmdXx8PHx9fQ3e39/v5u7cuTOsra1x5swZlJSU4NNPP8X8+fONrpvIHBgozSg7OxsA0L59e53rFQoFjh49CqBuoPxrmKzVs2dPAEBOTo7edfTs2RO3825iKXo3eptuCVMQMedhpG35FRc2fq/3ManpCw0JRbXI+B7vCRMm6NX+5s2bAIDdu3fjzJkzjdpm/PjxetfV1Ix/4kU4ODojLz8Pfn5+Wq9buuZ4/tbW1vUOVwKA77//HlVVNbNfjBgxAp06dTL4WLrC5MKFC3Hv3j1cuXIFJSUlOHnyJDIzM+/bAxoaGqqpiUhfXl5eSE5ONmhbBkozKisrAwCUl5frXL9t2zbI5XI4OTk1eInkwIEDAICIiAi968jPz8etvHygTePad10wCV3mT0B64n4cT9is9/GoecjNy0WV2vjLzrX/zxur9vuhvLy80dsa8kGqqVH97xKmSqlETk6O1uuWrjmev41N/XPwlpaW4tixmpsW7e3tMXXqVIOPU1+YtLa2hrW1NSZOnIhPP/0UALB37148/fTT9e4rNzcXlZWVBtdCZCgGSjPy8vJCYWEhzp49i6ioqDrr8vLysHDhQgBAZGQkRCJRvfvJycnBq6++ipEjRxo0V6WXlxes1GKgEZ1RXRdMQteXJuHqtgM4uuADvY9lCVaOdoh4ahTax/aGc4A3RBIxSm/cxI29Z5DywQ5U3C6ud1uRWIwJyR/AwdsdZ1f/F3+s+7betmJrKcJmjEDAuP5oFeoHiY0VyvJuI/fwH7j4/o8ovX6z3m1dQnwx/vA7AICfH3kVN0+mGn7CZuLj7WOSHsrGPDLur2pDpJ2dXaO3NeYyYlMh/t9NFmKJBL6+vlqvW7rmeP73uwnm0KFDqK6uBlAzLKl2PLy+7hcmaw0cOBCJiYkoLy/H0aNHMX369HpnSfDx8WEPJRnMy8vL4G0ZKM0oJiYGqampWLVqFYYPH47Q0FAAwOnTpzFjxgzI5XIA95/QvLS0FOPGjYO1tbXmE6q+kpOTUX2vAl8FTb9vuy7zJ9SEyW8O4cj89wG12qDjmZNzoDeGJ/4Ljn6eyP75JNK37odKoYBn91B0eGoUQqZEY9+MtyD/PV3n9r5Du8HB2x3FmXkInjSk3kBp6+GC4VtfgXvnQOQcOo9z//4a1WUVcOvQHsGToxE8aQgOPbseN/ac1rl9yNRhqCq5B2VFFUKmRDfJQHkl/Qqs7BuezqQhp0/rfg/qc/nyZSQmJiI2NrbRYyjXr19vQGVNy4r3vkJxaRm8vbwhk8m0Xrd0zfH8FQoFtm/frnPdH3/8ofk6JibGoP03JkwCNR+++vfvj3379qGyshJpaWno1q2bzn1euXLFoLvLiYzFu7zNKCEhAe7u7rhx4wY6duyIzp07IyQkBL1790ZgYCCGDh0KoO74yb8qLy/HmDFjNM+D9fb2Nlut4Y+PRLeEKSiV3UJe0h8IfHQAAuMGav54DxJu0vVaEjtrDPviZdh7ueG3mW/h4FP/xuXPf8GV/+zD0X++j5/H/gtiqRTDvlgEW3fdc3qGPDYUxZl5OP36F3D294JXv4462w35aAHcOwfi2MIPsXfKm7j00S6kb/0NJ//1KX4ctgAVd4ox+IMX0SpUe+yXSCpB0IRByPrpODK+S4L/mChIHYwPbi1FcHAw9uzZg+DgYKFLITKIWq3WPNnG2dkZPj4+eu+jsWGyVocOHTRf1x6bqClhoDQjPz8/JCUlYdSoUbC1tUVWVhbc3NywadMm7Nq1C1euXAGgO1BWV1djwoQJSE5Oxu7du+v8MDEHj65BAABHP08M3PA8Br0bX+dPl/g4sx6/MUKmDoNLsC8ufbQLst/Oaq2/ff4azqzcCjvPVuj03Dit9bYeLmgb0wPXvjkE2W9nUX7rLkIeG6bVzm94D3j17YDMHcdw5T/7tNaXXr+J4wmbIbWzQdeFk7XWtx3RE3aerXDtm4O4uu0grBzsEDCuv4Fn3fJIpVK4urqyF4Wardu3b6OkpAQAEBAQcN8hS7roGyZrj1OLgZKaIv5EN7OIiAjs3LlTa3lpaSmysrIgFou17gysnbvyt99+w88//4zevRt/d7ahjrz4Ho68+J7Zj2MM/9F9AUBnyKt19esD6P3G42g/qi+S39xSZ13QxMEQScS49s0hqJUqZHyXhLCZI3DCyb7OU4L8R0f97zh76z1Ozv7fUZYjh9+w7hBbS6GqUmjWhUwdipLsAhScqLnMfftCBkKmDEX61t/0P+kWSCaTYd26dZg/f36TvbuX6H4KCws1X+t75ciQMAkAbdq0gUgkglqtxt27d/Wumcjc2EMpkJSUFKjVaoSEhGgNrv7HP/6Bb775BvPnz4e9vT1OnDih+XPr1i2BKhZeq7B2qCq5h5Ks/HrbKMurUHQ1F07t20D6t/GBIVOHouBEKkplNe/h1a8PQmpng8DxA+oeJ7wtAOD2hcz71nP7YiakdjZwDvjzF4pdG1f4DumKa98c0iy7+vVBtO4VBpeQpnnjgaWVlpYiKSlJkCc/EZmCq6srJk2ahEceeQSRkY0fDlRVVYX169frHSYBQCwWY9y4cYiLi8OwYdpXVoiExkApkAsXLgDQfbl79+7dAIC33noLUVFRdf7s2rXLonU2JdZOdqgu1n7e+N9Vl9a0sXL8M1B69gxDqxA/XP36oGZZ4aVs3L6QiZCpQ+sex7Em4Dd0rOrSmulvrJ3//EAQPDkaIrEIV7/58zgZ3yVBWVWNkClD/74LImqGPDw88Oijj2LKlCno3r17o7eztrZGfHw8bGxsDHo295QpUzBx4kTN+HuipoSXvAVyv0BZ+0xYqquqpBxWTg1PzWHlaA+VUomKOyWaZSFTh0JZVY07FzPh5P/ntAi5B8+h8/Pj4RrRHoWpNRPRV9UGUmd7VN2tvxfNyrGmlqq/BM+QKdEovJQNkVhc5zg3T6chaMIgnFnxFdRKyz0+k4ialvDwcLz++uvw9fXls7mpRWGgFMj9AiXpdjftOryiOsLJ36vey94SO2u4BPugTCaHWlEzcbLU3hYBY/tBYm2FsfvW6twuZGo0Tr32ec1xLt+AR2QQ3DsHIC/pQr31uHcKgKK8EsWZeQAAr6iOmsvfccff1blN2+E9cP0X/abZIaKWxZhnfRM1VQyUAql9zjc1XtbOE/CK6ojQx4bhzIqvdLYJnjgEEmsrXNt+WLPMf2w/WDna1TyTPCNPa5uIJx9GYNwgJL/5H6iqFcj++SSCJw1B6GMx9QZK3+iucPD1QNauE5obcoKnDoWyogpJL2yEWqU9h2fU6mcQMnXYAx8oPT09ER8fr/MRo0RE1DwxUFKzkZ74GyKeGIkOz4xG/vEU5Bw4V2e9W+cAdF/8GO7l38Hlz37RLA+ZOhQVd0pw8f0fdV5ultrbYOCG59H2oV7I3nkcN/acRsGpVAQ80h85h87j6n/rhn9HP09ErX4GivJKnFuzDQBg5WQP/9F9kXPoPLJ+Oq6zfr+Y7giaMBh2rVuh/OZd496MZszd3R3Tpk0TugwiIjIhBkpqNpTlVfjt8VUYvvUVDNuyGNm7TiL/WArUSiU8ugYjaMJgVBWV4rfHV6FCXgQAcAn2QZve4Uj/7/56xy7e+DUZyqpqhD42FNk7a8Lgwaf+jeFfvYIB655DwNgoyH77HYp7FXD935NyxFIJDj+3HnfTbgAAAscPgNTOBtm7TtRbf/aukwiZMhTBk4bgwrs/mPbNaUaKi4tx6tQp9O7dG87OuiegJyKi5oWBkpqV4mu52DHspf89y7sP/IZ1g5VDzc0xhZevY/e4f9W9SWZqzfQa2T+frHefVUVlyD+WAu9BkbD3cce93Nsov3kXu0YvQdjMmmd5d0uYDIm1Fe4VFCLz+yO4+P6PKMku+MtxhkJVrcCNX5PrPU7u4fOoKrmH4MnRD3SgzM3NxZIlS/Dll18yUBIRtRAMlNTsVJeW449132qewy2SiDHkowVoH9sHwVOG4tLmPyeST35zi9YE57rsnbpca5myshqXPtqFSx81PFXTztiXG2yjqlJga+jMBtsRERE1N5yHkpo9tVKFQ8+sw419Z9B72eMImzlC6JKIiIgeKOyhpBZBVa3AbzNWCl0GERHRA4k9lERkUTY2NggLC4ONjY3QpRARkYmwh5KILCogIABbtjQ8rpWIiJoP9lASERERkVEYKInIotLS0tC/f3+kpaUJXQoREZkIAyURWZRarUZ1dTXUau3HUxIRUfPEMZREREQ6SCQSxMXFmWx/azZtQ0lZGZwcHLDwmclar01BIpGYZD9E+mKgJCIi0kEkEkEqNd2vSTUAlbrmb6lUqvWaqDnjJW8iIiIiMgo/EhGRRfn7+yMxMRG+vr5Cl0JERCbCQElEFmVra4ugoCChyyAiIhPiJW8isqi8vDwsX74ceXl5QpdCREQmwkBJRBZVVFSEHTt2oKioSOhSiIjIRBgoiYiIiMgoDJREREREZBQGSiIiIiIyCgMlEVmUWCxGt27dIBbzxw8RUUvBn+hEZFEqlQq///47VCqV0KUQEZGJMFASERERkVEYKImIiIjIKAyURERERGQUBkoisignJyeMHDkSTk5OQpdCREQmwmd5E5FF+fr64o033hC6DCIiMiH2UBKRRVVWVuLGjRuorKwUuhQiIjIRBkoisqjMzEzExcUhMzNT6FKIiMhEGCiJiIiIyCgcQ0lERERa1Go1lEql0GXoRSKRQCQSCV3GA4mBkoiIiLQolUps375d6DL0EhcXB6mU0UYIvORNREREREZhjCciiwoPD8epU6eELoOIiEyIPZREREREZBQGSiKyqOzsbMyePRvZ2dlCl0JERCbCQElEFlVeXo6LFy+ivLxc6FKIiMhEGCiJiIiIyCgMlERERERkFAZKIiIiIjIKAyURWZS3tzeWLVsGb29voUshIiIT4TyURGRRLi4uiI2NFboMIiIyIfZQEpFFFRYW4ptvvkFhYaHQpRCRhZWWluLmzZvIz8/HnTt3oFKp9Nr+4MGDyMvLM1N1ZAz2UBKRRRUUFGDNmjXo3LkzXF1dhS6HiMyosLAQSUlJuHLlCjIyMnDnzp066+3s7ODv74/AwEBERUUhKCgIIpFI57727NmDzz77DK6urnjttdc4bKaJYQ+lBcjlciQkJCA4OBi2trZo27Yt4uPjUVZWhjlz5kAkEuHdd98VukwiIiKTuHr1KtavX4958+Zh69atSE5O1gqTQM28tKmpqdi1axf+9a9/4ZVXXsHBgwe1ei5rwyRQE1JPnz5tkfOgxmMPpZmdO3cOsbGxyM/Ph4ODAzp06IDc3Fxs2LAB165d03yDde3aVdA6nYN80OWfE+HeOQD2bVwhtpKiLEcO2W9ncfH9H1F+866g9RERUdNXWVmJbdu2Yffu3VCr1XXW1fZGurq6QiwW4969e7h+/TrkcrmmTUZGBj788EMcOHAAzzzzDHx8fOqESQB45JFHMGbMGIudEzUOA6UZyeVyjBkzBvn5+ViwYAGWLl0KJycnAMDq1auxaNEiSKVSiEQiREZGClqrg7c77Fu3wvXdp1CWextqpRKu4e0QOj0GAeP6Y0fMS6i4XSxojURE1HTJZDKsXbsW+fn5mmUuLi6Ijo7GwIED4e3tDbFY+8JocXExzpw5g7179yIjIwMAkJaWhkWLFqF37944evSopu0jjzyCyZMn13tZnITDQGlGL7zwAmQyGebNm4e1a9fWWZeQkICtW7fi/PnzCAgIgLOzs0BV1sg7cgF5Ry5oLc8/kYrojxYgeHI0Lr7/owCVUUtjb2+PPn36wN7eXuhSiMhEMjMzsWLFCpSUlAAArKysMGnSJIwcORJWVlb33dbZ2RnR0dGIjo5GSkoKNm/ejIKCAlRXVzNMNiMcQ2kmqamp2LZtGzw8PLBy5UqdbXr06AEA6NKli2ZZUlISYmJi4O3tDRsbG/j5+WHy5MlITU21SN1/Vya7BQCwbuUgyPGp5WnXrh02btyIdu3aCV0KEZlAfn4+Vq5cqQmT/v7+eOuttzBmzJgGw+TfdezYEatWrUJYWFid5V27dmWYbOLYQ2kmiYmJUKlUmDZtGhwdHXW2sbOzA1A3UBYWFqJz58545pln0Lp1a8hkMqxcuRJRUVG4ePEi/Pz8zFq3xMYKUgdbSGys0Cq0LXq8Mh0AIPvtd7Melx4cSqUS5eXlsLOzg0QiEbocIjKCSqXCu+++i+LimiFRoaGhePnll426AnHo0CGkpaXVWXbx4kXk5ubC19fXqHrJfBgozWT//v0AgOjo6HrbyGQyAHUD5dixYzF27Ng67Xr16oWwsDBs374d8fHxZqj2TyGPDUPfFU9qXpdcL8Dhf7yDmyeF6SGllic9PR0zZ87El19+ifDwcKHLISIj7Nq1C1evXgUAeHl5YdGiRUaFyb/fgBMQEIDMzEwoFAp88MEHeOONN3SOwyThMVCaSXZ2NgCgffv2OtcrFArN2JC/Bkpd3N3dAQBSqfn/ua7/cgpFV3Ng5WALt04BaDuiF2zcnMx+XCIial7u3LmDr7/+GgAgEonw7LPPwsHB8OFRuu7mHj9+PF5++WXk5eXh6tWr2L9/P2JiYoyunUyPgdJMysrKANTMsaXLtm3bIJfL4eTkhICAAK31SqUSKpUK2dnZWLx4Mby8vDBp0iSDaunZsydu593EUvRusO29vDu4l1czldH1X04je9dJjN79FqR2Nriw8fv7bhs8aQh6v/kEijPzsXPkIgCArbszBm58Hk7tvaCsqsaJxR+h4ERNb+fA9+LhM6ATMn88ilOvfX7ffYutpRj0bjxahbcF1Gpk/ngM59/+BgDQdcEkhD/xEG4mX8H+x1cBAJwCvDDwnedh4+aE6pJ7OBL/Lu5eqekRfujb1+HWoT3Or/sWlz7a1eB7Up/H875FYWo2kpf/Bzn7f4d7lyD0eXM23Dr5I/fQeex/YrWmrf/Yfui6YCLs27hha/isBvcdPDkanZ8fD2VFFSrulODQs+tQebsYjn6eePTEu7ibeh1HXnwPd1Ky4DesO7omTIZrWDukfbmnznvZ4enRCH/8ISjKKrBj+MI6xwgNCUW1SL+nVOgyYcIEvdrfvHkTALB7926cOXOmUduMHz9e77qamvFPvAgHR2fk5efBz89P63VL96CfP9D83gNra+t67wEAgAMHDqC6uhoA8NBDD2mNe9SHrjBZO2Zy7ty5WLp0qabdsGHD6h1LGRoaiqqqKoPreNB5eXkhOTnZoG0ZKM3Ey8sLhYWFOHv2LKKiouqsy8vLw8KFNb/cIyMjdX5jDB48WNODGRwcjP3798PT09OgWvLz83ErLx9oo/+2hanZuHMxE+GzHmowUAJA/rGUOkGqxyvTcetMOvY+9n9w7xKEoZ8m4Ns+z0GtUCLpH++g64JJsHZp+PJIwLj+sPV0wQ+D50Nqa42plz5D6se7UFV8DwCQ8V1SnSDVb/UzuPKfvbj69UG0H9UXA96Zh52xLwMA9kx4HQPW/0PPd0K33Y+8qqmh/GYhTr32Gdw6B8BvaLc67bJ2HIP8bDrG7lurazd1iK2k6LvySfw4bAFKMvMxcOPzCBw/AKkf/wwAUJTWDYfFmXk4Ov99+I+JgpWDbZ19Xdq8E3cuZKL3G49rHSc3LxdVaqW+p6yl9sNTY9V+yCovL2/0tjk5OXrX1dSolErN3zk5OVqvW7oH/fyB5vce2NjY1LtOoVBg3759AGp6J0ePHm3wce4XJgEgLCwMYWFhSEtLw40bN3D58mVERETo3Fdubi4qKysNroUMx0BpJjExMUhNTcWqVaswfPhwhIaGAgBOnz6NGTNmaCZyrW9C808++QR3795FZmYm1qxZgxEjRuDo0aMG3Rnr5eUFK7UYMLAzSmJrDWvXmhuLHvr2dRSmZuPUqzXf/FZO9og7vhHfD56vc1v/sVH4Lup5AMDt89dwr+AOvKI6IC9Je4qi+3H088Sdi5mAWg2PbsGoKi1HVYnu3l9bd2e4dwnCr1PeBABk7zqBvivmwMnfCyVZ+Tq30SVs1kMInTYM+6atQPmtuwCAgRufx63fr+Lyp7u12tf27rYKNa6nwa51K6iqFSjJzId1K0e0CmuHzB3H6m1fnFHzXNv2sX30Oo6Pt49Jeij1vcRVGyLt7OwavW1LGIgv/t8NSGKJBL6+vlqvW7oH/fyB5vceWFtb17vu8uXLKCwsBFBzFczDw8OgYzQUJmsNHz5cc6PO0aNH6w2UPj4+7KE0gpeXl8HbMlCaSe08kzdu3EDHjh0RHh6OiooKXL16FbGxsfD398eePXvqHT9Ze+mgT58+GDlyJPz9/bF69WqDHtGYnJyM6nsV+Cpoer1t7DxbaULTX3n164hW4W2Rf+wSAODXyW/gkYPr4BLii6L0HIRMHQrZb7+jUsek5zaujhBLpXX2W3rjFhx89e9pFYnFgFqNoZ8tQruRvXDxwx3A357CUMvB1wPlBYVQK/8MS6U5cjj4eugVKNO+2AMbV0d0eHo0zvzff2Dr4QLfod1wYvHHetevD5FIBKhremUHrP8HSq4X4Pa5ayY/zpX0K7Cyt224YQP0fQSaQqHA448/Dicnp0aPC16/fr0BlTUtK977CsWlZfD28oZMJtN63dI96OcPNL/3QKFQYPv27TrXXbv258+k2inw9NXYMFl7DJFIBLVarZn8XJcrV65Y5H4D0sZbpczEz88PSUlJGDVqFGxtbZGVlQU3Nzds2rQJu3btwpUrVwA0fEMOALRq1QrBwcGaO+nMoe+qpzBq5wp0X/wYQmcMR8STD2PAhucxPPFfUJRWIHnZFwAAtVKF9P8eQPCkaEAkQvjjDyFVR2+duex/YhW2RT4J17C26DJfv7F7hkhP3I+A8f0hEosRNnMEMn84iupS3T2jppb541H8J2QGUj/9BbE/vFkTqlsAqVQKV1dX/tAnasYyMzM1XwcFBem9vT5hEqi5ouHt7Q0AuH79OhQKhd7HJPNqGb+hmqiIiAjs3LkTJSUlKCkpwcmTJ/H000+jrKwMWVlZEIvF6NSpU4P7uXnzJtLS0gz6pm2szO+PoOJOCYLiBqHPG0+gx5Jp8OwWjCtb9uLHYQtwJyVL0zZj+2EEjh+AtiN6ovJOCW6f1917VllYCpVSCTvPVppljm09UZZzy6hay2/dRfp/D8CzR6jO9WU5cti1cYVI8ud/b0dfD5TlyHW2v++xCgpRlJ4Dv5juCJ0eg8ufWS48A4BaoUTaF3tg7+0GG1fd85k2NzKZDAsWLGiSPTJE1Di5ubkAaj4g+vj46LWtvmGylr+/P4CantOCggL9CiazYxeBAFJSUqBWqxEaGqo1X9f06dMRHByMrl27olWrVkhPT8e6desglUoxf77ucYqmkPXTcWT9dLxRbe/l30HR1RxErXoayW9+ed+22T8dR9jMETj376/h3iUI9l5uyD9+SWdbj67B6L5kGn6dtEzn+tZ9IiB1sIWirAJt+nbA3bQbOttV3C7GnQuZCIobpLkppyzvTr2Xu9vF9ka72D448sJGneuvfXMIUaufwd20Gyi6mnvf89XHiK+X4uyKryA/p93zLLW3QZuoDig4fgnuXYJQXVaBiv/d5d3clZaWIikpCU899ZTQpRCRgVxcXODm5gZra2u9HlCwb98+g8IkALi6usLV1VXvp++QZTBQCuDChZobUnRd7u7bty++/PJLvPPOO6ioqEDbtm0RHR2NJUuW1DunpRCubT+MXktnImvH/UNo8vL/YOC7L+DRoxuhrFYgad4GqBW67yx2bOsJZUX9g6mt7G0xbv+/oSirQMn1mzg6/7162x5L2IQB6/+Bzi88iurSchx5sf62zgHeqC65V+/67N0nEbX6aVz+Yk+9bQDAOcgHD329FFI7a0hsrTHxzCb8seE7pOnYTiQWw61je5Tl3da5L7VShV5LZ0Fiaw31/963+ngP6IwB78yDlZMdRCIR2o+KwonFH+HGr4ZN/UBE1JBXXnnFoO38/PxgY2ODyspKvZ/NPWPGDMyYMcOg45L5MVAK4H6Bct68eZg3b56lS9JbmUyOe3l3oKq+/ziWCnkR9v7vbuuGtInqiAvv1j81Uc6B3xucr7JW8bVc/DymcT/wWvcK09y1rouyvAqVd0pQmn3/SyzF13LxTY9nGnVMt84BuL77FMoLCnUfs7JaM5dnQ/KOXGj0cYmIhBQeHo7FixcjJSUFjz76KJ/N3YJwDKUA7hcomzNFRRXcOvpj9C+rGtV+4HvxCIwbqJn+5+SSj3Hz1GW9j1tdVgG/4T0x9PPGBbCHvn0dbaI6oPpezVxl+59YjVKZ/uM6y28WYuR3b8D3b3NO6uI/th+Gffmy5o732+ev4dhLH+p9TJVKBUV5JcbuXQO3jv4Ntu/w9Gj0fetJVNwp0ftYRETmEB4ejri4OIbJFoY9lAKofc53c5Z/PEXryStZO44h6z7zJf5d0j/eaXTbc//+ut51KR/uQMqHOxq9rz0TXm9021rf9n5Oa9m2Lo0fA6jPe1Mqu1Xv03Tu5d7G192ebvRxL23eiUubdza6vSV4enoiPj7e4In6iYio6WGgJCKLcnd3x7Rp04Qug4iITIiXvInIooqLi7Fv3z4UF2tPhk9ERM0TAyURWVRubi6WLFmimceOiIiaPwZKIiIiIjIKAyURERERGYWBkoiIiIiMwkBJRBZlY2ODsLAw2NjYCF0KERGZCKcNIiKLCggIwJYtW4Qug4iITIg9lERERERkFAZKIrKotLQ09O/fH2lpaUKXQkREJsJASUQWpVarUV1dDbVaLXQpRERkIgyURERERGQU3pRDREREWiQSCeLi4ky2vzWbtqGkrAxODg5Y+MxkrdemIJFITLIf0h8DJREREWkRiUSQSk0XE9QAVOqav6VSqdZrat74L0hEFuXv74/ExET4+voKXQoREZkIAyURWZStrS2CgoKELoOIiEyIN+UQkUXl5eVh+fLlyMvLE7oUIiIyEQZKIrKooqIi7NixA0VFRUKXQkREJsJASURERERGYaAkIiIiIqMwUBIRERGRURgoicii3NzcMGvWLLi5uQldChERmQgDJRFZlFgshpWVFcRi/vghImop+BOdiCxKLpfj448/hlwuF7oUIiIyEQZKIiIiIjIKAyURERERGYWBkoiIiIiMwkBJRBbl5OSEkSNHwsnJSehSiIjIRKRCF0BEDxZfX1+88cYbQpdBREQmxB5KIrKoyspK3LhxA5WVlUKXQkREJsJASUQWlZmZibi4OGRmZgpdChERmQgveRMRERHpoFaroVQqhS6j0SQSCUQikSDHZqAkIiIi0kGpVGL79u1Cl9FocXFxkEqFiXa85E1ERERERmGgJCIiIiKj8JI3EVlUeHg4Tp06JXQZRERkQuyhJCIiIiKjMFASkUVlZ2dj9uzZyM7OFroUIiIyEQZKIrKo8vJyXLx4EeXl5UKXQkREJsJASURERERGYaAkIiIiIqMwUBIRERGRURgoiciivL29sWzZMnh7ewtdChERmQgDJRFZlIuLC2JjY+Hi4iJ0KUREzYpKpcLt27eFLkMnTmxORBZVWFiIffv2ISYmBq6urkKXQ0RkVtXV1cjIyEBGRgYyMzNx+/ZtKBQKSKVSuLi4IDAwEAEBAQgKCoKtrW29+1GpVHj//feRkpKCV199FT4+PhY8i4YxUBKRRRUUFGDNmjXo3LkzAyURtVgFBQXYt28fDh48iJKSknrbHTt2DABgZ2eHQYMGYfjw4fDz86vTpjZMHjlyBACwcuVKvP3227CysjLfCeiJl7wtQC6XIyEhAcHBwbC1tUXbtm0RHx+PsrIyzJkzByKRCO+++67QZdYhsbNG3In38Hjet+jzf3OELoeIiKhZqKiowCeffIIXX3wRP/30033D5F+Vl5djz549eOmll7BhwwbNdn8PkxKJBLNmzWpSYRJgD6XZnTt3DrGxscjPz4eDgwM6dOiA3NxcbNiwAdeuXcOdO3cAAF27dhW20L/ptnAKbN2dhS6DiIio2UhJScGHH36IW7duaZZJpVL06tULHTp0QGBgILy9vWFtbQ2FQoH8/HxkZGQgLS0NJ06cQGVlJYCaXsuUlBTMmTMHp06dqhMm58+fj549ewpyfvfDQGlGcrkcY8aMQX5+PhYsWIClS5fCyckJALB69WosWrQIUqkUIpEIkZGRAlf7J7fOAejw1CgkL9+C3q8/LnQ5RERETd6RI0fw/vvvQ6VSAQBsbGwwduxYxMTE6LwJUSqVwt/fH/7+/hg6dChmzpyJgwcP4rvvvkNZWRmKiorw9ttva9o35TAJ8JK3Wb3wwguQyWSYN28e1q5dqwmTAJCQkIAuXbpAoVDA398fzs5NozdQJBaj39q5yDlwDtd3nRS6HGqB7O3t0adPH9jb2wtdChGRSRw7dgzvvfeeJkxGRERg1apViIuLa/SMFg4ODhg1ahTWrl2L7t2711knFoubdJgEGCjNJjU1Fdu2bYOHhwdWrlyps02PHj0AAF26dKl3P7GxsRCJRHj99dfNUaaWDk+PhkuwL04u+dgix6MHT7t27bBx40a0a9dO6FKIiIyWnZ2N9957D2q1GgAQExODV199FV5eXgbtz8XFBXZ2dnWWqVSq+94B3hQwUJpJYmIiVCoVpk2bBkdHR51tav/D1Bcov/76a5w7d85cJWpxbNsaXRdOwvm3v0Wp7FbDGxAZQKlUorS0FEqlUuhSiIiMolAo8MEHH2h+nkVHR2POnDkQiw2LV7U34Bw9ehQAIBKJNOs2bdqEiooK44s2EwZKM9m/fz+Amv9c9ZHJZAB0B8ri4mK8+OKLWLt2rXkK1CFq9dMozS5AyqafLHZMevCkp6dj6NChSE9PF7oUIiKj/PTTT8jKygIAtG3bFrNnz64TAvWh627uf/7zn4iIiAAA3Lp1C1u3bjVJ3ebAm3LMJDs7GwDQvn17nesVCoXmE4iuQPnKK68gNDQU06ZNw/Tp042qpWfPnriddxNL0bveNoFxA+EzKBK7x78GtYI9Rw+C0JBQVItURu9nwoQJerW/efMmAGD37t04c+ZMo7YZP3683nU1NeOfeBEOjs7Iy8+Dn5+f1uuW7kE/f4DvQXM8f2tr63qHrVVVVWHXrl0AasY4zp071+CpfHSFydoxk23btsWiRYtQWVmJ/fv3Y8KECfXedxEaGoqqqiqDagAALy8vJCcnG7QtA6WZlJWVAaiZV0qXbdu2QS6Xw8nJCQEBAXXWJScn46OPPmr0L9uG5Ofn41ZePtBG93qxtRS9Xn8cst9+R/nNu3Dyrxn3Ye/tBgCwdraHk78XKu8Uo6r4nklqIuHl5uWiSm38h4fa/+uNVfs9UV5e3uhtc3Jy9K6rqVH975KYSqlETk6O1uuW7kE/f4DvQXM8fxsbm3rXHT9+HKWlpQCAfv36ISgoyKBj3C9MAjUhLyYmBrt27YJCocCBAwcwbtw4nfvKzc3VTD1kaQyUZuLl5YXCwkKcPXsWUVFRddbl5eVh4cKFAIDIyMg63eNKpRLPPPMM5s2bh44dO5qsFiu1GKinM0pqaw07Dxe0Hd4DbYf30FofNGEwgiYMxullXyLlwx0mqYmE5+PtY5IeSgcHB73a14ZIOzu7Rm/r6+urd11NjVgi0fzt6+ur9bqle9DPH+B70BzP39raut51tUPbAGDEiBEG7b+hMFlr+PDhmt7Q/fv31xsofXx8jO6hNBQDpZnExMQgNTUVq1atwvDhwxEaGgoAOH36NGbMmAG5XA5Ae0Lzd999FwUFBSa9qzs5ORnV9yrwVZDuS+fV9ypx4EntsZq27s6IWvU0ZPt/R/rW31CYmm2ymkh4V9KvwMre+LsGT58+rVf7y5cvIzExEbGxsQgPD2/UNuvXrzegsqZlxXtfobi0DN5e3pDJZFqvW7oH/fwBvgfN8fwVCgW2b9+utby6uhpXr14FAHh7eyMkJETvfTc2TAI1QS8iIgKpqakoKChAYWGhzkfXXrlyBVKpMNGOgdJMEhISsHXrVty4cQMdO3ZEeHg4KioqcPXqVcTGxsLf3x979uypM35SLpfj1Vdfxdq1a6FQKHD37l3NuoqKCty9exfOzs4G3z1WH7VCiexdJ7SWO/p5AgBKsvJ1ricyRHBwMPbs2VNnXlYioubk+vXrmju7Q0JC9L4RR58wWSs4OBipqakAgIyMDM3Ug00F7/I2Ez8/PyQlJWHUqFGwtbVFVlYW3NzcsGnTJuzatQtXrlwBUPeGHJlMhpKSEjzzzDNwdXXV/AGAVatWwdXVFdevXxfkfIhMRSqVwtXVVbBP0URExqq9sxuA1n0QDTEkTAJAYGCgzuM3FfyJbkYRERHYuXOn1vLS0lJkZWVBLBajU6dOmuXBwcE4cOCAVvvo6GjMmjULjz/+uFHjG/RVKruFz731u4OXqCEymQzr1q3D/Pnzm+ydnURE91N7Mw4AuLu7N3o7Q8Pk34/z1+M3FQyUAkhJSYFarUZoaGidx885OjpiyJAhOrfx9/evdx1Rc1JaWoqkpCQ89dRTQpdCRGSQ/v37Izg4GFVVVXr1UObm5mrGnev7bG5fX18sWrQIVlZW8PDwMKhuc2KgFMCFCxcA3P+Ri0RERNQ0eXh4GBTq/Pz8sHjxYqxduxZz587V69ncDg4O6Natm97HtBQGSgHoGyhrnw9KREREzVt4eDg2bNhQ5wplS8CbcgTAHkoiIqIHV0sLkwB7KAXx18lQiR40np6eiI+Ph6enp9ClEBGRiTBQEpFFubu7Y9q0aUKXQUREJsRL3kRkUcXFxdi3bx+Ki4uFLoWIiEyEgZKILCo3NxdLlixBbm6u0KUQEZGJMFASERERkVEYKImIiIjIKAyURERERGQUBkoisigbGxuEhYXBxsZG6FKIiMhEOG0QEVlUQEAAtmzZInQZRERkQuyhJCIiIiKjMFASkUWlpaWhf//+SEtLE7oUIiIyEQZKIrIotVqN6upqqNVqoUshIiIT4RjKB4TUzgbTrv1H6DKoCZHa8aYYIqL7kUgkiIuLM8m+1mzahpKyMjg5OGDhM5PrXWYMiURi9D4MxUD5gBCJRLCytxW6DCIiomZDJBJBKjVNVFIDUKlr/q7dp65lzRUveRMRERGRUZp3HCaiZsff3x+JiYnw9fUVuhQiIjIRBkoisihbW1sEBQUJXQYREZkQL3kTkUXl5eVh+fLlyMvLE7oUIiIyEQZKIrKooqIi7NixA0VFRUKXQkREJsJASURERERGYaAkIiIiIqMwUBIRERGRUXiXNxEZpVevXnq19/Pzw9KlSxETEwNvb28zVUVERJbEQElEFuXt7Y3XX39d6DKIiMiEeMmbiIiIiIzCQElERERERmGgJCIiIiKjMFASERERkVEYKImIiIjIKAyURERERGQUBkoiIiIiMgoDJREREREZhYGSiIiIiIzCQElERERERmGgJCIiIiKjMFASAOD111+HSCTS+nP16lWhSyOievz888/o2rUrbGxs4O/vj7ffflvokizq8OHDGDduHNq3bw+RSITly5cLXZJFrVmzBlFRUXB1dUWrVq0wYMAA/PLLL0KXZTFbtmxBjx494OrqCjs7O0RERODtt9+GWq0WujRB7N+/HxKJBMHBwYIcXyrIUalJ8vf3x/Hjx+ss8/T0FKgaIrqf5ORkjBs3Di+99BISExNx8uRJzJ07F/b29pg7d67Q5VlEaWkpOnTogMceewwvvvii0OVY3P79+zF79mz06tUL9vb2+PjjjzF69GgcOnQI/fv3F7o8s2vdujVeffVVhIWFwcbGBklJSXjuuecgkUgQHx8vdHkWlZ+fj1mzZmHEiBFIT08XpAYGStKQSCTw8vISugwiaoS3334bvXr1wsqVKwEAERERSElJwVtvvfXABMqHH34YDz/8MABg0aJFAldjebt3767zevXq1fjll1/w3XffPRCB8qGHHqrzOjAwED/88AMOHjz4QAVKlUqF6dOn4x//+AcqKioEC5S85E0aMpkMfn5+8PPzQ2xsLI4dOyZ0SURUj6NHj2LkyJF1lo0cORLZ2dmQyWQCVUVCUqlUKC4uhoODg9ClWJxarcapU6dw9OhRREdHC12ORb355psQiUSCf6hiDyUBAHr37o3PPvsMHTp0QHFxMTZt2oSBAwfil19+wfDhw4Uuj6jZuFdeAVm+XGu5QqnU/H0lU6b1+q982rjD0d7uvsfJy8vTuqJQ+zovLw9+fn4Gn4OxMm/koVqhrLNMn/N3tLeFTxsPyxRrBrcLi3H7brHWcn3eg6B2PpBI9OvzWbFiBe7evYunn37awMpNo1qhQOaNfK3l+py/h5sL3FycGjxWUVERfH19UVVVBZVKhaVLl+KFF14wwVkYR5Z/C/fKK+ss03W+9b0HNlZStPdr+IrhgQMH8OGHH+L333+HSCQyVfkGYaAkANBcNqo1cOBAyGQyrFmzhoGSSA821tb49fBpyPJv6Vx/r7wCn379c72v23i4Yt6s8Wav05xyC27jp990X+Fo6PxFAJ6aOtrcJZqVRCLG1h/3oaKySuf6ht6DHp1CERqg3weC999/HytWrMCOHTsE/TABAFKJBKfPp+JCWqbO9Q2dv4O9LV6cPaFRx3JycsK5c+dw7949HDt2DIsXL4aPjw/mzJlj3EkYqbSsHJ9/q/sGqb+fr65lE2IHNxgo5XI5pk+fjs8++6xJDFfjJW+qV1RUFLKysoQug6hZkUjEmDQ6GlKpRP9txTXbWkkb/qzv7e2N/Py6vUAFBQWadUKK6tERwe19Ddp2QK9IBLbzMXFFltXK2RHjhhs2hrGVsyPGxPTTa5u1a9di4cKF2LFjB2JiYgw6rimJRCI88tBAODncv5e9Po+OHAQnB/tGtRWLxQgODkZkZCTmzp2LhIQEvPLKKwYd15TCg9qhT9cIg7btENIePTqHNtju4sWLyM3NxejRoyGVSiGVSvHGG2/g2rVrkEql2Lp1q0HHNxQDJdXr7NmzaNu2rdBlEDU7rd1b4eEhffTeLmZAD/g28lJv//79sWfPnjrLfvnlF7Rv317wHiqxSISJDw+GrY21Xtu18XDFiEE9zVSVZXXtEIzI8EC9thEBmDQ6Wq/37bXXXsOyZcvw888/N4kwWcvBzhYTYgfrvV3PzmHoGOJv8HFVKhUqKioM3t6UHo7uC/dWznpt42hvh0cfGtSoy9e9evXChQsXcO7cOc2fuXPnom3btjh37hxGjRplaOkG4SVvAgD885//xOjRo+Hv74/i4mJ89NFH2Lt3L3788UehSyNqlvp274jUq9lIz8ppVPv2vm0wuE+XRu9//vz56NevH1555RXMmDEDJ0+exMaNG7Fu3TpDSzYpF2dHPDJiAP770/5GtZeIxZg8ZmijemdrlZaWaubKraqqQn5+Ps6dOwdHR0fB5uKrJRKJ8MiIAciU5aOk9F6jthnYOxKBbRvfu/ziiy9i06ZNSExMRFhYmKbH2s7ODi4uLgbVbUph/+ulO3kutVHtXV2cMHpYVKP3v3TpUgwcOBCBgYGorq7G4cOHsWrVKjzxxBOGlmxSNtZWmDQ6Gh9+taPRc2M+GjsIjo3s2XVwcECnTp3qLGvdujWsra21llsCeygJQM0g/pkzZyIiIgIjRoxAWloa9u3bhzFjxghdGlGzJBaJMOHhIbCztWmwrbWVFJNGRUMsbvyP5F69euGHH37Azp070aVLF7z22mv4v//7vyY1ZZA+vXTDB/aET2t3vfafnJyMbt26oVu3bsjLy8N7772Hbt264cknnzSkXJOzt7PFxEb20nl5umHEwF567f+dd95BRUUFxo8fD29vb82fpjRlzqjovnB3bbiXTgRg0qghevXOFhcXY+7cuejYsSP69u2LDz/8ECtXrmxSE/y3922DIX27Nqptr8hwdAhub96CzEikflCnlCcisoDzl64isYFeukdHDkLvLuEWqsiy7pVXYP2n36L4Pr10/n5eeHrqaL0CdXPyw69HcOL3S/Wul0jEmDdzPLz1DNTNxfWcAnzQQC/d4D5dEGvAMJHmQKFU4oMtPyKnQHv2h1puLk6IfyIONnoOE2lKWuZ3L5nNvfIKyPJ0371KRNq6dAhGl4igetdHBLdDr8gwC1ZkWfZ2tpjw8JB611tbW2HiqCEtNkwCwMND+sDDrf5L0CMG9myxYRIA2vm2QXRUt3rXe3m6YfiAljF2VhepRFJzo55E9416IpEIk0ZHN+swCTBQkp6STl/Au19+j137TwhdClGzMW7EADg7ak827WBni0dHNm4AfnMWGuCHqO4dda4bMzRK7xsXmhtraytMHhUNsY5/Z38/LwzsFSlAVZY1rF93+Hpp33AmkYgxZcxQg2ZFaE7aeLhi5JDeOtcN7tMF/o2Yc7KpazGBUiQSaX4o//TTTxg4cCCcnZ3h4eGBCRMm4Nq1a5q2O3fuxODBg9GqVSs4Oztj3Lhx931UUWVlJd555x3069cPrVq1gq2tLcLCwrBw4ULI5bq7sE+ePIlFixahV69e8PLygo2NDXx9fTFp0iScPn263mP9/PPPePjhh9G6dWtYWVnB3d0dERERmD17No4ePWrgu2MaZeUVOHbmIgC0iP/8RJZib2uDiQ9rj6XTZ3qU5i52SB94/q2XLiK4PXq24N7Zv2rr0xrR/er20llbW2FSC++drSWRiDF5lPZ0Wg8N7AUvTzeBqrKsfj06aU2n5d3aHTEDeghUkWm1uP/F7733HsaOHYuMjAwEBwfj3r172L59OwYPHoxbt25h/fr1GDNmDNLT0xEYGAiFQoEdO3Zg0KBBuHVL+1JuQUEBoqKi8OKLL+LkyZNo1aoVQkNDkZ2djbVr16JHjx7IyMjQ2m7atGlYvXo1MjIy4OHhgY4dO6KyshLffPMN+vXrh+3bt2tt8/7772PUqFHYvXs3VCoVIiMj0aZNG8hkMnz22WfYsmWLWd6zxjpy+gIqq6rh3dodHUKa78BhIiGEBPihX48/e+l6dA5Fx1B/4QqyMGsrKSaN/rOXzsHeFnEPQO/sXw2N6g4/L0/N67HD+sGthffO/lVrD1fEDv5znGRAW28M6NVZwIosq+ZGvT+n05JIxJh8n0vhzU2LC5QJCQn44osvkJOTg7Nnz0Imk6FXr17IycnB7NmzsWTJEnzxxRfIzc3F2bNncePGDfTo0QP5+fn497//XWdfarUakydPxu+//45Ro0bh2rVryMrKwh9//AG5XI7Zs2fj+vXrmD59ulYdr732GtLT03H79m1cvHgRZ8+exc2bN/Hdd9/B1tYWTz75JEpLSzXtFQoF/vWvfwGoCZYFBQ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\"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"layers = 2\\n\",\n \"qaoa_circuit = QAOAAnsatz(cost_operator=cost_hamiltonian, reps=layers)\\n\",\n \"qaoa_circuit.measure_all()\\n\",\n \"qaoa_circuit.draw(\\\"mpl\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we define the QAOA circuit, we must pass it to the pass manager to transpile it to the native gates of the backend.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create pass manager for transpilation\\n\",\n \"\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=generic_backend, seed_transpiler=seed\\n\",\n \")\\n\",\n \"\\n\",\n \"qaoa_circuit_transpiled = pm.run(qaoa_circuit)\\n\",\n \"qaoa_circuit_transpiled.draw(\\\"mpl\\\", fold=False, idle_wires=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we define the initial parameters of our QAOA model.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"init_params = np.zeros(2 * layers)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We define and execute an optimization method using the library `scipy`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" message: Optimization terminated successfully.\\n\",\n \" success: True\\n\",\n \" status: 1\\n\",\n \" fun: -3.709212907870921\\n\",\n \" x: [ 9.738e-01 9.892e-01 -5.693e-01 1.030e+00]\\n\",\n \" nfev: 56\\n\",\n \" maxcv: 0.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"objective_func_vals = []\\n\",\n \"\\n\",\n \"\\n\",\n \"def cost_func_estimator(\\n\",\n \" params: list, ansatz: QuantumCircuit, isa_hamiltonian: SparsePauliOp, estimator: Estimator\\n\",\n \") -> float:\\n\",\n \" \\\"\\\"\\\"Compute the cost function value using a parameterized ansatz and an estimator for a given Hamiltonian.\\\"\\\"\\\"\\n\",\n \" if isa_hamiltonian.num_qubits != ansatz.num_qubits:\\n\",\n \" isa_hamiltonian = isa_hamiltonian.apply_layout(ansatz.layout)\\n\",\n \" pub = (ansatz, isa_hamiltonian, params)\\n\",\n \" job = estimator.run([pub])\\n\",\n \" results = job.result()[0]\\n\",\n \" cost = results.data.evs\\n\",\n \" objective_func_vals.append(cost)\\n\",\n \" return cost\\n\",\n \"\\n\",\n \"\\n\",\n \"def train_qaoa(\\n\",\n \" params: list,\\n\",\n \" circuit: QuantumCircuit,\\n\",\n \" hamiltonian: SparsePauliOp,\\n\",\n \" backend: QiskitRuntimeService.backend,\\n\",\n \") -> tuple:\\n\",\n \" \\\"\\\"\\\"Optimize QAOA parameters using COBYLA and an estimator on a given backend.\\\"\\\"\\\"\\n\",\n \" with Session(backend=backend) as session:\\n\",\n \" options = {\\\"simulator\\\": {\\\"seed_simulator\\\": seed}}\\n\",\n \" estimator = Estimator(mode=session, options=options)\\n\",\n \" estimator.options.default_shots = 100000\\n\",\n \"\\n\",\n \" result = minimize(\\n\",\n \" cost_func_estimator,\\n\",\n \" params,\\n\",\n \" args=(circuit, hamiltonian, estimator),\\n\",\n \" method=\\\"COBYLA\\\",\\n\",\n \" options={\\\"maxiter\\\": 200, \\\"rhobeg\\\": 1, \\\"catol\\\": 1e-3, \\\"tol\\\": 0.0001},\\n\",\n \" )\\n\",\n \" print(result)\\n\",\n \" return result, objective_func_vals\\n\",\n \"\\n\",\n \"\\n\",\n \"result_qaoa, objective_func_vals = train_qaoa(\\n\",\n \" init_params, qaoa_circuit_transpiled, cost_hamiltonian, generic_backend\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After the optimization routine is executed, we can see how the cost function evolves with the number of iterations to check how the algorithm has converged.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12, 6))\\n\",\n \"plt.plot(objective_func_vals)\\n\",\n \"plt.xlabel(\\\"Iteration\\\")\\n\",\n \"plt.ylabel(\\\"Cost\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Nice! As you can see, our circuit has trained quite well, converging to a value that should correspond to the minimum energy of the cost Hamiltonian representing our problem. But are we done yet? How can we be sure that this really is the minimum energy? And equally important, although we might have found the minimum energy, what ground state does it correspond to? Or in other words, what is the actual solution to the Max-cut problem?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 2.4 Checking our solution \\n\",\n \"\\n\",\n \"After training our QAOA circuit using the Estimator primitive to obtain the ground energy of the cost Hamiltonian, we can obtain the ground state by using the Sampler primitive running the circuit with the optimized parameters of the QAOA.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Get the optimized parameters from the result\\n\",\n \"opt_params = result_qaoa.x\\n\",\n \"SHOTS = 10000\\n\",\n \"\\n\",\n \"\\n\",\n \"def sample_qaoa(opt_params, circuit, backend):\\n\",\n \"\\n\",\n \" # Submit the circuit to Sampler\\n\",\n \" options = {\\\"simulator\\\": {\\\"seed_simulator\\\": seed}}\\n\",\n \" sampler = Sampler(mode=backend, options=options)\\n\",\n \" job = sampler.run([(circuit, opt_params)], shots=SHOTS)\\n\",\n \" results_sampler = job.result()\\n\",\n \" counts_list = results_sampler[0].data.meas.get_counts()\\n\",\n \" display(plot_histogram(counts_list, title=f\\\"Max cut with {backend.name}\\\"))\\n\",\n \"\\n\",\n \" return counts_list\\n\",\n \"\\n\",\n \"\\n\",\n \"counts_list = sample_qaoa(opt_params, qaoa_circuit_transpiled, generic_backend)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It looks we have 8 different states with significant higher probabilities than the rest, which suggests there might be 8 different ground states. But how can we check that? Luckily for us, this Max-cut problem is not very big in size, so it can still be solved analytically to help us discern if our solution is a good or bad approximation. Let's take a look!\\n\",\n \"\\n\",\n \"To solve this Max-cut problem analytically, we diagonalize the cost Hamiltonian. Note that this can be done because the size of the problem is not too large; however, the complexity of this problem scales exponentially with the number of nodes (qubits), so the problem becomes intractable for a large number of nodes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The ground energy of the Hamiltonian is -5.0\\n\",\n \"The number of solutions of the problem is 8\\n\",\n \"The list of the solutions based on their index is [3, 5, 6, 7, 24, 25, 26, 28]\\n\"\n ]\n }\n ],\n \"source\": [\n \"eigenvalues, eigenvectors = np.linalg.eig(cost_hamiltonian)\\n\",\n \"ground_energy = min(eigenvalues).real\\n\",\n \"num_solutions = eigenvalues.tolist().count(ground_energy)\\n\",\n \"index_solutions = np.where(eigenvalues == ground_energy)[0].tolist()\\n\",\n \"print(f\\\"The ground energy of the Hamiltonian is {ground_energy}\\\")\\n\",\n \"print(f\\\"The number of solutions of the problem is {num_solutions}\\\")\\n\",\n \"print(f\\\"The list of the solutions based on their index is {index_solutions}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Great! We obtained 8 solutions as well, so let us now do some post-processing to check if the 8 possible solutions match with ours.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The analytical solutions for the Max-cut problem are: ['00011', '00101', '00110', '00111', '11000', '11001', '11010', '11100']\\n\",\n \"The QAOA ground states solutions for the Max-cut are: ['00011', '00101', '00110', '00111', '11000', '11001', '11010', '11100']\\n\"\n ]\n }\n ],\n \"source\": [\n \"def decimal_to_binary(decimal_list, n):\\n\",\n \" return [bin(num)[2:].zfill(n) for num in decimal_list]\\n\",\n \"\\n\",\n \"\\n\",\n \"# Convert the solutions to quantum states\\n\",\n \"states_solutions = decimal_to_binary(index_solutions, n)\\n\",\n \"# Sort the dictionary items by their counts in descending order\\n\",\n \"sorted_states = sorted(counts_list.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"# Take the top 'num_solutions' entries\\n\",\n \"top_states = sorted_states[:num_solutions]\\n\",\n \"# Extract only the states keys from the top entries\\n\",\n \"qaoa_ground_states = sorted([state for state, count in top_states])\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions}\\\")\\n\",\n \"print(f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Hurray! We've successfully found the right solution of the Max-cut problem using QAOA!\\n\",\n \"\\n\",\n \"However, it's important to note that these results were obtained using ideal quantum simulators, which do not account for the noise and imperfections present in real quantum hardware. In the next section, we'll explore how QAOA performs when executed on a noisy quantum simulator, which provides a practical view on how noise affects our quantum algorithms.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 3. Noisy quantum simulator \\n\",\n \"\\n\",\n \"In this section, we'll run the QAOA algorithm on noisy quantum simulators. These simulators are useful tools that aim to mimic the behaviour and noise of real quantum processors. They allow us to test and evaluate how our quantum algorithms could perform on actual quantum hardware, without consuming valuable quantum resources. Additionally, noisy simulators often run faster since they are executed locally on your machine, avoiding the potential delays and queues associated with cloud-based quantum devices.\\n\",\n \"\\n\",\n \"However, before we proceed, an important question arises. Which quantum device should I use? This is a key question that is sometimes overlooked. In the following, we'll explore how to choose the most suitable device (or simulator) for running a specific quantum algorithm.\\n\",\n \"\\n\",\n \"## 3.1 Choosing the backend \\n\",\n \"\\n\",\n \"Here we'll introduce a quantum backend that corresponds to a noisy simulator that mimics the noise of a quantum device.\\n\",\n \"\\n\",\n \"Let's see which backends are available for you.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The quantum computers available for you are [, , ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"real_backends = service.backends()\\n\",\n \"print(f\\\"The quantum computers available for you are {real_backends}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you are using the Open Plan (which is free), you should have `ibm_brisbane`, `ibm_sherbrooke`, and `ibm_torino`. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now create a noisy simulator for each backend you have access to, and see which gates they have.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Backend Availability\\n\",\n \"\\n\",\n \"Depending on your account, you may have different backends available. Use only 3 backends - if possible, use ibm_brisbane, ibm_sherbrooke, and ibm_torino - but if they are not available to you, feel free to use 3 different ones.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# backends=[service.backend(\\\"alt_brisbane\\\"),service.backend(\\\"alt_kawasaki\\\"),service.backend(\\\"alt_torino\\\")]\\n\",\n \"real_backends = [\\n\",\n \" service.backend(\\\"ibm_brisbane\\\"),\\n\",\n \" service.backend(\\\"ibm_sherbrooke\\\"),\\n\",\n \" service.backend(\\\"ibm_torino\\\"),\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The noisy simulators are [AerSimulator('aer_simulator_from(ibm_brisbane)'\\n\",\n \" noise_model=), AerSimulator('aer_simulator_from(ibm_sherbrooke)'\\n\",\n \" noise_model=), AerSimulator('aer_simulator_from(ibm_torino)'\\n\",\n \" noise_model=)]\\n\"\n ]\n }\n ],\n \"source\": [\n \"noisy_fake_backends = []\\n\",\n \"for backend in real_backends:\\n\",\n \" noisy_fake_backends.append(AerSimulator.from_backend(backend, seed_simulator=seed))\\n\",\n \"print(f\\\"The noisy simulators are {noisy_fake_backends}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we transpile the QAOA circuit to the different backends and do a relatively simple analysis of their potential errors.\\n\",\n \"\\n\",\n \"In the next exercise, you will count the accumulated total error of applying the different quantum circuits to the three backends. We can do that using [`backend.properties()`](https://quantum.cloud.ibm.com/docs/en/guides/get-qpu-information#dynamic-backend-information), [`circuit.data`](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.QuantumCircuit) and [`backend.configuration()`](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.QuantumCircuit), as they will provide us with the required information to account for the various errors introduced by each instruction.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 3: Error Counting \\n\",\n \"\\n\",\n \"**Your Goal:** Estimate the total error of the quantum circuit. \\n\",\n \"\\n\",\n \"In this third exercise you will estimate the total error introduced by all the instructions when executing quantum circuits on different backends by completing the `accululated_errors` function. \\n\",\n \"In particular, you'll account for the errors introduced by:\\n\",\n \"\\n\",\n \"- Single-qubit gates: Depending on the backend, these may include 'rz','x', or 'sx', and you'll have to access each instruction name.\\n\",\n \"- Two-qubit gates: Depending on the backend, these may include 'cz', 'cx', or 'ecr' gates. You’ll need to identify which gate is used on each backend.\\n\",\n \"- Readout errors: These contribute a constant error added at the end of the total accumulated error, only on the physical qubits where your circuit is transpiled.\\n\",\n \"\\n\",\n \"Keep in mind that each qubit has different error rates, so you cannot simply count all operations and multiply by average error values you see on the Compute resources page on IBM Quantum Platform. \\n\",\n \"While this approximation might yield similar results, the goal of this exercise is to teach you how to access detailed backend information and perform a more accurate estimation of the specific error rates per qubit.\\n\",\n \"\\n\",\n \"Also, don't count the measure gates as single-qubit gates, as their error is already included in the readout error.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define a function that calculates the accumulated total errors of single and two qubit gates and readout\\n\",\n \"\\n\",\n \"\\n\",\n \"def accumulated_errors(backend: QiskitRuntimeService.backend, circuit: QuantumCircuit) -> list:\\n\",\n \" \\\"\\\"\\\"Compute accumulated gate and readout errors for a given circuit on a specific backend.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Initializing quantities\\n\",\n \" acc_single_qubit_error = 0\\n\",\n \" acc_two_qubit_error = 0\\n\",\n \" single_qubit_gate_count = 0\\n\",\n \" two_qubit_gate_count = 0\\n\",\n \" acc_readout_error = 0\\n\",\n \"\\n\",\n \" # Defining useful variables\\n\",\n \" properties = backend.properties()\\n\",\n \" qubit_layout = list(circuit.layout.initial_layout.get_physical_bits().keys())[:n]\\n\",\n \"\\n\",\n \" # ---- TODO : Task 3 ---\\n\",\n \" # Goal: Define the following quantities:\\n\",\n \" # acc_total_error, acc_two_qubit_error, acc_single_qubit_error, acc_readout_error, single_qubit_gate_count, two_qubit_gate_count\\n\",\n \"\\n\",\n \" # TODO Define readout error (only for qubits in qubit_layout) using `properties.readout_error`\\n\",\n \" acc_readout_error = 0\\n\",\n \" for q in qubit_layout:\\n\",\n \" acc_readout_error += properties.readout_error(q) # TODO\\n\",\n \" # TODO Define two qubit gates for the different backends using `backend.configuration()`\\n\",\n \" if \\\"ecr\\\" in (backend.configuration().basis_gates): # TODO\\n\",\n \" two_qubit_gate = \\\"ecr\\\"\\n\",\n \" elif \\\"cz\\\" in (backend.configuration().basis_gates): # TODO\\n\",\n \" two_qubit_gate = \\\"cz\\\"\\n\",\n \" # TODO Loop over the instructions in `circuit.data` to account for the single and two-qubit errors and single and two qubit gate counts\\n\",\n \" for instruction in circuit.data:\\n\",\n \" if (\\n\",\n \" instruction.operation.num_qubits == 1 and instruction.name != \\\"measure\\\"\\n\",\n \" ): # TODO Count and add errors for one qubit gates\\n\",\n \" index = instruction.qubits[0]._index\\n\",\n \" acc_single_qubit_error += properties.gate_error(gate=instruction.name, qubits=index)\\n\",\n \" single_qubit_gate_count += 1\\n\",\n \" elif instruction.operation.num_qubits == 2: # TODO Count and add errors for two qubit gates\\n\",\n \" pair = [instruction.qubits[0]._index, instruction.qubits[1]._index]\\n\",\n \" acc_two_qubit_error += properties.gate_error(gate=two_qubit_gate, qubits=pair)\\n\",\n \" two_qubit_gate_count += 1\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" acc_total_error = acc_two_qubit_error + acc_single_qubit_error + acc_readout_error\\n\",\n \" results = [\\n\",\n \" acc_total_error,\\n\",\n \" acc_two_qubit_error,\\n\",\n \" acc_single_qubit_error,\\n\",\n \" acc_readout_error,\\n\",\n \" single_qubit_gate_count,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" ]\\n\",\n \" return results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, generate `errors_and_counts_list[]` and pass it to the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Backend aer_simulator_from(ibm_brisbane)\\n\",\n \"Accumulated two-qubit error of 70 gates: 0.272\\n\",\n \"Accumulated one-qubit error of 457 gates: 0.029\\n\",\n \"Accumulated readout error: 0.079\\n\",\n \"Accumulated total error: 0.380\\n\",\n \"\\n\",\n \"Backend aer_simulator_from(ibm_sherbrooke)\\n\",\n \"Accumulated two-qubit error of 70 gates: 0.278\\n\",\n \"Accumulated one-qubit error of 449 gates: 0.037\\n\",\n \"Accumulated readout error: 0.098\\n\",\n \"Accumulated total error: 0.413\\n\",\n \"\\n\",\n \"Backend aer_simulator_from(ibm_torino)\\n\",\n \"Accumulated two-qubit error of 70 gates: 0.181\\n\",\n \"Accumulated one-qubit error of 250 gates: 0.032\\n\",\n \"Accumulated readout error: 0.063\\n\",\n \"Accumulated total error: 0.276\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"qaoa_transpiled_list = []\\n\",\n \"errors_and_counts_list = []\\n\",\n \"for noisy_fake_backend in noisy_fake_backends:\\n\",\n \" pm = generate_preset_pass_manager(\\n\",\n \" backend=noisy_fake_backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed,\\n\",\n \" )\\n\",\n \" circuit = pm.run(qaoa_circuit)\\n\",\n \" qaoa_transpiled_list.append(circuit)\\n\",\n \"\\n\",\n \" errors_and_counts = accumulated_errors(noisy_fake_backend, circuit)\\n\",\n \" errors_and_counts_list.append(errors_and_counts)\\n\",\n \"# You can print your results to visualize if they are correct\\n\",\n \"for backend, (\\n\",\n \" acc_total_error,\\n\",\n \" acc_two_qubit_error,\\n\",\n \" acc_single_qubit_error,\\n\",\n \" acc_readout_error,\\n\",\n \" single_qubit_gate_count,\\n\",\n \" two_qubit_gate_count,\\n\",\n \") in zip(noisy_fake_backends, errors_and_counts_list):\\n\",\n \" print(f\\\"Backend {backend.name}\\\")\\n\",\n \" print(f\\\"Accumulated two-qubit error of {two_qubit_gate_count} gates: {acc_two_qubit_error:.3f}\\\")\\n\",\n \" print(\\n\",\n \" f\\\"Accumulated one-qubit error of {single_qubit_gate_count} gates: {acc_single_qubit_error:.3f}\\\"\\n\",\n \" )\\n\",\n \" print(f\\\"Accumulated readout error: {acc_readout_error:.3f}\\\")\\n\",\n \" print(f\\\"Accumulated total error: {acc_total_error:.3f}\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's plot the results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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otKfn1/+lf+0+ePInjx48r1DMwMABQOlm0trZG27Zt8cMPPyAtLa1U+7dv35b+37lzZ5w4cQKnTp1SWF7elPgvU+fOnVFcXFzqNf3uu+8gk8kQFBRUaRt3795VeK6hoSHNqPj0sLry7Ny5U2Eq9VOnTuHkyZOltt2oUSM0atQIa9aswfbt2zFw4MAXGqJaVFSEffv2QUdHRxoGqampCZlMptD7mZKSgp07dyqs27NnT2hoaGDGjBmlrokqqwepdu3aOHDgAB4+fIiOHTtKx6xZs2ZwdXXFggULpATvaU+fR1VVctyWLl2qUL548WKl2yKiNxd7roiInhIVFYWcnBx07969zOXvvvsuatWqhcjISAwYMAATJkzAtm3b0K9fP3zwwQdo1qwZ7t27h6ioKKxatQqNGzdGcHAwNmzYgLCwMJw6dQqtW7dGXl4eDhw4gNGjR6NHjx4wNTVFv3798P3330Mmk8HV1RW7du1CZmamyvexU6dOeOuttzB8+HBMmDABmpqaWLt2LWrVqoXU1NQK1+3atSt27NiBXr16oUuXLkhOTsaqVavg6emp8CVXX18fnp6e2LJlC95++21YWFigQYMGaNCgAZYvX45WrVqhYcOGGDlyJOrUqYOMjAwcP34cN2/exIULFwAAEydOxMaNGxEYGIixY8dKU7E7OTnh4sWLVd7fbdu2lRoiBjy5HsnGxqbK7TytW7duaNeuHb788kukpKSgcePG2LdvH/7v//4P48aNq9L0+iNGjMC9e/fQvn171K5dGzdu3MD3338PLy8vKWmpiJubG1q1aoWPP/4YBQUFWLx4MSwtLTFx4sRSdYODgzF+/HgAUHpI4J49e6QeuczMTPzyyy9ITEzE5MmTYWJiAgDo0qULFi1ahMDAQLz33nvIzMzE8uXL4ebmpvBaubm54csvv8TMmTPRunVr9O7dG7q6ujh9+jTs7e0xZ86cMvdz3759aNu2LQICAnDw4EGYmJhgzZo1CAoKQv369RESEgIHBwf8+++/OHToEExMTPD7778rtZ9eXl4YNGgQVqxYgaysLLRo0QIxMTG4fv26Uu0Q0RtOfRMVEhG9erp16yb09PREXl5euXWGDRsmtLW1xZ07d4QQQty9e1eMGTNGODg4CB0dHVG7dm0xdOhQabkQT6aI/vLLL4WLi4vQ1tYWtra2om/fviIpKUmqc/v2bdGnTx9hYGAgzM3NxahRo8Rff/1V5lTshoaGpeLy8/MT9evXL1X+9LTWJc6ePSt8fHyEjo6OeOutt8SiRYuqNBW7XC4Xs2fPFk5OTkJXV1c0adJE7Nq1SwwdOlQ4OTkpbOPPP/8UzZo1Ezo6OqWmsk5KShLBwcHC1tZWaGtrCwcHB9G1a1exbds2hTYuXrwo/Pz8hJ6ennBwcBAzZ84UP/300wtPxY6npr0vmYr9119/LdVGecdaCCFycnLEZ599Juzt7YW2trZwd3cX3377rcLU4EI8mYr9k08+KbX+tm3bRKdOnYS1tbX0OowaNUqkpaVVuF8lU7F/++23YuHChcLR0VHo6uqK1q1biwsXLpS5TlpamtDU1BRvv/12hW0/rayp2PX09ISXl5dYuXJlqf386aefhLu7u9DV1RV169YVERER5U77v3btWtGkSROhq6srzM3NhZ+fn3TbAyHKPmdPnjwpjI2NRZs2baRp38+fPy969+4tLC0tha6urnBychL9+/cXMTEx0nolMdy+fbvM/Xv6PHr48KEIDQ0VlpaWwtDQUHTr1k38888/nIqdiKpMJgSv5CQiInqd3blzB3Z2dggPDy93lkYiInpxvOaKiIjoNbdu3ToUFxdjyJAh6g6FiOi1xmuuiIiIXlMHDx7ElStX8M0336Bnz56lZhIkIiLV4rBAIiKi11Tbtm3x559/omXLlvj555/h4OCg7pCIiF5rTK6IiIiIiIhUgNdcERERERERqQCTKyIiIiIiIhXghBZlkMvluHXrFoyNjSGTydQdDhERERERqYkQAjk5ObC3t4eGRsV9U0yuynDr1i04OjqqOwwiIiIiInpF/PPPP6hdu3aFdZhclcHY2BjAkwNoYmKi5miIiIiIiEhdsrOz4ejoKOUIFWFyVYaSoYAmJiZMroiIiIiIqEqXC3FCCyIiIiIiIhVgckVERERERKQCTK6IiIiIiIhUgNdcERER0QsTQuDx48coLi5WdyhERErR1NSElpaWSm7BxOSKiIiIXkhhYSHS0tKQn5+v7lCIiJ6LgYEB7OzsoKOj80LtMLkiIiKi5yaXy5GcnAxNTU3Y29tDR0dHJb/+EhG9DEIIFBYW4vbt20hOToa7u3ulNwquCJMrIiIiem6FhYWQy+VwdHSEgYGBusMhIlKavr4+tLW1cePGDRQWFkJPT++52+KEFkRERPTCXuSXXiIidVPV3zD+JSQiIiIiIlIBJldEREREREQqwOSKiIiI6BXRtm1bjBs3rtq3M2zYMPTs2bPat/O8du7cCTc3N2hqar6U41GWhIQE2NraIicnBwCwbt06mJmZScunT58OLy+vlxpTSkoKZDIZ4uLilF5XmXjVsW/lGThwIBYuXKjuMKqME1oQERFRtXCe/L+Xur2UuV1e6vaqw44dO6Ctra3uMEpJSUmBi4sLzp8//1K+dI8aNQohISEIDQ2FsbFxtW+vLFOmTMGnn34qbX/AgAHo3LmzWmJRhfHjx+PTTz9VdxhKmzp1Ktq0aYMRI0bA1NRU3eFUij1XRERERCpSWFj4QutbWFioLZl4WYqKiipcnpubi8zMTAQEBMDe3r7M41FcXAy5XF5dISI1NRW7du3CsGHDpDJ9fX1YW1tX2zarS8kNvo2MjGBpaanucJTWoEEDuLq64ueff1Z3KFXC5IqIiIjeWNHR0WjVqhXMzMxgaWmJrl27IikpSVr+zz//oH///jAzM4OFhQV69OiBlJQUaXnJ8LpvvvkG9vb28PDwqHSbK1asgLu7O/T09GBjY4O+fftKy54dFujs7IxZs2YhODgYRkZGcHJyQlRUFG7fvo0ePXrAyMgIjRo1wpkzZ6R1yhrStXjxYjg7Oz/3cXBxcQEANGnSBDKZDG3btgXw5D5nM2bMQO3ataGrqwsvLy9ER0dL65UMY9uyZQv8/Pygp6eHyMjIcuOIjY2Vkqn27dtDJpMhNjZWGpIXFRUFT09P6OrqIjU1Fffv30dwcDDMzc1hYGCAoKAgJCYmSu2VrLdr1y54eHjAwMAAffv2RX5+PtavXw9nZ2eYm5sjNDQUxcXF0npbt25F48aN4eDgUKqtZ/3www/SrQj69++PrKwsaVnJ+TF79mzY2NjAzMwMM2bMwOPHjzFhwgRYWFigdu3aiIiIKPeYlOXq1ato0aIF9PT00KBBA/zxxx8Kx1Amk2HPnj1o1qwZdHV1cfTo0VLnRWxsLJo3bw5DQ0OYmZmhZcuWuHHjRpX37fTp0+jYsSOsrKxgamoKPz8/nDt3TmF9mUyGNWvWoFevXjAwMIC7uzuioqIU6vz1118ICgqCkZERbGxsMGTIENy5c0ehTrdu3bB582aljpG6MLkiIiKiN1ZeXh7CwsJw5swZxMTEQENDA7169YJcLkdRURECAgJgbGyMI0eO4NixYzAyMkJgYKBCD1VMTAwSEhKwf/9+7Nq1q8LtnTlzBqGhoZgxYwYSEhIQHR2NNm3aVLjOd999h5YtW+L8+fPo0qULhgwZguDgYLz//vs4d+4cXF1dERwcDCFEtRwHADh16hQA4MCBA0hLS8OOHTsAAEuWLMHChQuxYMECXLx4EQEBAejevbtCggMAkydPxtixYxEfH4+AgIBy42jRogUSEhIAANu3b0daWhpatGgBAMjPz8e8efOwZs0aXL58GdbW1hg2bBjOnDmDqKgoHD9+HEIIdO7cWaF3LD8/H0uXLsXmzZsRHR2N2NhY9OrVC7t378bu3buxceNG/PDDD9i2bZu0zpEjR+Dt7V3pcbt+/Tq2bt2K33//HdHR0Th//jxGjx6tUOfgwYO4desWDh8+jEWLFmHatGno2rUrzM3NcfLkSXz00UcYNWoUbt68Wen2SkyYMAGff/45zp8/D19fX3Tr1g13795VqDN58mTMnTsX8fHxaNSokcKyx48fo2fPnvDz88PFixdx/PhxfPjhhwo3AK9s33JycjB06FAcPXoUJ06cgLu7Ozp37ixdo1bi66+/Rv/+/XHx4kV07twZgwcPxr179wAADx48QPv27dGkSROcOXMG0dHRyMjIQP/+/RXaaN68OU6dOoWCgoIqHyN14TVX9GKmv+JjX6dnVV6HiIjeWH369FF4vnbtWtSqVQtXrlxBXFwc5HI51qxZI33pjIiIgJmZGWJjY9GpUycAgKGhIdasWQMdHZ1Kt5eamgpDQ0N07doVxsbGcHJyQpMmTSpcp3Pnzhg1ahQAIDw8HCtXrsQ777yDfv36AQAmTZoEX19fZGRkwNbWVuljAFR8HBo0aIBatWoBACwtLRW2sWDBAkyaNAkDBw4EAMybNw+HDh3C4sWLsXz5cqneuHHj0Lt370rj0NHRkYbeWVhYKGyrqKgIK1asQOPGjQEAiYmJiIqKwrFjx6QELDIyEo6Ojti5c6d0fIqKirBy5Uq4uroCAPr27YuNGzciIyMDRkZG8PT0RLt27XDo0CEMGDAAAHDjxo0qJVePHj3Chg0bpB6u77//Hl26dMHChQul2C0sLLB06VJoaGjAw8MD8+fPR35+Pr744gsAT67tmjt3Lo4ePSodx8qMGTNGes1WrlyJ6Oho/PTTT5g4caJUZ8aMGejYsWOZ62dnZyMrKwtdu3aVjku9evWU2rf27dsr1P/xxx9hZmaGP/74A127dpXKhw0bhkGDBgEAZs+ejaVLl+LUqVMIDAzEsmXL0KRJE8yePVuqv3btWjg6OuLatWt4++23AQD29vYoLCxEeno6nJycqnSM1IU9V0RERPTGSkxMxKBBg1CnTh2YmJhIQ+dSU1Nx4cIFXL9+HcbGxjAyMoKRkREsLCzw6NEjhSFzDRs2rFJiBQAdO3aEk5MT6tSpgyFDhiAyMhL5+fkVrvN0r4ONjY20zWfLMjMzqxRDWSo6DuXJzs7GrVu30LJlS4Xyli1bIj4+XqGsKolKZXR0dBSORXx8PLS0tODj4yOVWVpawsPDQ2H7BgYGUgIBPDlezs7OMDIyUih7+vg9fPgQenp6lcb01ltvKQwd9PX1hVwul3rfAKB+/foKN6i1sbFReP00NTVhaWmp1Ovn6+sr/V9LSwve3t5KHXMLCwsMGzYMAQEB6NatG5YsWYK0tDSl9i0jIwMjR46Eu7s7TE1NYWJigtzc3FLnzNOvmaGhIUxMTKR9vXDhAg4dOiS9v4yMjFC3bl0AUHiP6evrA0Cl75VXAXuuiIiI6I3VrVs3ODk5YfXq1bC3t4dcLkeDBg1QWFiI3NxcNGvWrMxrhEp6coAnXxirytjYGOfOnUNsbCz27duH8PBwTJ8+HadPny7zeh4ACrMHlvSglVVWMoRPQ0Oj1BDByiaRqOg4qIIyx6g8+vr6CsPWqurZ2RdlMlmZZU9PkGFlZYX79+8/X6Aq2L4qVHbMIyIiEBoaiujoaGzZsgVTp07F/v378e6771ap/aFDh+Lu3btYsmQJnJycoKurC19f31LnTEX7mpubi27dumHevHml2rezs5P+XzKM8On33auKPVdERET0Rrp79y4SEhIwdepUdOjQAfXq1VP4Qt20aVMkJibC2toabm5uCo8XmRJaS0sL/v7+mD9/Pi5evIiUlBQcPHhQFbsE4MkX0PT0dIUEq6L7IlV2HABIPXNPT/pgYmICe3t7HDt2TKHusWPH4OnpqYI9qVi9evXw+PFjnDx5Uior2ZcX3X6TJk1w5cqVSuulpqbi1q1b0vMTJ05Iw/+q04kTJ6T/P378GGfPni01rK8qmjRpgilTpuDPP/9EgwYN8Msvv0jLKtu3Y8eOITQ0FJ07d0b9+vWhq6tbaiKKyjRt2hSXL1+Gs7NzqffY08nhX3/9hdq1a8PKykrpfXzZmFwRERHRG8nc3ByWlpb48ccfcf36dRw8eBBhYWHS8sGDB8PKygo9evTAkSNHkJycjNjYWISGhio1+cDTdu3ahaVLlyIuLg43btzAhg0bIJfLVfplvG3btrh9+zbmz5+PpKQkLF++HHv27Cm3fmXHAQCsra2hr68vTThQMmvchAkTMG/ePGzZsgUJCQmYPHky4uLiMHbsWJXtT3nc3d3Ro0cPjBw5EkePHsWFCxfw/vvvw8HBAT169HihtgMCAnD8+HGFZLIsenp6GDp0KC5cuIAjR44gNDQU/fv3f+5r36pq+fLl+O2333D16lV88sknuH//Pj744IMqr5+cnIwpU6bg+PHjuHHjBvbt24fExESFBK2yfXN3d8fGjRsRHx+PkydPYvDgwdLwvar65JNPcO/ePQwaNAinT59GUlIS9u7di5CQEIVjf+TIEekax1cdhwUSERFRtXjVb+qroaGBzZs3IzQ0FA0aNICHhweWLl0qTTNuYGCAw4cPY9KkSejduzdycnLg4OCADh06wMTE5Lm2aWZmhh07dmD69Ol49OgR3N3dsWnTJtSvX19l+1WvXj2sWLECs2fPxsyZM9GnTx+MHz8eP/74Y5n1KzsOwJPetqVLl2LGjBkIDw9H69atpUQzKysLn3/+OTIzM+Hp6YmoqCi4u7urbH8qEhERgbFjx6Jr164oLCxEmzZtsHv37he+EXNQUBC0tLRw4MCBCmc3dHNzQ+/evdG5c2fcu3cPXbt2xYoVK15o21Uxd+5czJ07F3FxcXBzc0NUVJRSvToGBga4evUq1q9fj7t378LOzg6ffPKJNHEKUPm+/fTTT/jwww/RtGlTODo6Yvbs2Rg/frxS+1HS8zlp0iR06tQJBQUFcHJyQmBgoHSd2qNHj7Bz506FKf5fZTLxIvN2vqays7NhamqKrKys5/7j+cbgbIFERG+0R48eITk5GS4uLlWaAICopli+fDmioqKwd+9edYfyRlu5ciV+++037Nu3r1q3U9HfMmVyA/ZcERERERE9Y9SoUXjw4AFycnKkGxvTy6etrY3vv/9e3WFU2StxzdXy5cvh7OwMPT09+Pj4SDeqq8zmzZshk8nQs2dPhXIhBMLDw2FnZwd9fX34+/uXupkdERERkaodOXJEYVrpZx/0ZMhdecfn6fsdqZuWlha+/PLLl5pYzZ49u9xjExQU9NLieJWMGDGi2icIUSW191xt2bIFYWFhWLVqFXx8fLB48WIEBAQgISFBuolcWVJSUjB+/Hi0bt261LL58+dj6dKlWL9+PVxcXPDVV18hICAAV65c4ZAFIiIiqjbe3t4VzsxHwJo1a/Dw4cMyl1lYWLzkaF4tH330Efr371/mMmUniyD1UPs1Vz4+PnjnnXewbNkyAE/u0eDo6IhPP/0UkydPLnOd4uJitGnTBh988AGOHDmCBw8eYOfOnQCe9FrZ29vj888/ly6qy8rKgo2NDdatW1elO1/zmisl8JorIqI3Gq+5IqLXgaquuVLrsMDCwkKcPXsW/v7+UpmGhgb8/f1x/PjxctebMWMGrK2tMXz48FLLkpOTkZ6ertCmqakpfHx8ym2zoKAA2dnZCg8iIiIiIiJlqDW5unPnDoqLi2FjY6NQbmNjg/T09DLXOXr0KH766SesXr26zOUl6ynT5pw5c2Bqaio9HB0dld0VIiIiIiJ6w70SE1pUVU5ODoYMGYLVq1er9A7NU6ZMQVZWlvT4559/VNY2ERERERG9GdQ6oYWVlRU0NTWRkZGhUJ6RkVHmna2TkpKQkpKCbt26SWVyuRzAkxldEhISpPUyMjJgZ2en0KaXl1eZcejq6kJXV/dFd4eIiIiIiN5gau250tHRQbNmzRATEyOVyeVyxMTEwNfXt1T9unXr4tKlS4iLi5Me3bt3R7t27RAXFwdHR0e4uLjA1tZWoc3s7GycPHmyzDaJiIiIiIhUQe3DAsPCwrB69WqsX78e8fHx+Pjjj5GXl4eQkBAAQHBwMKZMmQIA0NPTQ4MGDRQeZmZmMDY2RoMGDaCjowOZTIZx48Zh1qxZiIqKwqVLlxAcHAx7e/tS98MiIiIiepW0bdsW48aNq/btDBs27JX+XrRz5064ublBU1PzpRyPspSMiMrJyQEArFu3DmZmZtLy6dOnlzsqSl1kMpk0g3ZNarsyL3q+rlq1SmHkW3VS+32uBgwYgNu3byM8PBzp6enw8vJCdHS0NCFFamoqNDSUywEnTpyIvLw8fPjhh3jw4AFatWqF6OhoThFLRET0Mr3s23W8Brff2LFjB7S1tdUdRikpKSlwcXHB+fPnX0pCMWrUKISEhCA0NPSl3sT3aVOmTMGnn34qbX/AgAHo3LmzWmKhF/PBBx9g5syZOHLkSJn3yFUltSdXADBmzBiMGTOmzGWxsbEVrrtu3bpSZTKZDDNmzMCMGTNUEB0RERFR1RQWFkJHR+e5138TbqJbVFRUYQKZm5uLzMxMBAQEwN7evsw6xcXFkMlkSv8AX1WpqanYtWsXvv/+e6lMX1//jbyR74uc05W91i+Ljo4O3nvvPSxdurTakyu1DwskIiIiUpfo6Gi0atUKZmZmsLS0RNeuXZGUlCQt/+eff9C/f3+YmZnBwsICPXr0QEpKirS8ZLjSN998A3t7e3h4eFS6zRUrVsDd3R16enqwsbFB3759pWXPDgt0dnbGrFmzEBwcDCMjIzg5OSEqKgq3b99Gjx49YGRkhEaNGuHMmTPSOmUNV1u8eDGcnZ2f+zi4uLgAAJo0aQKZTIa2bdsCeHKt/IwZM1C7dm3o6upKI5BKpKSkQCaTYcuWLfDz84Oenh4iIyPLjSM2NlbqKWrfvj1kMhliY2OlIXlRUVHw9PSErq4uUlNTcf/+fQQHB8Pc3BwGBgYICgpCYmKi1F7Jert27YKHhwcMDAzQt29f5OfnY/369XB2doa5uTlCQ0NRXFwsrbd161Y0btwYDg4Opdp61g8//ABHR0cYGBigf//+yMr6rwe15PyYPXs2bGxsYGZmhhkzZuDx48eYMGECLCwsULt2bURERJR7TJ5WWFiIMWPGwM7ODnp6enBycsKcOXMU6ty5cwe9evWCgYEB3N3dERUVpbD8r7/+QlBQEIyMjGBjY4MhQ4bgzp070vK2bdtizJgxGDduHKysrBAQECAtS0tLQ1BQEPT19VGnTh1s27ZNWlbea13ZOQIAly5dQvv27aGvrw9LS0t8+OGHyM3NLfc4nD59GrVq1cK8efMAAA8ePMCIESNQq1YtmJiYoH379rhw4YLCOt26dUNUVBQePnxYpWP9vJhcERER0RsrLy8PYWFhOHPmDGJiYqChoYFevXpBLpejqKgIAQEBMDY2xpEjR3Ds2DEYGRkhMDAQhYWFUhsxMTFISEjA/v37sWvXrgq3d+bMGYSGhmLGjBlISEhAdHQ02rRpU+E63333HVq2bInz58+jS5cuGDJkCIKDg/H+++/j3LlzcHV1RXBwMIQQ1XIcAODUqVMAgAMHDiAtLQ07duwAACxZsgQLFy7EggULcPHiRQQEBKB79+4KCQ4ATJ48GWPHjkV8fLzCl/VntWjRAgkJCQCA7du3Iy0tDS1atAAA5OfnY968eVizZg0uX74Ma2trDBs2DGfOnEFUVBSOHz8OIQQ6d+6MoqIiqc38/HwsXboUmzdvRnR0NGJjY9GrVy/s3r0bu3fvxsaNG/HDDz8oJApHjhyBt7d3pcft+vXr2Lp1K37//XdER0fj/PnzGD16tEKdgwcP4tatWzh8+DAWLVqEadOmoWvXrjA3N8fJkyfx0UcfYdSoUbh582al21u6dCmioqKwdetWJCQkIDIyslTS/PXXX6N///64ePEiOnfujMGDB+PevXsAniQh7du3R5MmTXDmzBlER0cjIyMD/fv3V2hj/fr10NHRwbFjx7Bq1Sqp/KuvvkKfPn1w4cIFDB48GAMHDkR8fLzCus++1pWdI3l5eQgICIC5uTlOnz6NX3/9FQcOHCh3VNvBgwfRsWNHfPPNN5g0aRIAoF+/fsjMzMSePXtw9uxZNG3aFB06dJD2GwC8vb3x+PFjnDx5stLj/CJeiWGBREREROrQp08fhedr165FrVq1cOXKFcTFxUEul2PNmjWQyWQAgIiICJiZmSE2NhadOnUCABgaGmLNmjVVGjqVmpoKQ0NDdO3aFcbGxnByckKTJk0qXKdz584YNWoUACA8PBwrV67EO++8g379+gEAJk2aBF9f33JvZVMVFR2HBg0aoFatWgAAS0tLhW0sWLAAkyZNwsCBAwEA8+bNw6FDh7B48WIsX75cqjdu3Dj07t270jh0dHRgbW0N4MkQyae3VVRUhBUrVqBx48YAgMTERERFReHYsWNSAhYZGQlHR0fs3LlTOj5FRUVYuXIlXF1dAQB9+/bFxo0bkZGRASMjI3h6eqJdu3Y4dOgQBgwYAAC4ceNGlZKrR48eYcOGDVIP1/fff48uXbpg4cKFUuwWFhZYunQpNDQ04OHhgfnz5yM/Px9ffPEFgCfXds2dOxdHjx6VjmN5UlNT4e7ujlatWkEmk8HJyalUnWHDhmHQoEEAgNmzZ2Pp0qU4deoUAgMDsWzZMjRp0gSzZ8+W6q9duxaOjo64du0a3n77bQCAu7s75s+fX6rtfv36YcSIEQCAmTNnYv/+/fj++++xYsUKqc6zr3Vl58gvv/wiHUdDQ0MAwLJly9CtWzfMmzdPmocBAH777TcEBwdjzZo10mt19OhRnDp1CpmZmdKtlRYsWICdO3di27Zt+PDDDwEABgYGMDU1xY0bNyo8xi+KPVdERET0xkpMTMSgQYNQp04dmJiYSL0AqampuHDhAq5fvw5jY2MYGRnByMgIFhYWePTokcKQuYYNG1b5mpSOHTvCyckJderUwZAhQxAZGYn8/PwK12nUqJH0/5Ivmg0bNixVlpmZWaUYylLRcShPdnY2bt26hZYtWyqUt2zZslRvRlUSlcro6OgoHIv4+HhoaWnBx8dHKrO0tISHh4fC9g0MDKTECnhyvJydnWFkZKRQ9vTxe/jwYZUmQnvrrbcUhg76+vpCLpdLvW8AUL9+fYVrw2xsbBReP01NTVhaWlbp9Rs2bBji4uLg4eGB0NBQ7Nu3r1Sdp4+RoaEhTExMpLYvXLiAQ4cOSeezkZER6tatCwAK53SzZs3K3P6ztzXy9fWt8LWuyjkSHx+Pxo0bS4lVyfJnj+PJkyfRr18/bNy4UUqsSvYpNzcXlpaWCvuVnJyssE/Ak+vmKnu/vSj2XBEREdEbq1u3bnBycsLq1athb28PuVyOBg0aoLCwELm5uWjWrFmZ1wiV9OQAUPhSWBljY2OcO3cOsbGx2LdvH8LDwzF9+nScPn26zOt5AChMCFDSg1ZWWckQPg0NjVJDBJ8eJleWio6DKihzjMqjr68v7asynp1QQSaTlVlWcvwAwMrKCvfv33++QFWw/fI0bdoUycnJ2LNnDw4cOID+/fvD399fYUhjRW3n5uZKPULPsrOzk/7/Iq+XKl7rsri6usLS0hJr165Fly5dpP3Mzc2FnZ1dmZPgPfueunfvnsJ7tzqw54qIiIjeSHfv3kVCQgKmTp2KDh06oF69egpfqJs2bYrExERYW1vDzc1N4WFq+vzTzGtpacHf3x/z58/HxYsXkZKSgoMHD6pilwA8SfzS09MVEqy4uLhy61d2HABIPXNPT/pgYmICe3t7HDt2TKHusWPH4OnpqYI9qVi9evVKXUNTsi8vuv0mTZrgypUrldZLTU3FrVu3pOcnTpyQhv9VFxMTEwwYMACrV6/Gli1bsH37doVriyrStGlTXL58Gc7OzqXO6aokRSdOnCj1vF69ehXGWtk5Uq9ePVy4cAF5eXkKy589jlZWVjh48CCuX7+O/v37Sz8YNG3aFOnp6dDS0iq1T1ZWVtL6SUlJePToUaXDcF8UkysiIiJ6I5mbm8PS0hI//vgjrl+/joMHDyIsLExaPnjwYFhZWaFHjx44cuQIkpOTERsbi9DQ0CpNPlCWXbt2YenSpYiLi8ONGzewYcMGyOVylX4Zb9u2LW7fvo358+cjKSkJy5cvx549e8qtX9lxAABra2vo6+tLEyCUzIg3YcIEzJs3D1u2bEFCQgImT56MuLg4jB07VmX7Ux53d3f06NEDI0eOxNGjR3HhwgW8//77cHBwQI8ePV6o7YCAABw/flwhmSyLnp4ehg4digsXLuDIkSMIDQ1F//79n/vat8osWrQImzZtwtWrV3Ht2jX8+uuvsLW1LbfX81mffPIJ7t27h0GDBuH06dNISkrC3r17ERISUum+AsCvv/6KtWvX4tq1a5g2bRpOnTpV7sQTJSo7RwYPHiwdx7/++guHDh3Cp59+iiFDhihcbwU8OQ8PHjyIq1evYtCgQXj8+DH8/f3h6+uLnj17Yt++fUhJScGff/6JL7/8UmEWzSNHjqBOnToKQ0SrA4cFEhERUfV4xW/qq6Ghgc2bNyM0NBQNGjSAh4cHli5dKk0zbmBggMOHD2PSpEno3bs3cnJy4ODggA4dOsDExOS5tmlmZoYdO3Zg+vTpePToEdzd3bFp0ybUr19fZftVr149rFixArNnz8bMmTPRp08fjB8/Hj/++GOZ9Ss7DsCT3ralS5dixowZCA8PR+vWraVEMysrC59//jkyMzPh6emJqKgouLu7q2x/KhIREYGxY8eia9euKCwsRJs2bbB79+4XvrdSUFAQtLS0cODAgQpnN3Rzc0Pv3r3RuXNn3Lt3D127dlWY3EHVjI2NMX/+fCQmJkJTUxPvvPMOdu/eXeX7fZX0Ik2aNAmdOnVCQUEBnJycEBgYWKU2vv76a2zevBmjR4+GnZ0dNm3aVGkvYWXniIGBAfbu3YuxY8finXfegYGBAfr06YNFixaV2Z6trS0OHjyItm3bYvDgwfjll1+we/dufPnllwgJCcHt27dha2uLNm3aKCRnmzZtwsiRI6t0nF6ETLzIvJ2vqezsbJiamiIrK+u5/3i+MaY//7CIl+IV/2AnIqrpHj16hOTkZLi4uFRpAgCimmL58uWIiorC3r171R0KvaDLly+jffv2uHbtWrlDeiv6W6ZMbsCeKyIiIiKiZ4waNQoPHjxATk6OdGNjqpnS0tKwYcOGF7pWsqqYXNUAzpP/p+4QypXCHymJiIgkR44cQVBQULnLc3NzX2I0r6agoCAcOXKkzGVffPGFdP8nddPS0sKXX375Urc5e/ZshXtQPa1169YVXjtH5fP3939p22JyRURERKQi3t7eFc7MR8CaNWvw8OHDMpdZWFi85GheLR999BH69+9f5jJ9ff2XHA09DyZXRERERCqir68PNzc3dYfxSnv6prukyMLC4o1PMGs6TsVORERERESkAkyuiIiIiIiIVIDJFRERERERkQowuSIiIiIiIlIBJldEREREREQqwOSKiIiI6BXRtm1bjBs3rtq3M2zYMPTs2bPat/O8du7cCTc3N2hqar6U41GWhIQE2NraIicnBwCwbt06mJmZScunT58OLy8vtcSmKs/ukypcuXIFtWvXRl5enkrbrSk4FTsRERFVi4brG77U7V0aeumlbq867NixA9ra2uoOo5SUlBS4uLjg/PnzLyWhGDVqFEJCQhAaGgpjY+Nq315ZpkyZgk8//VTa/oABA9C5c2e1xFJC1a9DdeyTp6cn3n33XSxatAhfffWVStuuCdhzRURERKQihYWFL7S+hYWF2pKJl6WoqKjC5bm5ucjMzERAQADs7e3LPB7FxcWQy+XVFSJSU1Oxa9cuDBs2TCrT19eHtbV1tW3zZSsqKqq2fQoJCcHKlSvx+PFjlbf9qmNyRURERG+s6OhotGrVCmZmZrC0tETXrl2RlJQkLf/nn3/Qv39/mJmZwcLCAj169EBKSoq0vGR43TfffAN7e3t4eHhUus0VK1bA3d0denp6sLGxQd++faVlzw4LdHZ2xqxZsxAcHAwjIyM4OTkhKioKt2/fRo8ePWBkZIRGjRrhzJkz0jplDVdbvHgxnJ2dn/s4uLi4AACaNGkCmUyGtm3bAgDkcjlmzJiB2rVrQ1dXF15eXoiOjpbWS0lJgUwmw5YtW+Dn5wc9PT1ERkaWG0dsbKyUTLVv3x4ymQyxsbHS8LWoqCh4enpCV1cXqampuH//PoKDg2Fubg4DAwMEBQUhMTFRaq9kvV27dsHDwwMGBgbo27cv8vPzsX79ejg7O8Pc3ByhoaEoLi6W1tu6dSsaN26scMPj8obQ/fDDD3B0dISBgQH69++PrKwsaVnJ+TF79mzY2NjAzMwMM2bMwOPHjzFhwgRYWFigdu3aiIiIKPeYPE3Vr0N5Qx03btwIZ2dnmJqaYuDAgdLQSAAoKChAaGgorK2toaenh1atWuH06dMKcXbs2BH37t3DH3/8UaX9ep0wuSIiIqI3Vl5eHsLCwnDmzBnExMRAQ0MDvXr1glwuR1FREQICAmBsbIwjR47g2LFjMDIyQmBgoEIPVUxMDBISErB//37s2rWrwu2dOXMGoaGhmDFjBhISEhAdHY02bdpUuM53332Hli1b4vz58+jSpQuGDBmC4OBgvP/++zh37hxcXV0RHBwMIUS1HAcAOHXqFADgwIEDSEtLw44dOwAAS5YswcKFC7FgwQJcvHgRAQEB6N69u0KCAwCTJ0/G2LFjER8fj4CAgHLjaNGiBRISEgAA27dvR1paGlq0aAEAyM/Px7x587BmzRpcvnwZ1tbWGDZsGM6cOYOoqCgcP34cQgh07txZoXcsPz8fS5cuxebNmxEdHY3Y2Fj06tULu3fvxu7du7Fx40b88MMP2LZtm7TOkSNH4O3tXelxu379OrZu3Yrff/8d0dHROH/+PEaPHq1Q5+DBg7h16xYOHz6MRYsWYdq0aejatSvMzc1x8uRJfPTRRxg1ahRu3rxZ6fZexuuQlJSEnTt3YteuXdi1axf++OMPzJ07V1o+ceJEbN++HevXr8e5c+fg5uaGgIAA3Lt3T6qjo6MDLy8vHDlypNJ9et3wmisiIiJ6Y/Xp00fh+dq1a1GrVi1cuXIFcXFxkMvlWLNmDWQyGQAgIiICZmZmiI2NRadOnQAAhoaGWLNmDXR0dCrdXmpqKgwNDdG1a1cYGxvDyckJTZo0qXCdzp07Y9SoUQCA8PBwrFy5Eu+88w769esHAJg0aRJ8fX2RkZEBW1tbpY8BUPFxaNCgAWrVqgUAsLS0VNjGggULMGnSJAwcOBAAMG/ePBw6dAiLFy/G8uXLpXrjxo1D7969K41DR0dHGqZmYWGhsK2ioiKsWLECjRs3BgAkJiYiKioKx44dkxKwyMhIODo6YufOndLxKSoqwsqVK+Hq6goA6Nu3LzZu3IiMjAwYGRnB09MT7dq1w6FDhzBgwAAAwI0bN6qUXD169AgbNmyQeri+//57dOnSBQsXLpRit7CwwNKlS6GhoQEPDw/Mnz8f+fn5+OKLLwA8ubZr7ty5OHr0qHQcy/MyXge5XI5169ZJPYhDhgxBTEwMvvnmG+Tl5WHlypVYt24dgoKCAACrV6/G/v378dNPP2HChAlSO/b29rhx40alx/B1w54rIiIiemMlJiZi0KBBqFOnDkxMTKShc6mpqbhw4QKuX78OY2NjGBkZwcjICBYWFnj06JHCkLmGDRtWKbECngyXcnJyQp06dTBkyBBERkYiPz+/wnUaNWok/d/Gxkba5rNlmZmZVYqhLBUdh/JkZ2fj1q1baNmypUJ5y5YtER8fr1BWlUSlMjo6OgrHIj4+HlpaWvDx8ZHKLC0t4eHhobB9AwMDKbECnhwvZ2dnGBkZKZQ9ffwePnwIPT29SmN66623FIYO+vr6Qi6XS71vAFC/fn1oaPz3ldvGxkbh9dPU1ISlpeVzv36qfh2cnZ0VrnOzs7OTYktKSkJRUZHCtrS1tdG8efNS29LX16/03H4dseeKiIiI3ljdunWDk5MTVq9eDXt7e8jlcjRo0ACFhYXIzc1Fs2bNyrxGqKQHAXjSc1VVxsbGOHfuHGJjY7Fv3z6Eh4dj+vTpOH36dLlTYj89e2BJD1pZZSVD+DQ0NEoNEaxsEomKjoMqKHOMyqOvry/tqzKenX1RJpOVWfb0BBlWVla4f//+8wWqgu1Xl6q8DqqK7d69ewpJ7ZuCPVdERET0Rrp79y4SEhIwdepUdOjQAfXq1VP4Qt20aVMkJibC2toabm5uCg9TU9Pn3q6Wlhb8/f0xf/58XLx4ESkpKTh48KAqdgnAk8QvPT1dIcGKi4srt35lxwGA1DP39KQPJiYmsLe3x7FjxxTqHjt2DJ6enirYk4rVq1cPjx8/xsmTJ6Wykn150e03adIEV65cqbReamoqbt26JT0/ceKENPyvOqj7dXB1dYWOjo7CtoqKinD69OlS2/rrr78qHfL6OmLPFREREb2RzM3NYWlpiR9//BF2dnZITU3F5MmTpeWDBw/Gt99+ix49ekgzsd24cQM7duzAxIkTUbt2baW3uWvXLvz9999o06YNzM3NsXv3bsjlcpV+GW/bti1u376N+fPno2/fvoiOjsaePXtgYmJSZv3KjgMAWFtbQ19fH9HR0ahduzb09PRgamqKCRMmYNq0aXB1dYWXlxciIiIQFxdX4YyAquLu7o4ePXpg5MiR+OGHH2BsbIzJkyfDwcEBPXr0eKG2AwICMGLECBQXF0NTU7Pcenp6ehg6dCgWLFiA7OxshIaGon///s997Vtl1P06GBoa4uOPP5ZmOnzrrbeka8iGDx8u1UtJScG///4Lf39/lW6/JmByRURERNXiVb+pr4aGBjZv3ozQ0FA0aNAAHh4eWLp0qTS9tYGBAQ4fPoxJkyahd+/eyMnJgYODAzp06FBuolIZMzMz7NixA9OnT8ejR4/g7u6OTZs2oX79+irbr3r16mHFihWYPXs2Zs6ciT59+mD8+PH48ccfy6xf2XEAnvS2LV26FDNmzEB4eDhat26N2NhYhIaGIisrC59//jkyMzPh6emJqKgouLu7q2x/KhIREYGxY8eia9euKCwsRJs2bbB79+4XvhFzUFAQtLS0cODAgQpnN3Rzc0Pv3r3RuXNn3Lt3D127dsWKFSteaNsVeRVeh7lz50Iul2PIkCHIycmBt7c39u7dC3Nzc6nOpk2b0KlTJzg5Oal8+686mXiReTtfU9nZ2TA1NUVWVtZz//FUJefJ/1N3COVK0XtP3SFUbHpW5XWIiOi5PXr0CMnJyXBxcanSBABENcXy5csRFRWFvXv3qjuUGqWwsBDu7u745ZdfSk2y8Sqr6G+ZMrkBe66IiIiIiJ4xatQoPHjwADk5OQqz51HFUlNT8cUXX9SoxEqVOKEFERERkYocOXJEmra9rAc9GXJX3vGZPXu2usOTaGlp4csvv3ypidXs2bPLPTYl95V61bm5uUn3ZXsTseeKiIiISEW8vb0rnJmPgDVr1uDhw4dlLrOwsHjJ0bxaPvroI/Tv37/MZfr6+i85Gnoer0RytXz5cnz77bdIT09H48aN8f3336N58+Zl1t2xYwdmz56N69evo6ioCO7u7vj8888xZMgQqc6wYcOwfv16hfUCAgIQHR1drftBREREbzZ9fX24ubmpO4xX2tM33SVFFhYWb3yCWdOpPbnasmULwsLCsGrVKvj4+GDx4sUICAhAQkICrK2tS9W3sLDAl19+ibp160JHRwe7du1CSEgIrK2tFWZzCQwMREREhPRcV1f3pewPERHRm4jzYxFRTaaqv2Fqv+Zq0aJFGDlyJEJCQuDp6YlVq1bBwMAAa9euLbN+27Zt0atXL9SrVw+urq4YO3YsGjVqhKNHjyrU09XVha2trfR4enpIIiIiUo2SKa/z8/PVHAkR0fMr+Rv2otP4q7XnqrCwEGfPnsWUKVOkMg0NDfj7++P48eOVri+EwMGDB5GQkIB58+YpLIuNjYW1tTXMzc3Rvn17zJo1C5aWlmW2U1BQgIKCAul5dnb2c+4RERHRm0VTUxNmZmbIzMwE8OTeUDKZTM1RERFVjRAC+fn5yMzMhJmZWYU3ja4KtSZXd+7cQXFxMWxsbBTKbWxscPXq1XLXy8rKgoODAwoKCqCpqYkVK1agY8eO0vLAwED07t0bLi4uSEpKwhdffIGgoCAcP368zAM2Z84cfP3116rbMSIiojeIra0tAEgJFhFRTWNmZib9LXsRar/m6nkYGxsjLi4Oubm5iImJQVhYGOrUqSPdSXzgwIFS3YYNG6JRo0ZwdXVFbGwsOnToUKq9KVOmICwsTHqenZ0NR0fHat8PIiKi14FMJoOdnR2sra1RVFSk7nCIiJSira39wj1WJdSaXFlZWUFTUxMZGRkK5RkZGRVmjhoaGtJMPF5eXoiPj8ecOXOk5OpZderUgZWVFa5fv15mcqWrq8sJL4iIiF6Qpqamyr6gEBHVRGqd0EJHRwfNmjVDTEyMVCaXyxETEwNfX98qtyOXyxWumXrWzZs3cffuXdjZ2b1QvEREREREROVR+7DAsLAwDB06FN7e3mjevDkWL16MvLw8hISEAACCg4Ph4OCAOXPmAHhyfZS3tzdcXV1RUFCA3bt3Y+PGjVi5ciUAIDc3F19//TX69OkDW1tbJCUlYeLEiXBzc1OYqp2IiIiIiEiV1J5cDRgwALdv30Z4eDjS09Ph5eWF6OhoaZKL1NRUaGj818GWl5eH0aNH4+bNm9DX10fdunXx888/Y8CAAQCeDEm4ePEi1q9fjwcPHsDe3h6dOnXCzJkzOfSPiIiIiIiqjUzwrn+lZGdnw9TUFFlZWTAxMVF3OHCe/D91h1CuFL331B1CxaZnqTsCIiIiIqrBlMkN1H4TYSIiIiIiotcBkysiIiIiIiIVYHJFRERERESkAkyuiIiIiIiIVIDJFRERERERkQowuSIiIiIiIlIBJldEREREREQqwOSKiIiIiIhIBZhcERERERERqQCTKyIiIiIiIhVgckVERERERKQCTK6IiIiIiIhUgMkVERERERGRCjC5IiIiIiIiUgEmV0RERERERCrA5IqIiIiIiEgFmFwRERERERGpAJMrIiIiIiIiFWByRUREREREpAJMroiIiIiIiFSAyRUREREREZEKMLkiIiIiIiJSASZXREREREREKsDkioiIiIiISAWYXBEREREREakAkysiIiIiIiIVYHJFRERERESkAkyuiIiIiIiIVIDJFRERERERkQowuSIiIiIiIlIBJldEREREREQqwOSKiIiIiIhIBZhcERERERERqQCTKyIiIiIiIhV4JZKr5cuXw9nZGXp6evDx8cGpU6fKrbtjxw54e3vDzMwMhoaG8PLywsaNGxXqCCEQHh4OOzs76Ovrw9/fH4mJidW9G0RERERE9AZTe3K1ZcsWhIWFYdq0aTh37hwaN26MgIAAZGZmllnfwsICX375JY4fP46LFy8iJCQEISEh2Lt3r1Rn/vz5WLp0KVatWoWTJ0/C0NAQAQEBePTo0cvaLSIiIiIiesPIhBBCnQH4+PjgnXfewbJlywAAcrkcjo6O+PTTTzF58uQqtdG0aVN06dIFM2fOhBAC9vb2+PzzzzF+/HgAQFZWFmxsbLBu3ToMHDiw1PoFBQUoKCiQnmdnZ8PR0RFZWVkwMTFRwV6+GOfJ/1N3COVK0XtP3SFUbHqWuiMgIiIiohosOzsbpqamVcoN1NpzVVhYiLNnz8Lf318q09DQgL+/P44fP17p+kIIxMTEICEhAW3atAEAJCcnIz09XaFNU1NT+Pj4lNvmnDlzYGpqKj0cHR1fcM+IiIiIiOhNo9bk6s6dOyguLoaNjY1CuY2NDdLT08tdLysrC0ZGRtDR0UGXLl3w/fffo2PHjgAgradMm1OmTEFWVpb0+Oeff15kt4iIiIiI6A2kpe4AnoexsTHi4uKQm5uLmJgYhIWFoU6dOmjbtu1ztaerqwtdXV3VBklERERERG8UtSZXVlZW0NTUREZGhkJ5RkYGbG1ty11PQ0MDbm5uAAAvLy/Ex8djzpw5aNu2rbReRkYG7OzsFNr08vJS/U4QERERERFBzcMCdXR00KxZM8TExEhlcrkcMTEx8PX1rXI7crlcmpDCxcUFtra2Cm1mZ2fj5MmTSrVJRERERESkDLUPCwwLC8PQoUPh7e2N5s2bY/HixcjLy0NISAgAIDg4GA4ODpgzZw6AJ5NPeHt7w9XVFQUFBdi9ezc2btyIlStXAgBkMhnGjRuHWbNmwd3dHS4uLvjqq69gb2+Pnj17qms3iYiIiIjoNaf25GrAgAG4ffs2wsPDkZ6eDi8vL0RHR0sTUqSmpkJD478Otry8PIwePRo3b96Evr4+6tati59//hkDBgyQ6kycOBF5eXn48MMP8eDBA7Rq1QrR0dHQ09N76ftHRERERERvBrXf5+pVpMxc9i8D73P1AnifKyIiIiJ6ATXmPldERERERESvCyZXREREREREKqBUclVcXIzDhw/jwYMH1RQOERERERFRzaRUcqWpqYlOnTrh/v371RUPERERERFRjaT0sMAGDRrg77//ro5YiIiIiIiIaiylk6tZs2Zh/Pjx2LVrF9LS0pCdna3wICIiIiIiehMpfZ+rzp07AwC6d+8OmUwmlQshIJPJUFxcrLroiIiIiIiIagilk6tDhw5VRxxEREREREQ1mtLJlZ+fX3XEQUREREREVKMpnVwBwIMHD/DTTz8hPj4eAFC/fn188MEHMDU1VWlwRERERERENYXSE1qcOXMGrq6u+O6773Dv3j3cu3cPixYtgqurK86dO1cdMRIREREREb3ylO65+uyzz9C9e3esXr0aWlpPVn/8+DFGjBiBcePG4fDhwyoPkoiIiIiI6FWndHJ15swZhcQKALS0tDBx4kR4e3urNDgiohpl+is8NHp6lrojICIieu0pPSzQxMQEqamppcr/+ecfGBsbqyQoIiIiIiKimkbpnqsBAwZg+PDhWLBgAVq0aAEAOHbsGCZMmIBBgwapPECiF9FwfUN1h1CuS0MvqTsEIiIiIlIhpZOrBQsWQCaTITg4GI8fPwYAaGtr4+OPP8bcuXNVHiAREREREVFNoFRyVVxcjBMnTmD69OmYM2cOkpKSAACurq4wMDColgCJiIiIiIhqAqWSK01NTXTq1Anx8fFwcXFBw4av7pArIno9OU/+n7pDKFeKnrojICIiInVSekKLBg0a4O+//66OWIiIiIiIiGospZOrWbNmYfz48di1axfS0tKQnZ2t8CAiIiIiInoTKT2hRefOnQEA3bt3h0wmk8qFEJDJZCguLlZddERERERERDWE0snVoUOHqiMOIiIiIiKiGk2p5KqoqAgzZszAqlWr4O7uXl0xERERERER1ThKXXOlra2NixcvVlcsRERERERENZbSE1q8//77+Omnn6ojFiIiIiIiohpL6WuuHj9+jLVr1+LAgQNo1qwZDA0NFZYvWrRIZcERERERERHVFEonV3/99ReaNm0KALh27ZrCsqdnDyQiIiIiInqTcLZAIiIiIiIiFVD6mquKZGZmqrI5IiIiIiKiGqPKyZWBgQFu374tPe/SpQvS0tKk5xkZGbCzs1NtdERERERERDVElZOrR48eQQghPT98+DAePnyoUOfp5URERERERG8SlQ4L5IQWRERERET0plJpckVERERERPSmqnJyJZPJFHqmnn3+IpYvXw5nZ2fo6enBx8cHp06dKrfu6tWr0bp1a5ibm8Pc3Bz+/v6l6g8bNkyKr+QRGBiokliJiIiIiIjKUuWp2IUQePvtt6WEKjc3F02aNIGGhoa0/Hls2bIFYWFhWLVqFXx8fLB48WIEBAQgISEB1tbWperHxsZi0KBBaNGiBfT09DBv3jx06tQJly9fhoODg1QvMDAQERER0nNdXd3nio+IiIiIiKgqqpxcPZ2oqNKiRYswcuRIhISEAABWrVqF//3vf1i7di0mT55cqn5kZKTC8zVr1mD79u2IiYlBcHCwVK6rqwtbW9tqiZmIiIiIiOhZVU6uhg4dqvKNFxYW4uzZs5gyZYpUpqGhAX9/fxw/frxKbeTn56OoqAgWFhYK5bGxsbC2toa5uTnat2+PWbNmwdLSssw2CgoKUFBQID3Pzs5+jr0hIiIiIqI3mVontLhz5w6Ki4thY2OjUG5jY4P09PQqtTFp0iTY29vD399fKgsMDMSGDRsQExODefPm4Y8//kBQUBCKi4vLbGPOnDkwNTWVHo6Ojs+/U0RERERE9Eaqcs/Vq2ju3LnYvHkzYmNjoaenJ5UPHDhQ+n/Dhg3RqFEjuLq6IjY2Fh06dCjVzpQpUxAWFiY9z87OZoJFRERERERKUWvPlZWVFTQ1NZGRkaFQnpGRUen1UgsWLMDcuXOxb98+NGrUqMK6derUgZWVFa5fv17mcl1dXZiYmCg8iIiIiIiIlKHW5EpHRwfNmjVDTEyMVCaXyxETEwNfX99y15s/fz5mzpyJ6OhoeHt7V7qdmzdv4u7du7Czs1NJ3ERERERERM9S+02Ew8LCsHr1aqxfvx7x8fH4+OOPkZeXJ80eGBwcrDDhxbx58/DVV19h7dq1cHZ2Rnp6OtLT05GbmwvgyRTxEyZMwIkTJ5CSkoKYmBj06NEDbm5uCAgIUMs+EhERERHR669K11w9fT1SZRYtWqRUAAMGDMDt27cRHh6O9PR0eHl5ITo6WprkIjU1VbqXFgCsXLkShYWF6Nu3r0I706ZNw/Tp06GpqYmLFy9i/fr1ePDgAezt7dGpUyfMnDmT97oiIiIiIqJqU6Xk6vz58wrPz507h8ePH8PDwwMAcO3aNWhqaqJZs2bPFcSYMWMwZsyYMpfFxsYqPE9JSamwLX19fezdu/e54iAiIiIiInpeVUquDh06JP1/0aJFMDY2xvr162Fubg4AuH//PkJCQtC6devqiZKIiIiIiOgVp/Q1VwsXLsScOXOkxAoAzM3NMWvWLCxcuFClwREREREREdUUSidX2dnZuH37dqny27dvIycnRyVBERERERER1TRKJ1e9evVCSEgIduzYgZs3b+LmzZvYvn07hg8fjt69e1dHjERERERERK+8Kl1z9bRVq1Zh/PjxeO+991BUVPSkES0tDB8+HN9++63KAyQiIiIiIqoJlE6uDAwMsGLFCnz77bdISkoCALi6usLQ0FDlwREREREREdUUz30T4bS0NKSlpcHd3R2GhoYQQqgyLiIiIiIiohpF6eTq7t276NChA95++2107twZaWlpAIDhw4fj888/V3mARERERERENYHSydVnn30GbW1tpKamwsDAQCofMGAAoqOjVRocERERERFRTaH0NVf79u3D3r17Ubt2bYVyd3d33LhxQ2WBERERERER1SRK91zl5eUp9FiVuHfvHnR1dVUSFBERERERUU2jdHLVunVrbNiwQXouk8kgl8sxf/58tGvXTqXBERERERER1RRKDwucP38+OnTogDNnzqCwsBATJ07E5cuXce/ePRw7dqw6YiQiIiIiInrlKd1z1aBBA1y7dg2tWrVCjx49kJeXh969e+P8+fNwdXWtjhiJiIiIiIheeUr3XKWmpsLR0RFffvllmcveeustlQRGRERERERUkyjdc+Xi4oLbt2+XKr979y5cXFxUEhQREREREVFNo3RyJYSATCYrVZ6bmws9PT2VBEVERERERFTTVHlYYFhYGIAnswN+9dVXCtOxFxcX4+TJk/Dy8lJ5gERERERERDVBlZOr8+fPA3jSc3Xp0iXo6OhIy3R0dNC4cWOMHz9e9RESERERERHVAFVOrg4dOgQACAkJwZIlS2BiYlJtQREREREREdU0Ss8WGBERUR1xEBERERER1WhKJ1cAcObMGWzduhWpqakoLCxUWLZjxw6VBEZERERERFSTKD1b4ObNm9GiRQvEx8fjt99+Q1FRES5fvoyDBw/C1NS0OmIkIiIiIiJ65SmdXM2ePRvfffcdfv/9d+jo6GDJkiW4evUq+vfvzxsIExERERHRG0vp5CopKQldunQB8GSWwLy8PMhkMnz22Wf48ccfVR4gERERERFRTaB0cmVubo6cnBwAgIODA/766y8AwIMHD5Cfn6/a6IiIiIiIiGoIpSe0aNOmDfbv34+GDRuiX79+GDt2LA4ePIj9+/ejQ4cO1REjERERERHRK0/p5GrZsmV49OgRAODLL7+EtrY2/vzzT/Tp0wdTp05VeYBEREREREQ1gdLJlYWFhfR/DQ0NTJ48WaUBERERERER1URVSq6ys7Or3KCJiclzB0NERERERFRTVSm5MjMzg0wmq7COEAIymQzFxcUqCYyIiIiIiKgmqVJydejQoeqOg4iIiIiIqEarUnLl5+dX3XEQERERERHVaErf5+rw4cMVPp7H8uXL4ezsDD09Pfj4+ODUqVPl1l29ejVat24Nc3NzmJubw9/fv1R9IQTCw8NhZ2cHfX19+Pv7IzEx8bliIyIiIiIiqgqlZwts27ZtqbKnr8dS9pqrLVu2ICwsDKtWrYKPjw8WL16MgIAAJCQkwNraulT92NhYDBo0CC1atICenh7mzZuHTp064fLly3BwcAAAzJ8/H0uXLsX69evh4uKCr776CgEBAbhy5Qr09PSU22EiIiIiIqIqULrn6v79+wqPzMxMREdH45133sG+ffuUDmDRokUYOXIkQkJC4OnpiVWrVsHAwABr164ts35kZCRGjx4NLy8v1K1bF2vWrIFcLkdMTAyAJ71WixcvxtSpU9GjRw80atQIGzZswK1bt7Bz506l4yMiIiIiIqoKpXuuTE1NS5V17NgROjo6CAsLw9mzZ6vcVmFhIc6ePYspU6ZIZRoaGvD398fx48er1EZ+fj6Kioqk+28lJycjPT0d/v7+CjH7+Pjg+PHjGDhwYKk2CgoKUFBQID1XZup5IiIiIiIi4Dl6rspjY2ODhIQEpda5c+cOiouLYWNjU6qt9PT0KrUxadIk2NvbS8lUyXrKtDlnzhyYmppKD0dHR6X2g4iIiIiISOmeq4sXLyo8F0IgLS0Nc+fOhZeXl6riqpK5c+di8+bNiI2NfaFrqaZMmYKwsDDpeXZ2NhMsIiIiIiJSitLJlZeXF2QyGYQQCuXvvvtuuddJlcfKygqamprIyMhQKM/IyICtrW2F6y5YsABz587FgQMH0KhRI6m8ZL2MjAzY2dkptFle8qerqwtdXV2lYiciIiIiInqa0sMCk5OT8ffffyM5ORnJycm4ceMG8vPz8eeff6Ju3bpKtaWjo4NmzZpJk1EAkCan8PX1LXe9+fPnY+bMmYiOjoa3t7fCMhcXF9ja2iq0mZ2djZMnT1bYJhERERER0YtQuufKyclJpQGEhYVh6NCh8Pb2RvPmzbF48WLk5eUhJCQEABAcHAwHBwfMmTMHADBv3jyEh4fjl19+gbOzs3QdlZGREYyMjCCTyTBu3DjMmjUL7u7u0lTs9vb26Nmzp0pjJyIiIiIiKqF0cgUAp0+fxqFDh5CZmQm5XK6wbNGiRUq1NWDAANy+fRvh4eFIT0+Hl5cXoqOjpQkpUlNToaHxXwfbypUrUVhYiL59+yq0M23aNEyfPh0AMHHiROTl5eHDDz/EgwcP0KpVK0RHR/MeV0REREREVG2UTq5mz56NqVOnwsPDAzY2Ngo3EH76/8oYM2YMxowZU+ay2NhYhecpKSmVtieTyTBjxgzMmDHjueIhIiIiIiJSltLJ1ZIlS7B27VoMGzasGsIhIiIiIiKqmZSe0EJDQwMtW7asjliIiIiIiIhqLKWTq88++wzLly+vjliIiIiIiIhqLKWHBY4fPx5dunSBq6srPD09oa2trbB8x44dKguOiIiIiIioplA6uQoNDcWhQ4fQrl07WFpaPvckFkRERERERK8TpZOr9evXY/v27ejSpUt1xENERERERFQjKX3NlYWFBVxdXasjFiIiIiIiohpL6eRq+vTpmDZtGvLz86sjHiIiIiIiohpJ6WGBS5cuRVJSEmxsbODs7FxqQotz586pLDgiIiIiIqKaQunkqmfPntUQBhERERERUc2mdHI1bdq06oiDiIiIiIioRlM6uSIiopqn4fqG6g6hXJeGXlJ3CERERCqhdHKloaFR4b2tiouLXyggIiIiIiKimkjp5Oq3335TeF5UVITz589j/fr1+Prrr1UWGBERERERUU2idHLVo0ePUmV9+/ZF/fr1sWXLFgwfPlwlgREREREREdUkSt/nqjzvvvsuYmJiVNUcERERERFRjaKS5Orhw4dYunQpHBwcVNEcERERERFRjaP0sEBzc3OFCS2EEMjJyYGBgQF+/vlnlQZHRERERERUUyidXH333XcKyZWGhgZq1aoFHx8fmJubqzQ4IiIiIiKimkLp5GrYsGHVEAYREREREVHNpvQ1VxEREfj1119Llf/6669Yv369SoIiIiIiIiKqaZROrubMmQMrK6tS5dbW1pg9e7ZKgiIiIiIiIqpplE6uUlNT4eLiUqrcyckJqampKgmKiIiIiIioplE6ubK2tsbFixdLlV+4cAGWlpYqCYqIiIiIiKimUTq5GjRoEEJDQ3Ho0CEUFxejuLgYBw8exNixYzFw4MDqiJGIiIiIiOiVp/RsgTNnzkRKSgo6dOgALa0nq8vlcgQHB/OaKyIiIiIiemMpnVzp6Ohgy5YtmDVrFuLi4qCvr4+GDRvCycmpOuIjIiIiIiKqEZROrkq4u7vD3d1dlbEQERERERHVWEpfc9WnTx/MmzevVPn8+fPRr18/lQRFRERERERU0yidXB0+fBidO3cuVR4UFITDhw+rJCgiIiIiIqKaRunkKjc3Fzo6OqXKtbW1kZ2drZKgiIiIiIiIahqlk6uGDRtiy5Ytpco3b94MT09PlQRFRERERERU0yg9ocVXX32F3r17IykpCe3btwcAxMTEYNOmTfj1119VHiAREREREVFNoHRy1a1bN+zcuROzZ8/Gtm3boK+vj0aNGuHAgQPw8/OrjhiJiIiIiIheeUoPCwSALl264NixY8jLy8OdO3dw8OBB+Pn54a+//lK6reXLl8PZ2Rl6enrw8fHBqVOnyq17+fJl9OnTB87OzpDJZFi8eHGpOtOnT4dMJlN41K1bV+m4iIiIiIiIlPFcydXTcnJy8OOPP6J58+Zo3LixUutu2bIFYWFhmDZtGs6dO4fGjRsjICAAmZmZZdbPz89HnTp1MHfuXNja2pbbbv369ZGWliY9jh49qlRcREREREREynru5Orw4cMIDg6GnZ0dFixYgPbt2+PEiRNKtbFo0SKMHDkSISEh8PT0xKpVq2BgYIC1a9eWWf+dd97Bt99+i4EDB0JXV7fcdrW0tGBrays9rKyslIqLiIiIiIhIWUpdc5Weno5169bhp59+QnZ2Nvr374+CggLs3LlT6ZkCCwsLcfbsWUyZMkUq09DQgL+/P44fP65UW89KTEyEvb099PT04Ovrizlz5uCtt94qt35BQQEKCgqk55xSnoiIiIiIlFXlnqtu3brBw8MDFy9exOLFi3Hr1i18//33z73hO3fuoLi4GDY2NgrlNjY2SE9Pf+52fXx8sG7dOkRHR2PlypVITk5G69atkZOTU+46c+bMgampqfRwdHR87u0TEREREdGbqco9V3v27EFoaCg+/vhjuLu7V2dMLyQoKEj6f6NGjeDj4wMnJyds3boVw4cPL3OdKVOmICwsTHqenZ3NBIuIiIiIiJRS5Z6ro0ePIicnB82aNYOPjw+WLVuGO3fuPPeGraysoKmpiYyMDIXyjIyMCierUJaZmRnefvttXL9+vdw6urq6MDExUXgQEREREREpo8rJ1bvvvovVq1cjLS0No0aNwubNm2Fvbw+5XI79+/dXOOyuLDo6OmjWrBliYmKkMrlcjpiYGPj6+irVVkVyc3ORlJQEOzs7lbVJRERERET0LKVnCzQ0NMQHH3yAo0eP4tKlS/j8888xd+5cWFtbo3v37kq1FRYWhtWrV2P9+vWIj4/Hxx9/jLy8PISEhAAAgoODFSa8KCwsRFxcHOLi4lBYWIh///0XcXFxCr1S48ePxx9//IGUlBT8+eef6NWrFzQ1NTFo0CBld5WIiIiIiKjKXug+Vx4eHpg/fz5u3ryJTZs2Kb3+gAEDsGDBAoSHh8PLywtxcXGIjo6WJrlITU1FWlqaVP/WrVto0qQJmjRpgrS0NCxYsABNmjTBiBEjpDo3b97EoEGD4OHhgf79+8PS0hInTpxArVq1XmRXiYiIiIiIKiQTQgh1B/Gqyc7OhqmpKbKysl6J66+cJ/9P3SGUK0XvPXWHUKGGLuVPwa9ul4ZeUncINRLfD8+H7wUiIqLno0xu8EI9V0RERERERPQEkysiIiIiIiIVYHJFRERERESkAkyuiIiIiIiIVIDJFRERERERkQowuSIiIiIiIlIBJldEREREREQqwOSKiIiIiIhIBZhcERERERERqQCTKyIiIiIiIhVgckVERERERKQCTK6IiIiIiIhUgMkVERERERGRCjC5IiIiIiIiUgEmV0RERERERCrA5IqIiIiIiEgFmFwRERERERGpAJMrIiIiIiIiFWByRUREREREpAJMroiIiIiIiFSAyRUREREREZEKMLkiIiIiIiJSASZXREREREREKsDkioiIiIiISAWYXBEREREREakAkysiIiIiIiIVYHJFRERERESkAkyuiIiIiIiIVIDJFRERERERkQowuSIiIiIiIlIBJldEREREREQqwOSKiIiIiIhIBZhcERERERERqYDak6vly5fD2dkZenp68PHxwalTp8qte/nyZfTp0wfOzs6QyWRYvHjxC7dJRERERESkCmpNrrZs2YKwsDBMmzYN586dQ+PGjREQEIDMzMwy6+fn56NOnTqYO3cubG1tVdImERERERGRKqg1uVq0aBFGjhyJkJAQeHp6YtWqVTAwMMDatWvLrP/OO+/g22+/xcCBA6Grq6uSNomIiIiIiFRBbclVYWEhzp49C39///+C0dCAv78/jh8//lLbLCgoQHZ2tsKDiIiIiIhIGWpLru7cuYPi4mLY2NgolNvY2CA9Pf2ltjlnzhyYmppKD0dHx+faPhERERERvbnUPqHFq2DKlCnIysqSHv/884+6QyIiIiIiohpGS10btrKygqamJjIyMhTKMzIyyp2sorra1NXVLfcaLiIiIiKiFzLdVN0RlG96lrojeK2oredKR0cHzZo1Q0xMjFQml8sRExMDX1/fV6ZNIiIiIiKiqlBbzxUAhIWFYejQofD29kbz5s2xePFi5OXlISQkBAAQHBwMBwcHzJkzB8CTCSuuXLki/f/ff/9FXFwcjIyM4ObmVqU2iYiIiIiIqoNak6sBAwbg9u3bCA8PR3p6Ory8vBAdHS1NSJGamgoNjf86127duoUmTZpIzxcsWIAFCxbAz88PsbGxVWqTiIiIiIioOqg1uQKAMWPGYMyYMWUuK0mYSjg7O0MI8UJtEhERERERVQfOFkhERERERKQCTK6IiIiIiIhUgMkVERERERGRCjC5IiIiIiIiUgEmV0RERERERCrA5IqIiIiIiEgFmFwRERERERGpAJMrIiIiIiIiFWByRUREREREpAJMroiIiIiIiFSAyRUREREREZEKMLkiIiIiIiJSAS11B0BERERE9CKcJ/9P3SFUKEVP3RHQy8KeKyIiIiIiIhVgckVERERERKQCTK6IiIiIiIhUgMkVERERERGRCjC5IiIiIiIiUgEmV0RERERERCrA5IqIiIiIiEgFmFwRERERERGpAJMrIiIiIiIiFWByRUREREREpAJMroiIiIiIiFSAyRUREREREZEKMLkiIiIiIiJSAS11B0BEREREROrRcH1DdYdQrktDL6k7BKWx54qIiIiIiEgFmFwRERERERGpAJMrIiIiIiIiFWByRUREREREpAJMroiIiIiIiFSAyRUREREREZEKvBLJ1fLly+Hs7Aw9PT34+Pjg1KlTFdb/9ddfUbduXejp6aFhw4bYvXu3wvJhw4ZBJpMpPAIDA6tzF4iIiIiI6A2n9uRqy5YtCAsLw7Rp03Du3Dk0btwYAQEByMzMLLP+n3/+iUGDBmH48OE4f/48evbsiZ49e+Kvv/5SqBcYGIi0tDTpsWnTppexO0RERERE9IZSe3K1aNEijBw5EiEhIfD09MSqVatgYGCAtWvXlll/yZIlCAwMxIQJE1CvXj3MnDkTTZs2xbJlyxTq6erqwtbWVnqYm5u/jN0hIiIiIqI3lFqTq8LCQpw9exb+/v5SmYaGBvz9/XH8+PEy1zl+/LhCfQAICAgoVT82NhbW1tbw8PDAxx9/jLt375YbR0FBAbKzsxUeREREREREylBrcnXnzh0UFxfDxsZGodzGxgbp6ellrpOenl5p/cDAQGzYsAExMTGYN28e/vjjDwQFBaG4uLjMNufMmQNTU1Pp4ejo+IJ7RkREREREbxotdQdQHQYOHCj9v2HDhmjUqBFcXV0RGxuLDh06lKo/ZcoUhIWFSc+zs7OZYBERERERkVLU2nNlZWUFTU1NZGRkKJRnZGTA1ta2zHVsbW2Vqg8AderUgZWVFa5fv17mcl1dXZiYmCg8iIiIiIiIlKHW5EpHRwfNmjVDTEyMVCaXyxETEwNfX98y1/H19VWoDwD79+8vtz4A3Lx5E3fv3oWdnZ1qAiciIiIiInqG2mcLDAsLw+rVq7F+/XrEx8fj448/Rl5eHkJCQgAAwcHBmDJlilR/7NixiI6OxsKFC3H16lVMnz4dZ86cwZgxYwAAubm5mDBhAk6cOIGUlBTExMSgR48ecHNzQ0BAgFr2kYiIiIiIXn9qv+ZqwIABuH37NsLDw5Geng4vLy9ER0dLk1akpqZCQ+O/HLBFixb45ZdfMHXqVHzxxRdwd3fHzp070aBBAwCApqYmLl68iPXr1+PBgwewt7dHp06dMHPmTOjq6qplH4mIiIiI6PWn9uQKAMaMGSP1PD0rNja2VFm/fv3Qr1+/Muvr6+tj7969qgyPiIiIiIioUmofFkhERERERPQ6YHJFRERERESkAkyuiIiIiIiIVIDJFRERERERkQowuSIiIiIiIlKBV2K2QCIiInrNTDdVdwTlm56l7giI6DXFnisiIiIiIiIVYHJFRERERESkAkyuiIiIiIiIVIDXXBEREdEbpeH6huoOoVyXhl5SdwhE9ALYc0VERERERKQCTK6IiIiIiIhUgMMCiYiIaijnyf9TdwjlStFTdwRERC8fe66IiIiIiIhUgMkVERERERGRCjC5IiIiIiIiUgEmV0RERERERCrA5IqIiIiIiEgFmFwRERERERGpAJMrIiIiIiIiFWByRUREREREpAJMroiIiIiIiFSAyRUREREREZEKMLkiIiIiIiJSASZXREREREREKsDkioiIiIiISAWYXBEREREREakAkysiIiIiIiIVYHJFRERERESkAkyuiIiIiIiIVIDJFRERERERkQowuSIiIiIiIlIBJldEREREREQqwOSKiIiIiIhIBV6J5Gr58uVwdnaGnp4efHx8cOrUqQrr//rrr6hbty709PTQsGFD7N69W2G5EALh4eGws7ODvr4+/P39kZiYWJ27QEREREREbzi1J1dbtmxBWFgYpk2bhnPnzqFx48YICAhAZmZmmfX//PNPDBo0CMOHD8f58+fRs2dP9OzZE3/99ZdUZ/78+Vi6dClWrVqFkydPwtDQEAEBAXj06NHL2i0iIiIiInrDqD25WrRoEUaOHImQkBB4enpi1apVMDAwwNq1a8usv2TJEgQGBmLChAmoV68eZs6ciaZNm2LZsmUAnvRaLV68GFOnTkWPHj3QqFEjbNiwAbdu3cLOnTtf4p4REREREdGbREudGy8sLMTZs2cxZcoUqUxDQwP+/v44fvx4mescP34cYWFhCmUBAQFS4pScnIz09HT4+/tLy01NTeHj44Pjx49j4MCBpdosKChAQUGB9DwrKwsAkJ2d/dz7pkrygnx1h1CubJlQdwgVKn5YrO4QyvWqnF81Dd8Pz4fvhdcT3w/Ph++H18+r/F4A+H54Xq/K+6EkDiEqfx3VmlzduXMHxcXFsLGxUSi3sbHB1atXy1wnPT29zPrp6enS8pKy8uo8a86cOfj6669LlTs6OlZtR95gpuoOoFLx6g6gXKYfv/pHj5Tzar+ifC/Qy/Vqv6p8P9DL9Wq/qnw/VFVOTg5MTSuOSa3J1atiypQpCr1hcrkc9+7dg6WlJWQymRoje7NkZ2fD0dER//zzD0xMTNQdDpFa8f1A9ATfC0T/4ftBPYQQyMnJgb29faV11ZpcWVlZQVNTExkZGQrlGRkZsLW1LXMdW1vbCuuX/JuRkQE7OzuFOl5eXmW2qaurC11dXYUyMzMzZXaFVMjExIR/MIj+P74fiJ7ge4HoP3w/vHyV9ViVUOuEFjo6OmjWrBliYmKkMrlcjpiYGPj6+pa5jq+vr0J9ANi/f79U38XFBba2tgp1srOzcfLkyXLbJCIiIiIielFqHxYYFhaGoUOHwtvbG82bN8fixYuRl5eHkJAQAEBwcDAcHBwwZ84cAMDYsWPh5+eHhQsXokuXLti8eTPOnDmDH3/8EQAgk8kwbtw4zJo1C+7u7nBxccFXX30Fe3t79OzZU127SURERERErzm1J1cDBgzA7du3ER4ejvT0dHh5eSE6OlqakCI1NRUaGv91sLVo0QK//PILpk6dii+++ALu7u7YuXMnGjRoINWZOHEi8vLy8OGHH+LBgwdo1aoVoqOjoaen99L3j6pOV1cX06ZNKzVEk+hNxPcD0RN8LxD9h++HV59MVGVOQSIiIiIiIqqQ2m8iTERERERE9DpgckVERERERKQCTK6IiIiIiIhUgMkVqYVMJsPOnTvVHQYR0WupOv7GTp8+vdz7RapLSkoKZDIZ4uLiyq0TGxsLmUyGBw8evLS4iCoybNgwzmD9GmNy9YaTyWQVPqZPn17uulX5UHtew4YNKzOewMBAlW+L6FWzbt26Sm9k/ip+0aWX4/bt2/j444/x1ltvQVdXF7a2tggICMCxY8ekOmlpaQgKClJjlBX7559/8MEHH8De3h46OjpwcnLC2LFjcffuXZVvq0WLFkhLS5NuAFqV91dJvbI+hzjz8Ovh6e8Z2tracHFxwcSJE/Ho0SN1h/ZcqvqZMH369DLP67p161Z/kG8IJldvuLS0NOmxePFimJiYKJSNHz9ebbEFBgYqxJKWloZNmzaVW7+oqKhUWWFh4XNt+3nXI/V4kR8JVOnPP/9E586dYW5uDj09PTRs2BCLFi1CcXGxyrc1fvx4hZulV/WXUP5wUfP16dMH58+fx/r163Ht2jVERUWhbdu2ComJra3tKztV899//w1vb28kJiZi06ZNuH79OlatWoWYmBj4+vri3r17Kt2ejo4ObG1tIZPJlF732c/EtLQ03Lhxo9z6ZX12CCHw+PFjpbf9vOtR1ZV8z/j777/x3Xff4YcffsC0adPUHVa1q1+/fqnz+ujRo+XWL+u8Li4uhlwuV3rbz7teTcLk6g1na2srPUxNTSGTyaTn1tbWWLRoEWrXrg1dXV3pHmQlXFxcAABNmjSBTCZD27ZtAQCnT59Gx44dYWVlBVNTU/j5+eHcuXNKx1byi+zTD3Nzc2m5TCbDypUr0b17dxgaGuKbb76RfrlZs2YNXFxcpF8YU1NT0aNHDxgZGcHExAT9+/dHRkaG1FZ561HN8Cr8SPDbb7/Bz88PtWvXxqFDh3D16lWMHTsWs2bNwsCBA6Hqu14YGRnB0tLyudblDxc114MHD3DkyBHMmzcP7dq1g5OTE5o3b44pU6age/fuUr2nhwWWjDLYsWMH2rVrBwMDAzRu3BjHjx9XaHv16tVwdHSEgYEBevXqhUWLFlXaw7NmzRrUq1cPenp6qFu3LlasWFHpPnzyySfQ0dHBvn374Ofnh7feegtBQUE4cOAA/v33X3z55Zdl7kcJMzMzrFu3TqHs6tWraNGiBfT09NCgQQP88ccf0rKnhwXGxsYiJCQEWVlZVfrx5enPxJJHyX04AaBt27YYM2YMxo0bBysrKwQEBEjb27NnD5o1awZdXV0cPXoUBQUFCA0NhbW1NfT09NCqVSucPn26VJzPrkfVp+R7hqOjI3r27Al/f3/s379fWi6XyzFnzhy4uLhAX18fjRs3xrZt26TlxcXFGD58uLTcw8MDS5YsUdhGcXExwsLCYGZmBktLS0ycOLHU50Fl50ZZva07d+6UfjBYt24dvv76a1y4cEE6r599jzxNS0ur1HltZWUlLXd2dsbMmTMRHBwMExMTfPjhh1IMUVFR8PT0hK6uLlJTU3H//n0EBwfD3NwcBgYGCAoKQmJiYqnYn13vtSaI/r+IiAhhamoqPV+0aJEwMTERmzZtElevXhUTJ04U2tra4tq1a0IIIU6dOiUAiAMHDoi0tDRx9+5dIYQQMTExYuPGjSI+Pl5cuXJFDB8+XNjY2Ijs7GypbQDit99+KzeWoUOHih49elQYLwBhbW0t1q5dK5KSksSNGzfEtGnThKGhoQgMDBTnzp0TFy5cEMXFxcLLy0u0atVKnDlzRpw4cUI0a9ZM+Pn5SW2VtR7VTE+fxw8ePBAaGhri9OnTQgghiouLhbm5ufDx8ZHqb9y4UdSuXVt6fvHiRdGuXTuhp6cnLCwsxMiRI0VOTk6F28zNzRWWlpaid+/epZZFRUUJAGLz5s1CCCEOHTokAIj79+9Ldc6fPy8AiOTkZIV9+O2334Sbm5vQ1dUVnTp1EqmpqdI606ZNE40bN5b+D0DhcejQoTJjrep7a8WKFaJbt27CwMBATJs2Tdre6tWrhbOzs5DJZEIIIW7cuCG6d+8uDA0NhbGxsejXr59IT08vFeez69HzKSoqEkZGRmLcuHHi0aNH5dZ7+m9scnKyACDq1q0rdu3aJRISEkTfvn2Fk5OTKCoqEkIIcfToUaGhoSG+/fZbkZCQIJYvXy4sLCwUPhOePueEEOLnn38WdnZ2Yvv27eLvv/8W27dvFxYWFmLdunXlxnX37l0hk8nE7Nmzy1w+cuRIYW5uLuRyean9KGFqaioiIiIU9q127dpi27Zt4sqVK2LEiBHC2NhY3LlzRwih+J4rKCgQixcvFiYmJiItLU2kpaWV+/5+9jOxLH5+fsLIyEhMmDBBXL16VVy9elXaXqNGjcS+ffvE9evXxd27d0VoaKiwt7cXu3fvFpcvXxZDhw4V5ubm0mdneetR9Xj2b+GlS5eEra2twufDrFmzRN26dUV0dLRISkoSERERQldXV8TGxgohhCgsLBTh4eHi9OnT4u+//xY///yzMDAwEFu2bJHamDdvnjA3Nxfbt2+XvhMZGxsrbLuyc6Osc/G3334TJV/j8/Pzxeeffy7q168vndf5+fll7vez7+OyODk5CRMTE7FgwQJx/fp1cf36dRERESG0tbVFixYtxLFjx8TVq1dFXl6e6N69u6hXr544fPiwiIuLEwEBAcLNzU0UFhZKsZe13uuMyRVJnn3z2tvbi2+++UahzjvvvCNGjx4thPjvQ+38+fMVtltcXCyMjY3F77//LpVVJbnS1NQUhoaGCo+n4wEgxo0bp7DetGnThLa2tsjMzJTK9u3bJzQ1NRW+mF6+fFkAEKdOnSp3PaqZnj2PmzZtKr799lshhBBxcXHCwsJC6OjoSF+oRowYIQYPHiyEeJIk2dnZid69e4tLly6JmJgY4eLiIoYOHVrhNnfs2CEAiD///LPM5W+//bb0QVrV5EpbW1t4e3uLP//8U5w5c0Y0b95ctGjRQlrn6Q/InJwc0b9/fxEYGCh9sBYUFJQZC3+4qPm2bdsmzM3NhZ6enmjRooWYMmVKqeNaVnK1Zs0aaXnJ38D4+HghhBADBgwQXbp0UWhj8ODBFSZXrq6u4pdfflFYZ+bMmcLX17fc2E+cOFHh3/9FixYJACIjI6PUfpQoK7maO3eutLyoqEjUrl1bzJs3TwhR+j1XlaSppB6AUp9DgYGBUh0/Pz/RpEkThfVKtrdz506pLDc3V2hra4vIyEiprLCwUNjb24v58+eXux5Vn6e/Z+jq6goAQkNDQ2zbtk0IIcSjR4+EgYFBqb/rw4cPF4MGDSq33U8++UT06dNHem5nZye9xkL8d36W/B2uyrlRWXIlRNWSppJ6Ghoapc7rUaNGSXWcnJxEz549FdYreT/ExcVJZdeuXRMAxLFjx6SyO3fuCH19fbF169Zy13vdaVVThxjVcNnZ2bh16xZatmypUN6yZUtcuHChwnUzMjIwdepUxMbGIjMzE8XFxcjPz1e6G7hdu3ZYuXKlQpmFhYXCc29v71LrOTk5oVatWtLz+Ph4ODo6wtHRUSrz9PSEmZkZ4uPj8c4775S5Hr0e2rZti9jYWIwfPx6xsbHo2LEjrl69iqNHjyIwMBCxsbGYOHEiAOCXX37Bo0ePsGHDBhgaGgIAli1bhm7dumHevHkKw4Gedu3aNQBAvXr1ylxet25dqU5VFRUVYdmyZfDx8QEArF+/HvXq1cOpU6fQvHlzhbpGRkbQ19dHQUEBbG1tK217165dMDIyUij74osv8MUXX0jP33vvPYSEhCjUKSwsxIYNG6T3yf79+3Hp0iUkJydL768NGzagfv36OH36tPTeenY9ejF9+vRBly5dcOTIEZw4cQJ79uzB/PnzsWbNGgwbNqzc9Ro1aiT9387ODgCQmZmJunXrIiEhAb169VKo37x5c+zatavMtvLy8pCUlIThw4dj5MiRUvnjx4+liSOCgoJw5MgRAE/+vl6+fFmqJyoZJqujo1Ph8mf5+vpK/9fS0oK3tzfi4+OVaqMsxsbGpYa16+vrKzxv1qxZmes+/fmUlJSEoqIihc9UbW1tNG/evFScZX2uUfUo+Z6Rl5eH7777DlpaWujTpw8A4Pr168jPz0fHjh0V1iksLESTJk2k58uXL8fatWuRmpqKhw8forCwUJpYIisrC2lpadLfceC/87PkPaDMuaEqHh4eiIqKUigzMTFReF7Weaijo6PwdyQ+Ph5aWloK+2dpaQkPDw+F2J9d73XH5IpUbujQobh79y6WLFkCJycn6OrqwtfXV+lrLQwNDeHm5lZpnaqUVXV79Prx8/PDTz/9hOLiYvzxxx/o1KkTbG1tERsbi0aNGuH69evS9YLx8fFo3LixwrnQsmVLyOVyJCQkwMbGRiEpef/997Fq1SrpeUVfGJX9sqilpSUlJ8CTBK3kB4Fnkytl8YeLmk9PTw8dO3ZEx44d8dVXX2HEiBGYNm1ahcmVtra29P+SazWe98Ly3NxcAE+u03r6ixUAaGpqAnhyPdbDhw8Vtu3m5gaZTIb4+PhSyRzw5JyqVauWdH2JTCYr9b4q6xrA6qKhofFcn0MVlVeGn0Uvz9PfM9auXYvGjRvjp59+wvDhw6Vz/H//+x8cHBwU1iuZLGbz5s0YP348Fi5cCF9fXxgbG+Pbb7/FyZMnVRqnhoaGSt8HOjo6z3Ve6+vrP9fEMM+7Xk3FCS2oTCYmJrC3t1eY2hcAjh07Bk9PTwD/fVl8dia0Y8eOITQ0FJ07d0b9+vWhq6uLO3fuvJzAy1CvXj38888/+Oeff6SyK1eu4MGDB9K+0OurTZs2yMnJwblz53D48GG0bdtW6s36448/YG9vD3d39yq3FxcXJz1mzJgBANL65f3KGB8fj7fffhvAkw9JQDERe5lfFoH/vlA8/Xg2ueIPFzWLp6cn8vLynnt9Dw8PhQvoAZR6/jQbGxvY29vj77//LnUulUx25ODgIJU5OTkBePKrdseOHbFixQop8SqRnp6OyMhIhQSxVq1aSEtLk54nJiYiPz+/VDwnTpyQ/v/48WOcPXu23J5kHR2dapnBsyKurq7Q0dFR+EwtKirC6dOn+Tn0itDQ0MAXX3yBqVOn4uHDhwqTLzx7jpf8oHTs2DG0aNECo0ePRpMmTeDm5oakpCSpTVNTU9jZ2SkkWyXnZ4mqnBu1atVCTk6Ownv82dvgqOO8rlevHh4/fqywf3fv3kVCQsIbfV6z54rKNWHCBEybNg2urq7w8vJCREQE4uLiEBkZCQCwtraGvr4+oqOjUbt2bejp6cHU1BTu7u7YuHEjvL29kZ2djQkTJpQaRlEVBQUFSE9PVyjT0tJSmNGmKvz9/dGwYUMMHjwYixcvxuPHjzF69Gj4+flx+MUbwMzMDI0aNcKyZcugra2NunXrwtraGgMGDMCuXbvg5+cn1a1Xrx7WrVuHvLw8KSE4duwYNDQ04OHhAQBl/toXEBAACwsLLFy4EC1atFBYFhUVhcTERCxevBgApB6ctLQ0afbLsu4V9/jxY5w5c0bqpUpISMCDBw9eqS+MT/9wUfJlgz9cVK+7d++iX79++OCDD9CoUSMYGxvjzJkzmD9/Pnr06PHc7X766ado06YNFi1ahG7duuHgwYPYs2dPhb82f/311wgNDYWpqSkCAwNRUFCAM2fO4P79+wgLCyt3vWXLlqFFixYICAjArFmz4OLigsuXL2PChAl4++23ER4eLtVt3749li1bBl9fXxQXF2PSpEkKPXAlli9fDnd3d9SrVw/fffcd7t+/jw8++KDM7Ts7OyM3NxcxMTFo3LgxDAwMYGBgUGZdIUSpzyHgyedfyQ8lVWFoaIiPP/4YEyZMgIWFBd566y3Mnz8f+fn5GD58eJXboerVr18/TJgwAcuXL8f48eMxfvx4fPbZZ5DL5WjVqhWysrJw7NgxmJiYYOjQoXB3d8eGDRuwd+9euLi4YOPGjTh9+rT0AwMAjB07FnPnzoW7uzvq1q2LRYsWKdzQuirnho+PDwwMDPDFF18gNDQUJ0+eLDUboLOzM5KTkxEXF4fatWvD2Ni43NsxPH78uNR5LZPJyh36Xh53d3f06NEDI0eOxA8//ABjY2NMnjwZDg4OL/T3qMZT4/Ve9Ip59oLJ4uJiMX36dOHg4CC0tbVF48aNxZ49exTWWb16tXB0dBQaGhrSReznzp0T3t7eQk9PT7i7u4tff/1VODk5ie+++05aD1WY0ALPzH4GQHh4eFTYRnkXdFZ1RjOq+cq68HfcuHFCU1NTDBgwQCpr3Lix0NTUFKtWrZLK8vLyhJ2dnejTp4+4dOmSOHjwoKhTp06lE1oIIcSvv/4qNDU1xciRI8WFCxdEcnKyWLNmjTA3NxcjR46U6hUWFgpHR0fRr18/ce3aNbFr1y7h4eFR5oQWzZs3FydOnBBnzpwR7777rnj33Xeldp49Z7/55hvx1ltviatXr4rbt29LMzU9a+jQoQoTX5Q8bt++LdWp6ntLLpcLLy8v0bp1a3H27Flx8uTJMie04HtLdR49eiQmT54smjZtKkxNTYWBgYHw8PAQU6dOVZgdDGVMaPH05EP3798vNavkjz/+KBwcHIS+vr7o2bOnmDVrlrC1tZWWl/VaRkZGCi8vL6GjoyPMzc1FmzZtxI4dOyrdj+TkZDF06FBhY2MjZDKZACB69+5dahaxf//9V3Tq1EkYGhoKd3d3sXv37jIntPjll19E8+bNhY6OjvD09BQHDx6U2ihrEpmPPvpIWFpaCgBi2rRpZcZYciF+WY//1969hUT1vWEcf6bRxlB0Sg3NpJRUUoqCDnYuMBSS7IAGSWYxHdGEEhGh8KLDTZYRJESxO9wEURAIClJoRHagiIQSiqKRMCskSVIzXf+LaGjMDtak8+v//cBc7LX32ut1cJh5Zm/eaW1tNcZ8bmhRVFTkNW+w9YwxpquryxQWFpqIiAjjcDjMggULPE2VfjQPf8f3mvscOnTIREZGms7OTtPf328qKytNUlKSCQwMNJGRkSY9Pd00NDQYYz6/HvPz801YWJhxOp1mx44dprS01Ot10tvba4qKikxoaKhxOp1m9+7dJi8vz2vtn/1vGGM83WPHjBljMjMzzcmTJ70aWnR3d5u1a9cap9NpJHleIwMN1l1WknE4HJ5jBn5mM+b7jWDa29vNhg0bTFhYmBkzZoxJT0/3dJX+0bx/GeEKwD/lR12VqqqqPGNFRUVGkmlubvY69ndasX9x/fp1k56ebkJDQz1vWF86ln3txo0bZtq0aSYoKMgsWrTIXLx4cdBW7JcuXTLx8fHG4XCYtLQ08+LFC885Bn7Qff36tVm+fLkJCQn5aSt2vrjAr3C5XGbhwoXDsta+fftMSEiIaWxsHJb1AOBvsRnj41+2BACou7tbWVlZamlpUUNDAw0d4PcOHz6s5cuXKzg4WDU1NdqzZ49OnDghl8s1LOtblqWOjg7t2rVrSLfcAYA/IVwBwF/S3d2tyspKJSQkeNr7Av4qJydH9fX1ev/+veLj41VYWKjt27ePdFkA8J9CuAIAAAAAH+C6OwAAAAD4AOEKAAAAAHyAcAUAAAAAPkC4AgAAAAAfIFwBAAAAgA8QrgAAAADABwhXAAC/k5+fL5vN9s0jIyPD72r68pg8efKI1QYA8A/8zhUAwO/k5+erra1NlmV5jTscDo0dO3bQOb29vQoMDPQa+/jxo0aPHj3k9Qeb19HRoa6uLs92dHS0LMvyBD673a7IyMghrwUA+Hdw5QoA4JccDoeioqK8Hl8HK5vNpqqqKq1cuVLBwcE6cOCAysvLNWPGDJ06dUpxcXEKCgqSJLndbmVlZSkkJEShoaHKyclRW1ub51zfm/e1sLAwr1okyel0KioqSmVlZdq0aZPX8b29vRo/frxOnz4tSVq6dKkKCgpUUFCgsLAwRUREaO/evfr6O86enh4VFxcrJiZGwcHBmjt3rurr6332nAIA/i7CFQDgP6u8vFyrV69WU1OTNm/eLEl6+vSpLl26pMuXL+vBgwfq7+9XVlaW2tvb1dDQoLq6Oj179kzr1q3zOtfAeUPhcrlUW1ur1tZWz1h1dbU+fPjgtc7Zs2cVEBCgO3fu6NixYzpy5IhOnTrl2V9QUKDGxkZduHBBDx8+VHZ2tjIyMvTkyZPfeHYAAMMtYKQLAABgMNXV1QoJCfEaKysrU1lZmWd7/fr131wx+vjxo86dO+e5Ra+urk5NTU16/vy5YmNjJUnnzp1TSkqK7t69q9mzZw86byjmz5+vpKQknT9/XiUlJZIky7KUnZ3t9TfExsbq6NGjstlsSkpKUlNTk44ePaotW7bI7XbLsiy53W5NmDBBklRcXKza2lpZlqWDBw8OuS4AwPAiXAEA/NKyZctUVVXlNTZu3Div7VmzZn0zb9KkSV4B6fHjx4qNjfUEK0lKTk6W0+nU48ePPeFq4LyhcrlcOnnypEpKStTW1qaamhpdu3bN65jU1FTZbDbP9rx581RRUaG+vj41NTWpr69PiYmJXnN6enoUHh7+23UBAIYP4QoA4JeCg4M1ZcqUnx7zK2O/ut6fyMvLU2lpqRobG3Xz5k3FxcVp0aJFvzy/s7NTdrtd9+7dk91u99o38AoeAMA/Ea4AAP+0qVOnqqWlRS0tLZ6rV48ePdK7d++UnJzss3XCw8O1atUqWZalxsbGb25XlKTbt297bd+6dUsJCQmy2+2aOXOm+vr69Pr16yGFMgCA/yBcAQD8Uk9Pj169euU1FhAQoIiIiCGdJy0tTdOmTVNubq4qKyv16dMn7dy5U0uWLBn0tsI/4XK5lJmZqb6+Pm3cuPGb/W63W7t379a2bdt0//59HT9+XBUVFZKkxMRE5ebmKi8vTxUVFZo5c6bevHmjq1evavr06VqxYoVPawUA+B7hCgDgl2praxUdHe01lpSUpObm5iGdx2az6cqVKyosLNTixYs1atQoZWRk6Pjx474sV9LnIBcdHa2UlBRPU4qv5eXlqaurS3PmzJHdbldRUZG2bt3q2W9Zlvbv3689e/bo5cuXioiIUGpqqjIzM31eKwDA9/gRYQAAfKSzs1MxMTGyLEtr1qzx2rd06VLNmDFDlZWVI1McAOCv48oVAAB/qL+/X2/fvlVFRYWcTqdWrlw50iUBAEYA4QoAgD/kdrsVFxeniRMn6syZMwoI4O0VAP4fcVsgAAAAAPjAqJEuAAAAAAD+BYQrAAAAAPABwhUAAAAA+ADhCgAAAAB8gHAFAAAAAD5AuAIAAAAAHyBcAQAAAIAPEK4AAAAAwAf+B0LjmOLVyr1LAAAAAElFTkSuQmCC\",\n 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_backend_errors_and_counts(noisy_fake_backends, errors_and_counts_list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations! 🎉 Your answer is correct.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex3(accumulated_errors)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We have seen that `ibm_torino` has a smaller accumulated error than the other noisy backends. Let's put these results into practice by running the whole QAOA algorithm with the different backends!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"Warning: 2 minutes needed. Do not skip!\\n\",\n \"\\n\",\n \"Running the following code will take approximately 2 minutes to execute, and will block this notebook during that time. \\n\",\n \"\\n\",\n \"
\\n\",\n \"Warning: 8 minutes needed. \\n\",\n \"\\n\",\n \"Running the following code will take approximately 8 minutes to execute, and will block this notebook during that time. \\n\",\n \"\\n\",\n \"If you want to skip this cell, go directly to [Section 4](#transpiler).\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" message: Optimization terminated successfully.\\n\",\n \" success: True\\n\",\n \" status: 1\\n\",\n \" fun: -2.792362076379236\\n\",\n \" x: [ 1.693e-01 1.030e+00 9.943e-01 -6.395e-02]\\n\",\n \" nfev: 87\\n\",\n \" maxcv: 0.0\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" message: Optimization terminated successfully.\\n\",\n \" success: True\\n\",\n \" status: 1\\n\",\n \" fun: -3.6908530914690854\\n\",\n \" x: [ 1.180e+00 -1.793e-01 8.893e-01 1.755e-01]\\n\",\n \" nfev: 59\\n\",\n \" maxcv: 0.0\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for noisy_fake_backend, circuit in zip(noisy_fake_backends[1:], qaoa_transpiled_list[1:]):\\n\",\n \" result_backend, _ = train_qaoa(init_params, circuit, cost_hamiltonian, noisy_fake_backend)\\n\",\n \" opt_params = result_backend.x\\n\",\n \" opt_params_list.append(opt_params)\\n\",\n \" counts_list_backend = sample_qaoa(opt_params, circuit, noisy_fake_backend)\\n\",\n \" counts_list_backends.append(counts_list_backend)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It appears that the three backends give the correct result. However, it looks like the `ibm_torino` backend presents lower noise levels, since the probabilities of measuring a state that is not a solution are lower.\\n\",\n \"\\n\",\n \"At this point, we can define a metric based on the probability of measuring a correct solution to compare backend performance. Since all three backends have predicted the same 8 solutions as the most probable outcomes, this metric provides a useful way to identify which backend is more reliable in practice.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Probability of measuring a solution for aer_simulator_from(ibm_brisbane) is 0.6364\\n\",\n \"Probability of measuring a solution for aer_simulator_from(ibm_sherbrooke) is 0.6794\\n\",\n \"Probability of measuring a solution for aer_simulator_from(ibm_torino) is 0.8019\\n\"\n ]\n }\n ],\n \"source\": [\n \"for noisy_fake_backend, counts_list_backend in zip(noisy_fake_backends, counts_list_backends):\\n\",\n \" solutions_counts = [counts_list_backend[key] for key in states_solutions]\\n\",\n \" print(\\n\",\n \" f\\\"Probability of measuring a solution for {noisy_fake_backend.name} is {float(sum(solutions_counts)/SHOTS)}\\\"\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As expected, `ibm_torino` provides us with the highest probability of measuring a solution. This confirms that our earlier error estimation analysis was correct in predicting which backend would execute the algorithm with minimum errors. To further understand the impact of noise, we can also compare these probabilities to that of the `GenericBackend`, which simulates an ideal, noise-free scenario.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Probability of measuring a solution for generic_backend_5q is 0.7623\\n\"\n ]\n }\n ],\n \"source\": [\n \"solutions_counts_noiseless = [counts_list[key] for key in states_solutions]\\n\",\n \"print(\\n\",\n \" f\\\"Probability of measuring a solution for {generic_backend.name} is {float(sum(solutions_counts_noiseless)/SHOTS)}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The probability of measuring a correct solution on the noise-free `GenericBackend` is, as expected, higher than on the noisy backends. However, the difference is not that big. This highlights an important point. Even with current noisy quantum computers, as long as we are careful and smart in our circuit design and use the right tools, we can still solve many problems with a good degree of accuracy!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 3.2 Estimating errors using NEAT \\n\",\n \"\\n\",\n \"The error estimation we have done above is a nice approximation of how noisy a device is going to be. Qiskit offers us another alternative, the Noisy Estimator Analyzer Tool (NEAT). NEAT is a function designed to help users understand and predict the performance of quantum estimation tasks, particularly when using the Estimator primitive. It leverages the `qiskit-aer` simulator to perform both ideal (noiseless) and noisy simulations of quantum circuits. \\n\",\n \"\\n\",\n \"A key feature of NEAT is its ability to simulate Clifford circuits efficiently. For non-Clifford circuits, NEAT can automatically convert them to their nearest Clifford equivalents that approximate their quantum state. This makes NEAT especially useful for performing a noise analysis of our quantum circuits before running them on real quantum hardware. However, this approximation introduces limitations, since Clifford equivalents do not perfectly replicate the behavior of non-Clifford circuits, and therefore the simulation may lose accuracy in certain cases.\\n\",\n \"\\n\",\n \"From a practical point of view, one way to use NEAT is to simulate a quantum circuit in a noisy and noiseless scenario measuring an observable whose expectation value is exactly 1 in the ideal (noiseless) case. In this setup, any deviation from 1 in the noisy simulation directly reflects the impact of noise on the circuit. An easy way to ensure that the observable meets the requirements is choosing:\\n\",\n \"$$\\n\",\n \"\\\\hat{O}=|\\\\psi\\\\rangle\\\\langle \\\\psi|, \\\\quad \\\\textrm{since} \\\\quad \\\\langle \\\\psi |\\\\hat{O}|\\\\psi \\\\rangle=1, \\n\",\n \"$$\\n\",\n \"where $|\\\\psi\\\\rangle$ is the quantum state generated by the circuit we want to simulate. \\n\",\n \"\\n\",\n \"Let's apply NEAT to our different noisy backends to see how they perform.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Running on backend: aer_simulator_from(ibm_brisbane)\\n\",\n \"Ideal results on aer_simulator_from(ibm_brisbane):\\n\",\n \"1.000\\n\",\n \"Noisy results on aer_simulator_from(ibm_brisbane):\\n\",\n \"0.718\\n\",\n \"\\n\",\n \"Running on backend: aer_simulator_from(ibm_sherbrooke)\\n\",\n \"Ideal results on aer_simulator_from(ibm_sherbrooke):\\n\",\n \"1.000\\n\",\n \"Noisy results on aer_simulator_from(ibm_sherbrooke):\\n\",\n \"0.658\\n\",\n \"\\n\",\n \"Running on backend: aer_simulator_from(ibm_torino)\\n\",\n \"Ideal results on aer_simulator_from(ibm_torino):\\n\",\n \"1.000\\n\",\n \"Noisy results on aer_simulator_from(ibm_torino):\\n\",\n \"0.784\\n\"\n ]\n }\n ],\n \"source\": [\n \"results = []\\n\",\n \"for backend, opt_params in zip(noisy_fake_backends, opt_params_list):\\n\",\n \" print(f\\\"\\\\nRunning on backend: {backend.name}\\\")\\n\",\n \"\\n\",\n \" # Prepare the QAOA circuit with optimized parameters\\n\",\n \" qaoa_neat = QAOAAnsatz(cost_operator=cost_hamiltonian, reps=layers)\\n\",\n \" qc = qaoa_neat.assign_parameters(opt_params)\\n\",\n \"\\n\",\n \" # Transpile the circuit\\n\",\n \" qc_transpiled = generate_preset_pass_manager(\\n\",\n \" optimization_level=3,\\n\",\n \" basis_gates=backend.configuration().basis_gates[:n],\\n\",\n \" seed_transpiler=seed,\\n\",\n \" ).run(qc)\\n\",\n \" # Use cost Hamiltonian as observable for Cliffordization\\n\",\n \" obs = cost_hamiltonian\\n\",\n \" analyzer = Neat(backend)\\n\",\n \" clifford_pub = analyzer.to_clifford([(qc_transpiled, obs)])[0]\\n\",\n \"\\n\",\n \" # Construct observable from Clifford circuit\\n\",\n \" state_clifford = Statevector.from_instruction(clifford_pub.circuit)\\n\",\n \" obs_clifford = SparsePauliOp.from_operator(Operator(DensityMatrix(state_clifford).data))\\n\",\n \"\\n\",\n \" # Apply layout\\n\",\n \" pm = generate_preset_pass_manager(backend=backend, optimization_level=1, seed_transpiler=seed)\\n\",\n \" isa_qc = pm.run(clifford_pub.circuit)\\n\",\n \" isa_obs = obs_clifford.apply_layout(isa_qc.layout)\\n\",\n \"\\n\",\n \" # Prepare pubs and simulate\\n\",\n \" pubs = [(isa_qc, isa_obs)]\\n\",\n \" result_noiseless = (\\n\",\n \" analyzer.ideal_sim(pubs, cliffordize=True, seed_simulator=seed)._pub_results[0].vals\\n\",\n \" )\\n\",\n \" noisy_results = (\\n\",\n \" analyzer.noisy_sim(pubs, cliffordize=True, seed_simulator=seed)._pub_results[0].vals\\n\",\n \" )\\n\",\n \"\\n\",\n \" # Store and print results\\n\",\n \" results.append({\\\"backend\\\": backend.name, \\\"noiseless\\\": result_noiseless, \\\"noisy\\\": noisy_results})\\n\",\n \" print(f\\\"Ideal results on {backend.name}:\\\\n{result_noiseless:.3f}\\\")\\n\",\n \" print(f\\\"Noisy results on {backend.name}:\\\\n{noisy_results:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using the NEAT tool, we can observe how the expectation value in a noisy simulation deviates from the ideal value of 1, in a way that agrees with the noise analysis performed in [Section 3.1](#choosing-backend). \\n\",\n \"\\n\",\n \"However, as mentioned before, we should be aware that here we are analyzing Cliffordized versions of the quantum circuits, which means that some gates have been transformed to ensure the circuit belongs to the Clifford group. As a result, while the transformed circuit is similar, it does not produce exactly the same quantum state. This means that NEAT provides a useful approximation of how much noise a circuit might accumulate on a given backend, but it doesn't offer exact predictions or guarantees. This is important to keep in mind!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 4. Transpiler \\n\",\n \"\\n\",\n \"The transpiler is one of the most important and useful tools for running quantum circuits on real quantum hardware. It serves as a bridge between the abstract, idealized version of a quantum circuit and the physical implementation on the actual quantum device. When you design a circuit, you typically use virtual qubits and ideal gates without considering the hardware's specific limitations. The transpiler translates this high-level circuit into a version that can be implemented on a real quantum computer, using only the physical qubit's gate operations available on the device.\\n\",\n \"\\n\",\n \"For example, suppose your circuit includes a CNOT gate between virtual qubits 0 and 1. On a real device, these two qubits might not be directly connected. In such cases, the transpiler inserts a series of SWAP gates to move the quantum states to physically adjacent qubits, enabling the CNOT operation. Alternatively, the transpiler might find a more efficient qubit mapping, such that virtual qubits 0 and 1 are reassigned to physical qubits that are directly connected - for example, qubits 3 and 4, hence avoiding the need for additional SWAP gates.\\n\",\n \"\\n\",\n \"Up to now we have been using the transpiler implicitly when executing the pass manager in `generate_preset_pass_manager`. However, now we will focus on understanding it better and leveraging it to its fullest for better circuit design.\\n\",\n \"\\n\",\n \"# 4.1 Minimizing the two-qubit gates \\n\",\n \"\\n\",\n \"One of the most important tasks of a quantum transpiler is to determine an optimal qubit layout for executing a quantum circuit. This involves finding the best mapping between the circuit's virtual qubits and the device's physical qubits. To do so, there are a few things to take into consideration. \\n\",\n \"\\n\",\n \"First, the transpiler must check for all the two-qubit gates in the circuit and ensure that the selected physical qubits are connected in a way that enables these operations and reduces the necessity of using additional SWAP gates. This requires inspecting the coupling map, which shows how physical qubits are connected.\\n\",\n \"\\n\",\n \"First we will perform a transpilation of the quantum circuit to a layout in the quantum computer that minimizes the number of two-qubit gates, since that will be the main source of error.\\n\",\n \"\\n\",\n \"In particular, we will consider the `ibm_brisbane` backend, where, as shown in [Section 3.1](#choosing-backend), two-qubit gate errors account for the majority of the total accumulated error.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The pairs of qubits that need to perform two-qubit operations are:\\n\",\n \" [[62, 72], [81, 72], [62, 61], [62, 63]]\\n\",\n \"The errors introduced by each of the two-qubit operations are:\\n\",\n \" [0.007, 0.005, 0.01, 0.007]\\n\",\n \"The accumulated errors introduced by each of the two-qubit operations are:\\n\",\n \" [0.192, 0.064, 0.128, 0.099]\\n\",\n \"The repetitions of each one of the two-qubit operations is:\\n\",\n \" [29, 13, 13, 15]\\n\",\n \"The number of two-qubit operations in total:\\n\",\n \" 70\\n\",\n \"The total accumulated error by two-qubit operations is:\\n\",\n \" 0.482\\n\"\n ]\n }\n ],\n \"source\": [\n \"# We select the `ibm_brisbane` backend\\n\",\n \"num_backend = 0\\n\",\n \"noisy_fake_backend = noisy_fake_backends[num_backend]\\n\",\n \"\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" backend=noisy_fake_backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed,\\n\",\n \" layout_method=\\\"sabre\\\",\\n\",\n \")\\n\",\n \"circuit_transpiled = pm.run(qaoa_circuit)\\n\",\n \"\\n\",\n \"\\n\",\n \"def two_qubit_gate_errors_per_circuit_layout(\\n\",\n \" circuit: QuantumCircuit, backend: QiskitRuntimeService.backend\\n\",\n \") -> tuple:\\n\",\n \" \\\"\\\"\\\"Calculate accumulated two-qubit gate errors and related metrics for a given circuit layout.\\\"\\\"\\\"\\n\",\n \" pair_list = []\\n\",\n \" error_pair_list = []\\n\",\n \" error_acc_pair_list = []\\n\",\n \" two_qubit_gate_count = 0\\n\",\n \" properties = backend.properties()\\n\",\n \" if \\\"ecr\\\" in (backend.configuration().basis_gates):\\n\",\n \" two_qubit_gate = \\\"ecr\\\"\\n\",\n \" elif \\\"cz\\\" in (backend.configuration().basis_gates):\\n\",\n \" two_qubit_gate = \\\"cz\\\"\\n\",\n \" for instruction in circuit.data:\\n\",\n \" if instruction.operation.num_qubits == 2:\\n\",\n \" two_qubit_gate_count += 1\\n\",\n \" pair = [instruction.qubits[0]._index, instruction.qubits[1]._index]\\n\",\n \" error_pair = properties.gate_error(gate=two_qubit_gate, qubits=pair)\\n\",\n \" if pair not in (pair_list):\\n\",\n \" pair_list.append(pair)\\n\",\n \" error_pair_list.append(error_pair)\\n\",\n \" error_acc_pair_list.append(error_pair)\\n\",\n \" else:\\n\",\n \" pos = pair_list.index(pair)\\n\",\n \" error_acc_pair_list[pos] += error_pair\\n\",\n \"\\n\",\n \" acc_two_qubit_error = sum(error_acc_pair_list)\\n\",\n \" return (\\n\",\n \" acc_two_qubit_error,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" pair_list,\\n\",\n \" error_pair_list,\\n\",\n \" error_acc_pair_list,\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \"(\\n\",\n \" acc_two_qubit_error,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" pair_list,\\n\",\n \" error_pair_list,\\n\",\n \" error_acc_pair_list,\\n\",\n \") = two_qubit_gate_errors_per_circuit_layout(circuit_transpiled, noisy_fake_backend)\\n\",\n \"two_qubit_ops_list = [int(a / b) for a, b in zip(error_acc_pair_list, error_pair_list)]\\n\",\n \"# We print the results\\n\",\n \"print(f\\\"The pairs of qubits that need to perform two-qubit operations are:\\\\n {pair_list}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The errors introduced by each of the two-qubit operations are:\\\\n {[round(err,3) for err in error_pair_list]}\\\"\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The accumulated errors introduced by each of the two-qubit operations are:\\\\n {[round(err,3) for err in error_acc_pair_list]}\\\"\\n\",\n \")\\n\",\n \"print(f\\\"The repetitions of each one of the two-qubit operations is:\\\\n {two_qubit_ops_list}\\\")\\n\",\n \"print(f\\\"The number of two-qubit operations in total:\\\\n {two_qubit_gate_count}\\\")\\n\",\n \"print(f\\\"The total accumulated error by two-qubit operations is:\\\\n {acc_two_qubit_error:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 4.2 Find the optimal layout \\n\",\n \"\\n\",\n \"Next, the transpiler must choose the \\\"best\\\" physical qubits that fulfill the connectivity constraints. However, defining \\\"best\\\" in this context is a challenging task, as it depends on multiple hardware-specific metrics such as coherence times ($T_1$, $T_2$), single-qubit gate error rates, readout errors, and two-qubit gate error rates. Optimizing across all these metrics at the same time is not straightforward, and it often involves trade-offs.\\n\",\n \"\\n\",\n \"In this exercise, we simplify the problem by focusing on two criteria: \\n\",\n \"1. The selected qubits must satisfy the required connectivity given by the transpiler\\n\",\n \"2. We choose the qubits with the lowest two-qubit gate error rates\\n\",\n \"\\n\",\n \"As discussed in [Section 3.1](#choosing-backend), two-qubit gate errors are typically the predominant source of noise in many quantum devices.\\n\",\n \"\\n\",\n \"In this exercise, look for a different qubit configuration (layout) that gives you a smaller total accumulated error when accounting only for the two-qubit gates. However, this may be too easy, and since we're approaching the end of the lab, you can increase the difficulty by looking for all the possible layouts that offer a smaller total accumulated error of two-qubit gates operations.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Building the following graph will help you visualize the possible configurations, and will also serve to find all the layouts that fulfill the desired conditions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# We build a graph with the connectivity constraints of our backend that includes the two-qubit gate errors as weights in the edges\\n\",\n \"graph = rx.PyDiGraph()\\n\",\n \"graph.add_nodes_from(np.arange(0, noisy_fake_backend.num_qubits, 1))\\n\",\n \"two_qubit_gate = \\\"ecr\\\"\\n\",\n \"graph.add_edges_from(\\n\",\n \" [\\n\",\n \" (\\n\",\n \" edge[0],\\n\",\n \" edge[1],\\n\",\n \" noisy_fake_backend.properties().gate_error(\\n\",\n \" gate=two_qubit_gate, qubits=(edge[0], edge[1])\\n\",\n \" ),\\n\",\n \" )\\n\",\n \" for edge in noisy_fake_backend.coupling_map\\n\",\n \" ]\\n\",\n \")\\n\",\n \"draw_graph(graph, node_size=150, with_labels=True, width=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Also take into account that we have to look for layouts that have the connectivity allowing for the gates in `pair_list`, which can be converted to a more interpretable list using `logical_pair_list`:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Physical qubit layout list:\\n\",\n \" [72, 62, 81, 61, 63]\\n\",\n \"\\n\",\n \"Original two-qubit gates list:\\n\",\n \" [[62, 72], [81, 72], [62, 61], [62, 63]]\\n\",\n \"\\n\",\n \"Remapped two-qubit gates list (in logical qubits):\\n\",\n \" [[1, 0], [2, 0], [1, 3], [1, 4]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"def remap_nodes(original_labels: list, edge_list: list[list]) -> list[list[int]]:\\n\",\n \" \\\"\\\"\\\"Remap node labels to a new sequence starting from 0 based on their order in original_labels.\\\"\\\"\\\"\\n\",\n \" label_mapping = {label: idx for idx, label in enumerate(original_labels)}\\n\",\n \" remapped = [[label_mapping[src], label_mapping[dst]] for src, dst in edge_list]\\n\",\n \" return remapped\\n\",\n \"\\n\",\n \"\\n\",\n \"layout_list = list(circuit_transpiled.layout.initial_layout.get_physical_bits().keys())[:5]\\n\",\n \"logical_pair_list = remap_nodes(layout_list, pair_list)\\n\",\n \"print(f\\\"Physical qubit layout list:\\\\n {layout_list}\\\")\\n\",\n \"print(f\\\"\\\\nOriginal two-qubit gates list:\\\\n {pair_list}\\\")\\n\",\n \"print(f\\\"\\\\nRemapped two-qubit gates list (in logical qubits):\\\\n {logical_pair_list}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 4: Good Mapping \\n\",\n \"\\n\",\n \"**Your Goal:** Find all the good qubit layouts. \\n\",\n \"\\n\",\n \"In this fourth exercise you will find ALL the possible qubit layouts that have a smaller two-qubit gate accumulated error than the layout transpilation of the above, whose value is stored in the variable `acc_two_qubit_error`. This problem can be reformulated as a graph problem in which you need to find ALL the paths in the graph above which the total edge weights sum to less than the specified threshold and that have the same connectivity of that in `logical_pair_list`.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"Warning: Don't try to solve it by brute force!\\n\",\n \"\\n\",\n \"If you try a brute force approach, it might take hours of computation, as there are too many paths. Instead, restrict the search to only the paths that have the same connectivity of `logical_pair_list`, which are <100, and your computation will take less than 1 second.\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \" Hint 💡: (Click to expand)\\n\",\n \" \\n\",\n \"The proposed workflow for this exercise is:\\n\",\n \"\\n\",\n \"1. Start from each node in the graph and iteratively build paths by extending them one step at a time. \\n\",\n \"\\n\",\n \"2. At each step, use the `logical_pair_list` to decide which node in the current path to expand from.\\n\",\n \"\\n\",\n \"3. Depending on whether the node we are expanding from in the path corresponds to the control or the target qubit (based on `logical_pair_list`), we choose how to access its neighbors:\\n\",\n \"\\n\",\n \" 3.1 If it's the control qubit, we use directed edges (`graph.neighbors`).\\n\",\n \"\\n\",\n \" 3.2 If it's the target qubit, we use undirected edges (`graph.neighbors_undirected`).\\n\",\n \"\\n\",\n \"4. As you extend each path, calculate the cumulative weight by multiplying the edge weight (`graph.get_edge_data(node_1,node_2)`) by the corresponding value in `two_qubit_ops_list`. Note that if you are using the undirected neighbors, you should use `graph.get_edge_data(node_2,node_1)`.\\n\",\n \"\\n\",\n \"5. When the path is complete, i.e., when it gets to the length of the `two_qubit_ops_list`, check to only keep paths whose total weight stays below the threshold.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You might find these functions useful for the next exercise: [rx.PyDiGraph.neighbors](https://www.rustworkx.org/dev/apiref/rustworkx.PyDiGraph.neighbors.html), [rx.PyDiGraph.neighbors_undirected](https://www.rustworkx.org/dev/apiref/rustworkx.PyDiGraph.neighbors_undirected.html), and [rx.PyDiGraph.has_edge](https://www.rustworkx.org/apiref/rustworkx.PyDiGraph.has_edge.html), which were used in the code below.\\n\",\n \"\\n\",\n \"Additionally, you might find [rx.PyDiGraph.get_edge_data](https://www.rustworkx.org/apiref/rustworkx.PyDiGraph.get_edge_data.html) particularly useful. It allows you to obtain the two-qubit gate error associated with each interaction in the graph.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"We found 8 valid paths\\n\"\n ]\n }\n ],\n \"source\": [\n \"def find_paths_with_weight_sum_below_threshold(\\n\",\n \" graph: rx.PyDiGraph,\\n\",\n \" threshold: float,\\n\",\n \" two_qubit_ops_list: list[int],\\n\",\n \" logical_pair_list: list[list[int]],\\n\",\n \") -> tuple[list[list[int]], list[float]]:\\n\",\n \" \\\"\\\"\\\"Find all valid paths through a graph whose weighted sum is below a given threshold.\\\"\\\"\\\"\\n\",\n \" valid_paths = []\\n\",\n \" valid_weights = []\\n\",\n \"\\n\",\n \" # Task 4 ---\\n\",\n \" # Goal: Find all valid paths through a graph such that the total weighted sum of the path is below a given threshold.\\n\",\n \"\\n\",\n \" # Iterate over all possible starting nodes in the graph\\n\",\n \" for start_node in range(graph.num_nodes()):\\n\",\n \" # Initialize the list of paths with a single-node path starting from the current node\\n\",\n \" paths = [[start_node]]\\n\",\n \" # Initialize the corresponding weights for each path (starting with 0)\\n\",\n \" weights = [0]\\n\",\n \" # Iterate through each step in the sequence of two-qubit operations\\n\",\n \" for i in range(len(two_qubit_ops_list)):\\n\",\n \" new_paths = []\\n\",\n \" new_weights = []\\n\",\n \" # Go through each current path and its weight\\n\",\n \" for path, weight in zip(paths, weights):\\n\",\n \" # We need to determine which node in the current path is involved in the next logical operation, so we can expand from it.\\n\",\n \" # Determine which node in the path we are going to expand from using logical_pair_list\\n\",\n \" if logical_pair_list[i][0] < logical_pair_list[i][1]:\\n\",\n \" index_of_expanding_node = logical_pair_list[i][0] # control qubit\\n\",\n \" node_to_expand_from = path[index_of_expanding_node]\\n\",\n \" # Explore all neighbors of the node_to_expand_from\\n\",\n \" for neighbor in graph.neighbors(node_to_expand_from):\\n\",\n \" # Ensure we don't revisit nodes and the edge exists\\n\",\n \" if neighbor not in path and graph.has_edge(node_to_expand_from, neighbor):\\n\",\n \" # Calculate the edge weight, scaled by the number of times the gate is applied which is in two_qubit_ops_list\\n\",\n \"\\n\",\n \" #### TODO ####\\n\",\n \"\\n\",\n \" edge_weight = (\\n\",\n \" graph.get_edge_data(node_to_expand_from, neighbor)\\n\",\n \" * two_qubit_ops_list[i]\\n\",\n \" )\\n\",\n \"\\n\",\n \" #### End of TODO ####\\n\",\n \"\\n\",\n \" # Extend the path and update the weight\\n\",\n \" new_paths.append(path + [neighbor])\\n\",\n \" new_weights.append(weight + edge_weight)\\n\",\n \" else:\\n\",\n \" index_of_expanding_node = logical_pair_list[i][1] # target qubit\\n\",\n \" node_to_expand_from = path[index_of_expanding_node]\\n\",\n \" # Explore all undirected_neighbors of the node_to_expand_from\\n\",\n \" for neighbor in graph.neighbors_undirected(node_to_expand_from):\\n\",\n \" # Ensure we don't revisit nodes and the edge exists\\n\",\n \" if neighbor not in path and graph.has_edge(neighbor, node_to_expand_from):\\n\",\n \" # Calculate the edge weight, scaled by the number of times the gate is applied which is in two_qubit_ops_list\\n\",\n \"\\n\",\n \" #### TODO ####\\n\",\n \"\\n\",\n \" edge_weight = (\\n\",\n \" graph.get_edge_data(neighbor, node_to_expand_from)\\n\",\n \" * two_qubit_ops_list[i]\\n\",\n \" )\\n\",\n \"\\n\",\n \" #### End of TODO ####\\n\",\n \"\\n\",\n \" # Extend the path and update the weight\\n\",\n \" new_paths.append(path + [neighbor])\\n\",\n \" new_weights.append(weight + edge_weight)\\n\",\n \" # Update paths and weights for the next iteration\\n\",\n \" paths = new_paths\\n\",\n \" weights = new_weights\\n\",\n \"\\n\",\n \" # After building all possible paths, filter those under the threshold\\n\",\n \" for path, weight in zip(paths, weights):\\n\",\n \"\\n\",\n \" #### TODO: Complete the condition for `if` ####\\n\",\n \"\\n\",\n \" if weight < threshold:\\n\",\n \" valid_paths.append(path)\\n\",\n \" valid_weights.append(weight)\\n\",\n \"\\n\",\n \" #### End of TODO ####\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" return valid_paths, valid_weights\\n\",\n \"\\n\",\n \"\\n\",\n \"threshold = acc_two_qubit_error\\n\",\n \"\\n\",\n \"valid_paths, valid_weights = find_paths_with_weight_sum_below_threshold(\\n\",\n \" graph, threshold, two_qubit_ops_list, logical_pair_list\\n\",\n \")\\n\",\n \"# Note that there could be no other paths with smaller errors\\n\",\n \"if valid_weights:\\n\",\n \" minimum_weight_index = valid_weights.index(min(valid_weights))\\n\",\n \" opt_layout = valid_paths[minimum_weight_index]\\n\",\n \"else:\\n\",\n \" minimum_weight_index = None\\n\",\n \" opt_layout = layout_list\\n\",\n \"print(f\\\"We found {len(valid_paths)} valid paths\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The path with smaller errors in its two-qubit gates is: [29, 28, 30, 27, 35] \\n\",\n \" With total accumulated error of 0.384\\n\"\n ]\n }\n ],\n \"source\": [\n \"init_layout = Layout({q: phys for q, phys in zip(qaoa_circuit.qubits, opt_layout)})\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" backend=noisy_fake_backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed,\\n\",\n \" initial_layout=init_layout,\\n\",\n \" layout_method=\\\"sabre\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"circuit_opt = pm.run(qaoa_circuit)\\n\",\n \"\\n\",\n \"(\\n\",\n \" acc_total_error_opt,\\n\",\n \" two_qubit_gate_count,\\n\",\n \" pair_list,\\n\",\n \" error_pair_list,\\n\",\n \" error_acc_pair_list,\\n\",\n \") = two_qubit_gate_errors_per_circuit_layout(circuit_opt, noisy_fake_backend)\\n\",\n \"\\n\",\n \"print(\\n\",\n \" f\\\"The path with smaller errors in its two-qubit gates is: {opt_layout} \\\\n\\\",\\n\",\n \" f\\\"With total accumulated error of {acc_total_error_opt:.3f}\\\",\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations! 🎉 Your answer is correct.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex4(find_paths_with_weight_sum_below_threshold)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Well done, you've completed the most complicated part of the work. Now all of the found combinations are better than the trivial threshold.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To this point, we've seen how optimizing the layout of qubits can significantly reduce the accumulated error from two-qubit gates. But can you imagine having to do this manually every time we want to run a quantum circuit on real hardware? That would be incredibly tedious. But don't worry - the transpiler can handle this task for us. By using one of the `layout_methods` provided, it smartly selects a layout of qubits that satisfies the connectivity necessities of our circuit, while also aiming to minimize the number of two-qubit gates.\\n\",\n \"\\n\",\n \"In particular, we will use the `sabre` method, which is a stochastic technique that aims to minimize the number of SWAP gates. To make the most of it, we'll perform a sweep over different values of the transpiler seed. This allows us to explore multiple layout configurations and choose the one that minimizes the two-qubit error rate, as we wanted. Note that here we could have selected to minimize the circuit depth or the number of two-qubit gates, the readout error, or any other metric we wanted to use.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 5: Best Mapping \\n\",\n \"\\n\",\n \"**Your Goal:** Use the transpiler to find the optimal layout.\\n\",\n \"\\n\",\n \"In this fifth exercise you will use the `sabre` `layout_method` to identify the layout that has the lowest two-qubit gate accumulated error. \\n\",\n \"\\n\",\n \"To do so, perform a sweep over the `seed_transpiler` values from 0 to 500 and use the `generate_preset_pass_manager` with `optimization_level=3` to select the best layout. Remember you can count the accumulated error of the two-qubit gates and the two-qubit gate number using the `two_qubit_gate_errors_per_circuit_layout` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def finding_best_seed(\\n\",\n \" circuit: QuantumCircuit, backend: QiskitRuntimeService.backend\\n\",\n \") -> tuple[QuantumCircuit, int, float, int]:\\n\",\n \" \\\"\\\"\\\"Find the transpiler seed that minimizes two-qubit gate error for a given circuit and backend.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # We initialize the minimum error accumulated\\n\",\n \" min_err_acc_seed_loop = 100\\n\",\n \" circuit_opt_best_seed = None\\n\",\n \" # First we loop over 500 seeds and transpile the circuit\\n\",\n \" for seed_transpiler in range(0, 500):\\n\",\n \" pm = generate_preset_pass_manager(\\n\",\n \" backend=backend,\\n\",\n \" optimization_level=3,\\n\",\n \" seed_transpiler=seed_transpiler,\\n\",\n \" layout_method=\\\"sabre\\\",\\n\",\n \" )\\n\",\n \" circuit_opt_seed = pm.run([circuit])[0]\\n\",\n \" # ---- TODO : Task 5 ---\\n\",\n \" # Goal: Find the transpiler seed that minimizes two-qubit gate error for a given circuit and backend looping from 0 to 500\\n\",\n \"\\n\",\n \" # TODO Use the `two_qubit_gate_errors_per_circuit_layout` function to count for the error of the transpile circuit\\n\",\n \" acc_total_error_seed, two_qubit_gate_count_seed, *_ = (\\n\",\n \" two_qubit_gate_errors_per_circuit_layout(circuit_opt_seed, backend)\\n\",\n \" )\\n\",\n \" # TODO Check if the error accounted above is smaller than min_err_acc_seed_loop. If so, assign the variables that this function returns\\n\",\n \" if min_err_acc_seed_loop > acc_total_error_seed:\\n\",\n \" min_err_acc_seed_loop = acc_total_error_seed\\n\",\n \" circuit_opt_best_seed = circuit_opt_seed\\n\",\n \" best_seed_transpiler = seed_transpiler\\n\",\n \" two_qubit_gate_count_seed_loop = two_qubit_gate_count_seed\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" return (\\n\",\n \" circuit_opt_best_seed,\\n\",\n \" best_seed_transpiler,\\n\",\n \" min_err_acc_seed_loop,\\n\",\n \" two_qubit_gate_count_seed_loop,\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best transpiler seed: 17\\n\",\n \"Minimum accumulated two-qubit gate error: 0.271\\n\",\n \"Two-qubit gate count for best seed: 70\\n\",\n \"Best layout (first n logical qubits mapped to physical qubits):\\n\",\n \" [42, 41, 43, 53, 40]\\n\"\n ]\n }\n ],\n \"source\": [\n \"(\\n\",\n \" circuit_opt_seed_loop,\\n\",\n \" best_seed_transpiler,\\n\",\n \" min_err_acc_seed_loop,\\n\",\n \" two_qubit_gate_count_seed_loop,\\n\",\n \") = finding_best_seed(qaoa_circuit, noisy_fake_backend)\\n\",\n \"\\n\",\n \"best_layout = list(circuit_opt_seed_loop.layout.initial_layout.get_physical_bits().keys())[:n]\\n\",\n \"print(f\\\"Best transpiler seed: {best_seed_transpiler}\\\")\\n\",\n \"print(f\\\"Minimum accumulated two-qubit gate error: {min_err_acc_seed_loop:.3f}\\\")\\n\",\n \"print(f\\\"Two-qubit gate count for best seed: {two_qubit_gate_count_seed_loop}\\\")\\n\",\n \"print(f\\\"Best layout (first n logical qubits mapped to physical qubits):\\\\n {best_layout}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations! 🎉 Your answer is correct.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex5(finding_best_seed)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, we sample the QAOA circuit with these circuits to highlight the importance of good transpilation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Warning: 5 minutes needed.\\n\",\n \"\\n\",\n \"Running the following code will take approximately 5 minutes to execute, and will block this notebook during that time. You can skip to the next cell.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" message: Optimization terminated successfully.\\n\",\n \" success: True\\n\",\n \" status: 1\\n\",\n \" fun: -2.0890691093089067\\n\",\n \" x: [ 9.921e-01 9.896e-01 -5.624e-01 1.031e+00]\\n\",\n \" nfev: 57\\n\",\n \" maxcv: 0.0\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Probability of measuring a solution for is 0.5526\\n\",\n \" message: Optimization terminated successfully.\\n\",\n \" success: True\\n\",\n \" status: 1\\n\",\n \" fun: -2.586044139558604\\n\",\n \" x: [ 1.007e+00 1.024e+00 -3.913e-02 8.720e-01]\\n\",\n \" nfev: 57\\n\",\n \" maxcv: 0.0\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Probability of measuring a solution for is 0.6674\\n\"\n ]\n }\n ],\n \"source\": [\n \"counts_list_transpiled_circuits = []\\n\",\n \"circuit_transpiled_list = [circuit_transpiled, circuit_opt_seed_loop]\\n\",\n \"opt_params_list_transpiled_circuits = []\\n\",\n \"for circuit in circuit_transpiled_list:\\n\",\n \" result_backend, _ = train_qaoa(init_params, circuit, cost_hamiltonian, noisy_fake_backend)\\n\",\n \" opt_params = result_backend.x\\n\",\n \" opt_params_list_transpiled_circuits.append(opt_params)\\n\",\n \" counts_list_transpiled_circuit = sample_qaoa(opt_params, circuit, noisy_fake_backend)\\n\",\n \" counts_list_transpiled_circuits.append(counts_list_transpiled_circuits)\\n\",\n \" solutions_counts = [counts_list_transpiled_circuit[key] for key in states_solutions]\\n\",\n \" print(f\\\"Probability of measuring a solution for is {float(sum(solutions_counts)/SHOTS)}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the following, remember to use the `sabre` layout method and loop over different seeds to minimize a specific metric - such as two-qubit gate counts, depth, or accumulated error - in order to maximize performance.\\n\",\n \"\\n\",\n \"In this section we have seen that transpilation is a key step in preparing quantum circuits to be executed on real hardware. Since different devices have different layouts and gate sets, a good transpiler helps to adapt your circuit to fit the hardware while trying to keep things like quantum depth and error rates low, thus achieving better performance from your quantum algorithms.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 5. Error Mitigation (EM) \\n\",\n \"\\n\",\n \"One of the main areas of research to address the inevitable noise in quantum devices is **error mitigation (EM)**. EM consists of a set of intelligent techniques designed to reduce the impact of noise without requiring complex error correction codes or additional qubits, resources that remain limited in today's quantum hardware. Instead of correcting errors as they occur, EM uses strategies such as loop repetition, calibration-based adjustments, and classical post-processing to improve the quality of the final results, leading to an improved performance in our quantum algorithms.\\n\",\n \"\\n\",\n \"This approach is especially valuable for current devices, which are small- to medium-scale and inherently noisy. Fully fault-tolerant quantum computing is still beyond our reach, but EM offers a practical way to take full advantage of the devices we have now.\\n\",\n \"EM integrates naturally with hybrid quantum-classical algorithms, those that alternate between quantum and classical computation, such as:\\n\",\n \"\\n\",\n \"- Variational Quantum Eigensolver (VQE),\\n\",\n \"- Quantum Approximate Optimization Algorithm (QAOA), and\\n\",\n \"- Quantum-enhanced machine learning models.\\n\",\n \"\\n\",\n \"These types of algorithms are especially sensitive to noise, and EM can greatly improve their reliability and accuracy.\\n\",\n \"\\n\",\n \"Remarkably, EM does not eliminate all imperfections, but it refines the result enough to make it useful and actionable. It is a tool for narrowing the gap between noisy quantum results and meaningful insights, paving the way for practical quantum applications even before large-scale error-correcting machines become a reality.\\n\",\n \"\\n\",\n \"There are several well-established techniques used in EM, each tailored to address different types of noise and imperfections in quantum computations.\\n\",\n \"\\n\",\n \"One of the most widely used methods is Zero Noise Extrapolation (ZNE). In this approach, the same quantum circuit is executed multiple times with deliberately increased noise levels. Then, mathematical extrapolation techniques are applied to estimate what the outcome would have been in the absence of noise. This method was introduced by [Temme et al. in 2017](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509).\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"## 5.1 Zero Noise Extrapolation (ZNE) \\n\",\n \"\\n\",\n \"ZNE is a powerful quantum error mitigation technique designed to reduce the impact of noise in quantum circuits without requiring additional qubits or full error correction. The process consists of three essential stages: noise amplification, execution at various noise levels, and classical extrapolation back to the zero-noise limit.\\n\",\n \"\\n\",\n \"\\n\",\n \"### Noise amplification\\n\",\n \"\\n\",\n \"The first stage, Noise Amplification, lies at the heart of ZNE. The idea is to run the quantum circuit in versions that have more noise than usual, but in a controlled and reversible way. By comparing how the output changes as noise increases, it becomes possible to infer what the result would be with no noise at all. This is typically achieved using a technique called circuit folding.\\n\",\n \"\\n\",\n \"Circuit folding artificially increases the noise in a quantum computation by inserting additional gates that, in theory, do not alter the logical outcome. These additional gates are the adjoint (inverse) operations of previously applied gates. For example, a unitary operation $U$ can be transformed into $ U \\\\cdot U^\\\\dagger \\\\cdot U$, which logically still computes $U$, but takes longer to execute, thus being exposed to more noise from the hardware.\\n\",\n \"\\n\",\n \"\\n\",\n \"There are two common types of folding: **Global Folding** and **Local Folding**. \\n\",\n \"\\n\",\n \"\\n\",\n \"#### Global folding\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"In Global folding, the entire quantum circuit is folded as a single block. This means the full unitary operation $U$ that the circuit represents is wrapped with its adjoint operation, yielding the transformation:\\n\",\n \"\\n\",\n \"$$U \\\\rightarrow U \\\\cdot U^\\\\dagger \\\\cdot U$$\\n\",\n \"\\n\",\n \"This global transformation is logically equivalent to the original circuit, since $U^\\\\dagger \\\\cdot U$ is the identity operation. However, because the circuit is now longer and includes more gate operations, it becomes more susceptible to environmental noise and hardware imperfections.\\n\",\n \"\\n\",\n \"Global folding is particularly useful for quickly applying a uniform increase in noise across the full circuit. It is straightforward to implement and does not require knowledge of the internal structure of the circuit. As such, it serves as a coarse-grained approach to noise amplification, suitable for general-purpose extrapolation when fine control is not needed.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 6a: Global Folding\\n\",\n \"\\n\",\n \"**Your Goal:** Implement global folding on the quantum circuits.\\n\",\n \"\\n\",\n \"In this part of Exercise 6 (Section a), you will create a function that applies global folding to any quantum circuit. Your implementation should allow evaluation of the circuit at different noise scaling factors, which simulate increased noise levels while preserving the circuit's logical output. The noise scaling factor represents the number of times a circuit $U$ or $U^\\\\dagger$ is applied, with 1 being the non-folding case.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def fold_global_circuit(circuit: QuantumCircuit, scale_factor: int) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"Apply global circuit folding for Zero Noise Extrapolation (ZNE).\\\"\\\"\\\"\\n\",\n \" if scale_factor % 2 == 0 or scale_factor < 1:\\n\",\n \" raise ValueError(\\\"scale_factor must be an odd positive integer (1, 3, 5, ...)\\\")\\n\",\n \" # We define the number of times we are going to \\\"fold\\\" the circuit\\n\",\n \" n_repeat = (scale_factor - 1) // 2\\n\",\n \" folded_circuit = QuantumCircuit(circuit.qubits, circuit.clbits)\\n\",\n \"\\n\",\n \" def remove_all_measurements(qc: QuantumCircuit) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"Remove all measurements from a quantum circuit.\\\"\\\"\\\"\\n\",\n \" clean_qc = QuantumCircuit(qc.num_qubits)\\n\",\n \" for instr in qc.data:\\n\",\n \" if instr.operation.name != \\\"measure\\\":\\n\",\n \" clean_qc.append(instr.operation, instr.qubits)\\n\",\n \" return clean_qc\\n\",\n \"\\n\",\n \" # Make a quantum circuit as a U (Unitary) by removing measurements, since measurements are not unitary\\n\",\n \" clean_circuit = remove_all_measurements(circuit)\\n\",\n \"\\n\",\n \" # ---- TODO : Task 6a ---\\n\",\n \" # Implement the global circuit folding. Use `QuantumCircuit.append` and `QuantumCircuit.inverse` functions\\n\",\n \" # add U^† (inverse of clean_circuit) then U(clean_circuit) to the main circuit (folded_circuit)\\n\",\n \"\\n\",\n \" # Apply U (U^† U)^n_repeat folding\\n\",\n \" folded_circuit.append(clean_circuit, folded_circuit.qubits)\\n\",\n \" for _ in range(n_repeat):\\n\",\n \" folded_circuit.append(clean_circuit, folded_circuit.qubits)\\n\",\n \" folded_circuit.append(clean_circuit.inverse(), folded_circuit.qubits)\\n\",\n \"\\n\",\n \" # --- End of TODO --\\n\",\n \"\\n\",\n \" return folded_circuit\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations! 🎉 Your answer is correct.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex6a(fold_global_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Local Folding\\n\",\n \"\\n\",\n \"\\n\",\n \"Local folding focuses on selectively increasing the noise in specific parts of the quantum circuit, usually around the most error-prone gates or subcircuits. Instead of folding the entire circuit as a block, this method focuses on individual quantum gates or groups of gates. This provides greater control and more precise calibration of the noise amplification process. Its operation is to take from the quantum circuit each quantum gate $G$. We can apply local folding by wrapping it with its inverse operation as follows:\\n\",\n \"\\n\",\n \"$$ G \\\\rightarrow G \\\\cdot G^\\\\dagger \\\\cdot G $$\\n\",\n \"\\n\",\n \"This transformation logically cancels the inserted $G^\\\\dagger \\\\cdot G$ pair, leaving the computation unchanged. However, the execution now involves three times as many gate operations, thereby increasing the local noise exposure. This makes it ideal for simulating different noise levels selectively and systematically.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 6b: Local Folding\\n\",\n \"\\n\",\n \"\\n\",\n \"**Your Goal:** Implement local folding on quantum circuits.\\n\",\n \"\\n\",\n \"In this second part of Exercise 6 (Section b), your task is to write a function that applies local folding to a quantum circuit. Unlike global folding, this method focuses on individual gates and selectively folds them to amplify noise in specific areas of the circuit. The function should allow flexible control of the gates being folded (for example, we cannot fold a measurement gate) and their evaluation at different noise amplification scales.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def fold_local_circuit(circuit: QuantumCircuit, scale_factor: int) -> QuantumCircuit:\\n\",\n \" \\\"\\\"\\\"Performs Zero-Noise Folding at the level of individual circuit instructions.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" if scale_factor % 2 == 0:\\n\",\n \" raise ValueError(\\\"scale must be an odd positive integer (1, 3, 5, ...)\\\")\\n\",\n \" # We define the number of times we are going to \\\"fold\\\" each instruction\\n\",\n \" n_repeat = (scale_factor - 1) // 2\\n\",\n \" qc_folded = QuantumCircuit(circuit.qubits, circuit.clbits)\\n\",\n \"\\n\",\n \" if scale_factor == 1:\\n\",\n \" return circuit\\n\",\n \" else:\\n\",\n \" for instruction in circuit.data:\\n\",\n \" # ---- TODO : Task 6b ---\\n\",\n \" # Implement the local circuit folding. Don't fold measurement gates!\\n\",\n \" if instruction.operation.name not in [\\\"measure\\\", \\\"barrier\\\"]:\\n\",\n \" qc_folded.append(instruction.operation, instruction.qubits)\\n\",\n \"\\n\",\n \" # Apply folding: (G · G^† ) ^ n\\n\",\n \" for _ in range(n_repeat):\\n\",\n \" qc_folded.append(instruction.operation, instruction.qubits)\\n\",\n \"\\n\",\n \" qc_folded.append(instruction.operation.inverse(), instruction.qubits)\\n\",\n \" else:\\n\",\n \" qc_folded.append(instruction)\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" return qc_folded\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations! 🎉 Your answer is correct.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using the following code\\n\",\n \"grade_lab2_ex6b(fold_local_circuit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Extrapolation\\n\",\n \"\\n\",\n \"\\n\",\n \"After amplifying the noise through folding, the quantum circuit is executed multiple times at different noise levels. Finally, in the third step, **Extrapolation**, the results from the noisy runs are post-processed using classical fitting methods, such as linear, polynomial, or exponential extrapolation, to estimate what the outcome would be in a hypothetical zero-noise scenario. Through this pipeline, ZNE helps recover higher-fidelity results from current noisy quantum hardware, making it a valuable tool in the near-term quantum computing landscape.\\n\",\n \"\\n\",\n \"\\n\",\n \"Now, we will integrate ZNE into the execution of a QAOA circuit, improving the accuracy of results on noisy quantum hardware.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def basic_zne(\\n\",\n \" circuit,\\n\",\n \" scales,\\n\",\n \" backend,\\n\",\n \" opt_params,\\n\",\n \" observable,\\n\",\n \"):\\n\",\n \" \\\"\\\"\\\"Basic Zero Noise Extrapolation (ZNE) loop using local folding.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" exp_vals = []\\n\",\n \" xdata = np.array(scales)\\n\",\n \" estimator = Estimator(mode=backend)\\n\",\n \"\\n\",\n \" for scale in scales:\\n\",\n \" # Apply local folding\\n\",\n \" folded = fold_local_circuit(circuit, scale)\\n\",\n \"\\n\",\n \" # Transpile for the backend\\n\",\n \" basis_gates = backend.target.operation_names\\n\",\n \" transpiled_folded = generate_preset_pass_manager(\\n\",\n \" basis_gates=basis_gates, optimization_level=0, seed_transpiler=seed\\n\",\n \" ).run(folded)\\n\",\n \" pub = (\\n\",\n \" transpiled_folded,\\n\",\n \" observable.apply_layout(circuit.layout),\\n\",\n \" opt_params,\\n\",\n \" )\\n\",\n \" # Evaluate the expectation value\\n\",\n \" job = estimator.run([pub])\\n\",\n \" results = job.result()[0]\\n\",\n \" exp_vals.append(results.data.evs)\\n\",\n \"\\n\",\n \" return xdata, exp_vals, pub\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"scales = [1, 3, 5, 7, 9, 11, 13]\\n\",\n \"xdata, ydata, pub = basic_zne(\\n\",\n \" qaoa_circuit_transpiled,\\n\",\n \" scales,\\n\",\n \" noisy_fake_backend,\\n\",\n \" opt_params_list[num_backend],\\n\",\n \" cost_hamiltonian,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To validate and analyze the results of the ZNE we just implemented, we can apply three types of extrapolation methods: linear, polynomial, and exponential. These approaches help estimate the circuit's output in the zero-noise limit based on the data collected at higher noise levels. To illustrate how this can be done, consider the following example code:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \" Extrapolation Method: LINEAR\\n\",\n \"⟨Z⟩ (ZNE Estimate): -2.858\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \" Extrapolation Method: QUADRATIC\\n\",\n \"⟨Z⟩ (ZNE Estimate): -4.694\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \" Extrapolation Method: EXPONENTIAL\\n\",\n \"⟨Z⟩ (ZNE Estimate): -8.288\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"methods = [\\\"linear\\\", \\\"quadratic\\\", \\\"exponential\\\"]\\n\",\n \"\\n\",\n \"for method in methods:\\n\",\n \" print(f\\\"\\\\n Extrapolation Method: {method.upper()}\\\")\\n\",\n \"\\n\",\n \" # Perform the extrapolation\\n\",\n \" zero_val, fitted_vals, fit_params, fit_fn = zne_method(method=method, xdata=xdata, ydata=ydata)\\n\",\n \"\\n\",\n \" # Print the extrapolated expectation value\\n\",\n \" print(f\\\"⟨Z⟩ (ZNE Estimate): {zero_val:.3f}\\\")\\n\",\n \"\\n\",\n \" # Plot the results\\n\",\n \" plot_zne(xdata, fitted_vals, zero_val, fit_fn, fit_params, method)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Conclusions \\n\",\n \"\\n\",\n \"Good job completing Lab 2! \\n\",\n \"\\n\",\n \"In this lab we explored the various sources of noise that can affect your quantum circuit, and - more importantly - the dedicated tools we can use to *cut through the noise*. With these tools at hand, you're now well-equipped to tackle any of your ongoing quantum algorithms and execute them on real quantum hardware. Remember, the key steps to follow are:\\n\",\n \"\\n\",\n \"- **Choose the right hardware** that is well-suited for your circuit.\\n\",\n \"- **Use the transpiler** to select the best possible layout and minimize the number of two-qubit gates and other metrics.\\n\",\n \"- **Apply error mitigation and suppression** to further reduce the impact of noise and improve the performance.\\n\",\n \"\\n\",\n \"Keep that in mind and you are all set to tackle your quantum challenges ahead!\\n\",\n \"\\n\",\n \"That's all, folks! Or is it?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Lab 0: 2/2 exercises completed (100%)\\n\",\n \" ✅ 2585 participants have completed this lab\\n\",\n \"Lab 1: 0/9 exercises completed (0%)\\n\",\n \" ✅ 2242 participants have completed this lab\\n\",\n \"Lab 2: 7/7 exercises completed (100%)\\n\",\n \" ✅ 1526 participants have completed this lab\\n\",\n \"Lab 3: 4/5 exercises completed (80%)\\n\",\n \" ✅ 1414 participants have completed this lab\\n\",\n \"Lab 4: 0/6 exercises completed (0%)\\n\",\n \" ✅ 1395 participants have completed this lab\\n\",\n \"Functions Labs: 0/8 exercises completed (0%)\\n\",\n \" ✅ 6 participants have completed this lab\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Check your submission status with the code below\\n\",\n \"from qc_grader.grader.grade import check_lab_completion_status\\n\",\n \"\\n\",\n \"check_lab_completion_status(\\\"qgss_2025\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Bonus challenge: Scaling it up! \\n\",\n \"\\n\",\n \"As a bonus, we've included a more complicated version of this problem, in which you'll implement the Max-cut problem on IBM quantum hardware. This is your chance to put everything you've learned into practice, including the error mitigation techniques!\\n\",\n \"\\n\",\n \"### The problem\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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u3aNq0KWbOnIl69erpbLg/KCgI/v7+SExMxObNm1GvXr1Uj9m6dSsSExPRsWPHNI8REBAAhUKBc+fOiYQylxBD3oIgCHoUHh6OgwcPYt++fThw4ADCwsJgbGwMf3//FHMfPTw8RO9jGkJDQ/H3339jzZo1uHz5MmxtbdGuXTt07twZ1apVy9dzIj8UEhKC3377DX/++SdkMhn69OmDgQMHokiRIjpt58WLFyhVqhSioqKwZMkS9O7dO83H1atXD5Ik4ejRo588VkBAAMqVK4fly5frNEYhe4geSkEQhByk0Whw8eJF7N+/H/v27cPFixdBEn5+fujVqxcaNWqEypUrw8TERN+hGiySOH78OObNm4edO3dCLpejadOm+Omnn9C0aVOYmprqO0SDERwcjJkzZ2Lx4sUwNjbG8OHDMXToUDg4pN4jO6tevnyJcuXKISoqCjNmzPhkMhkcHIyjR49i6dKlnz1e5cqVxaKcXEQklIIgCNksNDRU2wt58OBBvHnzBra2tmjQoAH69euHRo0awdXVVd9hGry4uDisW7cO8+bNw61bt+Dr64u5c+eiQ4cO2ZIg5WZBQUGYMWMGli1bBgsLC/z0008YPHhwtu1o9OrVK5QvXx5v377FyJEjMWrUqE8+dsOGDTA2Nkbbtm0/e8zKlStj4cKFiIiIyLM7MeUlYshbEARBx9RqNc6fP6/thbx8+TKA90N4jRo1QuPGjREYGChWr6bT06dPsXDhQixduhQRERFo3rw5Bg8ejLp164ppAB/577//MG3aNKxcuRK2trYYPnw4vv/++0zVj0yvkJAQBAQE4OXLl+jatStWrlz52d9L+fLlUaxYMWzatOmzx3348CGKFy+OAwcOoEGDBroOW9AxcTYTBEHQgVevXuHAgQPYt28fDh06hHfv3sHe3h4NGzbE4MGD0bBhQ7G6OANI4tixY9phbSsrK/Tu3RsDBgxA4cKF9R2ewXnw4AGmTp2KtWvXwtHREdOnT0e/fv2yvWh9aGgoAgMD8fLlSzRp0gQrVqz4bDJ5+fJlXLt27ZOruz9UtGhR2Nvb49y5cyKhzAVEQikIgpAJKpUKZ8+e1fZCXrt2DTKZDBUrVsTgwYPRqFEjVKxYEQpF6i3lhE9La1h74cKF6Ny5c77c0edL7ty5g8mTJ2Pjxo1wcXHB7Nmz0adPnxypAPD69WtUrlwZz58/R7Vq1bB169YvLoJatGgRPDw80Lhx4y8eXyaTITAwEOfOndNVyEI2EgllOiRX7hcEIX8LDg7WJpCHDh1CVFQUnJyc0LBhQ4wcORINGjSAk5OTvsPMlYKDgzF37twUw9pz5swRw9qfcP36dUyePBlbtmyBh4cH5s+fjx49euTYgqSwsDBUrlwZQUFBqFSpEg4ePPjFhWTR0dFYv349fvjhh3RP96hcuTLmzJkDkuLvwMCJhDINt4Ij8c/l57jw5C0ehcVo9xYt6mSJSoXs8U1AwXy1tyhJJCUlITExEXK5HAqFAkqlEgqFQpTkEPK0pKQknD59WptE3rx5E3K5HIGBgRg5ciQaNWqEgIAA8X+QBcHBwZgxYwYWL14MU1NTMaz9BZcvX8avv/6KHTt2oFChQli8eDG6du0KY2PjHIshPDwcVatWxdOnT+Hv748jR46ka0em9evXIz4+Hr169Up3W5UrV8a7d++08ykFwyUW5XzgaXgsRm25gQtP30Ihl0Ejpf7RJH+9krc9ZrYtC29Hwx2CiY+PR2hoKEJCQvDq1SuEhYUhJiYGsbGxiIuLQ2xsbLrux8XFQaPRfLKd5OQy+fbh5x/eNzc3h729Pezs7GBvb//F+5aWllCr1dpkNjExEWq1GsbGxjAxMYGpqSlMTExgZGQkrlwFnYmIiMC2bduwc+dOHDlyBNHR0ShQoIB2MU39+vVhb2+v7zBzveDgYEyfPh1LliyBubk5hg8fjkGDBsHGJv9crGfElStXMHbsWOzduxfFihXDmDFj0KlTJxgZGeVoHM+ePUOtWrUQFBSEsmXL4uTJk+naWYckAgIC4OHhgZ07d6a7vYiICNjZ2WHVqlXo2rVrVkIXsplIKP+/HdeC8cPmG9CQaSaSH1PIZVDIZJj1dVm09HPPgQjfkyQJb968watXrxASEqK9ffh58v3IyMgUz5XJZLCwsNDezM3NU3z88H7ysEnyMEPy8IRKpUJiYiKSkpJS3FQqlfZ+chKoVquhUqmgUqmgVqu1SWHy85O//rlkNb0+TDCTb1/6PDPP+fBzS0tLODs7i3ldeUB8fDx2796NDRs2YM+ePVCpVKhatSqaNGmCRo0awc/PT/RC6khyIrl48WJYWFhgxIgRGDRoULauQs7NXrx4gTFjxmDNmjUoUaIExo4di/bt2+tlbu6dO3dQt25dhIWFoVSpUjh58mS6LwAuXryISpUqYffu3WjatGmG2vX19UXt2rWxcOHCzIQt5BAx5I33yeTQjdeQnEYmBN1A6Iaf03ysS5ffYOJeEhqJ0IAYuvEaAOg0qYyLi8OjR4/w8OFDPHjwQPvxyZMnCA0NTZWA2djYwNXVFS4uLnBxcUH58uW1911dXeHs7AwzMzNtMhoeHp7m7enTp9r7Hyejn6JQKGBiYqLtNfz4vpmZGWxsbNL8XvLnxsbGkMvl2uTyw17JpKQkxMfHIzY2Fm/evEFoaGiqn4GVlRXs7Oxga2sLGxsbWFpawsLCAmZmZjAxMdEmwcm3qKioFJ8nJiYiISEh1efpvdYyNzeHs7MznJyc4OzsnOr+h587OTmJgtUGQqVS4ciRI1i/fj22b9+O6OhoVKxYEdOnT0f79u3h5uam7xDzlBcvXmh7JC0tLTF+/HgMHDhQJJKfEB0djZkzZ+L333+HpaUlFi5ciN69e+ut1NTZs2fRqFEjxMbGwtfXF8ePH89Qb/LixYtRsGBBNGrUKMNtV65cWSzMyQXyfUL5JDwWP2y+gbRSB6uA5jB2TTlnQ2mXsvgwAfyw+QbKedhmaPg7KSkJT548SZEwJn988eKF9nE2NjYoVqwYihcvjjp16sDV1RW2trYwNTWFQqEASURHR6dIDIOCgnDp0iXt52/evIEkSalisLOzg6Ojo/bm6+ub4nMnJyc4OjrC2tr6k0mjPq6SNRoNXr16hadPn+LJkyd4+vRpivvPnj1LkXC6urrCx8cHfn5+KFeuHPz8/ODj4/PZoSKSKXpV00o4o6OjERYWhtevX+P169fa+3fv3sXx48cRFhaG6OjoVMe2trZOM9lMKxF1cHAQtQp1SJIknD17FuvXr8c///yDsLAwlChRAiNHjkTHjh1RrFgxfYeY54hEMmPUajWWL1+OcePGITIyEsOGDcPo0aP1+vPat28f2rRpA41GgxIlSuDYsWOws7NL9/OjoqKwYcMGjBo1KlPvGZUrV8bq1asRGxsrRoQMWL4f8m636CwuP3uXYpg7uYfSsdVoWJSs/sVjKOQyBHjaYVPfKqm+FxMTg5s3b+L69eu4e/euNml8+vSpNukxNTVF4cKF4eLiAltbW5ibm2snWMfGxqbqSUxMTEzVjoWFRYpk8MOEMK2bvb19js+9ySlqtRrBwcHaJPPJkye4desWrl+/jsePHwMAjI2N4evrmyLJLFeuXIZOkukRHx+vTTTTSj4//Dw0NDTV71Ymk8He3v6ziaerqyuKFi0KZ2dnMZc0DSRx8+ZNrF+/Hhs2bMCzZ8/g4eGBjh07omPHjvDz8xM/t2zw4sULTJs2DUuXLoWVlRVGjBiBgQMHpmu+XX61f/9+jBw5Erdv30bnzp0xZcoUeHp66jWmtWvXokePHlAoFChUqBCOHz8OZ2fnDB3jzz//xKBBgxAUFAR394yP5t24cQPlypXD8ePHUbNmzQw/X8gZ+TqhvBkciebzT6X6+ocJpVkhf8iMTCCTf/mqasnXRZHw8gGuX7+Oa9euaRMYklAoFHB1dYWDgwPMzc0hk8mQkJCAd+/e4eXLlykSCSMjoy8mhB9+38HBIUdqjuUFUVFRuHHjRorf0c2bN5GQkAAA8PT0TJFg+vn5oXDhwjmScJBETEzMJxPOj++HhYVBrVZrn29lZYVixYqhaNGiKFasWIr7Tk5O+S5p+u+//7BhwwasX78ed+7cgb29Pdq1a4eOHTuievXqYk5kNomNjcWMGTMwa9YsWFhYYOTIkfj+++9FIvkZN2/exMiRI3Hw4EHUrFkTv//+OypUqKDvsDB79myMGDECFhYWcHNzw4kTJ+Di4pKhY5BE+fLl4e3tje3bt2cqDo1GAxsbG4wbN+6zWzoK+pWvE8rxO29h3flnUH+0CCc5oZQZm4FJ8YBMDpOCpWBXpydMXD8xJCZpEHV1L94dWgQTExNYWVlBLpcjLi4OMTEx2ofJZDK4uLjA09PzkzcHB4d89+avT2q1Gg8epLwQuHr1Kl6/fg0AcHR0RM2aNVG7dm3UqlULpUuXNohkRJIkREREIDg4GA8fPtTOu02+vXz5UvtYa2vrVMlm8ueOjo555u8tJCQEmzZtwvr163H+/HlYWFigVatW6NixI+rXr5+jpVXyG0mSsG7dOowePRpv3rzByJEj8eOPP4pE8jNevXqFcePGYfny5ShSpAhmzpyJli1b6v3/kSR++uknzJgxA9bW1nB0dMSJEycy1bt44cIFBAYGYu/evekqZv4pdevWha2tLbZu3ZrpYwjZK18nlI3nnsDdkNRz3BJe3EX0xW0wK1wBcnMbqMKfIerCNlCVAJfOs2DsUiTN4yWFPkbkxp/g7e39yWTR3d1dvKnlEiEhIbh27RpOnTqF48eP48KFC0hKSoK9vT1q1KiBWrVqoVatWihXrpxB7oYSGxuLx48fp5lsvnr1Svu45Hm6afVu5oaLm+QyP+vXr8fRo0ehUCjQuHFjdOrUCc2aNRNzrnLA2bNnMXToUFy4cAHt2rXDjBkz4O3tre+wDFZsbCx+//13zJw5EyYmJhg/fjz69etnEO8NarUaffv2xfLly+Hg4AArKyscP34800PvvXr1wpEjR/D48eMsnSd//vlnrFixAi9fvjT4c1J+la8TymK/7IVKk76Xr3r3Eq+WDYJJwVIo0D7tPUiVchkeTm4s/tjzqPj4eJw7dw7Hjx/H8ePHcfbsWSQmJsLGxiZFglm+fHmDX0gTGxurTTI/TjZDQkK0j7O1tU0z2SxWrJheazGmVeandu3a6NSpE9q0aSPqROaQ58+fY/To0Vi/fj38/f0xZ84c1KhRQ99hGSyNRoM1a9ZgzJgxCA8Px6BBgzBmzBidz93OrPj4eHTo0AF79+6Fo6MjjIyMcOLEiUxfHERGRsLNzQ0//fQTfvnllyzFtnPnTrRs2RJBQUF6n1cqpM2w3/WykSQx3ckkABjZucGsWCDiHpwBJU2acyrVEkECIp/Mm8zMzFCnTh3UqVMHAJCQkIALFy5oE8xx48YhPj4eVlZWqFatGmrVqoUmTZqgTJkyBneRYWFhgXLlyqFcuXKpvhcTE5NmsnnkyBGEhoZqH2dnZ5ciwSxbtiz8/f3h6emZLa9XrVbj8OHD2LBhA7Zt24bo6GhUqFAB06dPR7t27TI1HCdkTlxcHGbOnImZM2fC2toay5YtQ7du3Qyyp95QHD16FCNGjMC1a9fQrl07TJs2zaB2A3r37h1atGiBy5cvw9XVFRqNBv/++2+WeprXrVuHxMRE9OzZM8vxBQYGAgDOnTsnEkoDJXooM5BUvvt3OaLOb0XBYZsgN0m9zZQcxOE+vgZ1khByTlJSEi5duoTjx4/j2LFjOH36NGJjY+Ht7Y2WLVuiZcuWqFGjhsH3Xn5OdHR0msnmgwcPEBYWBgBwcHCAv78//P39ERAQAH9//0wvbEou87NhwwZs2rRJW+anU6dOosyPHpDE+vXrMXr0aLx+/RrDhw/Hzz//LOZJfsZ///2HIUOGYPfu3ahcuTJmz56NKlVSVwTRp3v37qFFixYIDw+Hvb09YmNjcezYMZQoUSLTxyQJPz8/FClSRGfzHgsXLozWrVvj999/18nxBN3K1wnlp+ZQfkrYtqmIf3wJBUdshkyWelFGUuh/eLViMAoVKoR69eqhfv36qFu3LhwcHHQZtpBLJCYm4tixY9ixYwd27NiBly9fwt7eHk2bNkXLli3RsGFDWFpa6jtMnSCJV69e4cqVK7h8+bL2Y3BwMID38zQ/TjKLFSv2ycVN7969w8qVK7Fw4UI8evQIHh4e6NChAzp16iTK/OjJhQsXMGTIEJw7dw5t27bFzJkzxcXzZ6jVavzxxx8YP348nJycMGvWLHzzzTcG97e7b98+dOjQAa6urlAoFAgLC8OxY8fg6+ubpeOeO3cOVapUwf79+9GwYUOdxNqpUycEBQXh9OnTOjmeoFv5OqEcv/MW1p5/lmqrRU1cJBTmKXcASAr9D69WDYdZ4QA4fz021bEUchm+8XNBVZMXOHToEA4fPox79+5BJpPB398f9erVQ7169VC9enXttoZC/iFJEi5fvqxNLm/dugUTExPUq1cPLVu2RPPmzTNcjiM3CA0NxdWrV7VJ5pUrV/D06VMAgKWlJcqXL58i0YyPj8eiRYuwbt06qNVqfP311+jbty9q1KhhECvr86Po6Gj8/PPPWLBgAcqWLYs5c+agdu3a+g7LoF28eBHfffcdbty4gSFDhmDSpEkGd/FIEr///jtGjRqFBg0aICwsDEFBQfj3339RpkyZLB+/R48eOHbsGB4/fqyz/925c+fixx9/RFRUlEEsYBJSytcJ5a3gSDRLow5lyPqfITcyhom7z/9f5f0cMdf3A3IlXLv8BiPHgmkeb/fA6ijt/n+J6IsXL3D48GHtLTQ0FKampvjqq6/ydBIhfNnjx4+1yeWpU6dAEpUrV9YOjZcsWVLfIWabN2/e4OrVq9pezMuXL2sLzgPvi86XL18e7du3R926deHr65tni/AbuoMHD6JPnz4IDw/H1KlTMXDgQDFP8jNiYmIwduxYzJs3D2XLlsWSJUsMop7kxxISEtCnTx+sXbsWI0aMwOnTp3Hv3j0cPXoU5cuXz/LxIyIi4Obmhl9++QU//5z2NsaZcf78eVSuXBkXLlxAxYoVdXZcQTfydUIJpL1TTtSlnYi9fQzqd68gJcVBYW4DU69ysKneEUZ2qff3lcuACl72ae6Uk4wkbt26hQMHDmDXrl3aJCIwMBAtWrRAy5Yt4ePjY3DDIUL2Cw8Px+7du7Fjxw4cOHAA8fHxKFmyJLp27YouXbrAw8ND3yFmi+DgYCxatAiLFy9GaGiotph8YmIirl27hvv374MkjI2NUbZsWVSoUAHVqlVD9erV4eXlJf5XstHbt28xYsQIrFy5EvXq1cPixYtRqFAhfYdl0Pbs2YMBAwYgLCwMkyZNwtChQw1yvvTLly/RunVr3LhxAwsXLsSKFStw/fp1HD58WGdJ2vz58zFs2DA8f/5cp50miYmJsLa2xm+//YZBgwbp7LiCbuT7hPJpeCwazDmBJE3qva7TgySgUWNQkUiM7Nct3c8LDw/Hnj17sHPnThw4cACxsbEoWrSotoeqatWqoicgH4qLi8Phw4exadMmbN26FQkJCahXrx66d++OVq1awdw89WKw3IQkjh8/jgULFmDbtm0wMzND165dMWDAAJQqVSrFY2NiYnDt2jXtUPn58+dx7949AICHhweqV6+O6tWro0aNGihVqpT4f9GRLVu24Pvvv0dCQgJmz56NHj16iOT9M0JCQjBkyBBs2rQJDRs2xJ9//mmwyfeFCxfQqlUryOVy/P333xg3bhwuXryIgwcP6myhkCRJKFOmDEqWLIktW7bo5JgfqlKlCgoXLox169bp/NhC1uT7hBIAdlwLxtCN15CZHwRJxB9ZiLBL+/Ddd99h3rx5MDExydAxEhIScOTIEezYsQO7du1CSEgIHBwc0KxZM7Rs2RINGjQQxZnzoaioKPzzzz9YuXIlTp06BWtra7Rr1w7du3dH1apVc9WbfHR0NNasWYOFCxfi9u3bKFmyJAYOHIguXbrA2to63ccJDw/HmTNncPLkSZw6dQqXLl2CWq2GjY2NtveyevXqqFixopirnEEhISEYOHAgtmzZgpYtW2LhwoVwc0s9IiO8J0kSli1bhlGjRsHIyAhz5sxBx44dDfb/cu3atejduzfKly+P9evXo2/fvjh9+jT279+v09qhu3fvRvPmzXHy5ElUr15dZ8dNNmzYMOzcuTPFNBnBMIiE8v/bcS0YIzZdg0qtgSwdPR0KuQwKmQwjqjtjynetER8fj6ioKPj5+WHz5s0oWDDteZZfIkkSLl68qJ1fd+fOnXyxeEP4vEePHmH16tVYtWoVnj17hqJFi6J79+7o0qWLQddku3v3LhYuXIhVq1YhNjYWrVq1wvfff486dero5I03Li4OFy5cwKlTp3Dq1CmcOXMG0dHRMDY2RsWKFbU9mFWrVjWY4tGGhiRWr16NYcOGQalUYv78+Qa5GtmQ3Lt3D3379sWJEyfQo0cPzJo1y2CreWg0Gvz000+YNWsWunfvjrlz56Jjx474999/sXfvXp0vsKpZsybUajVOnz6dLX9DGzduRIcOHRAaGgpnZ2edH1/IPJFQfqBqw5YIK9wQKlsvKOSyVKu/AQCUAJkcRawkLPuuLrwdLRAUFIR69eohKioKCoUCarUaGzdu1BbAzopHjx5h586dKRZviHmX+ZckSTh+/DhWrlyJzZs3Iz4+HnXr1kX37t3RunVrg+jJVqvV2LVrF+bPn4+jR4/C2dkZffr0Qd++fTN9oZWRtm/evIlTp07h5MmTOHnypHbnn9KlS6Nu3bqoX78+atWqJWonAggKCkLfvn1x4MABdO7cGX/88QccHR31HZbBSkxMxIwZMzBlyhR4enpi0aJFqFu3rr7D+qTIyEh06tQJ+/fvx2+//YYBAwagXbt22rn89evX12l7Z8+eRdWqVbF9+3a0bNlSp8dOFhQUBG9vb+zcuRPNmzfPljaETKJAkjx9+jQBcNu2bbz5IoLjdtxk47knWHTMHnqN3s2iY/aw8dwT7Lv4MI0LFGGxYsUoSZL2+S9fvmSpUqXo6OjISpUqUS6Xc+bMmdRoNDqLMSwsjKtWrWKbNm1oYWFBACxRogQnT57Mp0+f6qwdIXeIiori8uXLWbNmTQKgpaUle/bsyQsXLuglntDQUE6ZMoUFCxYkAFapUoXr1q1jQkKCXuIhSUmS+PjxY65cuZI9e/akp6cnAVCpVLJ69eqcOHEiT58+TZVKpbcY9UGSJC5YsICWlpb08PDg7t279R2SwTt58iR9fHyoVCo5ZswYxsXF6Tukz7p06RKLFi1KW1tb7t+/nyqVim3btqWxsTH37t2bLW22atWKJUqU0On73sckSaKLiwt//vnnbGtDyByRUP5/zZs3p4+PT5r/CBrN/yWOkiRp3zD379+f4nFhYWH09/enra0tu3TpQgBs3LgxQ0NDdR5vfHw8d+/ezS5dumiTy1q1anHp0qWMiIjQeXuCYXv8+DHHjx9PLy8vAmC1atW4efNmqtXqbG1XkiSeOXOG3377LY2NjWlqaspevXrxypUr2dpuZkmSxAcPHnDhwoVs3bo1bWxsCIDW1tZs0aIF//e///Hu3bspLhbzmvDwcDZv3pwA2LdvX0ZGRuo7JIP27t079u3blwBYuXJl3rx5U98hfZYkSZw9ezaNjIwYEBDAhw8fUq1Ws0OHDlQqldyxY0e2tHv37l3KZDIuXbo0W47/oVatWrFu3brZ3o6QMSKhJHnz5k0C4MqVK9P1+KlTp1Imk7Fy5cqp3ngiIiJYrVo1WlpacsaMGXR2dqaLiwsPHTqUHaGTJKOjo7l69WrWr1+fMpmMpqambN++PXfv3s2kpKRsa1cwPGq1mlu3bmWNGjUIgN7e3pw9e7bOk4a4uDguW7aM/v7+BMDChQvzt99+45s3b3TaTnZTqVQ8d+4cJ0+ezFq1atHIyIgA6OHhwe7du3PdunUMCQnRd5g6c+zYMbq7u9PBwYE7d+7UdzgG78CBA3R1daWVlRUXLFiQ7RdoWfX69Ws2bdqUADhixAgmJiYyMTGR7dq1o0Kh4ObNm7Ot7V69etHV1TVHRiSmT59OS0tLg/995DcioSTZuXNnFixYkImJiel6/IsXLyiTyQiA//77b6rvx8TEsF69ejQ1NeXatWu1id6PP/6Y7QneixcvOHPmTJYuXZoA6OzszCFDhvDSpUt5utdFSO3SpUvs3LkzlUolraysOGTIED5+/DhLx3z06BFHjBhBOzs7ymQyNmnShHv27MnWIa6cFBMTw71793L48OEsU6YMARAAy5Yty+HDh/Pw4cPpPk8YErVazfHjx1Mul7NmzZp8/vy5vkMyaAkJCRw2bBgBsEGDBrni53X06FG6urrS0dGRe/bsIfm+s6FBgwY0Njbm1q1bs63t4OBgGhsbc8aMGdnWxoeOHTtGALxx40aOtCekT75PKJ88eUKFQsG5c+dm6Hn169enhYUFv/rqqzS/Hx8fzxYtWtDIyIibNm3izJkzqVQqWalSpSy/qaeHJEm8evUqhw8fzgIFChAAfX19OW3aND579izb2xcMR3BwMH/++Wfa29tTLpezdevWPHHiRIYuMM6fP8/mzZtTJpPRzs6OI0eO5KNHj7IxasPw6tUrrl27lt27d6ebm5t2eLxdu3Zcs2YNw8PD9R3iFz1//pw1a9akXC7nxIkTRa/OF9y+fZvlypWjsbExZ8+ebfAXSyqVimPHjqVMJmPdunUZHBxM8v3UhsDAQFpZWfHo0aPZGsOoUaNobW2dY9OtYmJiKJfLuXjx4hxpT0iffJ9Qfv/993RwcGBMTEyGnrdu3Tpt78WZM2fSfExSUhI7dOhAuVzOVatW8fz58yxcuDCtrKy4fv16XYSfLiqVivv27WPHjh1pZmamPfGsWLGCUVFRORaHoF+xsbFctGgRfXx8CIABAQFcs2bNZ3vcTp8+zYYNGxIAS5YsyaVLlzI2NjYHozYckiTxypUrnDhxIitUqEAAlMvlrFGjBmfMmME7d+4Y3CjAjh07aG9vTw8PDx4/flzf4Rg0SZK4cOFCmpqa0sfHh1evXtV3SF8UFBTE6tWrU6FQcPLkydqLhRcvXtDX15eOjo68dOlStsYQERFBa2trjho1Klvb+Zifnx979uyZo20Kn5evE8rQ0FCamppy4sSJGX5ubGwsLS0t6ejoyKZNm37ycWq1mr169SIALliwgJGRkezUqRMBsEePHoyOjs7KS8iwyMhILl++nHXq1CEAmpmZsVOnTty3b1++W+maX2k0Gu7bt48NGjQgALq5uXHKlCkp5j8eP36cX331FQGwVKlS/Pvvv0XP1keCg4O5ePFiNm/enGZmZgTAIkWKcOjQoTxy5Ihe5y8nJCRw8ODBBMAWLVrkip5UfQoLC2OLFi0IgP37988VF03btm2jnZ0dPT09eerUKe3X79+/Ty8vL3p6evLevXvZHsfMmTNpbGys7RnNKf369aOvr2+Otil8Xr5OKH/55RdaWFhkeiFBr1696OjoSAC8fPnyJx+n0Wi0J/cZM2ZQkiSuXLmSFhYWLF68uN5WxAYFBXHq1KnaHitPT09Onz5dvPnkI7du3WKfPn1oampKKysrdu7cmdWqVdPOG9y8ebPBD/kZgri4OO7atYvfffeddmjcxsaG7du359q1a3N0sdL9+/dZvnx5Ghsbc968eQbXa2poDh06RFdXVzo4OGTbCmhdio+P58CBAwmArVu35tu3b7Xfu3z5Mp2cnOjj45MjU5sSEhLo6urKXr16ZXtbH1u5ciUB8N27dznetpC2fJtQJiUlsUCBAhwwYECmj3HixAkCYMGCBdmkSZPPPlaSJI4ZM4YA+Msvv1CSpBQn/jlz5ujtxC9JEi9cuMAePXrQxMSEpqam7NmzZ64Y8hGyTpIkbty4ke7u7gRAhULBjh07ihN1JkmSxMuXL3PChAkMCAjQ/kxr1qzJuXPn8sWLF9nW9qpVq7QXquL/9/MSEhI4YsQIAmC9evVyvIctM65fv85y5crRxMSECxcuTPGe8e+//9LKyoqVKlXKsU6BZcuWEQDv3r2bI+196N69ewTAgwcP5njbQtrybUK5ZcsWAuC1a9cyfQyNRkNnZ2ftUEl65ihNnz6dANinTx+qVKoUqwmbNm3K169fZzoeXQgLC+PUqVPp4eFBAKxevTo3btwoyg/lQZIkcc+ePQwMDCQAVqpUiWvWrOGQIUNoYmJCe3t7Tp06NcenZeQ1L1684KJFi9ikSRNtWaLq1avrNLlMSEjQTq3p1q2b+J19wd27d1m+fHkaGRnxt99+M/he+ISEBI4dO5ZKpZKlSpXi9evXU3x/27ZtNDExYf369XPsd6/RaFiiRAm2bNkyR9pLq31bW1tOmjRJL+0LqeXbhLJhw4YMDAzM8nF69uzJEiVKMCAggFWrVk1XL+Py5cupUCjYpEkT7T//nj176OTkRFdXV4PYtUKlUnHz5s2sVasWAdDd3Z2TJ0/OliLtQs6SJIk7duzQ9p5VrVqV+/fvT/G3++LFC37//fc0MjKio6MjZ82alSvmlRm6d+/eceXKlWzatCmNjIwok8m0yWVme8hCQkJYrVo1Ghsbc8WKFboNOI+RJIl//fUXzczMWLJkSYMtwP+hs2fP0tfXl0ZGRhw/fnyqRXTLly+nXC7nN998k6O7Um3fvv2zi1JzQsOGDT+7hkHIWfkyoXzy5AllMhmXLVuW5WMl/1OtWLGCANJdLHj//v20tLRkQECAtnDyy5cv2ahRIwLgt99+azBzGa9fv87evXvTzMyMxsbG7Nq1Ky9evKjvsIQM0mg03LJlC8uVK0cArFmzJo8cOfLZi6CgoCB+9913VCqVLFCgAP/44w+D33Iut/hUcjlv3rx0J5eXL19mwYIF6eLiwrNnz2ZzxLlbeHg4W7Vqpd0hyNAvkGJiYjhs2DDKZDJWrFgxzZqLs2bN0r6enF40V7VqVVavXj1H2/zY+PHj6eDgIOYJG4h8mVD+8ssvtLKyynCpoLTExMTQ1NSUv/32G+vWrctSpUql+x/7ypUrdHFxYaFChbSr8ZIX7NjZ2dHZ2ZmbNm0ymH+WN2/ecObMmdrt/apUqcL169fnykLP+YlarebGjRu1xe7r1q3LY8eOZegY//33H3v06EGFQkE3NzfOnz9fr3t05zWZSS43btxIMzMzBgQE5IrC2/p0+PBhurm50d7entu2bdN3OF905MgRFi5cWPve8nEFDkmS+OOPPxIAx4wZk+PvESdPnsxQB0p22bdvHwHw4cOHeo1DeC/fJZQqlYqurq7s37+/zo7ZrFkz1qpVi+fPnycArlq1Kt3Pffr0KX18fGhvb8/Tp09rv/7q1Su2adNGu5Lv5cuXOos3q9RqNbdv364tK+Pi4sIJEybw1atX+g5N+IBarea6deu0q/gbNGjAkydPZumYDx48YOfOnSmXy1mwYEEuWrRIXFDoWFrJZY0aNfi///2Pr1+/pkaj4S+//EIA7NSpk+gx/oykpCSOGjVKW3s3OxdE6cK7d+/Yu3dvAmCtWrXSTJRUKpV2vuzs2bP1ECXZvHlz+vr66n3u6Zs3bwiAa9as0Wscwnv5LqFMHqLW5QrIRYsWUaFQMDw8nG3atKGXl1eGem/evn3LmjVr0tTUlFu2bEnxvX/++YfOzs60tbXlihUrDKa3MtmtW7fYr18/mpub08jIiJ06dcr2QrrC56lUKq5atYrFixcnADZp0kTnw6F37txhhw4dKJPJWKxYMe7evdvg/jbzgrdv33LlypXaBT1KpZIuLi4EwMmTJ4uf+We8fv2atWrVolKp5MyZM/We/HzJjh076ObmRisrK/71119pxhsfH882bdpQoVBkqONCl27duqWd5mUISpQowe+//17fYQjMhwllkyZNWLFiRZ0eMzg4WHuVdOfOHcrl8gxv5RgfH8927dpRJpOlem54eDg7d+5MAGzYsCGDgoJ0Gb5OvHv3jrNnz2bhwoW1Scy5c+f0HVa+kpSUxGXLlml/By1atMj2ua7Xr1/X9lQ3atRIL+VD8ouLFy/SxcWFcrmcAGhra8t+/frxzJkzIrH8yOXLl+np6UlnZ2eeOHFC3+F81uvXr9mhQwftefNT9SOjoqJYt25dmpqa6nWouUOHDvTw8DCYkYlu3boxICBA32EIzGcJZVBQEGUyGZcsWaLzY1esWJHt2rUj+X7lt5OTU4a3NdRoNNq6aCNGjEh1hbpnzx56eHjQ0tKSCxYsMMgrbrVazfXr12uHWRs2bJhiKF/QvYSEBC5atEg7t7VNmzY5unpVkiRu27aNhQoVolKp5LBhw0QNSx07evQo7e3tWaRIEd6+fZt3797lzz//zIIFCxIAixYtykmTJvG///7Td6h6t379eu3c0pwo7p1ZKpWKf/75Jx0cHOjg4MC1a9d+8sLg9evXrFChAq2trfW6hebVq1cJwKD20P7zzz+pVCoNfpFVfpCvEsrx48fT0tIyW+p0/frrr7SysmJiYiKDgoJoYmKSqS0dSXLu3LmUyWRs164d4+PjU3wvMjKSffv21a7SffDggS7C17mPF4J89dVXBt9TkNvEx8dzwYIFLFiwoPbvJa2VoDkZz9SpU2lhYUEnJycuXrxYbNeYRZIkccGCBVQoFPzqq69S7bij0Wh45MgRduvWjRYWFtrzwtKlSxkREaGnqPVDrVbzhx9+IAB26dLFoOeWHj58WHtu7Nat22fLsQUFBbFEiRJ0dnbWe7H6Jk2asFixYga1TW9ykpvV+eFC1uWbhFKlUtHDw4N9+/bNluNfu3YtRdX+4cOH08rKKtOFyrds2UJTU1PWqFEjzW3bjh49ql0FOHPmTIP6B/+QRqPh5s2bWbZsWQJg7dq1efToUTFElwVqtZpLliyhm5sb5XI5O3XqxNu3b+s7LK0XL16wS5cuBMDy5cuLC4lMUqvVHDBgAAFw8ODBX/wfj4mJ4Zo1a1i/fn3KZDKampqyY8eO3Ldvn8GeH3TlzZs3bNCgAeVyOWfPnm2w55cHDx5oN8KoVq3aF6ek3Llzhx4eHvT29tb7SubkneE2btyo1zg+plKpaG5uzlmzZuk7lHwv3ySUu3bt+uKe21khSRI9PT05aNAgku93nLGysuKwYcMyfczTp0/T3t6ePj4+fPr0aarvf1inzN/f36CHljUaDbdv387y5csTAGvUqMFDhw4Z7InfUB09elRbR7Jjx47aclOG6MyZM6xYsSIBsEOHDgY599dQxcfHs23btpTL5Vy0aFGGn//8+XPOmDGDvr6+2koMP/zwg8GOaGTFzZs3WaRIEdrb2/PQoUP6DidNERERHDlyJI2MjOjp6cm///77i+e+8+fP08HBgaVLl9b7tpCSJLFatWosX768QU61qlWrFtu2bavvMPK9fJNQNm/enP7+/tnaxsCBA+nl5aU9UUyaNInGxsZZeiO9d+8eCxUqRBcXl0/Oizt79iz9/f21ZUQMuSadJEnctWsXK1So8MldWoTUHj58qC3KHBgYmGuKWGs0Gq5cuZIuLi40MzPjhAkTxFynL4iMjGTt2rVpamrK7du3Z+lYkiTx0qVLHDRoEO3s7LR1SDdu3GgwiyqyYuvWrbSwsGCZMmX4+PFjfYeTilqt5qJFi+jk5ERzc3P++uuv6RqKP3ToEC0sLFilShW+ffs2ByL9vOQOmf379+s7lDT9+OOPdHd313cY+V6+SChfvHhBuVzOv/76K1vbOXDgAAFo91mNjo6mk5MTe/TokaXjhoSEsEKFCrS0tPzkP7RarebSpUvp7OycoROXvkiSxL1796bYR1qUnknt3bt3HDFiBI2MjFiwYEGuW7cuV/6MoqKi+OOPP9LY2Jienp7cuHFjrnwd2S0kJITly5enjY2NzqcKxMXFcfXq1axWrRoB0NnZmT/++KNBJmJfotFoOG7cOALgN998o5NNKnTt33//1Y4mdO3aNd01MP/55x8aGxuzUaNGBvG6NBoNy5Qpw1q1ahns/+y2bdsIwKA7U/KDfJFQ/vHHHzQ2Ns72SeoJCQm0srLir7/+qv3avHnzKJfLszzHLSYmhk2bNqVCoeDy5cs/+biIiAiOGDGCSqWS3t7e3Lx5s8GeBMj3ieXBgwe1b3IBAQHcsWOHQcecE1QqFRcuXEhHR0eam5tz0qRJeaJn7+HDh9o5ZA0aNEhzKkd+9fjxYxYpUoSurq7ai9LscvPmTQ4aNIg2NjYEwPr163Pz5s1MSkrK1nZ1ITIyki1atKBMJuPUqVMN7lzx+PFjtm7dWrub2Pnz59P93EWLFlEmk7Fjx44G04O8du1ave/Z/SUvX74kAP7zzz/6DiVfyxcJZfXq1dmsWbMcaatVq1asXbu29vOEhAR6e3uzdevWWT62SqVinz59CIATJkz47In03r17bNq0KQGwTp062f4GlVWSJPHIkSOsWbOmdjHH4cOH9R2WXhw4cIClSpXSrgA19N09MmPPnj0sWLAgLSwsOH/+fIOcl5WTrl27RhcXFxYrVixHS//ExsZyxYoVrFy5snau5c8//8wnT57kWAwZcf/+fZYsWZLW1tbcvXu3vsNJ4cWLFxw4cCCNjY1ZsGBBrl+/Pt3JriRJnDp1KgFw4MCBBvP/kJiYyMKFC7NFixb6DuWLvLy8OGLECH2Hka/l+YTy5cuXlMlkOVbV//fff6eZmVmKq8vVq1cTgE4KfUuSxMmTJxMAe/bs+cUehb1797J48eKUy+UcMGAAw8PDsxxDdjt27BirVKlCAGzevDnv37+v75ByxN27d7UXAdWrV8/2ouT6FhkZyX79+mkXaeWX3/PHjh07Rmtra/r7+3+2fEx2u379OgcMGEBra2vKZDI2atSI27ZtM5gV4nv27KGNjQ1LlixpUIvRnj9/zu+//57Gxsa0t7fnlClTMjSaoNFoOHz48HR1FOS0BQsWUCaT8ebNm/oO5Yvat2/PatWq6TuMfC3PJ5QLFiygUqlMs/ROdkjez/vD5FGtVrN06dKsXbu2zk4Wq1atolKpZKNGjb5YVzMxMZG///47ra2taWdnx3nz5hn80JYkSfz777/p5eVFpVLJIUOG5NjvMKe9efOGgwcP1k5T2LRpk0G9qWS3f//9l0WKFKGpqSlnzJhhMAlMTti2bRtNTExYt25dRkZG6jscku+n1yxbtoyVKlUiALq5uXHs2LF6W2ksSRKnTZtGmUzG5s2bG0x9zWfPnnHAgAHaRHLq1KkZ3swiJiaGbdu2pUwm4//+979sijRzYmJi6OLiwi5duug7lHSZPXs2TU1NDWaqQH6U5xPKunXrskGDBjnWXlJSEs3MzPjbb7+l+PqePXt0Psfj4MGDtLKyor+/P1+9evXFx4eGhrJ3796UyWT09fU12BIbH0oulm1paUk7OzvOnTvX4JPh9EpKSuK8efNoZ2dHS0tLTps2LVUh+/wiNjaWI0aMoFwuZ4UKFfRaoD2nLFmyhHK5nN988w0TEhL0HU6arl69yn79+tHS0pJKpZLffvstL126lGPtJyUlsVu3bgTAsWPHGsRQ8LNnz9i/f/8sJZLk+4Llfn5+tLCwyPJq/uwwbdo0GhkZ5Zrdl86cOUMAeX5kx5Dl6YTy9evXma7jlhV16tRhq1atUn29efPm9PDw0OlOPdeuXaObmxu9vLx4586ddD3n8uXLrF69unbv2Jx8g8iskJAQ9unTh3K5nMWLF+fOnTtzbS+eJEncs2cPS5YsSZlMxt69e6frgiA/OH/+PEuVKkUjIyOOHz8+T/Y2SJLEKVOmEAAHDBiQK3YTioiI4OzZs+nt7a2dkrF58+Zs7U2OiYlhkyZNaGRkxPXr12dbO+mVnEgaGRnRwcGB06ZNy1QiSb6vMezs7Exvb2+DvHh6+/YtbW1tOXDgQH2Hkm7x8fE0MjIyuJ7e/CRPJ5TJPQA5PS9p7NixdHR0TJXw/PfffzQ1NeXo0aN12t6zZ89YqlQp2tnZpXv7qeQh5eLFixMAW7VqZfALd8j387y++uor7XaOuSHmD926dYsNGzbU7hqk763UDFFCQgLHjRtHpVLJ0qVL88KFC/oOSWckSeKIESMMcr5ceqjVam7dulW7eM7Ly4u//fabzvduDw8PZ+XKlWlhYaHdfUxfgoKC2K9fP20iOX369EwnkiS5YsUKGhsbs0aNGpneSS27jR49mubm5gwJCdF3KBlSqVIldu7cWd9h5Ft5OqFs2LBhihXXOeXgwYMEwLt376b63oQJE2hkZKTzSeXv3r1j7dq1aWJiwk2bNqX7eSqViqtWrWLhwoW1Nd1u3bql09h0TZIk7ty5U7vYqE+fPgZ/4gsLC+OAAQOoUChYpEgRbtu2LdclEznt2rVrDAgIoFwu58iRIw26rmp6SJKkXXyRF3pRLl++zK5du9LIyIgWFhYcOHCgTnbiefbsGX18fOjo6KjXi4mbN2+yd+/eKRLJrIwuqdVq7cVE7969Dbb3/eXLlzQzM+OYMWP0HUqGDR48mEWLFtV3GPlWnk0o3759S6VSqZcTd1RUFOVyOZcsWZLqe3FxcSxcuDDr16+v84QiISGBHTt2pEwm4+zZszP03KSkJC5dupReXl6UyWTs1KmTQa2kTEtiYiLnzJlDOzs7WllZGeQcxOQFUTY2NrS2tuZvv/1msPPlDJFKpeL06dNpYmLCokWL8vjx4/oOKVMkSeIPP/yQZ5LJD7169Yrjxo2jk5MTZTIZmzVrxsOHD2fq/Hb79m16eHjQy8tLL6v+1Wo1t23bxjp16hAAXV1ds5xIku+nDDRu3JhyuZxz58416IvJ/v37087OTue9zjlh/fr1BMCwsDB9h5Iv5dmEct26dQSgtxp+/v7+7NatW5rfS97GKjuKsGo0Gv74448EwKFDh2Z4EntiYiL//PNPuru7Uy6Xs1u3bnz06JHO49SlN2/ecMiQIVQqlfTy8krXPrnZTZIkbt++nUWLFqVcLmf//v0NdngrN7h37x6rVatGmUzGn376KVctzJIkSfs/OXfuXH2Hk23i4+O5fPlyli1blgBYunRpLl26NN09y2fOnKG9vT3LlCmT4yvK37x5w5kzZ9LLy0tbkHzDhg066UV88OABS5YsSVtbW70P33/Jo0ePqFQqOXPmTH2Hkin//fcfARhcjdL8Is8mlN27d2eZMmX01v7gwYNZpEiRT34/eYFOdm2tNX/+fMrlcn799deZ2mElPj6e8+bNo4uLCxUKBXv16mWwxY6T3bt3j82bN9fuEa6v1X7Xr19n3bp1tTuQ5IYabrmBRqPh9OnTqVQqWbly5Vyx+lSSJP70008EkOFRg9xKkiQePXpUu5uNo6MjJ02a9Nker927d9PMzIw1atTI0Z6xGzdusE+fPjQzM6OxsTG7du2q0/PG4cOHaWdnx+LFi+eKOqudOnWim5tbrp1eIkkSnZ2d+csvv+g7lHwpTyaUkiTR3d2dw4cP11sMycXMP1Vb7vHjxzQxMdH5Ap0Pbd++nebm5vTz88v0m29cXBxnz55NZ2dnGhkZsV+/fnz27JmOI9WtQ4cOsUyZMpTL5Rw2bFiO7Yf78Up0sTd59jh79iy9vb1pbW3Nv//+W9/hfJIkSfzll18IgL///ru+w9GLhw8fcuDAgTQ1NaWVlRV/+umnVD31q1atokKhYMuWLXMkkUlrWPvXX3/V6eJNSZI4f/58KhQKNmjQIFcMH589e5YAcrwqiq61aNGC9erV03cY+VKeTCjv3LlDANy3b5/eYrh27RoB8PTp0598THYt0PnQ9evXWbhwYdrZ2fHAgQOZPk5MTAxnzJhBBwcHGhsbs2/fvmkuOjIUKpWKM2fOpKmpKQsVKpStNTc1Gg0XLVqkLRw/Z86cXDUkmxtFRESwffv2BMBevXrl2EVDRowfP54Acu3woS6FhIRw1KhRtLS0pJmZGYcOHcoXL15w5syZ2kUq2V3QPjw8PMWwdtWqVblhwwad/68mJSWxb9++2mlHuaFQv1qtZvny5env758rylh9ztSpU2llZZXrX0dulCcTynnz5tHY2FivbzIJCQlUKpX8888/P/mYuLg4FipUiA0aNMjWnqw3b96wUaNGlMlknDp1apbaioqK4pQpU1igQAECYOPGjXnw4EGD7Yl78OABa9euTQDs0aMH3759q9PjP3z4UHv8nj175oqtLfMKSZK4bNkympubs2TJkrx27Zq+Q9KaMGECAXD69On6DsWgvHnzhuPHj6etrS0VCgUB8Pvvv8+280dCQgI3b97Mli1b0sjIKFuGtT8UFhbGWrVq0cjIiEuXLs2WNrLDggULdLY9sL4dPXqUAAy+WklelCcTyubNm+ulXNDHSpcuzf79+3/2MckLdDZv3pytsajVao4ZM4YA2KZNmyzVUSPfn6hXrlzJcuXKEQBLlSrFJUuWGOTcG41Gw8WLF9Pa2poFChTgP//8k+U3MLVazd9++41mZmYsVKgQDx8+rKNohYy6e/cuy5UrR2NjY86bN0/vFze//vorAXDq1Kl6jcNQJSUlaXuXLS0tqVAo2LVrV52NeGg0Gp44cYJ9+vShra0tAbBChQqcM2dOttYkvnXrFgsVKkRHR0eeOHEi29rRtdDQUNra2rJ37976DkUnkqus5KaEPq/IcwllUlISraysOGXKFH2Hwk6dOqVrs/pmzZqxYMGCOdKjum3bNlpZWdHHx0cnQ+2SJPHff/9NMQF/7NixBrnzS3BwMFu1aqUt5J7ZlaQ3b95kxYoVKZPJOHToUIMcbs1v4uPjOXjwYAJg8+bN9VY2JHkHnF9//VUv7Ru6mJgYNm7cmEZGRtywYQNjY2M5Z84curu7UyaTsV27dpnuab537x7HjBmj3c3Hy8uLY8aMSfcOYlmxc+dOWlpaskyZMga/ePFjPXr0oJ2dXZ4qtVO2bNk8kyDnJnkuoTx58iQBGMTuGjNmzKCVldUXS/ckL9D56aefciSuu3fvsmTJkrS2tuaOHTt0dtwHDx5w4MCBtLCwoLGxMbt162ZwO8FIksR//vmHzs7OtLGx4ZIlS9Ldo5WYmMjx48fTyMiIPj4+PHPmTDZHK2TUzp076eDgQDc3Nx49ejRH2542bRoBcOLEiTnabm4RHh7OwMBAWlpapprTnJCQwEWLFrFQoULai4L0DL+GhoZy7ty5rFChAgHQxsaGffr04YkTJ3Jk329Jkjh9+nTKZDK2bNlSp9vq5oTTp08TAP/66y99h6JT3333HUuXLq3vMPKdPJdQjh8/nnZ2dgYxIXffvn0EkK4V1smJSk6VloiMjGTr1q0JgGPHjtXpyfft27ecOXMmCxYsqN1icMeOHTlygk+vN2/esHv37gTAOnXq8OHDh599fPIe00qlkmPHjhXFyQ3YixcvWKdOHcpkMv7yyy85sihi7ty5BMDx48dne1u5UWhoKH19fenk5PTZ+YsqlYqrV69myZIlCYD16tXjsWPHUlz0vXnzhmvXrmWTJk2oUChoZGTEli1bcvPmzTm6sUF8fDw7d+5MABwzZoxBnd/SQ6VSsVy5cqxQoYJBvF/q0vLlyymTyT5ZZUXIHnkuoaxWrRrbtm2r7zBIvh9iBcDt27d/8bE5tUDnQxqNhlOmTKFMJmOTJk10vmBFpVJx48aNrFy5MgGwaNGi/OOPPwxqaOXgwYP09vamqakpZ86cmSr5iI2N5fDhwymXyxkQEGBQCz+ET1Or1ZwyZQoVCgWrVq2araWu1q5dSwAcOXKk3udvGqKwsDCWKVOGLi4u6Z5mo1aruWnTJu0cbX9/f/bs2ZPVq1enXC7XFh9fuHChXhbCvXz5koGBgTQ1NeX69etzvH1dmDdvHmUymUGM5ulacqUXMbc9Z+WphDIxMZHGxsYGsxuFJEl0cHDgpEmT0vX4nTt3EgC3bNmSzZGltG/fPtrZ2bFIkSK8ceNGtrRx9uxZtmvXjkqlkkZGRmzdujW3b99uEOV1YmJiOGzYMG3SmDxMf/ToURYuXPiTyaZg+E6fPk1PT086OjryyJEjOj/+7t27qVQq2aNHD5FMpuHNmzf08/Ojk5NThuYyJiQk8ODBgxw0aJC2okTykPaAAQP4/PnzbIz68y5dukR3d3e6ubnl2mQsJCSE1tbW/O677/QdSrbQaDS0sbHh5MmT9R1KvpKnEsqLFy8aXOmD2rVrs3379ul+fNOmTenp6ZnjCz0eP37MsmXL0tzcPFuLRb9+/Zpz585l+fLlCYBOTk4cOnSoQfT8nT9/nqVLl6ZCoaCfnx8BsGbNmrlihwvh08LCwlivXj3K5XLOnDkzQ4mfRvPpx548eZKmpqZs2bKluNhIw7t37xgQEEAHB4d07RYVGhrKFStWsE2bNrS0tCQAFixYkP379+eePXu4Z88ebYmuUqVKcf369Tk+VPv333/TzMyMFStWzPHtIXWpa9eutLe3z9NlzurXr89mzZrpO4x8JU8llAsWLKCRkVGOzqP5kp49ezIwMDDdj3/06BHNzMw4aNCgbIwqbTExMezYsSMBcMSIEdn+Jnn9+nUOHz6czs7OBMBy5crxjz/+yNbSHl+yZcsWWllZaXfQuH79ut5iEXRHrVZrt0D8+uuvP1k26+aLCI7bcZON5hxn0TF76DV6N4uO2cNGc45z3I6bvPkiguT7v10bGxvWqlXLoM43hiIyMpKVK1emnZ3dJxfmJSQk8NSpU/z1118ZGBhImUxGmUzGypUrc8qUKbx+/Xqayf+pU6fYuHFjAmCxYsW4adOmbO8d1mg0HDt2LAGwU6dOBlkeLb1OnDhBAFy8eLG+Q8lWY8eOpaOjoxg5yEF5KqHs2rUrK1SooO8wUpg0aRILFCiQoefMmTOHAHJ8lSr5fpj+jz/+oEKhYN26dVNtk5YdkpKSuHPnTrZp04ZGRkZUKpVs2bIlt27dysTExGxvn3zfc9qhQwdtsfZDhw6xdOnSNDU15V9//SVOSnnE1q1btWWzPqx7+CQsht/8dYZeo3ez8M/vE8mPb8lfbzH3X7oUK0t/f38x6T8N0dHRrF69Om1sbHjp0iXt1yMjI7lv3z7+/PPPrFGjBk1MTAiAVlZWbNu2LVeuXJmhi8lLly6xSZMmBMDAwECePHkyO14Oo6Oj2bp1a8pkMk6bNi1XnwtUKhXLlCnDSpUq5bpFRBm1Z88eAuCjR4/0HUq+ISNJ5BE+Pj6oW7cuFixYoO9QtNasWYOuXbsiNjYW5ubm6XqOJEmoU6cOnj17hhs3bsDKyiqbo0zt2LFjaNeuHczMzLB161YEBATkSLtv3rzBhg0bsHLlSly+fBkODg749ttv0a1bN5QvXx4ymUyn7ZHEhg0bMHjwYADA3Llz0alTJ8hkMsTHx2P48OH466+/0LZtWyxZsgR2dnY6bV/Ieffv30ebNm3w/PlzrF27FvQMwA+bb0BDQiN9+XQolwGSRo1fmxZHl5o+ORBx7hEXF4emTZvi8uXL2LBhA+Li4nDy5EmcPHkSN27cgCRJcHJyQo0aNVCjRg1Ur14dfn5+UCqVmW7z6NGj+OGHH3DlyhW0bNkS06dPR8mSJXXyeoKCgtCiRQv8999/WLduHVq0aKGT4+rL3LlzMWzYMFy8eDHHzun68ubNGzg6OmLt2rX49ttv9R1O/qDnhFZnIiIiCIArV67UdygpJA8vZLS47uPHj2lhYcG+fftmU2Rf9uzZM1asWJEmJiZcsWJFjrd/8+ZNjhw5Ujspv1ChQhw6dCiPHTumk+H4Z8+esWnTpgTA9u3bf7J3ZPPmzbS1taWnp+dn92YXco/o6Gh26NCBFdr2o/cHvZAFh/9Dm2odaVrIn3LT9/P4HJoMTbPH0nv0bm6/+kLfL8UgqNVqXrt2jb6+vlQqlXRzc9MupClSpAi7devGpUuX8v79+9nSw6fRaLhu3Tp6eXlRoVCwX79+Wd5cYefOnbS3t6e3t3e2LVbMSS9fvqS1tfUXd2/LS4oVK6aX6WP5VZ5JKA8fPpypxC27PXv2jAC4d+/eDD934cKFBMADBw5kQ2TpEx8fz169ehEABwwYkGND0B9SqVTcv38/+/Xrp32jsre3Z9euXblly5YML2DSaDT866+/aGVlRVdX13SVdXr69CmrVq1KhULBKVOm5Lm6bfnRf2ExLDZmb4ok0b3fMgKgwtqJJp5lPptQeo3ezWJj9vJJWP7aKSk8PJz//vsv586dy969e7NSpUo0MzPTJpBFixbloEGDuHHjxhxfuJKQkMDffvuNtra2tLCw4MSJEzNcbDwhIYFDhgzRFljPKwtXOnfuTEdHR75580bfoeSYLl26sGLFivoOI9/IM0Pe06ZNw/Tp0/Hu3TvI5XJ9h6Ol0WhgamqKefPmoX///hl6Lkk0aNAA9+7dw82bN2Fra5s9QaYjjsWLF2PQoEGoVKkS/vnnH7i6uuolFkmScPnyZWzfvh07duzA7du3YWpqinr16qFly5Zo3rw5ChQo8MnnP3z4EH369MHx48fRu3dvzJo1K90/V7VajQkTJmDq1KmoU6cO1qxZAzc3Nx29MiGntVt0FpefvUsxzE21ClJCDBSWdkh89RAhq4bBoclQWJatl+YxFHIZAjztsKlvlZwKO8ckJSXh/v37uHHjBm7evIkbN27gxo0bCA4OBgAYGxujVKlSKFWqFK5cuYJHjx7hn3/+MYhh4bdv32LatGmYN28e7O3tMXHiRPTs2fOLQ+sPHz5E+/btcfv2bcyaNQuDBg3S+TQbfThx4gRq1aqFZcuWoWfPnvoOJ8csXLgQQ4YMQVRUFMzMzPQdTp6XZxLKVq1aISYmBocPH9Z3KKkUKVIEX3/9NWbMmJHh5z579gylS5fG119/jeXLl2dDdOl39uxZfP311yCJzZs3o2rVqnqNBwAeP36MHTt2YPv27Th9+jRIokqVKmjZsiVatmyJEiVKAHifDM6ZMwdjx46Fq6srlixZgq+++ipTbR49ehSdO3eGSqXCypUr0bRpU12+JCEH3AyORPP5pz77mPQklMl2D6yO0u42ugwxRyQmJuL58+cICgrC06dPERQUhMePH+PWrVu4e/cuVCoVAMDT0xNly5ZNcStWrBhIon379tizZw927tyJhg0b6vkVpfT06VP88ssvWLduHXx8fDBjxgw0a9YszSRx7dq16N+/P1xdXfH333/D399fDxHrnkqlgr+/PywtLXH69GmD6nDJbleuXEFAQABOnz5tEO9XeV2eSSg9PT3RqVMnTJ8+Xd+hpPLVV1/B0dERGzduzNTzly1bht69e2PXrl1o1qyZjqPLmJCQEHzzzTc4f/485s6di379+hnMFXxYWBj27NmDHTt24MCBA4iPj0eJEiVQuXJlnD59Go8ePcKwYcPw66+/wsLCIsttde/eHXv37sWwYcMwbdo0mJiY6OiVCNlt/M5bWHf+GdSfWYST3oRSIZehc6AnJrYonR2hZkl8fDyePXuWImF8+vSp9v7Lly+R/BYgk8ng6uoKb29vlClTBmXKlEHZsmVRpkyZNHvx1Wo1vv32W2zbtg1bt27V+7npcy5fvoxRo0bh6NGjqFWrFmbNmoWKFSsCAGJiYjBw4ECsWrUKnTt3xsKFC/WyEDK7TJ06FWPHjsXFixfzTJKcXiqVCjY2Npg8eTKGDx+u73DyvDyRUMbGxsLS0hIrV65Et27d9B1OKr169cLNmzdx4cKFTD2fJJo1a4YrV67g9u3bsLe313GEGZOUlIQRI0Zg/vz56NGjBxYuXAhTU1O9xvSxuLg47N+/H7/++iuuXbsGAJDL5fDz80OtWrVQq1Yt1KhRI0s/S5KYM2cOfvzxR5QpUwZ///03ihUrpqNXkDuQRGRkJKKiohAVFYXo6Gjtx4zeT0pKSnHcj9tJz/cAwMjICGZmZilu5ubmKT4Pq9AHwbGff20Z6aE0T3yL4v9tQUJCAuLj45GQkJDqptFoIJPJIJPJIJfLtffT+zlJSJIEuVwOExMTGBsbQ6lUpvieSqVCUlISEhISEBERgbdv32pjlMvl8PDwgJeXF7y9vbUfk+8XLFgw3RdFGo0G3bp1w8aNG/HPP/+gVatW6XqePpHE/v37MWrUKNy6dQvt27dHly5dMGLECDx//hwLFy40yPePrLh+/ToqVqyIESNGYNq0afoORy9q1qwJFxcXbNq0Sd+h5Hl5IqG8evUq/P39ce7cOQQGBuo7nFTGjh2L1atXIygoKNPHCA4ORunSpdGkSROsW7dOh9Fl3urVq9G3b1+ULl0aW7Zsgaenp75D0goKCkLHjh1x4cIF/PTTT+jYsSPOnTuHY8eO4fjx43j27BlkMhnKli2rTTBr1qwJR0fHDLd1+fJldOjQASEhIfjzzz/RuXPnbHhFOS8pKQkvX75EcHDwZ2+JiYmfPIaZmRmsrKxgbW0NKyurFPc//vhxMvNxz/eHn3/qeyShUqkQHx+vvcXFxaX6/F65/tDw8z3rGUkoqVGDfw+CUqmEsbExjIyMYGRkpL0vk8kgSRI0Gs0nbx9/X5IkqNVqbZKqVqvTbFuhUGhfv0ajSZVgf8jY2BjW1tawsbGBvb09PDw8Ut0KFiwINze3TyaXkiShV69eWLNmDf7++298/fXXn/3ZGBqNRoOVK1dixIgRiIyMhKOjI3bt2oXKlSvrOzSdSkxMRKVKlUASFy9ezLcjKKNGjcLff/+NZ8+e6TuUPC/zxb8MyL179wBAO1/O0Njb26foKcgMd3d3zJs3D127dkXbtm3Rpk0bHUWXeV27dkXp0qXRpk0bBAQEYNOmTahTp46+w8LWrVvRq1cv2NjY4OTJk6hS5f2CCV9fX+2E9KdPn+L48eM4fvw4du3ahXnz5gEASpUqpU0wa9Wq9dkFPskCAgJw5coVfP/99+jSpQsOHTqE+fPnG/SwmUajwdOnT/Ho0aM0k8QXL14gLCwsxXPMzMzg4eEBd3d3eHp6okqVKnB3d4eLiwtsbGxSJYhWVlZZqi+YXTQaCUV+2afTY8oUSri4uUGtUkGtVkOtViMmJgZqtRoajUabYH54Mzc3T5V4fnwzMTGBvb19mjcHBwfY2tqm+hknJiZqe4yjoqK0Pcgf9iRHRkYiPDwcL168wNGjR/HixQtERkamOI6zs3OqZNPd3V27IG7p0qW5LpkEgKioKOzZsweRkZEIDAzEnTt30KhRI/z0008YPHhwnlm8MXHiRNy9excXLlzIt8kkAFSuXBmzZs1CcHAw3N3d9R1OnpYneignTJiAv/76CyEhIfoOJU0rV65Ejx49kJiYCGNj40wfhyRat26NM2fO4Pbt23ByctJhlJkXHh6Ojh074t9//8X06dMxfPhwvUz8TkhIwIgRI7Bw4UK0bdsWS5cuTfcK7ufPn2sTzOPHj+Phw4cA3i+oKleuHPz8/ODn54dy5cqhYMGCn5w3unr1agwYMADu7u7YuXOn3i9ykpKS8PDhQ9y5cwd3797F3bt3cefOHdy/fz9Fz6KzszPc3d0/e7O1tTWY+bLplZiYiDt37uD69espbla9VwByxeefm4EeSiOFDA8nN9Fl6DkuOjpaezHx4sULPH/+XHs/+fbxhbG9vT2KFy8OX19flCpVSvvRw8PDIP9Wzpw5g44dOyIqKgrLly9H69at8fr1a/z666/466+/4OrqismTJ6Nz5865evHKuXPnUK1aNUyaNAljxozRdzh69fLlS7i7u2PLli0G0RGTl+WJhDJ5uPHYsWP6DiVNO3fuRMuWLRESEpKuHq/PCQ0NRalSpVCnTh1s2rTJYE7aarUaY8aMwcyZM1G/fn2sXLkyR0vq3Lt3D+3bt8f9+/cxZ84c9O3bN0s/m5cvX+L48eO4ePEirl27huvXr2vfTO3s7FIlmb6+vtqLhQcPHqBVq1Z4+fIlNm3ahAYNGujkNX5ObGws7t27lypxfPz4MTQaDQDAyckJPj4+8PHxga+vL3x8fFCsWDG4ubll6ULHEMTHx+Px48d48OABHj58iFu3buH69eu4e/eudqi4aNGiKFeuHMqVK4ejxoF4EqH67DEzklD6ulpj7+AaOns9hmjr1q34+uuv0bt3b3Ts2BHPnz/H8+fPcf/+fdy+fRt3795FfHw8AMDKygq+vr6pEs3PXYxlJ0mSMGPGDIwdOxaBgYFYv349vLy8Ujzm4cOH+Omnn7Blyxb4+flpz2W5TVxcHMqXLw9bW1ucPn3aIEcJcpqnpyc6dOiAmTNn6juUPC1PJJR+fn4IDAzEokWL9B1Kmk6dOoUaNWrg7t27OtkSbOPGjejQoQM2bNiADh066CBC3Tl48CC6d++OxMRELF26FK1bt87W9khi5cqVGDhwIDw9PbFx40aULVs2W9p58eIFrl+/jmvXrmmTzEePHgF4vxDEx8dHm2QWKVIEc+fOxbFjxzB79mwMHjxYJ2+kJPH06VOcPXsWly9fxp07d3Dnzp0U84MKFiyYKnH08fHJ1PxQQ6JSqfDkyRM8fPhQmzgmf3z+/Ll27qC1tbX2d5GcQJYpUybFFITxO29h7flnn91qMS+s8taV8+fPo3bt2mjZsiXWr1+fZu+dJEl4+vQp7ty5g9u3b2v/Nu/cuYO4uDgAgKWlJXx8fFIkmb6+vvDy8sq2RDMkJARdunTBkSNH8PPPP2PChAmfTbLOnj2LkSNH4syZM2jYsCFmzJiBcuXKZUts2WHIkCFYvHgxrl69qrMtKHO7b775BqGhoThx4oS+Q8nTcn1CKUkSLC0tMWXKFAwbNkzf4aTpzp07KFWqlE5rYbVr1w5HjhzB7du34eLiopNj6kp4eDi+++47bNu2Db169cKcOXNgaWmp83aio6PRr18/rF+/Hj179sS8efOyXA4oMzHcvHlTm2Beu3YNN2/e1PbUKJVKqNVqeHh4oGnTpihSpAi8vb1RqFAheHt7w8HB4bNvpDExMbh06RLOnj2Lc+fO4dy5c3j9+jWA98PxpUqVSpE4lixZ0qDnbn5JQkICXr16hUePHqVKHJ88eaLtbTU1NUWxYsVQrFgxFC9ePMVHZ2fnLyYnt4Ij0ewTdSijLu+ClBALTcxbxFzdC/PiVWFUoDAAwDqgOeSmqf/GcmsdyvR4/PgxqlSpguLFi+Pw4cMZruggSRKePXuWIslMvh8b+36pvb29PSpXrowqVaqgatWqqFSpkk7OGQcOHEDXrl0hl8uxdu3adNeeJYnt27fjxx9/xKNHj9C1a1f8+uuvKFiwYJZjyk5Hjx7FV199hTlz5mDIkCH6Dsdg/P777xg7diwiIyNhZGSk73DyrFyfUAYFBcHb2xt79uxBkyaGOYfp1atXcHNz02kdyfDwcJQqVQqVK1fG9u3bDWboOxlJLFu2DEOGDIGbmxvWrVuHSpUq6ez4H66sXrRoETp16qSzY2eVRqPBw4cPce/ePTx9+hR79+7FkSNHYGpqCplMpn0TBd732CSXbvH29oaFhQViY2Px8uVL7VxHkrCyskJgYKD2TbdSpUq5psdRkiS8ffsWr169QkhIiPb24efJ9yMiIrTPUyqVKFy4cKqEsXjx4nB3d8/yHLe0dsoBgBcLe0IT9TrN57j3Wwal7f9NW8nLO+UA73ecqVKlCkjizJkzOv2bkyQJz58/x+3bt3Hp0iWcOXMG586dQ2RkJORyOcqUKYOqVauiSpUqqFKlCooUKZLu85xKpcIvv/yCmTNnomHDhli1alWmphupVCosWbIEEyZMQHR0NIYOHYrRo0fDxsbwLh4iIyNRtmxZFC5cGEeOHMnVc0B17fTp06hevTouX76c72px5qRcn1AePHgQDRs2xKNHj1CkSBF9h5OmhIQEmJmZYdWqVejatavOjrtt2za0adMGf/31F/r27auz4+rSw4cP8e233+LKlSsYP348fvrppyzN6SGJuXPnYtSoUShTpgw2btyIokWL6jDi7HHmzBm0bt0aZmZmWLNmDSwsLHDr1i2cOnUK169fx5MnT/DmzRtIkpTquQqFAra2ttrVvXZ2dl+8b2pqCoVCAaVSCYVCob19/PmHJWc+plKpEBcXh9jYWMTGxn7xfvLH6OhohIaGahPF0NDQVCVvbGxs4OLikuLm6uqqvZ/ck5ud87+ehsei3uxjUJMAMndBZqyQ4+DQmvB2zNme8ZyQmJiI+vXr4+7duzh79myO/J9JkqRt7+zZszhz5oy2ioeTk5O2B7NKlSqoUKECzM3NUx3jyZMn6NixIy5fvoypU6dixIgRWU6uoqKiMGvWLPz+++8wNzfHxIkT0a9fPygUn1/YlZN69eqFTZs24ebNm/D29tZ3OAYlPj4e1tbWmDt3LgYMGKDvcPKsXJ9QLl26FN999x0SExMNuivbwsICU6dO1fkwRP/+/bFixQqcPXsW5cuX1+mxdUWlUmHSpEmYOnUqqlSpgjVr1qBQoUIZPk54eDh69OiB3bt358rdaR4+fIgmTZogKCgIbm5u2rqkdnZ22p7HwMBAFClSBJGRkXj79i3evn2Ld+/effb+u3fvUvR6ZpRcLk+RcMrlcsTHx2u33fsSMzMzWFhYwMLCAubm5rC0tESBAgVSJIgfJowFChRIMxHIaUeOHEGbYVNh12QYkMEefpKQAZj9TRm0CfD64uNzG0mS0LlzZ2zduhX//vuvtvSWPrx9+xbnzp3TJpnnz59HTEwMlEol/Pz8tElmnTp1cPLkSfTu3Rt2dnbYsGGDzmtLvnz5EmPHjsXy5ctRqVIlLFq0CH5+fjptIzN27dqFFi1aYMmSJejdu7e+wzFIFStWRMmSJbFmzRp9h5Jn5fqE8tdff8X8+fMRGhqq71A+y8PDA71798aECRN0etyEhARUrVoVUVFRuHz5skEOxSQ7deoUunTpgjdv3mD+/Pno0qVLuoewTpw4gU6dOiEhIQErV6406G3ePvT48WMcOHAA+/fvx9GjRxEbGwsTExMkJiaiTZs2mDp1KooXL57lKQuJiYl49+6dNslM3pkluQ7i5+6n9fnHSeLH95M/mpmZ5cqhtXPnzqFevXqoXr06ekxYgFFbbkIml3+xlBDwfphbRiJs92w09nXCunXrDKqnShfGjBmDqVOn4p9//jG4WpMajQa3bt3CmTNntElm8uI4APDx8cEff/yBevXqZdvv5cyZM/juu+9w7949DBs2DBMmTMjx+dvJ3rx5g1KlSqFChQrYtWuXwU1/MhSDBg3CgQMH8ODBA32Hkncxl+vfvz/LlSun7zC+qFChQvzpp5+y5diPHj2itbU127ZtS0mSsqUNXYmIiGCXLl0IgO3bt+fbt28/+3i1Ws0JEyZQLpezVq1afPHiRQ5FmjkxMTHcvXs3Bw4cyKJFixIAlUola9euzenTp/PatWtUq9UcP348AbBDhw6Mi4vTd9j5yvXr12lra8tq1aoxJiaGw4cPp9LWldXGb6bX6N0s/PMeeo3enerm+cN2eo3ezXaLzvBJWAy3bt1KuVzOvn37Gvz/XUYsXryYADhr1ix9h5Iut2/fZsmSJWlkZMSKFSvS3t6eAGhnZ8f27dtz5cqVDAkJ0Xm7iYmJnDp1Kk1NTenl5cU9e/bovI30aNeuHe3s7Pjy5Uu9tJ9brF27lgAYHh6u71DyrFyfULZq1YqNGjXSdxhfVKxYMf7www/ZdvwtW7YQAOfOnZttbejS33//TVtbWxYsWJD//vtvmo958eIFa9WqRblczgkTJlCtVudskOkgSRJv3rzJ3377jfXq1aOxsTEB0Nvbm/379+eOHTsYFRWV5nM3bdpEMzMzBgQEGHyinFc8ePCABQoUYPny5RkREcGEhASamJjQzs7u/e/yRQTH7bjJxnNPsOiY94ll0TF72HjuCRZpN5rGBYrwyZMn2uOtWLGCADh69Gj9vSgd2r9/PxUKBQcMGGDwSbIkSVy2bBnNzMzo6+vLmzdvknx/EXrmzBmOHTuWFSpUIAACoL+/P8eMGcNTp05RpVLpLI5Hjx6xfv36BMBvvvkmRxO7v//+mwC4YcOGHGszt3r06BEB6C3xzw9yfUJZqVIl9ujRQ99hfFHJkiU5fPjwbG1j6NChNDIy4rlz57K1HV159uwZa9WqRZlMxlGjRjExMVH7vd27d9PBwYFubm48duyYHqNMLS4ujlu3bmWvXr3o7u5OADQzM2Pjxo05d+5c3r9/P91vxpcvX6aHhwddXFxyze8tt3r27Bk9PT1ZokQJhoaGkiSHDRtGAJw/f36az9Fo/u/3OH/+fALgsGHDUjzmjz/+IADOmDEj+4LPAdeuXaOVlRWbNm2q04QrO0RGRrJjx44EwN69ezM2NvaTjw0NDeXq1avZsWPHFL2X7dq144oVK/jq1assxyNJEtetW0dnZ2daW1tz4cKF1Gg0WT7u57x8+ZL29vZs165dtraTV0iSREdHR44bN07foeRZuT6hLFiwIMeMGaPvML6odOnSHDx4cLa2kZiYyMDAQHp6evLNmzfZ2pauqNVqTp8+nUZGRixfvjyvXbumfZNv2rQpw8LC9B0iSTIpKYl79uxhly5daGVlRQD08fHh8OHDefDgQcbHx2f62K9evWLlypVpYmLCtWvX6jBqIVloaChLlChBLy8vPn/+nOT7CwMTExPa2tqm6wLgzZs3lMlktLGxSZVwjR07lgC4ePHibIk/uz1//pzu7u709/dndHS0vsP5rKNHj7JQoUK0srLKcM+cWq3m2bNnOW7cOFasWJEymYwAWL58eY4bN4537tzJUmxv3rxhnz59CICVK1fm9evXs3S8T5EkiU2aNGGBAgUM5hyZGzRr1owNGjTQdxh5Vq5OKCVJopGR0Sd7FwyJn58fBwwYkO3tBAUF0d7ens2aNcv2K2Rdunz5MgsXLkyZTEa5XM7Zs2frfchNrVbz6NGj/O6777Q9GyVLluTEiRN57949nbYVHx/Prl27EgAnTJig99eel7x+/Zply5ZlgQIF+ODBA+3XhwwZkuFpIlWrViUA7tixI8XXJUni999/T5lMxvXr1+ss9pwQGRnJsmXLsmDBggY9Dy8iIoLfffcdAbBGjRp89OhRlo/5+vVrrlmzhp06daK1tTUB0M/PjzNnzuSzZ88yfdyTJ0/S19eXSqWSP/7442d7UDPjr7/+IgDu2rVLp8fN6yZPnkwbG5tc9d6Ym+TqhDIsLIwAuGXLFn2H8kUBAQHs27dvjrS1e/fuXDcEt379elpaWmpP6k2bNtUOS+YkSZJ45swZDh48mC4uLtr5kKNHj+b169ezNdGTJImTJ08mAA4ePFic9HQgNDSUpUuXprOzM2/duqX9enR0NI2NjWljY5OhubkrV64kAH711VepvqfRaNi9e3fK5fJck1SqVCo2bNiQ1tbW2jmIhmjnzp10c3OjpaVltg0nx8fHc+vWrfz6669pamqqTVwXLlyYqV7AxMRETp48mSYmJvT29ua+fft0EuelS5dobGzMfv366eR4+cnhw4cJIMs90ULacnVCeePGDQLgmTNn9B3KFwUGBrJXr1451t7o0aOpUCh44sSJHGszM2JiYtizZ08CYKdOnRgZGcldu3bRycmJzs7OOTKBWpIkXr16lT/++CO9vLwIgK6urhw6dCjPnTuX472FCxcupEwmY9euXQ1+LpshCwkJoa+vLwsUKMDbt2+n+N7gwYMJgLNnz87QMSMiIqhQKCiTydLswVKr1dqkct26dVmKPyeMGDGCSqWShw4d0ncoaXr9+rV2rmTjxo0ZFBSUI+1GRkZy9erVbNSoERUKBZVKJRs3bszVq1d/cpHdpzx48IBfffWVtqpDVuZsvnnzht7e3qxQoQITEhIyfZz8KjIykjKZjMuXL9d3KHlSrk4ok682Hj9+rO9Qvqhq1ars3r17jrWnUqlYo0YNurm56aWnLz2uX7/OkiVL0tzcnMuXL0+RuIWEhLBJkyYEwAEDBuh8yIgk7969y/Hjx7NEiRIEQAcHB/bt25f//vuv3leUb9iwgUqlki1atMjS/Mz86tWrV/Tx8aGrqyvv3r2b4nsRERE0MTGhmZlZioVg6dWkSRNt5YG0qNVq9ujRw+CTyuTKEBlNqnNC8iIXBwcH2tvbc82aNXqbBvL69WsuWLCA1atXJwCamprym2++4bZt29Kd1EmSxDVr1tDR0ZE2Njb866+/MtzLqtFo2LRpU9rZ2aWoNCBkTOnSpfndd9/pO4w8KVcnlNu3bycAg02YPlSzZk127tw5R9sMDg6mk5MT69Wrp/cE6UOSJPHPP/+kiYkJy5Qp88nhB0mSuGDBApqamtLHx4dXrlzJcttxcXFcsWIFK1WqRAC0srJi165duW/fPiYlJWX5+Lq0Z88empqasnbt2oyMjNR3OLlGcHAwS5QoQTc3N96/fz/V9ydNmqS9UMmMv/76izKZjB4eHp9MCjQajTapNMSFVvfv36eVlRW//vprg5uv+/z5czZr1owA2K5dO4M6vwcFBXHGjBn08/MjANrY2LBnz548dOhQus6x4eHh2hGZqlWrZmiawZQpU0TZGx3o3bs3y5Ytq+8w8qRcnVCuW7eOABgTE6PvUL6oRo0aOZ5QkuShQ4cok8k4ceLEHG87LR8uPunfv3+6inrfvn2bfn5+NDIy4owZMzI1f+r+/fscNmwY7ezsCIANGzbk5s2bDb7378SJE7S2tmaFChXEas50ePHiBYsVK0Z3d/cUC3CSxcXFaefpPnz4MFNtPHz4UFvb8FM1VEnDTSpjY2NZpkwZFi9e3KAuVDQaDf/66y9aWVnR1dWV27Zt03dIn3Xnzh2OHTtWu4GBu7s7J06cmK6FTceOHWOJEiWoVCr5008/ffE8ePjwYcrlco4dO1ZX4edbS5cupVwuz/DUBeHLcnVCmbyjQ25YvODv759ji3I+NmHCBMpkMh4+fFgv7SfLSnmchIQE/vDDD5TJZKxdu3a6VmCqVCpu3bqV9erV0w5p//DDDzpZHZqTrly5QmdnZ5YsWVJb8kZI7fnz5yxatCgLFiz4yd/xggULtCVdMkuSJBYsWJDW1tbs3bv3Zx/7YVK5Zs2aTLepK5IksWvXrjQ3NzeoRTgPHz5k7dq1CYC9evXiu3fv9B1SukmSxAsXLvC7776jubk5lUol27Vrx+PHj3+29zchIYETJ06ksbExCxcuzIMHD6b5uBcvXtDJyYn169c3qJGm3OrWrVsEwKNHj+o7lDwnVyeUc+bMobm5ub7DSJcSJUpke2HzT1Gr1axXrx6dnZ0ZHByslxiuXLlCDw8Purq68vz585k+ztGjR+nh4UFbW1tu3LgxzccEBwdz4sSJ2qLjVapU4erVqw2+N/Jz7t+/T09PT3p6eqbZ85bfBQUFsXDhwvT09PzknGqVSqX9m8jqziI9evSgk5MTbW1tvziPTqPRsGfPngaRVC5atIgA9B5HMpVKxVmzZtHU1JSFChXS+0VvVr17945z587VzssuXbo0Fy5c+NnesHv37rFOnTrahYkfDvEnJSWxatWq9PDw4OvXr3PiJeR5Go2G1tbWnDp1qr5DyXNydUI5ZcoUOjk56TuMdClYsKBehytCQ0Pp6urKGjVqZGohQlYkbzFYoUIFnWwx+PbtW7Zr144A2LVrV759+5aSJPHIkSNs27YtFQoFLSws+N133/Hq1atZfwEG4vnz5yxZsiSdnZ3z1OvKqqdPn7JQoUL09vb+7GKF5L18bWxssrxCNvlYANI1NGsISWVyuZn+/fvrpf2P3bhxQ1tcfOjQobli6lJ6SZLEw4cPs3Xr1pTL5bSysuL333+fqtrAh49fuXIlHRwcaGdnxyVLllCj0XDo0KFUKpW5opJJbvLVV1+xRYsW+g4jz8nVCeXPP/9Mb29vfYeRLvb29pw+fbpeYzh16hSNjY3Zo0ePHJmIr9FoOH78eG25jPTMl0wvSZK4atUqWlpaaudcAaCvry/nz5/PiIgInbVlSMLCwhgQEEAbGxuePHlS3+Ho3X///UcvLy8WKlSIT58+/eTjNBoNS5UqRSMjI52MFAQHB2trlH799dfpeo5Go2GvXr0ok8lyPKlMLjdTsWJFvZebSUhI4Lhx46hUKunr68uzZ8/qNZ7s9uzZM/7yyy8sUKAAAbBOnTr8559/0lwEGBYWxu7du2s3UQDAefPm6SHqvG3MmDF0dnY2uAVpuV2uTiiHDBlCX19ffYeRLiYmJvzf//6n7zC4evVqAuC0adOytZ2YmBi2bduWADh58mSd/+M+ePCAvXv3pqmpqXb7tEqVKuWKElJZFRkZyVq1atHMzCxfr/h8/PgxPT09WaRIkS/Oqd21a5e2R/HjMkKZ5ePjw8DAQJqYmKT7AkYfSWVyuRl7e/vPJt054dy5cyxVqhSVSiXHjh2r9+Q2JyUmJnLDhg3a8kNubm6fXMSzcuVK7a5hY8aMydXTdQxR8vngv//+03coeUquTij79OnDihUr6juML1Kr1QTAZcuW6TsUkv+37/A///yTLccPCgqin58fLSwsuHXrVp0e+8aNG+zQoQPlcjldXV05ZcoUhoSEcOfOnSxYsCDNzMw4Y8YMgysBpGtxcXFs0aIFlUpllucD5kaPHj2ih4cHixUr9sWFSpIksWrVqrSxsWH16tV1FsPAgQPp6emZ4ULJHyaVq1ev1lk8nzJ58mTKZDLu3bs329v6lNjYWA4fPpxyuZwBAQHZtsd1bnHt2jX27ds3xSKec+fOkXx/MV6qVCkWL16co0ePppGREYsWLSoWkejQ69evCSDX7GiVW+TqhLJTp06sXbu2vsP4oqioKALg33//re9QSL5/g+3QoQNNTU2ztEAmLadPn6azszO9vLx0+qZx/vx5tmjRQjvM+Oeff6a6ao+Ojta+aZUpUybPD6WpVCp26dKFMpmMf/75p77DyTH379+nu7s7ixcvnq5FZsePH9f2Tuoygdu2bZt2xXi9evUy9FyNRsPevXtne1JpCOVmjh49ysKFC9PU1JQzZ84Uuz99ICIignPnzmXx4sUJgPXq1WP9+vVpYWGhnW959+5d1qxZkwA4YsSIfNWrm52KFCnCwYMH6zuMPCVXJ5Tt27dPc09dQ/Pq1SsC4K5du/QdilZ8fDyrVKnCAgUK6GwYbOXKlTQ2Nmb16tV1Voz4+PHjrF+/PgGwRIkSXLly5Rd7Hy9fvswKFSpQJpOxf//+eXY+Jfk+MRk0aBABcO7cufoOJ9udO3eOjo6O9PHxSVe9P5Js3LixdocSXc7jfffuHeVyObt27Uq5XJ7ueJJ9mFQuWrRIZ3El03e5mYiICPbp04cAWLNmTVGd4DPUajU3bdqkrUJQsmRJHjp0SDtVSKPRcNasWTQyMmK5cuVS7EsvZM63337LwMBAfYeRp+TqhPLbb7/NFT2Ujx49Msi6V6GhofT29mbp0qWzVOBYrVZzxIgR2hpyWV1FLkkS9+3bp51rVLZsWW7cuDFDb4pqtZpz586lpaUlXVxcuGnTpjw7AVuSJI4cOZIAuHTpUn2Hk2127dpFMzMzVqtWjW/evEnXc65evUoAtLa25qBBg3QeU8WKFdm2bVsaGxtnagvDDy8IJkyYoLO/0Q/LzeijIP7OnTvp5uZGKysr/vnnn7miVrC+nT9/nkZGRmzatCkrVKig7f3evXu39u/i6tWr9PHxoampKf/3v//l2XNaTvjf//5HY2Nj0eOrQ7k6oezatatO50RllzNnzhCAQRUSTnb79m1aW1uzUaNGmRqKioiI0O5tPGfOnCyd4DQaDbdu3cqAgAACYGBgIHft2pWlYz5//pytW7cmADZp0iTP7oErSRL79+9PmUyWJ+cFLVmyhHK5nK1bt85QL2P79u3p7OxMALxx44bO4xo9ejQLFCjA1q1bMyAgIFPHkCSJU6dOJQD27dtXJ72JQ4cOpZGRUY5P+3j9+jU7dOhAAGzcuHG6NiAQ3m/J6OnpycDAQCYmJmovqqtWrUoA9Pf359atW6nRaBgXF8eBAwdqf8avXr3Sd/i50sWLFwkgz0+Nykm5OqHs2bMnq1Spou8wvmjLli0EwPDwcH2HkqaDBw9SoVBwwIABGUreHj58SB8fH9rY2HD//v2Zbl+lUnHt2rUsVaqUtqzG4cOHdXr1vX37dnp4eNDc3JyzZs3Kk/O4NBoNu3XrRoVCYfDb1qWXJEmcMGGCdu/tjCRbDx8+pFwup4+PT5Z2xvmcQ4cOEQBnz55NALx3716mj7VixQoqFAq2atUqS0PzmzZtyvFyM5Ikcc2aNXRwcKCDgwPXrl0res/SKSEhgTVr1qSDgwODgoJSfC+5vm7yLkKlS5fm33//TbVazb1797JAgQJ0cnLizp079RR97pWYmEhTU1P+8ccf+g4lz8jVCWVuWeWd3LVuyCfY5B000jsP78iRI7Szs2OxYsUy/SaqUqm4bNkyFilSRNuDePr06UwdKz2ioqI4ZMgQyuVylitXTucLkgyBSqXiN998Q2Nj4ywl+YZApVKxd+/eBMApU6Zk+P/nu+++o6OjY7ZWWIiLi6OJiQlnzZpFa2trjhs3LkvH2717N83MzFi9enW+ffs2w89/8OABLS0t2aFDhxw735w/f17bk9auXTudzZ/ODzQaDTt27EgTE5MvnvtOnjzJhg0baueTr1q1isHBwWzevLm2dzsvFYfPCdWqVWP79u31HUaekasTyv79+9Pf31/fYXzRzz//TE9PT32H8UUjRoygXC7n7t27P/u4BQsWUKFQsH79+pl605MkiXv37tX2SLZp04aXL1/ObNgZdvHiRfr7+1Mmk3HgwIFZmj9qiBITE9msWTOampry2LFj+g4nU2JiYtisWTMqFAquWLEiw88PDg6msbExW7VqRYVCka17Q9epU4fNmzdnjx49WKRIkSwncmfPnqW9vT1LlSqVob3b1Wo1q1SpwiJFijA6OjpLMaTH8+fP2blzZwJgmTJlcv22ifrw888/EwA3bdqU7ud8WPGicOHCXLx4MefPn08zMzOWKFGCly5dysaI85YRI0bQy8tL32HkGbk6oRw4cCDLli2r7zC+qEePHrliNZlarWbLli1paWnJa9eupfp+UlIS+/XrRwAcMmRIpoaNr127xnr16mlXfl68eFEXoWeYSqXiH3/8QQsLC7q5uXHz5s0G3YOcUfHx8fzqq69oaWmprW+XW7x+/ZqBgYG0sLDgvn37MnWMH374gdbW1mzatGm2z7MeP348HR0defjwYQLQyc/77t279PT0ZMGCBT+5Xd/Hpk+fTrlcnq29/OT7ZH/8+PE0MzOjk5MTFy1apJdV5LndkiVLCIAzZ87M1POvXr3Kr7/+mjKZjIUKFeIff/xBf39/KpVKTps2TfxO0uGff/4hgAxXaBDSlqsTyqFDh+aKnXIaNWrEVq1a6TuMdImJiWH58uXp4eGR4p8sPDyctWvXppGREZcsWZLh47548YI9evSgTCZj8eLFuWPHDoNI4IKCgrRDRs2bN081hyk3i4mJYbVq1Whra5tr9v5+/PgxixUrRmdn50xfbLx9+5aWlpb84YcfaGlpySlTpug4ypQ2btxIAAwNDaWrq6vOVpMHBwezTJkytLOz+2KSeOPGDRobG3PUqFE6aTstGo2Ga9asobu7u7atvFySKzsdOHCACoWC/fr1y/J58NatW2zWrJl2VXjXrl0pk8lYs2bNPHU+yw7Pnz8ngDwz51zfcnVCOWLECJYoUULfYXxRuXLl2L9/f32HkW4vXrygu7s7AwICGBMTw1u3brFw4cJ0dHTk8ePHM3Ss6Ohojhs3jubm5nR0dOT8+fMNbhcbSZK4detWurm50cLCgr///nueWbQTERHBChUq0MnJiXfu3NF3OJ91+fJlFihQgEWLFuWjR48yfZxZs2bR2NhYuxguu6dTXL9+nQB4+vRpDh8+nM7Ozjr7+3n37h1r1apFU1NT7tixI83HJCYm0s/Pj6VKlcq2EiinT59mxYoVCYBt27bNF1ucZpdr167RysqKTZo00el55vDhwyxXrhwB8KuvvqKbmxttbGzyZNUHXXJ3d+ePP/6o7zDyhFydUI4ePZqFChXSdxhf5OzszEmTJuk7jAy5cuUKLSwsWKVKFVpaWrJMmTIZKrmjUqm4ePFiFihQgCYmJhw9erTB92ZERkZy0KBBlMlkLF++vN6G43UtPDycpUuXppubW5YStex04MABWlpasmLFilla1KHRaFikSBF26tSJo0aNYoECBbK9BmJcXBxlMhmXLVvGy5cvE4BOF0TFx8ezbdu2lMvlaY4O/PLLL1Qqlbxy5YrO2kz29OlTbRmg8uXL59o5uYYi+WK9fPny2TLPVa1Wc9myZXRxcaGxsTF9fX0JgN9++63Bn3/1pW3btqxVq5a+w8gTcnVCOX36dNrb2+s7jM9KSkoigEwNE+uTJEns1q2bdqvD9C5c+XjBTefOnXW2E09OOX/+PP38/CiXyzl48GBGRUXpO6QsCwkJYfHixenl5WVwtQFXrVpFpVLJJk2aZHmV6oEDBwiAJ0+eZNmyZdm1a1cdRfl5hQoV4g8//EBJkli4cGGdj0io1WoOGDCAAPjrr79qh0nPnz9PhUKh8wvW6OhojhkzhqampnRxceHy5cvFnLwsioyMZLly5ViwYMF0bRmaFckjQ2ZmZrSysqKpqSm9vLx44sSJbG03N5o1axbNzc3zzKiUPuXqhHLx4sWUyWQGvQvDkydPCCDTiwv0IT4+nl26dNGW8gHAkSNHfnGuz4cLbmrVqpWre/hUKhV/++03mpub093dPU/MsXn27Bm9vLxYrFgxgyiGrNFoOGnSJAJgz549dXJCb9WqFUuXLq2dG7VhwwYdRPpljRs3ZvPmzUmSgwcPZsGCBXU+R1iSJE6ePJkA2L9/f0ZHR7NEiRKsWLGizt4MNRoNly9fThcXF5qYmHDMmDF54oJK35KSktiwYUNaW1tnS4H9T3n+/Dm7du1KADQzM6NcLufPP/9scNOO9OnkyZMEkGvmmRuyXJ1Qbt68mQAyVbomp+zbt48A+N9//+k7lHR59eoVAwMDaWpqynXr1pEk582bp90aLi0vX77ULrgpUaKEwSy40YWnT5+yadOmBMCWLVsaXO9eRj1+/Jhubm4sXbq0XgvtR0VFaXcwmjhxok7+Xp4/f065XM4FCxZw2bJllMvlOfYahw4dyuLFi5N8v1EAAF6/fj1b2lq6dCnlcjkLFy5MExMTnc2NPX78OP39/QmA7du3z3UjC4ZKkiT26dOHSqWShw4d0ksMly5dYs2aNQmAMpmMpUuX5v379/USi6GJjY2lUqnkn3/+qe9Qcr1cnVAePXqUAAx2XhhJzpkzh6amprliuOjevXv09PSkq6srL1y4kOJ706ZNIwDOmjVL+zWNRsM///yTNjY2dHR05IIFC/Lkla8kSfznn3/o6upKS0tL/vHHH7l6eOTu3bt0cnJiQECAXuZVPXjwgL6+vrSysuL27dt1dtxx48bRwsKCkZGR/Oabb7Jtd5y0/PXXX1QoFExMTGRCQkK2ry5P/n8sUKAAHz58mKVjPX78mG3btiUAVqxYkadOndJRlAL5f7+r5cuX6zUOSZK4bds2enh4EACVSiV/++23PHPxnxX+/v7s1q2bvsPI9XJ1Qnn16lUCSJX8GJJ+/frlilqZly5doqOjI319fT9ZTHnMmDEEwAULFvD27dusVq0aAbB3794G3UusKxERERwwYIC2J3b79u259mR87do12trasn79+jl6EbBnzx7a2NiwePHivHv3rs6Om5SURFdXV/br148qlYq2traf7FHPDseOHSMAbW9h27Ztsy2hjYqKopeXFytWrMhixYrR1tY2U4uAIiMjOWrUKBobG9Pd3Z1r1qwx6OlDudGGDRsIgGPHjtV3KFqJiYmcMWMGjY2NCYClSpUyiCkw+jRgwIBcUTHG0OXqhDIoKEjnKyp1rXbt2mzXrp2+w/iso0eP0srKioGBgZ8dIpQkiQMHDiQAKhQKlihRIl+u+rxy5Yp2rmiNGjVyXeHwZEePHqVSqWTfvn2zPTFOnv8nk8nYrFkznfeMJk9/uX79Ok+dOkUAObq1ZkhICAFw69atJN/vyy2TybJlG8LevXvT0tKS//33H9+9e8cmTZpQLpdz5syZ6fo9qtVqLlq0iE5OTjQzM+P48ePFln3Z4OTJkzQ2Nmbnzp0N8sLzzZs32vqVSqWS//vf//Qdkt6sXr3a4KfP5QZK5GL29vYAgLdv3+o5kk+7f/8+atSooe8wPmnbtm3o0KEDatWqha1bt8LS0vKTjz1x4gQOHjwIuVwOSZLwyy+/oFatWjkYrWEoX748Dh48iIMHD+KHH35A5cqV8c0332DatGkoUqSIvsNLtzp16mDx4sXo2bMnihcvjuHDh2dLO9HR0ejevTu2bt2KcePGYfz48ZDL5To5dlJSEqKjozF79myUK1cOkZGRWLBgASwtLXHlyhWcOHECUVFRiI6OBknIZDLI5XLI5XLt/bS+lpHvJ78WMzMzrFq1CmFhYYiJiQEA/Pzzz6hRowbkcjmsra1hZ2cHe3t72NnZwc7ODmZmZpDJZOl+vXv37sXSpUuxePFiFCpUCACwc+dOjBs3DqNGjcLVq1exdOlSmJubp/n8I0eOYPjw4bhx4wY6d+6MadOmwcPDI4u/BeFj9+/fR8uWLVG1alUsXbo0Q7/jnGJvb49du3Zh//79+OabbzBo0CCsWLEChw4d0r635heVK1cGAFy4cAENGzbUczS5l4wk9R1EZpGEiYkJ5syZgwEDBug7nFQiIyNha2uLdevWoVOnTvoOJ5Xly5f/P/bOOiyK9X3j9+7SDYIgIIKKiihiKwZ297Ebu7sVW2zF7m48dncrBsYxjy2SIt2xc//+8Md85QBKLCmf65qL3Yn3fWbZnbnnfZ/AwIED0bFjR+zevRuqqqop7hccHIxJkyZh69atqFWrFjZu3IjFixfj4MGDOHbsGFq1apXNluce5HI59uzZgxkzZuDbt28YOnQonJ2dYWhomNOmpZmpU6di8eLFOHbsGNq2bavQtt+9e4d27drB09MTe/bsQbt27SAIAiIjIxEWFiaKvcTXv1qX0vrY2NhU+5ZKpdDW1oaOjg60tbXFByGSEAQhyeuU1qV33/j4eACARCKBIAhp+nxUVFREcfmrxcDAADKZDE5OTihXrhxOnToFDQ2NJELFzc0NTk5OKF26NI4fPw4LC4sk/4cJEybg5MmTqFmzJlxdXVGtWrUM/lcL+BX+/v5wcHCAiooK7t69C319/Zw26bfExsaiY8eOOH36NJSVlbFu3ToMHDgwp83KNkjC0NAQo0aNwqxZs3LanDxLnhaUAGBiYoIRI0ZgxowZOW1KMh48eIDq1avDw8MDlSpVymlzkrBkyRJMnjwZQ4YMwdq1ayGTyZLtQxJubm4YNWoUYmJisGTJEgwcOBBSqRQJCQno0qULzpw5g9OnT6NRo0Y5cBa5h6ioKKxatQoLFy6ERCLB1KlTMXr0aKirq+e0ab9FEAR07twZ586dw61btzL8XY2JiYGXlxe+fv0KT09PXLlyBYcOHYKSkhLMzMwQHR2dZLQwNdTV1aGjoyMKwcTXqa37+++/cePGDZw5cwYSiQS1a9fGli1b0L9//2wdGerbty/evHkDd3d3AMD8+fOxePFi+Pv7QyqVIiwsDMHBwUmWoKCgZOv+u0RGRqbYX0piFABu3boFuVyOfv36wcbGBhcuXMDZs2dhbGyMJUuWoFu3brlyxCw/EBAQgPr16yMoKAh37twRR5HzCqdPn0bXrl0RGRmJqlWr4ty5cyhUqFBOm5UttGzZEoIg4Ny5czltSp4lzwtKOzs71KlTB+vWrctpU5Kxe/du9OnTB+Hh4b+cSs5OSGLy5MlYunQpnJ2dMWfOnBRvLl++fMGwYcNw9uxZdOzYEatWrYKpqWmSfeLi4tCuXTvcuHEDFy5cQO3atbPrNHItAQEBmD9/PtavXw8TExPMmzcPvXr1SlGw5yaioqJQr149eHt748GDBzAzM0uyPSEhAb6+vvj69au4eHp6JnkdEBCQrF0dHR3Url0bxYsXR6FChVIUiD+LRG1tbSgrK6fZ7ujoaJiZmWHAgAFYsmQJ9u7di169esHf3x+FCxfO9OeSHhYtWoRFixYhODgYEokEz549g729PS5dupSpB664uDjs3LkTgwcPxuzZs1G1atVUxWdQUBACAgLw8eNHxMXFJWtLWVkZFhYWsLS0hJWVFaysrMTXlpaWMDExKRCbGSQwMBANGjSAn58fbty4gTJlyuS0SRkiOjoabdu2xaVLl6CqqooNGzagb9+++f57MW/ePKxcuRLfv39XmEvOH0f2umwqntatW7Nly5Y5bUaKTJ06lWZmZjlthkh8fDz79+9PAHR1dU11nxUrVogJvVOrH5xIVFQU69WrRx0dnTydyFzRvHv3jp06dSIA2tnZ5erAMUEQ+O3bN54/f56GhoY0Nzfn6NGj2bVrVzo4OLBo0aKUyWQEIC7a2tosW7YsmzVrxoEDB3Lu3LncuXMnT58+zSZNmoiRrVkdNbxjx44kqcNGjx5Na2vrLO0zNY4dO0YAYsSsIAgsWrQoR40alal2fX19WahQIXbs2PG3wR0RERFcvnw5CxcuLP6v2rdvz8ePH/Ps2bNct24dJ0yYwI4dO7Jy5co0MDBI8n9VU1Nj6dKl2axZMw4dOpSLFy+mm5sbHzx4wICAgFwZXJIbCAoKYsWKFWloaMgXL17ktDkKYdu2bVRSUhILVXz58iWnTcpSEvPHvnnzJqdNybPkeUE5cuRI2tra5rQZKdKkSRO2atUqp80g+aP6Tfv27SmTybh79+4U93ny5AkrV65MiUTCkSNHprncYlhYGGvUqEEDA4NsrQKRF7h37x5r165NAGzcuHGOVGOIj4/n69evee7cOW7evJkzZsxgnz592KBBA5YsWZJqampJRAUAamho0NHRkb169eL06dO5ceNGnjlzhs+fP081Qvvdu3csV64ctbS0xGjnrKZatWps2rSp+L5hw4bs0KFDtvT9X169ekUAvHbtmrhu6NChtLKyypQQ69atG42MjBgQEJDqPuHh4Vy8eDGNjIwok8no5OTEd+/ecceOHVRRUaGDgwN9fHxSPDY0NJTPnj3jiRMn6OrqytGjR7Nt27a0s7OjtrZ2ku+FlpYWy5Urx9atW3PkyJFcsWIFjx49yidPnvyxtaJDQkJYtWpVGhgY8OnTpzltjkJ5/fo1zczMKJFIqKamxjVr1uSJnMoZISQkhBKJhDt37sxpU/IseV5QLl++nJqamrnuyVkQBOrp6Sm8xm5GCA0NZf369ammpsZTp04l2y6Xy7l48WIqKyuzfPnyGUqDExwczIoVK9LY2LigAsN/EASBx48fZ+nSpSmRSNirV68sedoXBIFfv37l2bNnuXjxYvbs2ZP29vZUVVUVBYFUKqWZmRlr1KjBzp07c/z48XR1deXRo0f58OFD+vn58dSpU5RKpRw3blya+3Zzc6Oenh6tra358uVLhZ9bSnh4eBBAkuToxsbGnDlzZrb0/18CAwMJgH///be47syZM0nyU6aXxOINqd3kQkNDuWDBAhYqVIjKysocOHBgsqpc7u7uNDU1pampabpTKQmCwMDAQD569Ih///03ly5dyuHDh7NFixa0sbGhurp6EsGpp6fHihUrsn379hw3bhxXr17NU6dO8dWrV3m6GEBqhIWF0cHBgXp6evTw8Mhpc7KE0NBQsVoYANaoUSPfjML+l7Jly3LIkCE5bUaeJc8LyiNHjhDAL5/ec4K3b9/mihyZ/v7+rFSpEnV1dXnz5s1k2728vNigQQNKJBJOmjSJsbGxGe4rICCAZcuWpbm5OT98+JAZs/Ml8fHx3LBhAwsXLkxVVVVOmjSJwcHBGWorODiYN2/e5Pr16zl06FDWrl2benp6SUaSatSowYEDB3LVqlW8cuUKv3z5kuYk5mvWrCGA35YjCw8Pp5OTEwGwY8eOGT6fjDBgwACam5uLQuXbt28EQDc3t2yz4WdiY2OTib/o6GhqaGhwyZIl6W4vLi6ONjY2rFWrVjLXgeDgYM6ZM4f6+vpUUVHh0KFDf1kq0cfHhzVq1KCqqio3btyosAdwQRDo5+dHd3d3HjhwgC4uLhw0aBAbN25Ma2trMXk2AKqqqrJKlSocMGAA165dy9u3b+fpOuERERGsU6cOdXR0sjXnaU4gl8vFPLKamppUUlLizJkzM3W/yI3069eP9vb2OW1GniXPC8rEUYrc5r+3d+9eAmBgYGCO2fD582eWKlWKxsbGKU7FHD16lAYGBjQ1NeWVK1cU0qePjw+tra1pamqab59iM0tYWBhnzZpFDQ0NGhgYcOXKlYyJiUlx35iYGD558oS7d+/mxIkT2bx5c7F0Gv4/IXG5cuXYtWtXLliwgCdPnuTHjx8V4rs4cuRIymQyXrhwIcXtDx48YMmSJampqcnt27dn6yxBSEgINTQ0kswAXLt2jQCybYQ0JVRUVLh27dok69q0acM6deqku62lS5dSKpUm+e0GBgZy5syZ1NXVpZqaGkeOHJlqZav/EhMTw6FDhxIAO3TokC3XJrlczq9fv/LatWt0dXVl3759aW9vT2VlZfE7XLJkSXbs2JHz58/nmTNn6O3tnetmnP5LZGQk69evTy0tLd65cyenzck2zp49Sz09Perr61Mmk7FKlSqZLv2Zm9i8eTOlUmlBov8MkucFZeI0U06NSqTGqFGjciw4gCRfvnxJMzMzWllZJat1HhERwYEDB4oO+7+qjpMRfH19aWdnRwMDg3z/5J4ZfHx8OHDgQEqlUlpZWYlTz3PnzmWnTp1YpkyZJMEwxYoVY6tWrTh16lTu27eP//zzT5aOEMTHx7NFixbU0dFJ8nCQkJBAFxcXKikpsUqVKnz79m2W2ZAaa9eupZKSUhK/wLVr11JZWTlH68kbGBhw0aJFSdYl3qTSI+C+fv1KTU1NMaAnICCAU6dOpba2NtXV1Tlu3LhUfSJ/R+KDpJmZWRJ/z+wkNjaWT58+5Y4dOzh69Gg6OjpSV1dX/K4bGRmxcePGnDhxIvft28eXL1/mminz6OhoNmnShBoaGrxx40ZOm5PtvH//nuXLl6eamhqNjY2ppaXFXbt25fqHgLTwzz//EMAfWQFOEeR5QSkIArW1tTM0pZSV1KhRgz169MiRvt3d3WlgYMDy5csnu+l4eHiwdOnSVFdX56ZNm7LsIhAcHEwHBwdqaWkpbPQzv+Dn58dLly5xxYoV7NevH8uVK5dEOOro6LBevXocMWIEN23axLt376Y5QErRhIWFsXz58ixWrBj9/Pzo6elJR0dHSiQSTp06NcfEW40aNZIFvA0ZMoTly5fPEXsSsbCw4PTp05Os8/b2JgDu27cvze106dKFxsbGfPv2LSdOnEhNTU1qampy0qRJCinn+PXrV9arV48SiYTTp0/PURGeiCAI/PTpE48dO8ZZs2axbdu2LFasWJII9KpVq3LgwIFct24d7969y/Dw8Gy1MSYmhi1atKCamtoffV2LiIhgt27dxFrgANi9e/ccu04pioSEBGppaSV7KCwgbeR5QUmS5cuX57Bhw3LaDJHY2Fiqqqpy1apV2d73xYsXqampSQcHhyR1SeVyOZcsWUJlZWVWrFiRr1+/znJbIiIi2KxZM6qoqPDYsWNZ3l9uxM/Pj0eOHOHYsWNZv359GhkZJblBVq5cmX379uXy5cu5ZMkSVqhQgQDYunXrHJ26/ZkvX77Q2NiY1tbW1NPTy9GRLZL8+PFjigKtdu3a7NatWw5Z9QNbW1uOHj062frKlSuza9euaWrj8uXLBMBmzZpRXV2d2tranDZtmsL9xBMSErhgwQLKZDLWqFEjWTBPbiEwMJBXr17lihUr2Lt3b9rZ2YnpbCQSCUuVKsXOnTvTxcWFZ8+epY+PT5Y8KMfFxbFt27ZUVVVN1Q3kT0IQBK5YsYIymYy2trbU0tKilZUV7927l9OmZYoGDRqwXbt2OW1GniRfCMrWrVuzefPmOW2GyKNHjwgg239Ybm5uVFZWZvPmzRkZGSmu9/LyYsOGDQmAEyZMSNVfLyuIjY1l586dKZVKuWPHjmzrNycQBIH//vsvt23bRicnJ1pbW4vi0dLSkh07duTs2bN55MgRvn37NsX0G3K5nAcPHqSVlRWlUikHDhyY4alNRREeHs7WrVsTAIsWLapwF4n04uLiQg0NjSSjU4lZFRYsWJCDlpHVq1dn//79k62fOnUqjY2Nfyt0Pnz4QH19fUokEuro6HDmzJlZ7ut47949WllZUUdHJ12jqDlJTEwMHz9+zO3bt3PUqFGsW7cudXR0xN9b4cKF2aRJE06aNIn79+/nv//+mymRGR8fz7/++ovKyso8c+aMAs8k73Pt2jUaGRnR1NRUnG2ZP39+nk0vNG3aNJqYmOSLKfzsJl8IykmTJtHCwiKnzRBZv349lZSUGB0dnW19bty4kRKJhD169EgyfXXs2DEx8ObSpUvZZs/PJCQkcNCgQQTAlStX5ogNWUFcXBwfPHjAFStWsH379uLoo0Qiob29PUeMGMFDhw7Ry8sr3W3HxMRw5cqVNDAwoKamJmfNmpXt03sk+fDhQ1pbW1NTU5NDhgwhgGRBJ9lNYhDSzyROK/+cQignaNiwIbt06ZJs/blz5wgg1ZRaX7584dChQ0XXh+HDh2drxHxISAi7d+9OAOzdu3eejL4WBIEfP37kkSNH6OzszNatW7No0aKiyDQ2Nmbnzp25fv16vnr1Ks2CISEhgV27dqWSktJvCz38qXh6erJq1apUVVVl69atKZFIWK9evTQHjOUmTp48SQC/zJpQQMrkC0GZGFGdnRfgX9GtWzdWqVIlW/oSBIELFiwgAI4cOVKM7o2IiBBFXLt27XJ8VEkQBE6ZMkWsoJIXn/7CwsJ48eJFzpw5kw0aNKCGhoY4dV2vXj3OmDGD58+fV2iC5+DgYE6aNImqqqo0NDSki4tLtvgpJSQkcOHChckCb0aOHEllZWU+ePAgy21IiefPnxMAT548mWT9hQsXklTMySnatWvHFi1aJFsfGhpKqVTKrVu3Jln/8eNHDhw4kMrKytTT06OKigqHDx+eXeYmQRAE7tq1i1paWixZsmSO/Y8Vzffv33nu3DlOmTKFNWrUEKfLCxcuzE6dOnHt2rV88eJFitekhIQE9urVizKZLEl+0QKSEx0dLVZia9euHc3MzGhgYJDn3J38/f0JgAcPHsxpU/Ic+UJQJkZmpZRnMbuRy+U0MjLilClTsqWvsWPHEgDnzJkjXhCfPHmSLYE3GWHx4sXiCExWl+XLLD4+Pjx8+DBHjx7NypUri6NHhQoVYtu2bbl06VK6u7tnSy62xBEsFRUV6unp0dnZOcseEn4O2JgyZUqS84uJiWHVqlVpaWmZxEc3u5g2bRr19fWTfebLly+nurp6jn+nevbsybp166a4rVKlSuzduzfJH1WFnJycKJPJaGRkxCVLlrB9+/Y0MTHJ8cCGd+/esWrVqlRSUuLixYtz/DNVNOHh4bxw4QKnTZtGBwcHUWAaGhryr7/+4po1a8QMCn379qVUKuWBAwdy2uw8gSAI3LhxI5WVlVm9enU2b96cADhkyJAkbli5HSsrK44ZMyanzchz5AtBGRsbS2Vl5RyfiiPJp0+fEkCWRwDGxcWxV69elEgkXLdunbh+7969VFNTo729fbYE3mSEzZs3UyKRsHv37rkiupT8cSF88+YNt2zZwj59+rBEiRLiVFmJEiXYp08fbtmyha9fv85Rge7t7c2xY8dSQ0ODmpqanDhxolg7OrPI5XJu2bJFDLy5evVqivt9+vSJenp6bNOmTbZ+FoIg0MrKigMGDEi2zcnJKdtmBX7FkCFDWKlSpRS3jRkzhmZmZuzVqxelUilNTEy4YsUKRkREiHWEc4sPY2xsLCdPnkyJRMKGDRvS29s7p03KMiIiInjp0iVOnz6dtWvXFnNkKisri5Wtnj17lu+EdVZy9+5dmpqaskiRIpw4cSLV1dVZtmxZPnv2LKdNSxPdunVjzZo1c9qMPEe+EJQkWaFCBQ4aNCinzeDSpUuprq6epYEvUVFRbNWqFZWUlMQn5/j4eHG0snfv3oyKisqy/hVBYgBRy5Ytc8TW2NhYuru7c9myZWzXrh0NDQ3F0oSVKlXiqFGj6ObmluMBManx7ds3Tps2jTo6OlRTU+OIESMyVc7x9evXrFu3LgHQycnpt4EgJ06cIAAuW7Ysw32ml3v37hFAikK3WrVq7Nu3b7bZkhoTJkxgqVKlkq2/f/++WNPd2NiYq1evFr/3MTExLFWqFB0dHXPVbAL5I+K8SJEiNDQ05JEjR3LanGzB29ub5cuXp5KSEm1tbcVqPwYGBmzXrh1XrlzJJ0+eFAjM3+Dr6ysKdGdnZ5YvX56qqqpcvXp1rvue/5dVq1ZRVVU1WwNY8wP5RlD26tWLNWrUyGkz2KRJEzZt2jTL2g8ODmadOnWooaHBc+fOkfyR9LhBgwaUyWRctWpVrv+xJnL+/HlqaGiwbt26CvU7TImwsDBeuHCBzs7OrFevnliDWF1dnfXr1+fMmTN58eLFPBeMEBwczHnz5tHAwIDKysrs379/uipXxMTEcM6cOVRRUWHJkiXTNbI+ceJEymQy3r59OyOmp5uRI0eySJEiyaJH5XI5NTU1uXTp0myx41fMmTOHpqamJH/4lO3cuZNVq1YVI+QBJMt24OLiQplMlmsrSwUEBLBdu3ZiIYT8PFqZKCb19fV59+5dkj8e4K9evcpZs2bR0dGRqqqqYt3yNm3acMWKFfTw8MizUc1ZSWxsLEeMGCEOdAwbNowA2KVLl1w9BX7//n0CKCjMkU7yjaBcunQpNTU1c/SpMTo6mmpqalk2auPn58cKFSokudg9efKExYoVo6GhYY7mBswod+7coZ6eHitWrKiQhM0/8++//3LFihVs0KCB6CdlZGTE9u3bc/ny5bx//36umXLPLOHh4Vy6dCmNjY0plUrZvXv33wqUW7du0cbGhkpKSpw+fXq6R4rj4uJYq1YtmpmZ8du3b5kx/7fEx8fT2NiYY8eOTbYtICCAAHj06NEstSEtLF++nFpaWpw0aRILFSpEAGzatClPnjzJhIQE2tjYcPDgweL+X758obq6OsePH5+DVv8eQRDo5uZGY2Nj6ujocNOmTfluhO7ff/9lsWLFaG5u/sscsNHR0bx+/Tpnz57N+vXriwJTV1eXrVq14rJly/jo0aMCgfkTu3btopqaGqtUqcINGzZQU1OT9vb2/PTpU06bliI5mUs6L5NvBGVuiPJMTEicUt3szOLn58cyZcqwSJEifP78OUly//79VFdXZ6VKlTI13ZnTPHv2jMbGxixVqlSmkivHxsby8uXLHDNmjJgDUlVVlS1atOC6desynYsuLxAVFcW1a9eKo2Ht27fno0ePkuwTHBzMwYMHEwBr1Kghfp8ywtevX2loaMimTZtmqcC4dOkSAaQYefzu3btUp8KzC7lczgsXLtDOzk6sdjRmzJhkaYIGDRrEsmXLiu87dOhAU1PTPDMyHhQUJEby1q1bl2/evMlpkxTCgwcPaGhoSBsbG3p6eqbr2OjoaN64cYNz585lgwYNqKamJn4HWrZsySVLltDDwyPfX3t+h4eHhzj4sXXrVlpZWeXqgZCaNWvmeKGEvEa+EZS+vr45PkoxefJkFi5cWOE31oCAAJYrV45FihTh27dvGR8fz/HjxxMAe/bsmev9JdPCu3fvWKJECRYqVChddVT9/Py4fft2/vXXX9TW1iYAmpmZcfDgwTx16lSunlbJSmJjY7lt2zaWLFmSANi8eXPeunWLbm5uNDExoba2NtetW6eQ7+qFCxcokUg4f/58BVieMk5OTixRokSKN+XEQgKPHz/Osv5TIzg4mCtXrhQfYMzNzQkgVReOxBRn3759E3NT5sUI4qtXr7JkyZJUVVXl/PnzsyXTQVaRWF2sRo0aCsmcEBMTw1u3bnH+/Pls1KiR6F5jbm7OYcOG8cKFC3n688oMAQEBbNSoEWUymSjAZTIZ16xZk+sE99ixY2llZZXTZuQp8o2gFASBxsbGyeroZieVK1dm9+7dFdpmYGAg7e3tWbhwYb569Yrfv39nw4YNKZPJ6Orqmut+hJnh+/fvrF+/PpWUlLhp06YU9xEEgR4eHpwzZw6rVasmJhKvWbMm58+fz6dPn+arzySzxMfHc9++fSxVqpQYtV6rVi2FJxx2dnamVCrNklHC6Oho6urq0tnZOcXtiTMD2Vk68NmzZxw0aBA1NDSopKTErl278tatW1yxYgU1NTVTPc7T05MAePjwYdrY2LB+/fp59vsaFRXFKVOmUCaTsVy5cnR3d89pk9LN/v37qayszBYtWjAiIiJL+oiNjeWVK1c4atQosTa5jo4Ou3Tpwv3792e5/3huIz4+npMmTRJ9KRN9LPv165ergmAOHTpEAPTz88tpU/IM+UZQkmT79u1Zr169HOn727dvlEgkCi0vGBwczMqVK7NQoUJ8/vw5nz59SktLSxoaGubo9F5WEhcXJzpujxw5kvHx8QwPD+exY8c4YMAAFilSRPRX6ty5M3fv3p3l/nt5mYSEBLq6ulJTU5P6+vpiOqTq1avz1KlTChMzCQkJrF+/Po2NjRWWxiiRo0ePEgBfvXqV4vYjR44QQJaXKIyNjeXBgwfFaG1TU1POmTMnSSaAWbNm0czM7JftFCtWjE2aNCGAZO4IeZEnT56wcuXKlEgkHD16dI5UdMoIrq6uYrBIdvlSC4LAp0+fcs6cOaxYsSIBUElJiY0bN+batWvTPd2el3Fzc6OmpibLly/PJUuWUFVVlTVq1Mg1QV9fvnwhgILqSOkgXwnK5cuXU01NLUemE7Zs2UKpVKqwwJLQ0FBWr16d+vr6fPLkCQ8cOEB1dXVWrFjxjygJNXfuXEqlUjF6GQDLlCnD8ePH89q1a/kmmCYrefr0KatWrUqJRMLhw4czJCSEgiDw3LlzrFWrFgGwQoUKdHNzU0gAga+vL01MTFivXj2FBiR06tSJ9vb2qW7ftm0bATA+Pl5hff6Mt7c3Z86cSRMTEwKgo6Mj3dzcUvwOjh49OomPZEp0796dKioq7NSpU5bYmxPEx8dz2bJlVFdXp4WFBc+ePZvTJqWKIAicOnUqAXDixIk5OkL85csXrlmzho0aNRIDBytVqsQ5c+b8EbMtz58/Z8mSJamnp8dVq1bRzMyMRYoU4b1793LaNAqCwCJFinDq1Kk5bUqeIV8JysRQ/5z4MjZr1oyOjo4KaSs8PJy1atWirq4u3d3dOXHixHzlL5kScXFxvH79OidOnEgbGxsCoEwmo7KyMo2MjHjhwoWcNjHP4O/vL9aFtrW1FTMC/IwgCLx+/TobNWokJm9fvXp1poNDrl27RqlUyhkzZmSqnUTCwsKopqbGxYsXp7pPYmS1IhEEgTdu3GCnTp2opKRETU1NDh069LcBTH379qWDg8Mv9+nSpQsB0MPDQ5Em5wo+fPjAxo0bEwC7d++e62YP4uPj2a9fv2zPoZoWgoODuX//fnbp0kX0B7e0tOSoUaN45cqVfPsQHRwczJYtW1IqlXLx4sV0cHCgiooKt2/fntOmsX379qxfv35Om5FnyFeCMi4ujhoaGtmejy4oKIhKSkpcs2ZNptuKjIyko6MjtbW1eenSJTZu3JgymYwrV67Md0+rAQEB3LNnD7t06UI9PT0CoImJCfv168ejR48yLCyM79+/Z9myZamrqyvm3SwgZaKiorhgwQJqa2tTT0+PS5cuTdNo/f3799m1a1fKZDLq6Ohw3LhxmfJHdHFxIQCF/L92795NAL/MYjBz5kyam5tnui/yx8Pchg0bWK5cOQJg6dKluXr16jT7uaVWyzuRsLAw6uvrEwDPnz+vEJtzG4k1wQ0MDFioUCHu3LkzV6QYioqKYps2baikpMTdu3fntDm/JDY2lhcuXOCwYcNoZmZGANTX12fPnj15+PDhPJMVIK0kJCSIvpSTJ0/mgAEDRLennBTSixcvpqamZkEKqDSSrwQlSTZo0IBt27bN1j537dpFAPTy8spUO1FRUWzUqBE1NTV57NgxMcFuVpdxzC4EQeCzZ8+4YMECOjg4UCqVEgCrVKnC2bNn8+HDhyneeEJDQ9mqVStKpVKuWLEi3wnrzCKXy7l7926am5tTWVmZY8aMyVC06tevXzllyhTq6+tTKpWyQ4cOvHnzZro/b7lczubNm7NQoUKZ9glr3779b0ugjRo1iuXKlctUP2/evOGoUaOoo6NDqVTKdu3a8fLly+k+9/r16/8y1cicOXOoqqrKQoUKcdq0aZmyObfj7+/Pbt26EQCrVauWbQnwUyIoKIi1a9emhoZGrp6OTwlBEPjw4UPOmDGD5cuXJwCqqKiwefPm3LhxY66t5pVeBEHg0qVLCYC9evXi6tWrqaSkxHr16jEgICBHbLpx4wYB5JmSkTlNvhOUM2fOZKFChbL1ibhNmza/neb6HTExMWzWrBnV1dW5Y8cOFi1alBYWFqkGIuQVBEHg7du3OXz4cDE3opaWFtu3b89t27al+WKYkJAgRgY6OTnlqmjAnOTq1auic/9ff/2Vrio5qREZGcmNGzeyTJkyok/X7t270/WZf//+nebm5mzQoEGGf4uxsbHU0tLiggULfrlfnz59WKtWrXS3n5CQwOPHj4vT/kZGRpw6dWqmcrpWrFiRQ4cOTXFbQEAAtbW1OX78eP7111+sU6dOhvvJS1y/fp2VKlUiAHbs2JEfPnzI1v6/fPnCcuXK0cDAIFf45mWWjx8/cuXKlaxXrx5lMpkYZOfi4sKXL1/m+QfuAwcOUEVFhQ0bNuSZM2doZGTEYsWK8cmTJ9luS0REBGUyWapZRwpISr4TlBcvXvxlRKiiCQsLo6qqKpcvX57hNmJjY9m6dWuqqalx5cqV1NPTo52dXa6JdssIL1++5LRp02hpaSnmYBs1ahQvXryYKTG4e/duqqiosFatWgqvrJOXePXqFVu1aiXeTLJi9Ecul/P8+fNs1qyZ6I4wd+7cNH/uicnI161bl6H+r1y5QgC/vZG0bduWLVu2THO73t7edHFxoYWFhZjcfc+ePQp5SLGysuKUKVNS3DZu3Dhqa2szICCAq1atooqKCqOjozPdZ15ALpdz165dNDMzo4qKCidMmMDg4OAs7/fGjRuiIMnrD+cp8f37d+7evZt//fUXNTU1CYAlS5bk+PHjefPmzTw7VXv9+nXxPnj//n1WqlQpx0aXK1asSCcnp2zvNy+S7wRlWFgYpVIpN2/enC39HThwgAAyXEIqLi6O7du3p4qKCqdPn05VVVXWr18/T+Ym8/Ly4tKlS2lvby/Wuh04cCCvX7+u0BHje/fu0djYmBYWFllSlSg34+/vzyFDhlAmk9HKyoqHDh3KlhGJV69ecciQIVRXV6eqqir79euXpmmgIUOGUENDI0MVrMaPH88iRYr89vwcHR1/m/81KCiIW7ZsYf369SmRSKimpkYnJyeFp+0xMDDgokWLkq339PSkqqoq58yZQ5J0d3cnAD58+FCh/ed2IiMjOXfuXGpoaLBQoUJcs2ZNlvjICYLAtWvXilOmuS04KCuIjo7mmTNnOHDgQBobGxMADQ0N2bdvX546dSrPBfW8ePGCRYsWpbm5OR88eMDWrVtTJpNx586d2WrH0KFDaWNjk6195lXynaAkyUqVKrFXr17Z0lfHjh1ZpUqVDB0bHx/PLl26UFlZmYMHD6ZEImG3bt3y1HRucHAwt2zZwnr16lEikVBVVZUdO3bksWPHsvQ8PD09xafW3FDDOauJjIxMEnCzbNmyHPmeBAYGctGiRWJFmAYNGvD48eOppuwJDw+nlZUV69Spk+6HChsbG/bv3/+3+9nb23PYsGHJ1kdGRvLgwYNs06YNlZWVKZVK2ahRI27bti1LRscEQaBMJuPGjRuTbevfvz+NjIzEYIqIiAhKJBJu3bpV4XbkBby9vdmvXz9KJBKWLl1aoTlRY2JixPKQo0ePznNCShHI5XLeu3ePU6ZMEbNmFC5cmGPGjMmRqeOM4u3tzQoVKlBXV5eXLl0Sg3UWLVqUbVP7iTES2TGintfJl4Jy/PjxNDExyXI/ysjISGpoaHDhwoXpPjYhIYE9e/akkpISO3ToQAAcP358roiG/B3R0dH8+++/xZFViUTChg0bcvv27dk6shoZGcnOnTsTAGfNmpVnp3d+ReJUYWYDbhRNXFwcDx48yOrVq4vlLmfOnJliEM7169cJgCtXrkxz+x8/fiQA/v3337/d19LSUgxwiYuL4+nTp9mjRw9xCrB69epctWqVwhOu/5fw8PAUSym+fv2aUqmUrq6uSdaXKlWKI0eOzFKbcjtPnjxhgwYNxIeTzIodHx8f1qxZk6qqqgotMpHXefr0KceOHcvChQsTAO3s7Lhs2bIs/00ogtDQUDZu3JjKysrcu3cvnZ2dxYeF7Lhf/vvvvwTAixcvZnlfeZ18KSgTb2D379/P0n727NlDAOmezpPL5ezbty+lUinr1q1LAFyxYkUWWakYEhISeOXKFfbr14+6uroEwMqVK3P58uU56uspCALnzZtHiUTCevXqZTrSPreQkJDAQ4cO0c7OTqEBN1mBh4cHBw0aRC0tLUqlUrZq1YqnTp1KIvBHjRpFNTU1vnnzJk1trlu3jkpKSml6QNHX1+fgwYM5ZMgQFipUiABYtmxZzp8/P0NT7Rnl69evKaZL6tixIy0sLJKNKHfq1Il169bNNvtyK4Ig8NSpUyxdujQlEgn79euXoWvKvXv3WKRIEZqammb5tT+vkvjA1alTJ6qoqFAqlbJ58+Y8ePBgrvbnjYuLY58+fcTRyXXr1lEikbBz585ZPlMjCAL19fU5d+7cLO0nP5AvBWV8fDz19fWzvK53nTp12KBBg3QdI5fLOXDgQEokEpYvX54qKirJRjRyC4Ig8PHjxxw/fjxNTU0JgMWLF6ezszNfv36d0+Yl4erVqzQ1NWWhQoV46tSpnDYnw8TFxXHHjh0sXbo0AbBx48Y5mm4lPYSFhXHTpk1iRG/RokU5Z84cenl5MTIyktbW1qxRo0aaRpJbtmz5yzKqid/NxKT/AGhhYcHJkyfz2bNnORLp+vz5cwJIkkj+4cOHBJDiaNn8+fOpq6ub56NyFUVcXBzXrl3LQoUKUUNDg3PmzElzfe1t27ZRRUWFDg4OeWLULTcQFBTEjRs3smbNmmI524EDB/L27du58jspCII4Ojls2DC6ublRVVWVDRo0YGhoaJb23axZs1/mly3gB/lSUJJkz549aWdnl2Xtv3r1igB48ODBNB8jCAKHDx9OiURCCwsL6ujo5Mqa3B8/fuT8+fNF3xsjIyOOGDGC9+7dy5UXmkQCAgLEyOfRo0fnKV/U6Ohorl+/nsWKFSMAtmnTJk+Psjx8+JADBgyghoYGZTIZ27Zty2XLlhHAL6vekD8+C3V1dS5ZsiTZtrdv33LOnDliSiNDQ0OqqKhw+PDhOe4ucvv27WQZJho3bsyyZcumKKJPnTpFAH9EKdX0EBwczAkTJlBFRYVFihThihUrGBkZmeK+cXFxYkLsgQMH5qnffG7i7du3nDFjhpj5oESJEpwzZ06Gg02zks2bN4vXlAsXLlBXV5f29vZZ+iAxe/ZsGhgY5Or7X24g3wpKNze3LL1Yjx07loaGhmm+gAmCwDFjxhAACxUqRFNT01yVLPXbt29ct24dHRwcCICamprs0aMHz549m6ec2gVBEFOyVKxYkf/++29Om/RLIiIiuHz5chYpUoQSiYRdu3bNVd+LzBIaGsr169ezQoUKBEAdHR3KZDJeu3Yt1WPOnz9PAHzx4gXJH475K1asYNWqVcU8pr169eK5c+cYFxdHMzMzzpo1K3tO6BccP36cAMQbW2Lao9SCxjw9PQmAJ0+ezE4z8wwfPnygk5MTlZSUaGRkxEWLFiWpEPPt2zc6OjpSSUmJGzZsyEFL8w9yuZzXrl1j3759qaWlRQCsW7cut23bluWjgOnhzJkz1NDQYPXq1Xn9+nWamprSysqKb9++zZL+Eq9JWdV+fiHfCsrQ0FAqKysrpBzif4mOjqaBgQEnTJiQpv0FQRCTcmtqatLGxiZTyZMVRUREBPft28cWLVpQSUmJMpmMLVq04L59+9I81ZRbefz4Ma2trampqcldu3bltDnJCA4O5rx581ioUCEqKSnRyckp14vfzCAIAt3d3dm7d29KJBICYLt27XjhwoVko3ejRo2iubk5N2/ezAYNGlAikVBFRYXt27enm5tbsnr2pUuX5vjx47PzdFJk0aJF1NbWpiAIFASB1apVY7Vq1VId1Uj0zZo3b142W5q3+PTpEwcPHkxlZWXq6+tzzpw5vH79Oi0sLFi4cGHevHkzp03Ml0RERHDPnj1s1KgRJRIJ1dXV2b179xR/sznBw4cPWbhwYZYsWZLXr19nmTJlaGhoyAcPHii8r6CgIALI9SU7c5p8KyjJH9NNjRs3Vni7+/fvJ4A0C4BEvw9lZWXWqlWLgYGBCrcprcTHx/Ps2bNJomBr1qzJtWvX5rtcbeHh4aIjd8+ePXNF/dtv375x2rRp1NHRoaqqKocNG/bHTXleuXKFEolEjDg1NzfnlClT6OHhwYMHD1JTU5NSqTTNaX6qVKnCwYMHZ98JpEKvXr3EMpHHjh0jgN+WTa1Xrx47duyYHebleb5+/cqRI0dSWVlZTLT/p+WhzSk8PT25cOFC0dXE1NSUkyZN4suXL3PUrg8fPrBUqVI0NDTkhQsXWKNGDWpoaCQLjFMEZcqUSTE9WQH/I18LyjVr1lBZWVnhQ/X16tWjo6NjmvadO3cuAYijf/8dXckOBEHg/fv3OWLECBoZGREAy5Qpw3nz5mV7GbScYM+ePdTS0mLJkiUVnsg6rXh5eXHMmDHU0NCgpqYmJ0yYkG9q8GaEadOmUUlJidu3b2eLFi2ooqIiBtckPgCk9fOpV6/ebxObZwcVK1bkgAEDmJCQwLJly6bpYXb06NG0trbOBuvyPgkJCWIQVtmyZamurk5NTU1OnDiRfn5+OW3eH0HivWTYsGHU19cXs32sXr06x+ptf//+nQ4ODlRXV6ebmxtbtWpFJSUlhY8m9u3bl5UqVVJom/mNfC0oP3/+TAB0c3NTWJuJOan27dv3230XLVokisnWrVtnu8N4ZGQkt23bJkbdFilShOPGjaOHh8cf51z89u1bVq5cmcrKyly+fHm2BXB4eHiwf//+VFFRoZ6eHp2dnXNFHsmcJCAggDt37qSenp5Yi7h06dLs1q0bS5UqRQBUUlJi27ZteeTIkd/+blq1asU2bdpkk/Upk5CQQFVVVbq6uvLgwYMEkKapt+3bt1MikeR5F5OsJjAwkE2aNKFMJuPKlSspCAIDAgI4bdo0amtrU01NjaNGjco3acPyAjExMTxy5Ajbtm1LJSUl8Td79OhRxsbGZqstUVFR7NChA6VSKdesWcN+/foRAJcsWaKwe93GjRspk8lSDRArIJ8LSpK0s7Njz549FdbehAkTaGBg8NucXStWrCAASqVStmvXLlt/YG/fvuXYsWOpp6dHiUTCFi1a8PTp07nC7yUniY2N5bhx4wiALVq0yLIp/qioKO7cuTNJ0u+FCxfmKqf27CQhIYHu7u6cNWsWq1evLvpQWltbUyqVctCgQeJFv0mTJnR0dOSaNWtYpUoVAqC+vj6HDh1Kd3f3FG8O3bp1S3f6LkWT+KB56dIlVqxYMc2uNo8ePSIA3rt3L4stzLvcuHGDRYsWpYGBAS9fvpxse1BQEOfMmUN9fX2qqKhw8ODBuTI6OT/z7ds3rlq1ipUrVyYAGhgYcPjw4Xzw4EG2DV4kJCRw1KhRBMDJkydz+vTpBMBp06YpxIanT58SQIHP7i/I94JyxowZ1NfXT7U0XHqIiYmhoaEhx44d+8v91qxZI4rJ9u3bZ0uUdEJCAo8fP84mTZqIP+iJEyf+EVPa6eXMmTM0NDRkkSJFfuvjlh7evXvH8ePH08DAgADYpEkTHjt2TCHfvbyGv78/d+/eze7du4vJxvX09NipUydu375dTFw9e/ZsymQyPnz4kBEREVRRUUlSUefly5ecMmUKzczMCIClSpXi/Pnzk/idDhw4kNWqVcvuU0zCkSNHxNkQACkKn5SIjo5OtVzjn05cXBynTZtGiURCR0fHFKsw/UxoaCgXLlxIQ0NDMdCtwMcy+3n+/DknTpzIIkWKEABtbGy4bNkyBgUFZXnfgiBw+fLlBMAePXqIs4SKEJXx8fHU1NRMMZ1ZAT/I94Ly/v37BMAbN25kuq3Eqayf88z9l40bNxIAJRIJO3bsmOVi0s/PjwsWLBDzh1WvXp27du3KEV/NvIS3t7cYQTxt2rQMjyDHx8fz2LFjSYT8+PHj/7j0EgkJCbxz5w5nzJghjiwCYKVKlTh9+nTevn07RWEdFxfHSpUqsWzZsvz7779TDXZLSEjg5cuX2bt3bzGYrF69ety6dSuHDh1KGxub7DjNVJk7dy4LFSrERo0asVKlSum6eZUtW5ZDhw7NQuvyHu/fv2e1atUok8m4YMGCdM2uREREcMWKFeJDSK1atbh///5sn4b900lISOD58+fZtWtXKisrU0NDg0OGDPnl/VNRHDp0iCoqKqxfvz7nzZtHAJw6dWqmRWW9evXYoUMHBVmZ/8j3glIul9PExEQhaUUaNGjAOnXqpLp9586dopjs3Llzlo1MCYLA27dvs3v37lRWVqaamhr79euXYwEneZWEhAQuWLCASkpKtLW1Tde0o4+PD+fOnUtzc/M/Vsj7+vpyx44d7Ny5s+igb2BgwK5du3LXrl1pDpR4/vw5lZWVWblyZRYvXvy3F/3w8HDu3r2bDRs2pEQioVQqpZqaGrdt25ZjGRQ6d+4s+iqnp9gB+WPK3sHBIYssy1sIgsCdO3dSS0uLJUqUyFRy/7i4OP7999+sX78+AbBw4cKcPn36b0c6C1A8vr6+nD17No2NjcUKYKdOncpSX/YbN25QT0+P5cqV44wZMxQiKqdMmcIiRYr8cTEIaSXfC0ryx5RYZiMp3717RwDcs2dPittPnTpFqVRKiUTCbt26ZYmYDA8P56ZNm8Qk0SVLluTy5ctzNA1RfuDp06esUqUKJRIJR48ezfDw8BT3EwSBV69eZadOnaikpEQNDQ0OGDCAHh4e2WxxzhAfH8+bN29y6tSptLe3Fx+eqlatypkzZ/LevXsZ9tN1dnamRCJhp06d0nWcr68v27VrRyUlJUokEiopKbFZs2bZLi7Lli1La2trFi9ePN2//UWLFlFLSyvHK/3kNMHBwezSpQsBsG/fvgpN8/Xq1SuOGDGC2traol/7pUuXCoRBNhMTE8M9e/aIsxglSpTgypUrGRISkiX9vXz5khYWFjQzM+P48eMJgFOmTMnw/z2xeEHBQ0nK/BGCMrHEWWLljYwwefJk6uvrpzgCdefOHTHtSffu3RUe/PL69WuOGjWKOjo6lEqlbNOmDc+fP//H34AUSXx8PJctW0Z1dXUWK1aMFy5cELf5+fnR1dVVzMFWpkwZrl69+pe5EfMLXl5e3Lp1K//66y/q6uqK5Q579OjBvXv3KiywydvbmwBYvnz5dF/s169fTyUlJfr4+HDt2rV0dHRMIi63b9+epeIyJiaGMpmMEomE69atS/fx586dIwC+f/8+C6zLG9y8eZMWFhbU1dVN9whveggLC+OGDRtYrlw5MbuAq6trlv6W4+LiGBQURE9PT7569YqPHj2ih4cHnz59yn/++YcvX77k69ev+fbtW3748IGfPn2ip6cnvb296evry2/fvjEwMJDBwcEMCwtjZGRknhfCgiDw3r177NatG5WUlKilpcURI0bwzZs3Cu/Lx8eHFStWpI6ODocMGZIpUenr66vwzDH5CQlJIp8TGxsLU1NTDBw4EIsWLUr38ZGRkShWrBh69uwJV1fXJNtevXqFatWqITIyEj179sTOnTshk8kybXNCQgJOnjyJdevW4erVqzAyMsKAAQMwePBgFCtWLNPtKwKSiIqKQnh4OCIjIxEREYHIyMgkr1Nal/g6OjoaEokESkpKkMlkSZbfrVNWVoa+vj4MDAxSXNTU1DJ0Th8+fMCgQYNw9epV1KxZE8rKyrh9+zakUinat2+PYcOGwdHRERKJRMGfZu4gLi4Od+/exblz53D+/Hn8888/kEgkqF69Opo3b47mzZujcuXKkEqlCu33/PnzaN68OQDg2LFjaNeuXZqP3bNnD3r37o2YmBioqqoCAHx9fXH06FEcPnwYN2/ehEwmQ6NGjdC5c2e0a9cO+vr6CrP9+fPnsLOzg66uLnx8fKChoZGu4319fWFqaoojR46gQ4cOCrMrLxAfH4+5c+fCxcUFtWrVwp49e7Ll+kYSt27dwvr163HkyBGoqKigZ8+eGDZsGCpUqJBk36ioKHh7e4uLv78/IiIiEBERgfDwcPF1autiY2MVbr9UKoW+vn6SxcDAINX3P7/W1NTMVdcvHx8fbNy4ERs3bkRAQACaNWuG0aNHo0mTJgq7zoSHh6NTp064evUqOnXqhP3792Py5MlYuHBhuj8LKysr/PXXX1i2bJlCbMtP/BGCEgBGjBiBY8eOwdPTM92Cz9XVFRMnTsT79++TXOy+fv0Ke3t7BAUFoXv37ti9e3emxaSvry+2bt2KTZs2wdvbGw4ODhg2bBg6duwo3iyzg/DwcPj6+sLHx0dc/vvex8cHUVFRv2xHKpVCU1MTmpqa0NLSSvJXXV0dJCGXy5GQkAC5XJ5k+e+6n9/HxcUhJCQEYWFhKfarrq6eqtg0MjKCpaUlrKysYGVlBQMDA0gkEkRERODkyZM4cOAAzp07B7lcDmVlZfTt2xcuLi4wNDTMio86RyEJT09PXLhwAefOncOVK1cQHh6OwoULo1mzZmjevDkaN26MQoUKZakdM2fOxPr161G9enW8ePECr1+/TrMwO378ONq3b4/v37+naGeiuHRzc8OtW7cgk8nQuHFjdOrUCW3btoWBgUGmbN+8eTMGDx6MqVOnwsXFJd3Hk4Senh6mT5+OSZMmZcqWvMTHjx/RvXt3PHr0CLNnz8bUqVMV8jCeVkgiMDAQT58+xa5du3D69GmEhISgcOHCMDExgVwuh4+PD4KDg5Mcp6mpCR0dHWhpaUFLSwva2tri67S819DQAEkIggC5XC7+TetruVyO8PBwBAcHIygoCMHBweLy8/vUro3KysriNbBYsWKwtLRMslhYWGT4gTwzxMTE4NChQ1i1ahWePHmCUqVKYeTIkejTpw+0tbUz3X58fDwGDx6MHTt2oEWLFjh79myGRGW3bt3w9etX3L59O9M25Tf+GEH58OFDVKtWDefPn0fTpk3TfFxcXBxKlCiBBg0aYNeuXeL6oKAglCtXDr6+vujWrRv27t2b4acpkrh58ybWr1+Po0ePQkVFBT169MCwYcNgb2+foTZ/15+fnx/evHmDN2/e4MOHD8kEY0RERJJjtLW1YWpqiiJFisDU1FRcjI2Noa2tnaJg1NTUhJqaWpY+DcfHxyMkJARBQUFpXvz9/REeHi62oa6uDhUVFYSHh0MQBFhaWqJx48Zo2rQp9uzZgxMnTqB169ZYv349zM3Ns+xcsoOYmBh4eHjg3r17cHd3x7179+Dj4wOpVIqaNWuKo5D29vYKH4X8FQ0bNoSWlhaWL18OW1tbTJo0CfPmzUvTsZcvX0bjxo3x6dMnWFpa/nJfX19fHDlyBIcPH8atW7cgkUhQq1YttG7dGq1bt0bp0qXT/X2tU6cO7ty5g4CAgAwL7woVKqBWrVpYv359ho7PS5DE3r17MXz4cBgaGmL//v2oUaNGlvX19etXvHz5Eq9evcLLly/x/v17cbTx59FDqVQKPT09xMfHIzw8HGpqarCzs0OjRo1Qv359WFhYwMzMDJqamlliq6JJSEhASEhIimLTz88PX758wefPn/H582d4eXlBEATx2CJFiqQoNhMFp7q6epbZTRJ3797FqlWrcPToUWhqaqJfv34YPnw4SpYsmem2Z8+ejblz56JBgwa4evUqJk2ahEWLFqX5d79q1SpMmTIFoaGhUFFRyZQ9+Y0/RlCShK2tLezt7bF///40H7dz5044OTnhxYsXsLW1BfBjCsTOzg4fPnxAly5dsH///gzdfMPDw7Fnzx6sX78eL1++RKlSpTBs2DD06dMHenp66W7vv8TFxeH9+/f4999/RfGYuCQ+vSopKcHS0hJmZmYpCsYiRYqgSJEiCnlCzC3ExcXh2LFj2L17N65evYqYmBgYGhrCwMAA8fHx8Pb2RlxcnLi/rq6uOBLbpEkT9O/fH5UqVYKFhUWumjr6LyTx5csXUTjeu3cPT58+RXx8PNTV1VG1alXUrFkTNWrUgKOjo0KngdNDQkIC9PT04OzsjMmTJ2PmzJlYvHgxXrx4AWtr698e/+rVK9ja2uLGjRuoW7dumvv19fXF6dOncerUKVy+fBnR0dEoWbKkKC5r164NZWXlX7YRGRkJfX19FClSBF++fElz3/+lbdu2iI+Px9mzZzPcRl4gNDQUQ4cOxYEDB9CrVy+sXbsWOjo6mW5XEIRkwvHVq1d49eqV+HCsoaGBsmXLolSpUjA3N4eZmZn418zMDCYmJlBSUgIAvH79Grt27cKBAwfg6ekJc3NzdO3aFd26dUPFihVz9e8+I8THx8PLyyuJyPx5+fr1axLBaWJiAhsbG1SoUAF2dnaws7ND2bJlFS40v379ig0bNmDz5s0ICgpCy5YtMWrUKDRq1ChT/wNXV1eMHTsWtWrVwp07d9IlKu/fv48aNWrg4cOHqFKlSoZtyJdko79mjrNo0SKqqamlOaJMLpezTJkyScq6xcXFiRGuHTp0yFBgzIsXLzhs2DBqaWmJyc8vX76cYUfrkJAQ3rlzh9u2bePEiRPZunVrWltbi2Xt8P9JpWvWrEknJycuWrSIx48f55s3b7Il6XpuICEhgdeuXeOgQYPERNtlypThnDlzkjmCy+Vyenl58datW9y9ezfnzJnDbt260cTEJEm9aT09PdarV49jx47lrl27+OzZsxz9PCMjI3nz5k0uXryY7dq1S2JviRIl2LNnT65bt44eHh656v/u4eFBALx16xbJH+dRrFgxNm/ePE2/icSgmE2bNmXYhsjISJ46dYqDBg2iqakpAVBXV5ddu3blvn37Ug3qWbVqFQGwf//+Ge6b/FHTO6dzaWY1t2/fZrFixaijo8P9+/dnqA1BEPjp0yeePn2aS5YsYZ8+fVi1alUxNykAampqsmrVquzbty+XLFnCM2fO8NOnTxm6Vsvlct6+fZvDhw+nkZGRmFx/9uzZKeZLza/ExcXx06dPvH79Onfu3MmZM2fyr7/+orW1tVj5SiqVskyZMuzcuTMXLFjAU6dO8cuXLwoJIIqKiuK2bdtoZ2cnJktfv359pkqWbtmyRcxSAYATJ05M8/VGRUWFa9asyXDf+ZU/SlB6eXlRKpVyy5Ytadr/6NGjScqiCYLABg0aiFVQ0nOBiouL46FDh+jo6EgANDY2prOzc7rTD0RHR9Pd3Z1r1qxhr169WLp0afFCKpFIWLx4cbZo0YJjx47lpk2beOPGDfr7++f5qMCM4O/vz71797Jnz57izcDS0pJTpkzh06dPM/SZXL58mcWKFaOysjIbN27MDh06sESJEuL/QEVFhZUqVWK/fv24evVq3rp1K0tKLgqCwA8fPnDv3r0cMWIEK1euTCUlJfGGWr9+fU6dOpUnT57MshKTimLNmjVUVlZOkkHh2LFjBMBjx46lqQ1ra+vfVrBKK4Ig8NGjR5w1a5aYW1Imk7Fu3bpcunSp+AASFxcnis8jR45kqs+VK1dSXV09X/5Oo6OjOW3aNEqlUtaqVStdZREjIyN5/fp1uri4sFWrVmIVKgDU0tJitWrV2LdvXy5dupRnzpzh58+fsyz7RXx8PM+fP88+ffpQW1ubAFi5cmUuW7aMX79+zZI+8wLh4eF0d3fn5s2bOWLECNatW5d6enri/0lXV5d16tTh8OHDuWnTJrq7u2dYCAqCwBs3boh1u/X09Dh+/Hh+/PgxQ+3t37+fMplMTMWXVlFZvXp19ujRI0N95mf+KEFJ/qgVXLt27d/uJwgCq1atSkdHR3Fdt27dCIBVq1ZNc665kJAQLl68WLzx1K1blwcPHkxT1Qa5XM5Xr15x586dHDZsGKtUqUJlZWVRuFSrVo0jRozg7t27+ezZsz8qqXZKxMfH89atW5w+fbpYUxYAK1asyKlTp/LevXsKuWFHRkZywoQJlEqlrFixIj08PBgaGspbt25x9erV7NevHytVqiSmkkocIezduze3b9/Ojx8/ptuOiIgIXr9+nQsXLmSbNm1YuHBhsW1ra2v27t2bGzZs4JMnT/Jcqcdu3bqxRo0aSdYJgsDmzZuzWLFijIyM/G0brVq1YvPmzbPEPi8vL27cuJEtW7akmpqa+Jk3b95cHJnJbB69RAGd1mTweYWrV6/S2tqaKioqnDdv3m+/m56enjx48CBHjRrFKlWqiLMs2trabNKkCWfPns0zZ84obOQro0RFRfHvv/9mhw4dqKqqKpaH3LhxI79//55jduUWBEGgp6cnT58+TRcXF3bt2pU2NjaUSqXi4EepUqXo5OTEbdu28e3bt+n+f37+/JmTJk2ivr4+JRIJ27ZtyytXrqS7nePHj1NFRYVly5ZNs6gcPXo0S5Qoka5+/gT+OEG5b9++FHO+yeVJv0BXrlwhAJ4/f54kOWbMGDFvWUxMzG/78fLy4sSJE6mtrU0VFRX269eP//zzT6r7C4LAr1+/8siRI5w8eTLr168vPgVLJBLa2NiwT58+XLduHR8+fJgmG/4EPD09uWXLliR5EgsVKsTu3btz9+7dWXqDfvjwIe3s7CiVSjlo0KBkfcXGxvLZs2fctWsXR40axYoVK4rTQxYWFuzVqxe3bdvGDx8+JLmACYLAd+/ecffu3Rw2bBgrVqwo3li1tLTYsGFDzpgxg6dPn84XNy8LCwuOGzcu2fp3795RRUWFM2bM+G0bEyZMYPHixbPCvCRERkby5MmTHDBggDgiLJVK2ahRIy5evJgeHh4ZGiF78uQJAdDd3T0LrM5+vn//TicnJwJgnTp1Uiy3Fxsby/v373PlypXs1KmTWCoR/1+0oXfv3ty4cSP/+ecfhef2VSQhISHcsWMHmzRpQqlUSiUlJbZs2ZK7du2iv79/TpuXq4iOjqaHhwd37NjB4cOHs0KFCuI1sXDhwuzQoQNXrlzJhw8fpvnBODIykps2baKtrS0BsFy5cty8eXOaHkQTuXjxItXV1WltbU0AnD59+i/3P3DgAAHk+tmf7OaPE5SRkZHU0dHh8BmLOPPEczZzvcGS08+w2JTTLDn9DJu53uDME89Zp0132tvbUxAEscC8mZnZb4fqX758SScnJyorK1NHR4eTJ0+mt7d3ivt++fKF27dvZ48ePcQRTAA0Nzdnhw4duHDhQl65ciXLqgjkRaKjo3nx4kWOGzdOfKKUSqV0cHDg3Llz+eDBg2y9+cTFxXHVqlXU19entrY2Fy5cyOjo6FT3DwoK4okTJzh27NgkAtPIyIj29va0s7MTyxgm+nk6OTlx06ZNuf7GmhG+fv36yynjGTNmUEVFhe/evftlO1u3bqVEIvnlZ69IEpORq6iosEWLFmzevDk1NDTExO9dunThli1b0jy9GxwcnKGyjbkNQRC4b98+GhkZUVdXl5s3bxYFdkxMDC9evMjJkyezTp064mivqqoqa9euzUmTJvH48eN5WoT5+flxzZo1rFmzpjgYUKVKFTo7O/Pu3bv57verCEJCQnju3DlOmzaNdevWpaqqqui606hRI86ePZtXrlz57b1XEAReuXKFbdu2pUQiob6+PidNmsQvX76kyY6bN29SW1ubFhYWBMDly5enuu+nT58IgKdOnUrXueZ3/jhB+SkgglUm72OxKadZfNoPIfnfpfjUH3/rzTvOZRt3iTWKU6umIAgCb968yVatWonCc+nSpcl854KCgnjkyBEOHTpUfBKSSCSsXLmyeDFNTXz+qcTExNDd3Z0rVqxgixYtqK6uLn7G/fv3p5ubG4OCgnLaTAYGBnL06NFUUlKipaUlDx06lGzU0cvLi5cuXeKqVas4ZMgQOjo6ir6dicI48bWhoSE7duzILVu2ZNg/KC9w6NAhAqCvr2+K29MaoHP79m0C+OUsgCKpX7++6L/85MkTkj++q9evX+f06dNZvXp18f9ZsmRJDh06lEeOHPnld1VXV5eLFi3KFvuzgo8fP7Jp06YEwM6dO9PHx4efP3/mhg0b2Lp1a1Fwm5iYsGPHjlyxYgXd3d3T5P6TF/H19eXOnTvZpUuXFGvd52XhnJXExMTwzp07XLx4MVu3bi1+djKZjFWrVuW4ceN49OjRX35+Hz584Pjx46mrq0upVMquXbvy2bNnv+374cOHNDAwEF2Ktm/fnuJ+giDQ2Nj4tyOZfxp/TNogADjx1BsT//4HchJy4fenLZUA8vg4hF/egOend8DMzCzJdrlcjpMnT2LJkiVwd3dH2bJlMWnSJHTr1g0qKiqIiYnBnTt3cPnyZVy+fBkeHh4gCWtrazRs2FDMb5bZxMr5Bf5/zjh3d3cx1c3jx48RFxcHVVVV1K5dG82aNUOzZs1ga2ubK1N3vHr1CiNHjsTVq1dhaWmJcuXKISAgAK9fvxZTNamqqqJUqVIoW7YsbGxsYGNjg3LlyqFMmTIICwvD7du3cf36dVy/fh1PnjyBIAgoX7482rZti3bt2qFSpUq58twzwpgxY3Dq1Cl8+PAh1X0SE5cfP34cbdu2TXGfwMBAGBoaws3NDZ06dcoqcwH8L6dtmzZt8ODBA/j4+KT4/wgODsb169dx6dIlXL58Ge/evYNUKkWVKlXQuHFjNGrUCDVr1hQLFlSsWBE1atTAhg0bstR+RZOQkABXV1fMnDkThoaGGDx4MEJCQnD27Fm8evUKMpkMtWrVQvPmzdGiRQuUL18+33x/00pCQgIePHiAc+fO4dy5c/Dw8AAAVK5cWfxcqlWrlq2J3fMKgiDg9evXuHXrFm7fvo3bt2+LKbpsbW3RokULtGzZEg4ODsnSfEVERGD37t1YunQpPn/+jJYtW2LatGlwcHBItb8XL16gUaNGiI2NRWhoKP7+++8UK1i1a9cOERERuHz5smJPOA/zxwjKE0+9MebQU/x8skyIR8itvYh8eQ1CTASUjSyhV7cX1K0q/rQTAYkEq7rYo639D0EZExODPXv2YNmyZXj79i3q1q2LSZMmoUmTJnj27JkoIG/fvo3Y2FgULlwYjRo1QsOGDdGwYcNcUzoxp4mKioKHh0cSAenr6wvgR3mrGjVqiHkSK1SokKuSyMbGxuLdu3d4/fq1uLx69Qpv375FTEwMgB+JkgVBQPHixdGlSxfUqFEDZcuWhZWVVZpvHKGhobh8+TKOHz+OM2fOIDg4GObm5qK4dHR0/G2uxNxM1apVUaZMGezZsyfVfUiiRYsW4mecWgUdIyMjjBo1Cs7OzlllLgCgU6dOePr0KVRVVVG1alXs2LEjTcd9+fIFly9fxqVLl3DlyhV8//4dqqqqqFatGurUqYNr165BXV0dV65cyVL7FcmjR4/g5OSEly9fonjx4vDz80NkZCRMTExEodSoUSOF5NXNT/j7+4vVqS5cuIDg4GAYGBigSZMmaN68OZo2bQpjY+OcNjPX8vXrV9y6dQuXLl3CuXPn4O/vDx0dHTRp0gQtW7ZEs2bNYGJiIu4fHx+PQ4cOYeHChXj16hUcHR0xbdo0NG7cOMWHm3fv3qFhw4YICgpCXFwczp49i0aNGiXZZ9GiRXBxcUFwcDAkEimk0j/rISkl/ghB+el7JJq63kScXEiyPuDEEkT9ewc6VdpCycAUkc8vI9b3HYy7uUCtqG2SfVVkUhzuVwFnDu7E6tWr8e3bN7Rv3x6DBg1CQEAATp06hYsXLyIkJASampqoV6+eKCLLlSv3xz2R/xeS+Pjxo1ihxd3dHc+ePUNCQgI0NTVRtWpVUUBWr14911xMIyIi8ObNG1HMJIrHDx8+QC6XAwAMDQ2TjDba2NigbNmyMDExwY4dOzBjxgyEh4djwoQJmDRpErS0tDJkS3x8PG7duoUTJ07g+PHj8PT0hK6uLlq2bIm2bduiWbNmCkkSnV1ERkZCV1cXa9euxZAhQ36577t371CuXDlMnjwZc+fOTXGf2rVrw9LSEnv37s0KcwEA3t7eKFasGGbPng1nZ2ccPHgQXbp0SXc7giDg2bNnuHnzpjjy4u/vDwCws7NDnTp1ULt2bdSuXTvXVWeKj4/HlStX4OzsjEePHgEAJBIJHBwcRBFZoUKFbK20lJf51ehlnTp1ULNmTTg4OOS670FuQRAEPH78GGfPnsXZs2fx4MEDkETlypXF0csqVapAJpNBEAScPHkSLi4uePjwISpXroxp06ahXbt2yb6vnp6eaNCgAby8vCCVSnHt2jVUr14dAPDCOxRbrrzAhScfINc0RLycUJZJUNJIC9WsDNCpclGUM9PNiY8jR/kjBGXnTffg4RmcZJo71udf+O0eD736/aBb/cdwNhPi4LN1OGSaujDplbTwuwREnPdrBLo5o3379rC0tMS9e/dw+/ZtyOVyVK1aFS1atEDjxo1RrVq1PD1qlBlIwt/fP4n4ev36Nf755x98//4dAFC6dGnUqFFDFJC2trZihYqcIjAwMJlofP36NTw9PcV9ihYtmkw02tjY/LbGd1hYGBYuXIiVK1fCwMAALi4u6N27d6ZuuCTx7NkzHD9+HCdOnMDTp0+hoqKChg0bom3btmjTpg2KFCmS4fazg+vXr6N+/fp49uwZ7Ozsfrv/jBkzsGzZMrx48SLFEmwDBgzA06dPRZGTFcydOxeLFy/GokWLMGbMGAQEBCjEZYUknJ2dsXjxYvTq1Qu3b9/Gu3fvAACWlpaiuKxTpw7KlCmT7WItISEBV69exf79+3H48GGxclSlSpUwbtw4NG/evMB1R0Ekjl5evHgRd+/exadPnwAA5ubmcHBwQM2aNVGzZk1UrFgxV83a5Ba+ffuGCxcu4OzZszh//jxCQkJgaGiIZs2aoWXLlmjSpAn09fVx5coVLFy4EFevXkWZMmUwZcoUdO/ePcm929fXFw0bNsT79++hpqaGIxdvYsuzaDz4HASZVJKi61zi+mqWBljylx0sDfNGqU5FkO8F5XPvULRem7yIe/C17Qh7cBxFxxyEVPV/U2ih99wQcmM3zIbtgJKOUbLjlC4vw4dH16GmpoaGDRuiTZs2aNWqFUxNTbP0PHIbgiDA09MziQBLfB0SEgIAUFZWRqlSpWBjYwNbW1tUr14d1atXz9YbT0JCAvz8/ODt7Q0fH58kf39+nejfKJVKUaJEiWSisUyZMpkuP/n582dMnjwZbm5uqFSpElauXJmuUoG/a/vkyZM4fvw4bt68CblcjurVq6NHjx7o1q3bb0VvTuDi4oLFixcjKCgoTS4AUVFRsLGxgZ2dHU6dOpVs+7JlyzB79myEh4dnyYyAXC6HlZUVmjRpgpCQEPj4+ODu3bsKa//EiRNo164dfH19YWJiAn9/f9y5c0ccwXzy5AnkcjkMDAxQq1Yt1KlTBw4ODrC3t8+S+tIkce/ePRw4cABubm749u0btLS0EBERgerVq2PPnj1pKo1ZQObw8/PDvXv3cPfuXdy7dw+PHj1CbGws1NTUULly5SQi8+dp3gJ+XP/d3d1x9uxZnDlzBv/88w+kUilq1qyJli1bokWLFoiKisKiRYtw8uRJWFhYYNKkSejXr59YRjIwMBCNGjXC+wR9GLUcC4lMlqYYDJlUAplEgqUd7UR3ufxOvheUs06+wL77nkj4zxfA/+AMyMMDYTowqQN89Oen+HZwBoz+coaGdfWkjQlymMd8xsQGxdCoUaMsuYjnNiIiIvD161e8efMmiXh88+aNOEqhqamJMmXKJBFgNjY2KFGiRJaNPJJESEhIMmH439f+/v74+SuurKwMU1NTsXZ5Yh3fYsWKwcbGBtbW1lBTU8sSmxO5c+cOxo4di4cPH6JDhw5YsmQJSpQoobD2g4KCcObMGRw5cgRnzpyBRCJBq1at0KdPH7Ro0SLXjJ63a9cOkZGRuHTpUpqPOXjwILp164abN2+iTp06SbadPn0arVu3xtevX7NkevDMmTNo1aoV7t69i2bNmmHChAkK9dd89uwZ7O3tce/ePdSoUSPZ9oiICLi7u4uBCffu3UNUVBSkUilKly6NypUro1KlSqhcuTLs7e0z7P7w/Plz7N+/HwcOHMCXL19gamoKW1tbuLu7Q1VVFStXrkSPHj3+eDeenCIuLg5PnjzBvXv3RKHp5eUF4IfveeIUec2aNWFnZ5fjsz+5CS8vL5w7dw5nzpzB5cuXERkZCUtLS3Ts2BEVK1bE6dOncejQIRgaGmLs2LEYOnQodHV1ceDuO0w99S+AH9/5uIAvCL29H3F+7yGPDIFEWRXKhYpCp3qHZLpBAsD1pxiM/Ey+F5TNV93Ea7/wZOt9tg6DTFMPxt1ckqyP++4J363DYNB0OLQrNk92XNkiOjg7qk6y9XkNuVyOb9++icIrtSVx5A4A9PX1RcH4s3AsWrSoQqfgYmJi4OPj88sRRR8fH0RHRyc5zsjIKEWx+PPrQoUK5QrfLkEQsH//fkydOhXfvn3D0KFDMXnyZIVPUwcEBODAgQPYtWsXHj9+DCMjI3Tv3h19+/aFvb29QvtKLyVLlkTbtm2xfPnyNB8jCAKqVKkCDQ0N3Lp1K4moef/+PaytrXHp0qVkDvSKoE2bNvDy8oKrqyscHR3x4MEDVK1aVWHth4aGQk9PDwcOHEDXrl1/u398fDxevHiBx48f4/Hjx/Dw8MCzZ8/EoDBra2tRZCYu+vr6Kbb18eNHHDhwAAcOHMDLly9hYGCAjh07okKFCtizZw/c3d3Rp08fLFu2LFeOdv/peHl5JRnFfPz4MeLj46GmpiZer21tbcW/6QkMzK/Exsbi5s2bOHLkCI4ePYqAgABYWFigUaNGCAwMxNmzZ6GhoYHewyfgvKQS4uT/k0rRHx4i7NEpqJqVgUzLAIyPRdS/dxHr9RIGzUZA275Zkr5UZFJcHFM3309/53tBaT3jLOLlyU/Re+MAKBmYwbjznCTr40P84LNxAPQbDoRO1eQpSpRlEryb3yLL7M0IJBEbG4vw8HCEh4cjLCxM/Js4iufl5ZVEKPr6+opBJQCgpKSURHj9vJibm6N06dIoXLhwmkYlBEFAVFQUwsPDERERIdqV2uvg4OAkgjEwMDBJexoaGr8UiaampihSpIiYfiUvERUVheXLl2P58uWIiYnBoEGDMGnSpCwZYXv+/Dl27dqFvXv3wt/fH3Z2dujbty+6d++e7UFQUVFR0NLSwtatW9GvX790HXvhwgU0a9YMp06dQqtWrcT1iQFey5cvx4gRIxRqr5eXF4oVK4Z169bB09MTW7Zsgb+/v8IfTgwMDDBp0iRMmTIlQ8cnJCTg9evXSUTm06dPERkZCeDHCFbiKKalpSU+ffqEkydP4v79+9DQ0EC7du3QrVs3lCxZEjNnzsThw4dhb2+PFStWoH79+oo81QKykJiYGHh4eODhw4d4+fIlXr16hZcvXyI0NBTAj9RlZcqUSSIyy5Yti+LFi/+RI5oJCQm4desWDh8+jCNHjuDbt2/ifcXXtitUzWzwu1luCnL47hwDJsTDbNDGJNtkUgkqW+jDbXDNLDyLnCdfC0pBIIpPP5vitoyOUAKAsG8YVFVVoKamBjU1Nairq0NTU1Nc1NTUoKqqChUVFSgrKyd5nfj3v/xXqJFEVFSUKA7/KxT/uy4+Pj7Vz0FXVzdFoZi4mJiYQENDA5GRkUnEXlrEYEqvIyMj8buvlaamJrS1taGtrQ09Pb1fikVdXd18P70WGhqKNWvWYMWKFYiMjET//v0xZcoUWFhYKLyvhIQEXLhwATt37sTJkychl8vRvHlz9O3bF61atcoWYf748WNUrlwZ7u7uYuRkWiGJBg0a4Pv373j69GmSkRY7OztUr14dW7ZsUai9c+bMwdKlS+Hj44N69eqhbNmyWRJNXqlSJVStWhWbNm1SWJtyuRzv3r2Dh4cH7t+/j4sXL+L9+/fiA6WamhrKlSuHZs2aoVy5crh27Rp27NgBQ0NDLFiwAL169frjR7PyAyTh6+ubRGAm/k30e1dVVUXp0qWTiEwbGxsUK1Ys1XRd+Q25XI5bt27h77//xtHrj6DSOu1uLd8Oz0Gs3zsUHZnyteH0iNr5Ovo7Xz+KSKUSKMskKY5QyrQMIA8PTLZeHhEkbk8JyhMQERGOkJAExMfHQy6XQxAECILwWxGVGRIF1c9/f15UVVXF11KpVPybOILi6+sLPz8/PH78OIk4i4yMTDZ1/F+kUqko/rS0tJK8Njc3T7buv6//u05TUzNXTDvnJnR1dTFjxgyMGjUK69atw/Lly7F161Y4OTlh6tSpsLS0VFhfSkpKaNmyJVq2bImgoCAcPHgQu3btQseOHWFgYIBu3bph4MCBqFChgsL6/C8vX74EAJQtWzbdx0okEixcuBA1a9bEgQMH0LNnT3Fb3bp1cf78eYXZCfwQ4Fu3bkX37t0RGRmJJ0+eYNy4cQrtIxFjY2MEBAQotE2ZTAaJRIJHjx5hz549CA0NhaOjIxo3bgxjY2O8ffsWjx49wooVK0S/aG1tbdjZ2eHjx484cuQISpYsCWtr60wHphWQc0gkEpiamsLU1BSNGzcW15OEn59fMpGZmB8zEQMDA5ibmydZihYtmuR9RlOi5SZkMhnq1auHevXqQf/Ec+x/8DXVIBwhLgZMiIUQG4Xod/cR/dEDGjYpu8TJpBIc9viarwVlvh6hBFL3oQy+uh1hD1OI8r7rhpCbqUd5x3/7iGKvD6Bu3bqoW7cuatWqBV3d/31B4uPjERkZiaioKERFRYmvf/4bGxsL/ih7CQDia0W8T88xwI+Rwt8JQjU1tXw/QpjbiIiIwPr167Fs2TIEBwejT58+mDZtGooXL55lfb5+/Rq7du3C7t274evri3r16mHMmDFo1aqVwkeoJk+ejIMHD4oVLzJC+/bt8fTpU/z7779i+pRjx46hQ4cO+Pz5s8IKCJw6dQpt2rTBo0ePcOXKFcycORM+Pj5Zkq2ge/fu8PX1xbVr1zLdVnx8PE6ePIkNGzbgypUrMDQ0RL9+/TB48GDxe0QSx48fx+TJk/H+/Xt07twZjRs3xsePH8Up858FrrGxMaytrUWBmfi6ZMmSBWIzn5GYAu7Nmzf4+vUrvLy8ki3fvn1Lcoyurm6qYtPY2Bi6urrQ0dGBjo5OrgkO/BWp6YdEAs+vRcTT/3+AlUihUaomDJqPhEwtZWGdX2IwUiPfC8pZJ19g733PZE8YKeehjIfPtmGQquugSO/kgQIyCVBBMwwqz0/gxo0b8PPzg1QqRYUKFUSBWadOHRgZJReiBRSQESIjI7Fp0yYsWbIE379/R69evTBt2rQsTdcSHx+PY8eOYdWqVbh79y6KFy+OkSNHol+/fgpLnN6qVSsIgoCzZ1N2SUkLL1++hJ2dHVatWiX6TAYHB8PQ0BBbtmxJt29marRu3Ro+Pj549OgRSpUqherVq2dZ8vThw4fjzp07ePr0aYbb8Pb2xpYtW7Blyxb4+PjAwcEBQ4cORceOHZNkMHj06BHGjx+PmzdvokmTJli2bBnKly+frL2goCC8e/cO79+/x7t375IsiVOlAGBiYpKq2MwPI1cFJCcxgDIlsZm4+Pn5pTh7p66uDh0dnSQiM/H1f//+/FpdXT3ZbNyvXqe2LTH2ICYmRlyio6OTvJ/1Sh8CUh9MiQ/8ioTwQMjDAxH15jYgU0KhpsMg00w5+C03xmAoknwvKF94h6JVCnkoASDg+CJEvb0HnaptoaRvisjnVxDr+xbGXRdAzaJcisesbW2OVg4VQBIfPnzAzZs3xSUxAW3ZsmXRpEkTNGvWDHXr1hXzWRVQQEaJiorCli1bsHjxYvj7+6NHjx6YPn06SpcunaX9PnjwAKtWrYKbmxvU1dXh5OSEkSNHpphYPD1YWVmhU6dOWLJkSabacXJywtmzZ/HhwwdRtFStWhWlSpXCvn37MtU28KPEm6WlJTZs2ABra2s0aNAAN27cUFj+0P/i7OyMXbt2JUmonxZI4sqVK9iwYQNOnDgBNTU19OzZE0OHDk3muuDp6Ylp06Zh3759KFeuHJYtW4amTZtmyN7AwMBUxWZiAAgAFClSJJnYtLa2RokSJf6I9Gt/MvHx8fD19YW/vz/CwsIQFhaG0NDQFP+mti5nZIoExaYkz3f7K/wPOkOIjYBJ7xWpzup9XNAi35ZpzPeCEki5Ug7wozJOyM0ftbzlMRFQKWwJvTo9oV68cvJGBDkS/N/BZ/dEdOzYEVOnTkXFihWT7JJYX/Tq1au4cOECvLy8oK6ujnr16qFZs2Zo1qwZrK2tC6aPC8gwMTEx2Lp1KxYtWgQfHx907doVM2bMyJAvYnrw9vbGhg0bsHHjRgQFBaF169YYM2YM6tWrl+7vc0REBLS1tbFz50706dMnU3Z9+fIFpUqVgrOzM2bMmAEAmDJlCnbt2gUfH59M/9ZmzZqFFStWwMfHB4MHD8bjx4/x+vXrLPsNr1ixArNmzUJ4eOrTbD8THByMnTt3YuPGjXj79i1sbW0xdOhQ9OrVK9loclhYGBYtWoSVK1dCV1cX8+bNg5OTU5ZE9ZIUxWZKgvPndGSmpqapis0/JRCkgNQRBAGRkZGiwIyOjk7m5pUYw5DS+8QYh+DgYPj7+8Pf3x/fvn0TR1b9/f0RHBwsptv6GYuJxyGRpf33Ef70PILOr4XpwI1QLpQ8U0fBCGU+4PP3SDRJoZZ3uhAS4L15KKqVtYKPjw8+f/6MZs2aYdq0ackSLAM/LqivXr3C+fPncf78edy8eRNxcXGwsrISxWX9+vUL/I4KyBCxsbHYsWMHFi5ciK9fv6JTp06YMWNGilOWiiQ6Ohr79u3DqlWr8OLFC9jZ2WH06NHo3r17mhPCP3z4ENWqVcPDhw9RpUqVTNs0duxYbN++HR8+fIChoSEuXbqEJk2a4MWLF7C1tc1wuwkJCbC0tESrVq2wYMECmJqaYsGCBZgwYUKmbU6NHTt2oF+/foiLi/ulj9mjR4+wfv16HDx4EAkJCfjrr78wdOhQ1KlTJ5nYTUhIwJYtWzBr1ixERERg/PjxmDRpUo5de0ji+/fvqYrNn8W0mZkZrK2tUbx4cRgbG6Nw4cIwMjJC4cKFxcXQ0DBP+OMVoHhiYmLw/fv3JEtAQAACAgLw9etXeHp6wtPTE1+/fkVsbKx4XOKUdyL6+vqwsLCAjY0NKlWqhNq1a8POzg4dt3r80ofyv4Q9PIHgK1tg0ns5VE2Tzx4V+FDmE0489caYQ0+RkZOVAFjZuQL8H5zBlClTIJFI0LFjR9y7dw8vXrxA7dq1MW3aNDRr1izVkYvIyEhcv35dFJjv37+HsrIyateuLQrM8uXLF4xeFpAu4uLisGvXLri4uODz589o2LAhRo4cmSWBND9DElevXsWqVatw+vRpGBoaYvDgwRg2bNhvk7Pv3LkTTk5OiIiIUMh0Z0BAAEqUKIFBgwZh2bJliIqKgr6+PpYuXYpRo0ZluN2TJ0+ibdu28PDwwI0bNzB58mR4e3tnqY/08ePH0b59e3z79i1ZPyRx9uxZLFy4EHfu3IGFhQUGDx6M/v37p5hHNHH/iRMn4s2bN+jVqxcWLFiQJTlOFQVJBAQEJBObnz59wrdv3/Dt27cUs1Lo6+uLAvO/gvPn90ZGRjAwMChIg5QLSUhIQFBQEAICApKJxP8KxsTXiflVf0ZFRUX0tRQEAaGhoYiIiAAA6OnpoXLlyqhSpQpsbW1ha2uLMmXKpDoSPmjTZVz6FA1KkmYlkUeGQKapl2Qd5Qnw2z0e8YFeMB+1F1KVpK5uMqkEPatbYE6blN3p8gN/jKAEfojKiX//AzmZ4Vqc/v7+mDBhAvbu3QsHBwd07doV+/btw/3792Fvb49p06ahQ4cOv71gvX//HhcuXMD58+dx9epVREVFwdTUFE2bNkWzZs3QqFGjbK15XUDeJj4+HocPH8aaNWvg7u4OS0tLDB8+HP3790+1OoqiePfuHdasWYMdO3YgISEBgwcP/mXVn4kTJ+LIkSP4+PGjwmyYO3cuXFxc8O7dOxQtWhT169eHjo4OTpw4keE2W7ZsiW/fvuHBgwewtbVF+fLlcejQIYXZnBI3b96Eo6Mj3rx5I/rHyuVyHD58GIsWLcKzZ89Qs2ZNTJ48OdWHBpI4f/485s6dC3d3d9SvXx/Lli1DpUqVstT27CIyMlIUlwEBASm+/vn9f3P0SqVSGBoa/lZ8Jr7/E/LgKppEIZcWUZi4/JyiKBGpVIpChQrB0NAwyWJkZARDQ0NoaGggODgYPj4++PDhA16+fClmjtDV1UWVKlVQtWpV8W/RokV/+78UBAGnT5/GkiVL8PC9H4o4rUq2z7cj88G4KKgWLQeZdiHII4IR+eo6EgK9oN+gP3SqtU+x7fyeh/KPEpTAj+nvSUf+wYPPQZBJJSkKSykIARKUN1HHmh7VUyyXdPXqVQwbNgzv37/H6NGjUa9ePaxevRqXL19GqVKlMHnyZPTs2VNMZ/IrYmNjcfv2bXH08sWLF5BKpahevTqaNWuG9u3bo1y5cgUXtQLSxMOHD7FmzRocOnQIMpkMPXv2xMiRI7N8OjwkJERMzh4TE5OqsGzRogVkMhlOnUqfw/uvCA8PR4kSJdCmTRts3boV8+bNw7JlyxAYGJghH0FPT09YWlpi8+bNKFOmDOrUqYPLly+jYcOGCrM5JZ4/fw47Ozvcu3cPFStWxJ49e7B48WK8f/8eTZs2xdSpU1G3bt0UrwUkcerUKcybNw+PHj1CzZo14ezs/MuZk/wOSYSGhqYqNv/7+vv37xCEpK5RysrKorg0NDSEuro6VFVVxSWxkMWv1qX3vZKSkkL+ZyQhl8sRGxuL2NhYxMXFia//+z6t21LaLzo6GkFBQUkE4s+V2BLR1dVNURimtBgZGUFPT0/MWRwZGYmnT5/i4cOHePToER49eoR///0XwI/0d4kjj4nisUSJEun6DGNjY7F3714sW7ZMfKALDg6GtPF4qBUtC/4U7R356gYi/rmEuIDPEKLDIVVRh4pJSWhXbp2sljdQUCkn3/PCOxSHPb7i4edgvPsWjng5oSyTwLqwNiqa62DX7CFwLF/8l5GisbGxWLZsGVxcXKChoYG5c+fC3t4eS5cuxbFjx2Bubo6JEydiwIAB6XIu9/LyEkcvL126hNDQUNjY2KBz587o0qULbGxsFPERFJDP+fbtGzZv3owNGzbAx8cHjo6OGDVqFNq0aZOl5dVCQkKwevVqrFixArGxsRgyZAgmTZokCksLCwt0794dixYtUmi/q1evxtixY/Hy5UsEBQWhVq1auH//PqpVq5butmbOnAlXV1f4+Phg+PDhuH37Nt69e5flCfm9vLxQtGhRDBw4EGfPnoWPjw86dOiAqVOnonLlFIIF8WNE5dixY5g3bx6ePXuGunXrYubMmWjQoMEfKyQzilwuR3BwcKrC8/v374iJiRHF1M+vU3r/qwpmv0Iqlf5WpMpksjQJvszc4mUymVjpLbHflF6rqamlOJL4szg0MDBI0wAL8L8YhBs3buDRo0d4+PAhXr16BUEQoKqqiooVKyYRj6VLl86wG0NISAg2bdqEVatWwdfXFw0aNEBYWBgePXqEli1bYsKcxRh0zDNTMRgFtbz/MASBSUL5N23ahKFDh+Kff/5BuXK/9nnw9vbG9OnTsWvXLtja2mL58uUwNzfH4sWLsX//fujr62PMmDEYOnRouqex4+LicOnSJbi5ueH48eMICwtDuXLl0KVLF3Tu3BmlSpXK0PkW8OcQHx+Po0ePYs2aNbhz5w6KFi2KYcOGYcCAATA0NMyyflMSlkOHDkXp0qWxZ8+eJBVuFEFsbCxKly6NKlWq4MCBAzAwMMC0adMwderUdLWTkJCAYsWKoU2bNnBxcYGpqSlmzZqV4fraaSU4OBgrV67EvHnzIJPJ0KtXL0yePBllypRJcf/EqfD58+fj5cuXaNiwIZydneHo6JildhaQdgRB+KXgzMx7uVyeRGD+TvRlZL/s8jUliU+fPuHq1au4cuUKrl27Bn9/fygpKcHOzi7J1LWtra1CgrC8vLywatUqbNq0CbGxsejatSsAYN++fbCysoKrqytatmwJIPMxGK5d7EW3uXwNC0iR2NhYWllZsUOHDmk+5tGjR6xTpw4BsHnz5nz16hU/fvzIYcOGUVVVlerq6hw0aBCfP3+eIZuio6N54sQJdu/enVpaWgRAe3t7uri48P379xlqs4A/i8ePH9PJyYmqqqpUU1Njv379+OTJkyztMzg4mLNnz6auri5VVFQIgBcuXMiSvnbt2kUAfPDgAVu2bMmGDRumu43jx48TAJ88ecI1a9ZQSUmJvr6+WWDtD3x8fDhx4kRqaWlRTU2NUqmUc+bMSXX/+Ph47t69m6VLlxavNXfv3s0y+wooICvw9vbm3r176eTkxGLFihEApVIpq1WrxilTpvDixYuMjIxUeL8vXrxg3759qaysTF1dXU6aNInLly9n4cKFqampyYULFzImJibZccefeLH4lFO0mHiCxaac/u1SfNoZWk8/y+NPvBR+DrmVAkH5C3bu3EkAfPToUZqPEQSBf//9N62srCiTyTh8+HAGBATQ39+f8+fPp6mpKQGwQYMGPH78OBMSEjJkW1RUFI8cOcIuXbpQQ0ODAFi5cmUuXryYnz59ylCbBfw5BAQE0MXFhUWLFiUA1q5dm4cOHWJcXFyW9RkcHMw2bdoQAFVVVTl27FiFC7WEhATa2tqyYcOGXLFiBdXU1BgdHZ2uNlq2bMmqVatSEASWL1+e7du3V6iNiXz8+JFDhw6lqqoqdXR0OHXqVPr5+dHY2Jhz585Ntn9cXBy3bdvGEiVKEADbtGnDBw8eZIltBRSgaAIDA3nkyBEOGzaMZcqUIQACYPny5Tl69GieOHGCwcHBWdK3IAi8ceMGW7ZsSQA0MzPj0qVL+ffff9POzo4A2L17d3p5pS7+AgICWMiyDO3GbhcFY2pCstiU0+y86S4/BURkyfnkVgoE5S+Ij49n6dKl2aJFi3QfGxMTwyVLllBHR4e6urpcvnw5Y2NjGRcXxwMHDrBmzZoEQEtLSy5btixTP6SIiAi6ubnxr7/+opqaGgGwWrVqXL58OT09PTPcbgH5n/j4eP799990dHQUL7Rz587lly9fsqS/sWPHsnjx4uKIpZqamsKF5YkTJwiAmzZtIgBeuXIlzcf6+/tTJpNxw4YNdHd3JwCeO3dOYbaR5IcPH9irVy/KZDIaGhpywYIFSX7/ZcqU4dixY8X3MTEx3LBhgziK89dff2X5qHIBBWSWsLAwnjlzhuPHj2fFihUpkUgIgCVLluSgQYN48OBB+vn5ZakNide36tWrEwBtbW25a9cu3rlzh/Xq1SMA1qlTJ00j/AMGDKCuri79/Pz43CuEM088Z/NVN1ly+g8BWXL6GTZfdZMzTzznc6+QLD2v3EqBoPwNBw8eJADeuXMnQ8f7+/tzyJAhlEqlLFmyJI8dO0ZBEEiSDx48YM+ePamsrEwNDQ0OHTqUr169ypS94eHh3L9/P9u1a0dVVVUCoIODA11dXent7Z2ptgvI3zx79owDBw6kuro6JRIJ69evz+3btzM0NFRhfbRt25bNmzcn+WPEctasWdTV1aWmpibnzZvHqKioTPchCAKrVq3KunXr0sjIiNOmTUvzsatXr6aSkhK/f//Ofv360cLCIsOzCP/l27dvHDVqFJWVlVmkSBGuXr06xSk9BwcH9u3bl1FRUVy9ejXNzMwokUjYtWvXDLvLFFBAVhMdHc2rV69yxowZdHBwoJKSEgHQ1NSUvXr14o4dO7LsQfW/+Pn5cd68eTQ3NycAOjo68syZM/z333/ZqVMnUVyeOnVKvB//inv37hEA165dm+J2ufz3bfwJFAjK3yCXy1m+fHk2aNAgU+08f/6cTZo0IQDWq1cvyQiDr68vZ8+eTWNjYwJg48aNeerUKcrl8kz1GRoayj179rBVq1ZUVlamVCpl69ateerUKcbHx2eq7QLyL2FhYdy5cycbNGhAiURCdXV1duvWjefOncv096Zy5cocNGhQknVBQUEcP348lZWVWbRoUe7bty9NF/lfkThK2bhxY9ra2qb5uGrVqrFNmzYMCQmhhoZGilPP6SU8PJxz586ltrY2dXR06OLi8kvfsKZNm9LW1pYmJiaUSqXs1asXX79+nWk7CihA0QQHB3PPnj3s0KGD6HpVqFAhduzYkevXr+e//6eLAYwAAHj1SURBVP6b6d9yWhEEgTdv3mTXrl2prKxMdXV19u/fnx4eHvT19eXQoUOppKREc3Nz7tixI80PivHx8bS3t2elSpUU9nCZXykQlGkg0Uk/PVNnKSEIAs+cOcMyZcpQIpGwd+/efPfunbg9NjaWe/fuZdWqVQmAJUqUoKurK0NCMj98HhwczI0bN7JSpUoEQHNzc86aNatgSryAX+Lp6cmFCxeybNmyBEBjY2OOHTuWT548ydCNIjX/QJJ89+4d27dvTwCsXr16hmcFyB8PgnZ2dqxYsSIBpGlk7+3btwRANzc3uri4UEVFJVOj+nFxcVy/fj2NjY2poqLCcePG8fv376nu/+XLF06ePJkqKiqUSCTs169fkutDAQXkBnx8fLh+/Xo2btxYHIWsVq0aFy5cyKdPn2Z6ICS9hIeHc8OGDSxfvjwB0NramitXrmRQUBBDQ0M5c+ZMampqUl9fn0uXLk33LMjq1aspkUh4//79LDqD/EOBoEwDiVNoVapUUciPJS4ujmvXrqWJiQllMhn79u2b7MZx7949duvWjUpKStTS0uKIESP45s2bTPdN/ohGHzRoELW0tCiVStmyZUueOHGiYNSygFQRBIGPHj3i6NGjWbhwYQJguXLluGTJkl86sv9MbGwsAXD79u2/3O/atWuiEOzSpQs/f/6cIZsPHTpEANTU1OSMGTN+u/+sWbOoo6PDgIAAGhoacsiQIRnqVxAEHj58mNbW1pRIJOzVq1eq5yAIAq9du8YOHTpQKpVSV1eX9vb2LFWqVIb6LqCArODdu3dcsmQJa9asSYlEQplMxoYNG3Lt2rX8+vVrjtj08uVLjhgxgtra2pRKpWzXrh0vXrxIuVzO2NhYrl69mkZGRlRTU+PkyZMZFBSU7j58fX2po6PDwYMHZ8EZ5D8KBGUauXnzJgFw9+7dCmszKiqKrq6uSYTlf9P/eHt709nZmUZGRgTAZs2a8dy5cwoRtmFhYdy8eTOrVKki+ro4Oztn+AZewJ9BXFwcT58+zS5dulBVVZUSiYSNGzfm7t27GR4enupxnz59IgBevHjxt30kJCRw+/btNDExoaqqKqdOncqwsLB02ZmQkMDSpUuzaNGitLa2/uWIqiAILFGiBPv168eVK1dSJpNlKFvCtWvXWK1aNTGdz9OnT1PcLzIykps3bxZHVWxsbLhhwwaGh4dz/PjxLF26dLr7LqAARSEIAp88eUJnZ2eWK1eOAKimpsa2bdty586dvxxpz0ri4uJ4+PBhMaCmcOHCnD59uuibKZfLuX//fhYvXpxSqZT9+vXL1Cxcjx49aGhoyMDAQEWdQr6mQFCmg44dO9LMzEzhubH+KyydnJySCcvo6Gju3LlTHLkpVaoU16xZk+6bbGo8fvyYQ4cOpba2NiUSCZs3b85jx45laRqZAvI+ISEh3Lp1qxglrqmpyV69evHixYvJ/I1u3bpFAHz58mWa2w8PD6ezszPV1NRobGzMLVu2pMuPKTEvJQA+fvw41f0Sne7Pnz9PU1NT9unTJ819kD8Cmpo3b04ArFq1Kq9evZrifp8+feLEiROpr69PiUTCNm3a8PLly0nE7sSJE2ltbZ2u/gsoILMkJCTw5s2bHDt2LC0tLQmAurq67NmzJ48cOcKIiJxLgePt7c3Zs2eLafdq167N/fv3J8kXeenSJdGlq02bNnzx4kWm+rx27RoBcNu2bZk1/4+hQFCmg/fv31NFRUUhjvopkRZhKQgCb9++zc6dO1Mmk1FbW5ujR49WmK9VeHg4t23bJo6yFClShNOnTy/IbVnAb/n06RPnz5/PUqVKiSPe48aN482bN5mQkMADBw4QQIaixj09PdmjRw8CYIUKFdLszxwXF0crKyuqqKhw8uTJqe43fPhwmpubc/369ZRIJGl2L/n8+TN79epFiURCa2trHj58ONlIqCAIvHLlCtu1a0epVEo9PT2OHz+eHz58SLHNKVOmsESJEmnqv4ACMkNMTAzPnDnDAQMGiK4sJiYmHDJkCC9cuMDY2Ngcsy3RHaRjx46UyWTU1NTk4MGDk4z6C4LAixcviiOWDg4OvHXrVqb7jomJoY2NDR0cHLLdJzQvUyAo08mECROooaGRpSl4oqKiuHLlShobG6cqLMkfN9mpU6eyUKFClEgkbNWqFS9evKiwqLqnT59y+PDh1NHRoUQiYdOmTXn06NGCUcsCfokgCLx//z6HDx8uZi4wNDRklSpVqKamlqmRDnd3dzGHa+vWrfnvv//+9pjEfJSmpqZJfhuJqT7i4uJoaGjIcePG0dLSkp07d/5tm4GBgRw3bhxVVFRobGzM9evXJ/tdREREcOPGjbS1tRXTlGzatOm35z9t2jRaWVn91oYCCsgICQkJPH/+PLt3705tbW0xN+TEiRN59+7dHBdQoaGhXLt2rRgIWKZMGa5evTpJcKpcLufRo0dFd60qVarw+PHjCrv3TZs2jcrKygVputJJgaBMJ8HBwTQ0NKSTk1OW9xUZGZkmYRkVFcVt27aJGf9tbGy4fv36X/qzpYeIiAju2LGDNWrUEG/MS5YsUWh+wgLyJ3K5nPfu3eOUKVNoYGAg+mK1atWKW7ZsyVBiY0EQePDgQRYrVowqKip0dnb+ZeRmTEwMjYyMqGJcgkO2XmUz1xtJkhHXWXCW+o0Hc/TsZQSQqt8j+eNmvGHDBhoYGFBLS4tz585N9jv7+PEjx48fTz09PTFY4MqVK2m+2c2YMYMWFhZp+zAKKCCNvH37ltOmTRNzM9rY2HDOnDl8/vx5tqX2+RX//PMPhwwZQk1NTcpkMv7111+8evVqEtvi4uK4a9cu2tjYEADr16+v0EEU8kfQqkwmy7KZyPxMgaDMAOvWraNEIvmlT5Yi+a+w7N27d4qVMgRB4PXr15NEjI4fP54fP35UmC3Pnj1j//79xTqoU6ZMoY+Pj8LaLyD/0rFjRzo4OHDZsmWsW7cupVIpJRIJa9asyYULF/LVq1fpujFERUXR2dmZysrKLFGiBM+fP5/ifp8CIlhv/o/6u8Wnplx31+r/19uO3ppqubRbt27R3t6eANi3b98k1X0EQeDly5fZpk0bSiQS6uvrc+LEiRlyFZk1axbNzc3TfVwBBfyXsLAwbtu2jbVr1xZ9IocMGcL79+/nChEZGxvLAwcOsE6dOuJ0+8yZM5NljoiKiuK6devEalGtW7fOkvr1sbGxtLOzo729fcFMXAYoEJQZID4+njY2Nqxfv362/igjIyO5YsUKsf6yo6Mjjx07lmKQwufPnzlp0qQkzv/Hjx9X2I/E29ubEydOpLa2NlVUVDhw4MA0TT8W8OdSo0aNJCP7AQEB3LlzJ9u3by8mRba2tuaECRNEv8u08Pr1a9avX58A2Llz5yTuKMefeNF6+tlU6+6mVIfXevpZHn/yvxuat7e36L9ZpUoV3rt3T9z27ds3rly5UpyeK1++PDdv3pypwL05c+bQ1NQ0w8cX8GeTOLDQp08fampqilkY9u/fr5BKVIrg69evnDFjhugS4+joSDc3t2T3p9DQUC5atIjGxsaUSqXs3r07//nnnyyza/bs2VRSUioobZpBCgRlBjlz5gwB8Pjx49ned3x8PN3c3Ojg4EAAtLKy4ooVK1JMgB4ZGclNmzaJIyuGhoYcNWoUPTw8FCKGQ0JCuGjRIpqYmFAikbBDhw4FCWALSBFzc3M6OzunuC0qKoqnT5/mwIEDk/hd9u3bl8eOHfut36EgCNy7dy8LFy5MbW1trlq1ikc9PGn5k1g06bOC2pVaUtnQghJlVcp0jKhRpjZNB21KJiwtp5zm3w8/c/HixdTS0qKRkRG3bt1KuVzOhIQEnj17ln/99ReVlZWprKzMjh078tq1awr5Tc2bN4/GxsaZbqeAP4svX75w7ty5LF68uFgYY968edlW7vB3CILAS5cusX379pTJZNTS0uLw4cNTjMb+9u0bZ8yYQV1dXaqoqHDQoEEpunspkmfPnlFJSSlNOWsLSJkCQZlBBEFg48aNaW1tnaORcPfv32f37t3FBOijRo1KNeL72bNnHDdunHjDtrW15ZIlSxQSYBQdHc3NmzfT2tpaLC959uzZXDGtUkDOEx8fT5lMxk2bNv12X7lcTnd3d06dOlUc+Uv0u1y3bt0vy7kFBQVxyJAhVDYwpdXkk0lEokZpB8o09alduTUNmo+kbp2elGrqUaKsxiL91yYfsZx4nCqFzDhq1CgGBwfz/fv3nDZtGs3MzMTRSFdXVwYEBCj0s3JxcaGRkZFC2ywgfxIVFcV9+/axUaNGlEgk1NTUZN++fXnjxo1cc+0NDg6mq6urmP3B1taW69evTzHlnaenJ0ePHk11dXVqampy/PjxWRoAm0hcXBwrVapEW1vbJKmICkgfBYIyE/zzzz+USqV0dXXNaVPo7e3N6dOn09DQkBKJhK1bt041ECA+Pp5nzpxh586dqaqqSqlUyqZNmypkSiQhIYFHjhwR0w7Z2dlx7969Bf4ofzheXl4EwDNnzqT72Hfv3nH58uV0dHSksrIyAbBo0aLs168f9+/fT39//2THNF9+MZm/pHHPpbSYeCzJOtNBmwiZMjVt66UwSnmKzZZe4O7du8U8m7q6uhw6dCgfPnyYZTfsRYsWsVChQlnSdgF5H0EQ6O7uzsGDB1NXV5cAWKdOHW7fvl1hgZiZJTY2lidPnmSXLl2orq5OJSUldunSJVWh++TJE/br14/KysrU19fnrFmzsjV5uouLC6VSKR88eJBtfeZHJCSJAjLM4MGDcfjwYbx//x4GBgY5bQ6io6Oxf/9+uLq64sWLFyhfvjzGjBmD7t27Q01NLdn+ISEhcHNzw+7du3Hnzh3o6OigU6dO6NOnD2rXrg2JRJIhO0jixo0bWLx4Mc6fP49ixYph3Lhx6N+/PzQ1NTN7mgXkMe7fv48aNWrg2bNnsLOzy3A7ERERuHnzJi5fvoxLly7hxYsXAIAKFSqgUaNGaNy4MQxK2qPT1kdpbtN3x2gAQBGnValur1XWAv3790f79u2hoaGRYfvTwtKlS+Hi4oLg4OAs7aeAvMX379+xY8cO7NixA69fv4a5uTn69OmDvn37omTJkjltHgRBwN27d7Fv3z4cPnwYgYGBKFeuHHr06IE+ffqgSJEiSfaPiYnB4cOHsX79eri7u8PMzAxjx47FoEGDoK2tnW12v3r1ChUrVsSYMWOwePHibOs3P1IgKDOJv78/rK2t0a9fP7i6uua0OSIkce3aNbi6uuL06dMoVKgQBg0aBCcnp1QvPu/fv8fu3buxe/dufPnyBVZWVujduzd69+6N4sWLZ9iWZ8+eYcmSJTh06BD09PQwYsQIjBgxAoaGhhlus4C8xZEjR9CxY0cEBgYq9MHLz88PV65cwaVLl3Dp0iX4+PjAsOkw6FRqAXkarmwk4b2+L5QNLWDcZV6y7VIQbcoawLWXg8Js/h0rVqzAnDlzEBoamm19FpB7ef36NVxdXbF7926QRPv27eHk5ISGDRtCJpPltHl4+fIl9u3bh/379+PLly8oWrQounXrhh49eqT48Pjhwwds2rQJ27dvR2BgIBo3boyhQ4eidevWUFJSylbb5XI5atWqhZCQEDx9+jTFQZcC0k6BoFQAixYtgrOzM16+fIlSpUrltDnJeP/+PdasWYOdO3ciLCwMtWrVQt++fdGpUyfo6uom218QBNy6dQu7du3C4cOHERERgTp16qB3796pHpMWPn/+jBUrVmDr1q0AgNGjR2Py5MnQ09PLzOkVkAdYtWoVpkyZgqioqAyPev8Okvj333/R9+C/8ItN240p4sU1BJ5ejkLNR0GrQpMU9ylbRAdnR9VRpKm/xNXVFc7OzggPD8+2PgvIXZDE1atXsWLFCpw9exYmJiYYMWIEBg8enCsexL28vHDgwAHs27cPz549g76+Pjp16oQePXqgdu3akEqlSfaXy+U4c+YM1q9fjwsXLkBfXx9OTk4YPHhwjt4zly9fjokTJ+L27dtwcMi+h8b8ivT3uxTwO8aMGQMzMzNMmjQpp01JkZIlS2LVqlXw8/PD/v37oampiUGDBsHExAQ9evTAxYsXIZfLxf2lUikcHR2xfft2+Pv7Y+/evVBTUxOP6d69O86fP5/kmLRgaWmJ1atXw9PTE2PGjMGqVatQvHhxLF26FNHR0Yo+7QJyEd7e3jAzM8syMQkAEokEZcqUQWCCcpr2jw/8iqBLG6BqVgaa5Rumut+7b9kr7KRSKQRByNY+C8gdxMbGYteuXbC3t0ejRo3g5eWFXbt24fPnz5g+fXqOismQkBBs3boV9evXh4WFBWbOnIlSpUrh+PHj8PX1xaZNm1C3bt0kYtLPzw8LFiyAlZUV2rZti6CgIOzYsQPe3t5Yvnx5jorJt2/fYsaMGRgzZkyBmFQQBYJSAaipqWHRokU4ceIErl27ltPmpIq6ujq6deuGCxcuwNPTE7NmzcLjx4/RtGlTFCtWDFOnTsWbN2+SHKOhoSGKzq9fv2L27Nl4+vQpmjdvjqJFi2LSpEmiH1taMTQ0hIuLCz58+IAuXbpg6tSpKFWqFLZt24aEhARFnnIBuYSQkJBs8TEWBCI+DXPd8ohgfDs8B1JVTRi2mwqJNPWpw3g5IQjZN5EjlUrT/bBWQN4mMDAQCxYsgKWlJfr27YuiRYviypUrePr0KXr37g1VVdUcsSsmJgZHjhxBhw4dYGxsjMGDB0NJSUkcbHBzc0Pbtm2T2JfoP9+1a1cULVoUCxYsQOPGjfHw4UM8ePAAffv2hbq6eo6cTyKCIKB///4wMzPD/Pnzc9SW/ETBlLeCIAkHBwfExMTg0aNHucK3JS2QxIMHD7Br1y4cOHAAISEhqF69Ovr06YOuXbtCX18/xWM8PDywe/du7N+/H4GBgahUqRL69OmDbt26wcjIKF02vHv3DjNmzICbmxvKlCkDFxcXtGvXLktHswrIXrp164Zv377hypUrWd6X9YyzvxSVQkwk/PZPhTwsAMY9F0PF0OKX7SnLJHg3v4WizUyVDRs2YPTo0YiLi8u2PgvIGf7991+4urpi165dIIk+ffpgzJgxKFOmTI7ZJJfLcf36dezbt+//2rvzuBjX/g/gn5kWSZZSqGQ5RNEiElJJZUtECNnikH0pu3Ps67HvW8hDIluWJClSFEppU8lO5Ui79pm5f3+cp/mdHqFlpnuq7/v1mpfM3HPd36Gmz1z3teDKlSvIyclBjx49MGHCBIwbN+67yTWlsrOzcfbsWRw5cgQvXrxAp06dMGfOHEyePLnc3yNs2r9/PxYuXIjAwED069eP7XLqDOqhFBEOh4M9e/bg+fPnOHnyJNvlVBiHw0GvXr1w+PBhpKam4uLFi1BWVsb8+fPRqlUr2Nvb49atW2V+uXE4HBgaGmL//v1ISUmBl5cX2rZtiyVLlkBNTQ22tra4evUqioqKKlSDpqYmPD09ER4eDg0NDdjZ2cHY2BgPHjwQ18smNSw3N7dGZm5++PABilI//r5jeMX4cnkDeJnJaDFmzS/DJAB0UBbvrO7/RZe867bSCZPDhg2DlpYWvLy8sGrVKnz8+BFHjx5lJUwyDIOIiAgsXrwYbdq0gZWVFR48eICFCxciISEB4eHhcHZ2/i5MFhQU4PLlyxg1ahRatmyJRYsWQVtbGwEBAUhISMDChQslLky+efMGK1euxNy5cylMihj1UIrY1KlTce3aNcTHx6NVq1Zsl1Nlnz9/xrlz53D69GnExsaiSZMmGDp0KGxtbTFkyBA0adLku+d8/foVFy5cwJkzZxAWFgYlJSWMGzcOU6ZMQc+ePSvc4xgQEIAVK1YgPDwcgwcPxtatW9GtWzcRv0JSk/r16wcNDQ24u7uLtF2BQICIiAjcvHkTN27cwPPnz9F84Gw06W4NAcp+vzECPtKubkHBm3C0GPUnGnbo+cv2GT4PBTF3YSqfijFjxmDo0KFiD8bHjh3DnDlz6LJ3HVNcXIwLFy5g9+7diIqKgq6uLlxcXDB+/HjWLmm/efMGHh4eOHfuHBISEqCiooKxY8diwoQJ6NWrV7nv2TweDwEBAfDw8ICXlxdyc3PRvXt3ODg4YPz48VBTU2PhlVSMQCCAlZUV3rx5g9jYWCgoKLBdUp1CgVLE0tPToaWlBUtLS1y4cIHtcqqNYRhER0fj2rVruHbtGp4/fw4ZGRlYWFhgxIgRGD58eLlvIC9evMCZM2fg7u6O5ORkdOrUCSNHjoStrS169er13SzA8s57+fJl/PHHH0hKSoKDgwM2btxYreWLCHt69OgBIyMjHDlypNptFRQU4P79+7hx4wZu3ryJlJQUNGvWDEOHDsWwYcPQtpsJxp1+/t3zMvyPIzf8Bhp2NIK81vezthV0+pd7vjGNXyPQ6yzCw8MhJyeHwYMHY8yYMbCxsSn3g1V17d69G+vWrUNOTo7I2yY1Lz09HceOHcPBgweRmpqKIUOGwMXFBZaWlqwM60lLS8PFixdx7tw5hIaGolGjRhg5ciQmTJgAKyurcpfuEQgECA0Nxfnz53Hx4kWkpaWhU6dOwhApiaublOfo0aOYPXs27t69CysrK7bLqXtqaAH1esXd3Z0BwHh7e7Ndisi9e/eO2bdvH2NhYcFISUkxABgjIyNmy5YtTFxc3He7IPB4PMbPz4+ZNm0ao6KiwgBgWrZsyUyfPp25efPmL3fmKS4uZo4dO8aoqqoyMjIyzLx585jPnz+L8yUSMdDU1GSWLl1apefyeDzmyZMnzObNmxkLCwumQYMGwr2KnZ2dmfv373+3E9OYoyHMb6tuldn5poGGDgPgh7f/3Snnt1W3mDFHQ4Rtvn37ltmxY4dwF6gGDRoww4cPZ9zc3JjU1NRq/fv827p16xg1NTWRtUfYkZ6ezqxYsYKRl5dnGjRowMyYMYOJi4tjpZZv374x586dY6ytrRlpaWlGWlqaGTp0KOPh4cF8+/at3OcIBALm+fPnzPLly5m2bdsyABh1dXVmyZIlzLNnzyRma8eKevfuHaOgoMDMmDGD7VLqLOqhFAOGYTB48GAkJCQgLi6uznarZ2RkwMfHB9euXYOvry/y8vKgqakJW1tbjBgxAr179y4zOYnP5yM0NBTXr1/H9evXkZSUBHl5eQwaNAi2trawsbFB8+bNyz1Xfn4+9u/fj23btoHH48HFxQVLliwRSw8RET1VVVXMmTMHq1ev/uWxzH/XkwwICIC/vz8CAwORlZWFxo0bo1+/fsIdcbS1tX/Yw/Puax4G7g1CMb/qYxFlpbjwW2SGdsrf7+z0/v17XLlyBZcuXcKTJ0/AMAwMDQ1hbW2NoUOHwtDQ8Je98D+yZMkS3Lx5E4mJiVWunbAnJycHe/fuxa5du8Dj8bBgwQI4OzujRYsWNVpHVlYW/Pz8hO+3eXl5MDY2xoQJEzBmzJgfTp58/fo1zp8/j/Pnz+PFixdQUlLCmDFj4ODgUO4ak7WBQCDA4MGDER8fj9jY2CqvpUx+jgKlmLx58wY6OjqYNWsWdu/ezXY5YldYWIiAgABcv34dN27cwN9//w0VFRUMHjwY/fv3h7m5Odq1aycMAAzDICEhQfhm9/jxY3C5XJiamsLW1ha2trblXt7OyMjAX3/9hf3796Np06bYtWsXHBwcaEa4hFNQUMDGjRvh7Oxc7uMpKSkICAgQhsjk5GTIyMigT58+sLS0hJWVFXr27AkZmYqtMQkA158nY5Hnc1TlDY4DYO/YbrDtpv7LY798+QJfX1/4+Pjgzp07yMrKEn7vDx06FAMHDqzUxISZM2fi2bNnCA+v+PaRhH15eXk4dOgQ/vrrL+Tl5WHOnDlYsWJFjQXJ0g9i3t7euHXrFoKDg8Hn86Gnpwd7e3s4ODigffv25T4vOjoat2/fhpeXF54+fYpGjRphxIgRGD9+PAYMGABZWdkaeQ3ismPHDixbtgx37tzBwIHlb2BAqo8CpRjt2LEDK1aswJMnT2BoaMh2OTVGIBDgyZMnuHbtGvz9/REZGQmGYaChoQFzc3PhrX379sIg+PnzZ9y8eRPXr1+Hv78/ioqKoKOjIwyXPXr0KPPJ+NOnT3BxccGlS5fQv39/HD58mNWlNsiPCQQCSElJwdXVFdOnTwfwzxIjDx48gL+/PwICAvDixQsA/78nt6WlJUxNTavdu3/9eTKWXo5CMY8PcH7dsyLF5UCKw8GO0XoVCpP/i8fjITQ0FD4+PvDx8UF0dDS4XC6MjY2FvZe6uro//QA0YcIEpKSkSPSatuT/FRUV4fjx49i8eTPS09Mxffp0/Pnnn1BXr/z3T1XO/eDBA9y6dQve3t548+YN5OTkYGlpCRsbG1hbW6NNm+9XMsjJyYG/vz9u376N27dvIzk5GY0aNcKAAQMwbtw4DBs2TOx71teUJ0+ewMTEBC4uLrRXt5hRoBQjHo+Hnj3/mUn69OnTSvWu1CVZWVkIDg5GYGAgHjx4gMjISAgEArRu3bpMwPztt9/A4XDw7ds33LlzB9evX4e3tzcyMzOhpqaG4cOHY8SIETA3NxfOivT19cW8efPw4cMHLF26FH/88UedeSOsKzIyMtC8eXP8/vvvEAgEePr0KV68eAGGYdCuXTtYWVnBysoK/fv3F0tvztaDJ7AvNB1ybXQgxeWAX84i5RwwYMBBz7bNsGN0t3Ivc1fFx48fcfv2bfj4+MDf3x95eXlQV1cXhktLS8vvQvPw4cMBADdu3BBJDUQ8SkpKcPr0aWzcuBHJycmYPHky1qxZU24voCilpqbCx8cHt27dgp+fH/Ly8qChoQEbGxsMHToU/fv3/+49kGEYxMXFCb8XHz58CB6PBy0tLVhbW2PIkCEwNTVlbba5uGRlZcHAwAAtW7ZEcHBwvf0dXFMoUIpZeHg4evXqhW3btmHp0qVslyMRsrKy8PDhQwQGBiIwMFAYMNXV1YXh0szMDJqamuDz+Xj48CGuX7+Oa9eu4d27d2jcuDGGDBkCW1tbWFtbQ05ODn/99Re2bt0KVVVVHDhwADY2Nmy/zHqJYRi8evUKT58+xdOnTxEWFoZnz56huLgYXC4X3bp1Q8+ePWFkZCT8ECFOfD4fnTt3hoGBAdbuPYFLzz4i7F0mkr7kooTPQEaKA80WjdGxKQfHljvi7P4tsLe3F0stRUVFCAoKEvZevnz5ErKysjAzM4OFhQVMTU1haGiIIUOGQFVVFR4eHmKpg1QPn8/H+fPnsW7dOuFuX+vXr0fnzp3Fcr7SpbG8vb3h7e2NZ8+egcvlok+fPhg6dChsbGygo6PzXa/3t2/fEBAQIAyRHz9+RMOGDWFhYSEMkeIOv2xiGAZjx46Fn58fIiMj6/RrlRg1OgWonnJ2dmYaNmzIvH79mu1SJFJWVhbj7e3NLFmyhDE0NGS4XC4DgGnSpAljZmbGLFiwgDl16hQTERHBhIeHMxs2bGB69OjBAGCkpaUZS0tLZv/+/UxgYCAzcOBABgAzYsQI5v3792y/tDovNTWVuXHjBvPnn38yAwcOZBQVFYWzpjt27Mg4ODgwK1euZAAwd+/erfH6PD09GQBMWFjYd4/x+WVnqfbu3ZuxtrauqdKYpKQkZt++fczgwYMZBQUFBgAjKyvLKCgoMN26dWO8vb2ZzMzMGquH/Byfz2cuXbrEaGtrMwAYW1tbJioqSiznysnJYa5cucJMmzaNadmyJQOAadasGTNu3Djm7NmzTFpa2nfPEQgEzIsXL5hdu3YxlpaWjKysLAOA0dTUZBYsWMD4+voyBQUFYqlXEh07dowBwFy6dIntUuoN6qGsAd++fUPXrl2hpaUFX19fmkDyC9nZ2QgNDUVkZCSeP3+O58+fIykpCQzDQEZGBl26dIGBgQHatWuHrKwsREdHIzg4GCUlJdDX14empiYePHiAvLw8rFmzBs7OzrV+UDnbvn79ioSEhDK36OhofPz4EQDQokULGBkZCW+GhobCGfvPnj2DoaEhIiIiYGBgUGM1M/+ded2sWbMKbfl49OhRzJs3D58+farxTQl4PB6io6Px8OFD/PHHH2AYBnl5eeBwONDR0YGJiQlMTU1hYmICDQ2NGq2tvmMYBj4+Pli9ejUiIyMxcOBAbNy4EUZGRiI9z6tXr4RjIR88eICSkhJ06dJF2AtpbGxcZo3IgoIChIeHIyQkRHj7+vUrGjRogP79+2PIkCEYMmQINDU1RVpnbRAbG4uePXvC0dFRJGvfkoqhQFlDfHx8MHToULi7u2PChAlsl1Pr5ObmIiYmRhgwIyMjERMTI9zesW3btmjRogUKCgrw5s0b5OfnQ0FBAXl5eWjTpg1OnjwJS0tLll+FZOPxeHj79q0wMCYmJgq/Tk9PB/DPtoDt27eHlpYWunTpIgyQGhoaP/ygFBgYiP79+yMpKQkdO3assdfj7++PAQMGVHhmZ2ZmJlRVVbF582YsXry4Biosn7q6OmbMmIFJkybh4cOHCA4OxsOHD4XLCLVp00YYLk1NTaGtrV0rl3KpDQICAvDnn3/i8ePHMDU1xaZNm2BmZiaStktKSvDw4UNhiExMTISsrCz69+8vHA/578u0ycnJZcJjREQEeDweGjVqhN69e8PY2BjGxsYwMzOr1+PI8/Pz0bNnT0hJSeHJkydo2LAh2yXVGxQoa9C4ceOEe5z+aL1FUnE8Hg+JiYnCgFn6Z0ZGRrnHKygowMTEBAMHDoSVlRU0NTUhJydXw1WzRyAQID09HampqUhJSUFKSgpev34tDI1JSUkoKSkBADRq1AhaWlrf3Tp27Fjpf7ObN29i+PDh+Pz5M1q2bCmOl1auAQMGID09Hc+ePavwVQF7e3skJCQgKiqKtSsJTZo0wdq1a78LtWlpaXj48KHwVhooFBUVYWxsDAMDA+jp6UFPTw8dO3YsswYsqZyoqCg4Ozvj/v376NmzJzZt2oQBAwZU+3siLS0Nt2/fhre3N+7cuYOcnByoqqoKeyFLJ2mVlJQgOjq6TID88OEDAKB9+/bC8GhsbAwdHZ1yd7epr2bMmAEPDw+Eh4dDW1ub7XLqFQqUNejz58/Q1tbGiBEj4ObmxnY5dRLDMEhLS8Pbt2/x9u1bvHnzBmFhYXj06BHS0tK+O75x48Zo164dunbtig4dOkBNTQ1KSkrf3Zo0aSKxvUAMwyAjI0MYEv8dGP/999TUVGFgLKWurg5tbe3vgqOamprIApWHhwcmTJiAvLy8Gus5Kb3MfuHCBYwdO7bCz7t16xZsbGxq/PJ8KYZhICUlhaNHj8LJyemnx+bl5eHJkyd4+PAhQkJCEB0djdTUVACAnJwcdHR0hAGz9EYfZH8uMzMTa9asweHDh9G5c2ds27YNw4YNq/LPAp/PR3R0NHx8fODt7Y0nT54AAHr27CnshdTW1sbLly8RFxeHmJgYPH78GE+fPkV+fj5kZWXRo0cPYXjs06cPVFVVRfmS65QLFy5g/PjxOHnyJKZNm8Z2OfUOBcoaduLECcyYMQP+/v50CbaGff36FUuXLsXp06ehoqKCpk2b4sOHD8IZyDIyMigpKYFA8P3uKlwuF4qKiuWGTSUlJSgqKkJOTg5SUlJlbtLS0hW6r7CwEN++fUNeXh7y8vKEX5d33/8+/uXLFxQXF5ept3nz5lBTUxPeVFVVv/t7q1atamRs6fHjxzF79mzweLwa6/UbO3YswsPDkZiYWKnem5KSErRu3RoODg7Ys2ePGCss37dv39C4cWN4eHhg/PjxlX5+WloaYmJiEB0djejoaERFRSEuLk44NERdXf27kNm5c+d6v5yKQCDAqVOnsHLlShQVFWHdunWYP39+pf9dCgoKEBYWJuxFDgkJQXZ2Nho3bgwrKyt069YNysrKSE5ORlxcHF68eIHXr18L33PU1NTQq1cvYYDs3r17vbqKUh2vX7+GgYEBhg0bBnd3d5qrwAIKlDVMIBCgf//+SE5ORkxMDI3vYEFISAhmzZqF+Ph4rF27FhYWFsJljB4+fIjc3FzIyclBX18fXbp0Qfv27aGoqIjc3FxkZGT88FZcXFxuGK2KRo0aoVGjRlBQUCjzZ3lfq6iolAmLrVq1kqj15Pbs2YM1a9YgNze3Rs73/v17/Pbbbzhw4ADmzJlT6ee7uLjA3d1duFtPTUpNTYWamhpu3rwpsqWveDwekpKShCGz9FZ6CbV0opu2tjbatWtX5tamTZs6/x719OlTzJs3D2FhYZg0aRL++uuvCvcCpqenIyQkRDjWNTw8HCUlJWjUqBE6dOgARUVF8Hg8fP78GW/fvhW+P6irq6Nr167o0qWL8M8uXbqgWbNmYnyldVdxcTH69u2LzMxMRERE0Ja8LKFAyYKEhATo6+vDxcUFW7duZbuceqm4uBjr16/Htm3b0KtXL5w9exYdOnQAj8dDZGQkHjx4gMDAQAQHByMnJwcNGzZEnz59YG5ujn79+qFXr17lhjaGYcDn88vceDzeL++Tk5MTBsWGDRtK7OX1qti7dy9Wr15dY4Fyw4YN2L59Oz5//lylnXYiIiLQo0cP3L17F1ZWVmKo8MdevnyJzp074/79+zA3NxfruTIzM4W9mVFRUUhKSsK7d+/w8ePHMh+MWrVqJQyYbdu2LRM427ZtW2sDZ1paGlauXImTJ09CX18fBw8ehImJyQ+PZ/67xuqtW7cQGBiIZ8+e4dOnTwCAhg0bQkFBAQKBABkZGSj9tdq6detygyPtJS1aixcvxoEDBxAaGooePXqwXU69RYGSJRs3bsT69evx8OFD9O7dm+1y6q1Hjx5h8uTJ+Pvvv7Fnzx5Mnz69zKUSPp+P58+fC3f5CQoKQnZ2NuTk5NC7d29hwOzduzddmvqB/fv3Y+XKlcjLyxP7uQQCATp27Ih+/fpVeZwywzBo3bo1xo0bh127dom4wp8rDbPh4eGs/WIsKSlBcnIy3r17h3fv3uH9+/fCr0sDJ5/PFx7fsmVLYW+msrKycAhI6RCR0q9L/y4vL8/q5Ugej4djx47hzz//BABs2rQJEyZMQEFBAbKzs5GSkoJPnz7h48ePiImJwYsXL/Dp0ydkZ2d/dwVCRkYGampqaNu2LdTV1dG6dWvhCghdunShnrIaUDruec+ePVi0aBHb5dRrFChZwuPxYGpqii9fvuD58+do3Lgx2yXVW7m5uVi8eDFcXV1hY2ODEydO/HA2cukg+9JdfoKCgpCVlYUGDRqgd+/e6NevH8zNzdG7d+9a23MjagcPHsSSJUtQWFgo9nOVLlH04MGDai3vMmPGDDx8+BDx8fEirO7XHjx4AHNzcyQmJqJTp041eu6K4vF4wsD577D5/v174fCPzMzMH/ZIy8jI/DRscrlcSElJCf+szNcMw+Dbt2/CW25ubpm/JycnIykpCYWFhcKfz4KCgp++Xg6HgxYtWqBjx44wMDCAqakptLS0oK6uDiUlJRqrx6Lk5GR069YNvXv3xo0bN+j/gmUUKFn0+vVrdOvWDaNHj6ZZ3xLg5s2bmD59OgQCAVxdXTFixIhfPofP5yMmJkZ4ifzBgwfIzMyErKwsevXqVaYHs1Ej0ewPXdscOXIECxcu/G7ikDhMmTIFjx49QlJSUrV+uXh5ecHOzg6vX78W+/aQ5Z3377//Fsu+5jWppKQEWVlZyMzMRGZmpjBo/ujrjIwMFBYWCoeBCASCMn/+7Ot/k5OTQ+PGjaGgoCC8ycrK4s2bN/j48SNatGiBwYMHQ01NDV++fMGnT5/w5s0bvH37Fnw+HwoKCujbty/69esHExMT9OzZk64+SCA+nw8rKyskJSXh+fPnUFZWZrskIv7NeMjPnD59mgHAeHp6sl0KYRjm77//ZmxtbRkAzNSpU5ns7OxKPZ/P5zNRUVHMvn37GDs7O6Z58+YMAIbL5TJaWlrMuHHjmG3btjG+vr7M58+fxfQqJMvRo0cZKSkpsZ8nJyeHkZeXZzZu3FjttrKzsxkZGRnmwIEDIqis4g4cOMDIysoyfD6/Rs9b2wkEAqakpITh8Xhl7i8uLmZ27tzJKCgoMEpKSsy0adOY33//ndHS0hJuEaqhocE4ODgwhw8fZqKjo+nfvpZYv349w+VymQcPHrBdCvkv6qFkGcMwGDduHPz8/BAdHU3bqkkAhmHg5uaGhQsXQllZGWfOnIGpqWmV2hIIBIiLi8PTp0+Fu/w8f/4c3759A/DPhIdu3bqhW7duMDAwQLdu3dCxY8c6NSnH1dUVM2fOFNkM+B85efIkZsyYgffv34vk58jS0hINGjSAj4+PCKqrmBUrVuDixYt48+ZNjZ2zrhEIBHjz5g3OnTuHQ4cOIS0tDfLy8sjPzy+zlWXprU2bNmyXTCrpwYMHsLCwwNq1a7FmzRq2yyH/RYFSAmRmZkJPTw8dOnRAQEAA7XAhId68eSO8hLp06VJs2LBBJMvxCAQCvH37tswOP8+fP0dycjKAf5YM0tPTEwbNbt26QUdHp9Zup3by5EnhUAJxjnEyMTGBvLw8/Pz8RNLe7t278ccffyA9Pb3G/u0nTpyIDx8+ICgoqEbOV5sxDIO///4bsbGxiImJQUxMjPDr0vG60tLS6NatG6ysrGBiYgJjY2MoKiqyXDmpjvfv36Nnz57Q0dHB3bt36felBKFAKSECAwNhYWGBrVu3Yvny5WyXQ/6Lz+dj586dWL16NbS1teHu7g5dXV2xnCstLQ1RUVFlejLj4+MhEAjA5XLRuXPnMr2Z+vr6tWKc3enTpzF16lTweDyxvfmXLrdT1QXBy5OQkABtbW3cunUL1tbWImnzV8zNzaGmpgYPD48aOV9tkZubK9xJ5t8B8uvXrwD+WbZHW1sb8vLyCA8Ph7y8PDZs2IDZs2fXqd7++i4vLw99+/ZFTk4Onj59SuMmJQwFSgmycuVK7Ny5E48fP6a1tCTM8+fPMXHiRCQlJWHLli1wdnaukV9UBQUFiI2NLRMyo6KihEvwqKmplenJ7NatGzp06CBRv0TPnDmDKVOmoLi4WGwLha9atQqHDx9GamqqyGbXMwyDDh06wNraGgcPHhRJm7/SsWNH2NnZYfv27TVyPklTUlKCxMTE73od3759C+CfHas0NTWhq6sLHR0d6OrqQldXF/Ly8pg9ezZu3ryJyZMnY9++fbRIeB0jEAhgb2+PO3fuIDQ0FDo6OmyXRP4HBUoJUlxcDGNjY+Tm5iIiIqLezgqWVIWFhfjjjz+wZ88emJub4/z58z9cXkicBAIBXr9+XSZkPn/+HCkpKQD+uWSur69fJmR26dKFte8nd3d3TJo0CQUFBWKZLcvn89G2bVsMGzYMR44cEWnb8+bNw61bt/DmzRuxL0nCMAwaNmyI7du3Y8GCBWI9F9sYhsGHDx++u1SdkJAg3G9eXV29TGjU1dWFlpbWdx8YPD09MWfOHMjIyODYsWOwtbVl4yURMVu/fj3Wr18PLy8v+j+WUBQoJUxiYiK6d++OiRMn4tixY2yXQ8px//59jB8/HlJSUrhy5YrELEz/5csXREVFlRmXmZiYKJwMo6ysjPbt26N9+/Zo165dma/btm0rtqVRzp8/DwcHB+Tl5YllLKKvry+GDBmCp0+fomfPniJt28fHB0OHDsWLFy+gra0t0rb/V3p6OpSVlXHlyhXY2dmJ9Vw1hWEYfP36FXFxcd/1OpauU9mkSRNhYCwNkDo6OlBSUvpp22lpaZg7dy4uXbqEsWPH4uDBg3QJtI66cuUKRo8ejc2bN2PVqlVsl0N+QJrtAkhZnTt3xt69e+Hk5IQhQ4ZUaC1EUrP69++PiIgIjBkzBmZmZti3bx9mzZrF+qK6LVq0wIABAzBgwADhffn5+YiJiUFSUhLevn2Lt2/f4t27d3j69Ol3O56oqqp+FzhL/9TQ0Kjy5erSy+/imuXt5uaGrl27wtDQUORtm5ubQ05ODj4+PmIPlKXb+Kmrq4v1PKL07ds3fPz4ER8+fMDHjx+Ft3//vXThcBkZGWhra0NXVxe2trbC4KihoVHpn51r165h5syZ4PP58PT0hL29vTheHpEAz58/x+TJkzF27FisXLmS7XLIT1APpQRiGAZ2dnYIDg5GdHQ01NTU2C6JlKO4uBiLFy/GwYMHMWXKFBw5cqRW7Y7D4/Hw6dMnYcj83z+Tk5OFexJzuVy0bt263N7N9u3bQ01N7YcTbi5dugR7e3tkZWWJfA/jjIwMqKqqYsuWLVi8eLFI2y41dOhQFBYWIiAgQCztlyrdQu7Tp08SESqLioqQnJz808CYlZUlPJ7D4UBVVRUaGhrCW5s2baChoQFtbW1oampWewxtZmYmFixYAHd3dwwfPhzHjh1Dq1atqvlKiaT68uULevbsCRUVFQQFBdXalS7qC+qhlEAcDgeurq7Q09ODo6MjfH19JWqSBfmHrKwsDhw4ACMjIzg5OSE6OhpXrlxB+/bt2S6tQqSlpdGuXTu0a9eu3MeLiorw4cOH78JmQkICfH198ffffwuPlZGRQZs2bcrt3czJyQEgnh7K8+fPg8/nY+LEiSJvu5S1tTWcnZ2Rk5Mj1r2ZP336BCkpqRoJSHw+H58/fy63R7H07//+/wUAJSUlYUg0NTX9LjSqqalBVlZWbDXfvn0b06dPR15eHv7zn/9g0qRJrF8VIOJTXFyMUaNGoaioCNeuXaMwWQtQD6UEu3v3LgYOHIjdu3fD2dmZ7XLITzx//hx2dnbIzs6Gh4cHBg0axHZJYpefn4/379+XCZv//jojI6PM8U2aNEHz5s3L7N9c3u1/93hu2rTpDz9QGRoaQl1dHdevXxfb63zz5g06dOiAq1evYuTIkWI7z+rVq3H69Gl8/PixUs9jGAZ5eXnIyclBTk4OcnNzhV/n5OQgOzsbKSkpZUJjcnIyeDyesI1GjRqV27P47xtbk7pycnLg4uKCkydPYtCgQThx4gRat27NSi2kZjAMAycnJ5w5cwaBgYHo06cP2yWRCqBAKeFKL6k+ffoU+vr6bJdDfiIjIwMTJ06Er68vNm7ciJUrV9brnuWcnBy8e/cOnp6e2LJlC1avXo2SkhLhHs7/e8vKyiq3F5PD4aBp06bfBU+GYXDlyhVMmjQJJiYm5YbTn4XRytDW1oaJiQlcXV2r3VapkpKSMuFvxYoVePv2LdauXVtuMPz37d+P5ebm/rT3t0GDBlBVVf0uJP7774qKihLZ2xcQEIBp06YhIyMDu3fvxvTp0yWyTiJaBw4cwIIFC3D69GlMmTKF7XJIBVGglHBFRUUwMjICj8fD06dPaSkhCScQCLB+/Xps2LABw4cPx5kzZ0Q+brC28fb2xrBhw5CamvrTy7kCgQA5OTk/DJz/e4uOjkZaWhqaNGmC7OxslPdWVhpGGzRoAC6XCy6XCw6H893X5d3376+Tk5ORlZUFfX194WMVbYvD4aCgoOC7UFi6m0t5OBwOGjdujCZNmpS5VfS+0vsbN24skt2dalpeXh6WL1+OQ4cOwdzcHG5ubj8cmkHqFn9/fwwePBiLFi3Czp072S6HVAIFylrgxYsXMDIywrBhw+Dh4UGf0GsBb29vTJw4ES1atMDVq1fr9SK8fn5+GDRoEN69e4e2bduKpE2GYdCmTRuMHDkS+/fv/2UYLS4uBsMwEAgEEAgE5X79s8c/fPiAmzdvws7ODkpKSpVuS15e/qfBr0mTJnBwcED//v2xZ88eyMvL19ve7YcPH8LR0REpKSn466+/MHfu3Hr7b1HfJCUloVevXujVqxe8vb1pW8Vahibl1AJdunSBm5sb7O3tYWhoKLbZrER0bGxsEB4ejpEjR6JXr144deoUxo4dy3ZZrCjdsSQrK0tkgfLly5f49OkThgwZAuCfWejNmjVDs2bNxDIpqri4GIqKijA2Nhbbz9+XL1+gpaUFBQUFsbQv6QQCATZt2oR169ahd+/euH37NjQ1Ndkui9SQ7Oxs2NraokWLFjh//jyFyVqIPvbVEmPGjMHy5cuxbNky+Pv7s10OqYCOHTvi8ePHsLW1xbhx4+Di4iLcBaQ+KV2gOjMzU2Rt+vv7Q1paGqampiJr82dkZWWhq6uL6OhosbRfOhayvk42+fr1K6ytrbFu3TqsXbsWwcHBFCbrET6fDwcHB6SkpODGjRu0bWYtRT2UtcjmzZsRGRmJcePGITw8nMYU1QKNGjXCuXPn0KtXLyxZsgQRERG4ePEiWrRowXZpNUZRUREAvpv1XR3+/v7o06dPjfbm6enpISwsTCxtly5qXh8D5ZMnTzBmzBgUFBTgzp07ZRbmJ/XDqlWr4Ovri9u3b6NTp05sl0OqiHooaxEpKSmcP38eTZo0wciRI5Gfn892SaQCOBwOFi5ciHv37iEhIQF9+vTBy5cv2S6rxpT2Noiqh5LH4+H+/fuwsrISSXsVpaenhxcvXoill7n0++G3334TeduSimEYHDhwAKampmjdujUiIiIoTNZD7u7u2L59O3bt2oWBAweyXQ6pBgqUtYySkhKuXbuGly9fwsnJqdyZrUQymZqa4smTJ5CVlYWxsTEeP37Mdkk1QkpKCk2bNhVZoIyIiEB2dnaNB0p9fX0UFxcjMTFR5G3HxcVBUVGx3uz6kpubi/Hjx2PBggWYO3cuAgMDoaGhwXZZpIYFBATg999/x9SpU7Fw4UK2yyHVRIGyFtLT08PJkydx7tw57Nu3j+1ySCW0bdsWjx49gra2NiwsLHDjxg22S6oRioqKIrvk7e/vj8aNG6Nnz54iaa+idHV1AUAs4yhjY2PRtWvXerGCQ1xcHHr27AkfHx9cunQJe/bsEesOO0QyhYeHY8SIEbCwsMDRo0frxfd+XUeBspYaN24clixZgiVLluD+/ftsl0MqQUlJCXfv3oW1tTVGjhyJI0eOsF2S2CkqKoqsh9Lf3x/m5ubV3he6spo1a4Y2bdqIJVDGxcWha9euIm9X0ri7u8PIyAiysrIIDw/H6NGj2S6JsCAhIQFDhgyBrq4uLl++TB8o6ggKlLXY1q1bYW5uDnt7e3z48IHtckglyMnJwdPTE/PmzcOcOXOwatWqOj18QVSBMj8/H48eParxy92l9PT0EBUVJdI2eTweEhMT6/RapYWFhZg1axYmTZqE0aNH4/HjxzT5op76+PEjBg4ciJYtW8Lb25s266hDKFDWYtLS0rhw4QIaNWoEOzs7FBQUsF0SqQQpKSns3bsXO3fuxNatWzFlyhQUFxezXZZYKCkpiSRQPnz4EMXFxbC0tBRBVZWnp6cn8h7KV69eobi4uM72UL59+xYmJiY4ffo0XF1dcfr0acjLy7NdFmFBeno6Bg0aBC6Xizt37giXFCN1AwXKWk5ZWRleXl6Ii4vDrFmz6nQvV13E4XCwePFinD9/Hp6enhg6dChycnLYLkvkRDWG0t/fH61atUKXLl1EUFXl6evrIyUlBV+/fhVZm3FxcQBQJwPlzZs30b17d2RmZiI0NJT24q7Hvn37Bmtra3z9+hV3796Furo62yUREaNAWQcYGBjA1dUVZ86cwaFDh9guh1TBuHHjcOfOHYSFhcHMzAwpKSlslyRSorrkHRAQACsrK9ZCiZ6eHgAgJiZGZG3GxcVBWVm5Tq1NyuPxsHLlSgwfPhz9+vXDs2fPYGBgwHZZhCVFRUWws7NDfHw8fH19adH6OooCZR0xceJELFq0CM7OzggKCmK7HFIF5ubmePjwIdLT09GnTx+8ePGC7ZJERhSB8uvXr4iMjGRt/CTwz+5HcnJyIh1HWdcm5Hz+/BkDBgzAjh07sH37dnh5edHOJ/UYn8/H5MmTERQUhBs3bqB79+5sl0TEhAJlHbJ9+3aYmJhgzJgx+PjxI9vlkCrQ0dFBaGgomjZtir59+yI4OJjtkkSidAylQCCochv3798HwzCsjZ8E/hm33LVrV5GOoyxdMqguePr0KQwMDJCQkIB79+5h6dKldIm7HmMYBvPnz8fly5dx4cIFmJubs10SESMKlHWIjIwMPD090aBBA9jZ2SEvL4/tkkgVtG7dGsHBwTAwMMCAAQNw+fJltkuqNkVFRTAMU63xof7+/tDS0mJ9e0JRTswpLi7Gy5cv60Sg9PLygrm5Odq3b4/IyEiYmZmxXRJh2bp163DkyBEcP34cI0aMYLscImYUKOuYFi1a4Nq1a4iPj8e4cePA4/HYLolUQdOmTXH79m3Y2dnB3t4ee/fuZbukaindz7s6l739/f1ZvdxdSl9fH3FxcSL52UpKSgKPx6vVSwYxDIM9e/Zg1KhRGDZsGO7du1dvdvwhP7Z//35s2LAB27Ztw++//852OaQGUKCsg7p3747Lly/D19eXZn7XYg0aNIC7uzuWLl0KZ2dnrFmzptb+X1Y3UL59+xZv3ryRiECpp6eHwsJCJCUlVbut2j7Dm8fjYf78+XBxccGyZctw/vx5yMnJsV0WYZmHhwcWLlyIJUuWYNmyZWyXQ2qINNsFEPEYPHgwTpw4AUdHR6irq2P9+vVsl0SqgMvl4q+//kLz5s2xfPlyMAyDDRs21LpxaaXrzVV16aCAgABwuVz069dPlGVVyb+3YNTW1q5WW3FxcWjZsiWaN28uitJq1Ldv3zB+/Hjcvn0bx48fx4wZM9guiUiA27dvY8qUKXB0dMT27dtr3XsVqToKlHXYlClTkJKSglWrVkFNTQ0zZ85kuyRSRcuWLQOHw8GyZcvAMAw2btxYq96oVVRUAPwzA7gqwsPDoaOjIxGzhZWVlaGmpobo6GiMHTu2Wm3V1hneKSkpsLGxwatXr3Dr1i0MGjSI7ZKIBAgJCcGoUaNgbW0NV1fXWvUeRaqPAmUdt2LFCiQnJ2POnDlo1aoVbG1t2S6JVFHpjNmlS5eCYRhs2rSp1rxhKygoQFlZGe/fv6/S8xMTE6GlpSXiqqpOR0dHJMs6xcXFYcCAASKoqObExMTA2toawD87F5WuzUnqt9jYWAwdOhQ9e/bEhQsXIC1N8aK+oTGUdRyHw8G+ffswcuRIjBs3DqGhoWyXRKphyZIl2LVrF7Zs2YI//vijVo2pbNeuHd69e1el5yYmJqJz586iLagaWrVqVe3dcoqKipCUlFSreij9/PzQt29fqKio4MmTJxQmCQAgPj4eAwYMQNu2bXHjxg00bNiQ7ZIICyhQ1gNSUlJwd3dHz549YWNjg4SEBLZLItXg4uKC3bt3Y+vWrVi1alWtCZVVDZQ5OTlITU2VqECppKRU7a0k4+Pjwefza02gdHV1hbW1NczMzBAUFAQ1NTW2SyISICYmBv369YOysjLu3LmDpk2bsl0SYQkFynpCTk4O169fh6qqKgYPHlzntvarb5ydnbF7925s27YNK1eurBWhsqqB8uXLlwBQ5wJlaGgopKWlJX7nEIFAgJUrV8LJyQkzZ87EtWvXoKCgwHZZRAJERETA3Nwc6urquH//Plq2bMl2SYRFNMihHlFUVMTt27fRp08fWFtb48GDB/RpshZzdnYGh8OBs7MzGIbBtm3bJHpMZbt27fD+/XsIBAJwuRX/LJuYmAgA6NSpk7hKq7TSQMkwTJX/zUNCQmBgYAB5eXkRVyc6hYWFcHR0xMWLF7Fr1y7h9xwhT58+xaBBg6CpqYk7d+4IlwYj9Rf1UNYzGhoa8PX1xfv372FnZ4eioiK2SyLVsGjRIuzduxfbt28XLiskqdq1a4eSkpJK944nJiZCVVUVTZo0EVNllaekpITi4mLk5+dXuY1Hjx7B2NhYhFWJ1tevX2FlZYXr16/j8uXLcHFxoTBJAPzzvWtlZYWuXbvC39+fwiQBQIGyXtLR0cH169fx6NEjODo6Vmt/ZcK+hQsXYt++fdixY4dwWSFJ1K5dOwCo9GVvSZuQA/z/uprp6elVen5qairevn2Lvn37irIskUlKSkKfPn2QlJSEwMBA2NnZsV0SkRD379/HoEGD0KNHD/j6+krUBz3CLgqU9ZSZmRnOnTsHT09PLF26lO1ySDUtWLAA+/fvx86dO4XLCkmauhgoqzqOMiQkBAAksocyJCQEvXv3hrS0NB4/foxevXqxXRKREH5+frC2toaxsTFu3bpFY2lJGTSGsh4bNWoU9u/fj/nz50NdXR0uLi5sl0SqYf78+eBwOJg/fz4YhsHOnTsl6hJlo0aNoKKiUqlAKRAI8PLlS0yZMkV8hVWBKAJl27Ztoa6uLsqyqu3+/fsYNmwYevTogWvXrtGlTCLk7e2NUaNGYcCAAbh8+TJtsUm+Q4Gynps3bx6Sk5OxePFiqKioYNKkSWyXRKph3rx54HA4mDdvHhiGwa5duyQqVFZ2pvenT59QUFAgURNygOoHSkkcP+nn5wdbW1uYmZnBy8tLoicLkZrl5eWFsWPHwsbGBhcuXICsrCzbJREJRIGSYMuWLUhLS4OjoyM4HA4mTpzIdkmkGubOnQsOh4O5c+dCRkYGf/31F9slCVU2UJbO8Ja0S95NmzYFl8utUqAsKChARESERH14o94n8iOenp6YMGECRo8ejbNnz0JGRobtkoiEokBJwOFwcPz4cTAMg8mTJ4NhGIn6ZUcqb86cOSgpKcGiRYugpqaGhQsXsl0SgH8CZURERIWPT0xMhIyMjHD8paTgcrlQVFSsUqAMDw9HSUmJxPRQXr16FePGjaPeJ/KdM2fOYOrUqZgwYQJOnTpF2ymSn6LvDgLgn1+Qrq6u4HA4mDJlijBcktpr4cKFSElJgbOzM1RVVWFvb892SWjXrh0+fPgAPp8PKSmpXx6fmJiIjh07SuQvsqoubh4SEgIFBQXo6uqKoarKuXDhAiZOnIgxY8bgzJkz1PtEhE6cOAEnJydMmzYNx44dq9DPK6nfJO9dmrCGy+Xi+PHj4HA4cHR0BMMwEjcZglTO1q1bkZKSgkmTJkFFRQX9+/dntZ5/r0WpoaHxy+MlcYZ3qaoGykePHqFXr16sh+TS3qeJEyfi1KlTFBiI0KFDhzBv3jzMmTMHBw4cqNRGBKT+ou8SUgaXy8WxY8cwffp0TJ06FadPn2a7JFINXC4XJ0+eRL9+/TBixAhER0ezWk/79u0BVHzpoLoWKBmGQUhICOvrT7q6usLR0RHTpk2Dm5sbhUkitHv3bsybNw/Ozs44ePAghUlSYfSdQr7D5XJx9OhRzJgxQ/gLh9ResrKyuHLlCjp06IAhQ4bgw4cPrNXStm1bABULlPn5+fjw4UOdCpQvX75Eeno6q+MnDx06BCcnJ8yZMwfHjh2jwEAA/PNhZ8OGDVi8eDFWrlwpcStEEMlH7ySkXFwuF0eOHIGTkxN+//13nDp1iu2SSDU0btwYPj4+aNCgAQYPHlzl5W6qS15eHi1atMDbt29/eeyrV68ASN4M71JVCZQhISHgcDjo3bu3mKr6udLeJxcXF7qUSYSKi4sxbdo0rF27Fps2bcLmzZspTJJKozGU5Ie4XC4OHz4MDoeD6dOnAwCmTZvGclWkqlq1agVfX1/07dsXw4YNg7+/Pxo2bFjjdWhqagqXA/qZL1++AABUVVXFXVKVKCkpVXrrxUePHkFHRwdNmzYVU1U/tmXLFvzxxx9YtWoVNm3aRIGBAACysrIwatQoBAcH4+zZs7RsHKkyCpTkp7hcLg4dOgQOh4Pff/8dDMPg999/Z7ssUkWdOnWCt7c3LCws4ODggMuXL9f4+Dk9PT0EBQX98rj8/HwA/+ywI4maNm2KrKysSj0nJCQEZmZm4inoBxiGwbp167BhwwasX78eq1evpjBJAADv37+HtbU1UlNTcffuXfTr14/tkkgtRtc7yC+VhsrZs2dj+vTpOHHiBNslkWro1asXLl68iJs3bwp31KlJ+vr6SEhIQGFh4U+PKw2UkrpjS0lJCRo0aFDh4zMyMhAfH1+jE3IYhsHKlSuxYcMGbNu2DWvWrKEwSQAAYWFh6NWrFwoKChASEkJhklQbBUpSIRwOB4cOHcKcOXMwY8YMuLq6sl0SqYahQ4fi+PHjOHr0KLZs2VKj59bX1wefz8eLFy9+elxeXh4AsHJZviLy8vIqFXYDAwMBAKampmKqqCyGYeDs7Iy//voLe/bswfLly2vkvETyXb9+Hf369UP79u3x+PFjaGlpsV0SqQPokjepMA6Hg4MHD4LD4cDJyQkMw8DJyYntskgVTZs2DcnJyfjzzz+hpqaGqVOn1sh5dXV1weFwEBUVhe7du//wuPz8fMjJyUnskjb5+fmVuhzv4+MDLS2tGtn1RyAQYN68eThy5AgOHz6M2bNni/2cpHbYt28fnJ2dYWdnh7Nnz0rsBzZS+1CgJJXC4XBw4MABcDgczJw5EzweD3PmzGG7LFJFf/75J5KTkzFjxgy0bNkS1tbWYj9no0aN0LFjR0RFRf30uPz8fIm93A1Urj6GYeDj44Px48eLuap/zrV48WIcPXoUJ0+epIl0BADA5/Ph7OyMAwcOYOnSpdi2bRvN8iciRYGSVBqHw8H+/fshLS2NuXPnIjU1FRs2bKCxWbVQ6VCGz58/Y8yYMbh//z6MjIzEfl59ff1fBsrKXlKuaZWpLyoqCqmpqTUS2Ldu3Yq9e/fi8OHDFCYJgH++V8ePH49bt27hyJEjmDVrFtslkTqIPp6QKuFwONi9ezf++usvbNq0CVOnTkVJSQnbZZEqkJKSwvnz59GtWzcMHTq0wrvYVEdpoPzZhKDKXlKuaZXpofTx8YGCggJMTEzEWtOxY8fwxx9/YP369XSZmwAAUlNT0a9fP9y/fx/e3t4UJonYUKAkVcbhcLBs2TKcO3cOHh4esLGxQW5uLttlkSpo2LAhbty4gcaNG2PkyJHCGdbioq+vj8zMTHz69OmHx9SGS94VDbw+Pj6wsrKq1Kzwyrp8+TJmz56NefPmYfXq1WI7D6k9YmNj0bt3b6SmpiI4OBhDhgxhuyRSh1GgJNXm4OAAX19fPH78GP369UNqairbJZEqaN68Oa5du4aXL18KJ12Ji76+PgD89LK3pF/yrmjgzczMRGhoqFgvdwcEBGDChAkYN24c9u3bR8NPCO7evYu+ffuiWbNmePLkCbp168Z2SaSOo0BJRMLCwgLBwcH48uUL+vTpg4SEBLZLIlWgp6eHkydP4ty5c9i3b5/YzqOhoYFmzZr9NFBKeg9lRQOvn58fBAKB2HqHwsLCMGLECFhYWOD06dM00YLg5MmTsLa2hrGxMR4+fIjWrVuzXRKpB+idh4iMnp4eQkNDoaCggL59++LRo0dsl0SqYNy4cViyZAmWLFmC+/fvi+UcHA7nlxNzasMYyorU5+PjAz09PbH8Uk9ISMCQIUOgq6uLy5cvQ1ZWVuTnILUHn8/HqlWrMH36dEyfPh03b95E48aN2S6L1BMUKIlIaWho4OHDh9DV1YWlpSWuXr3KdkmkCrZu3Qpzc3PY29vjw4cPYjlHRQKlJPdQVqQ+gUCA27dvi6V38uPHjxg4cCBatWoFb29viQ7fRPzS0tJgbW2Nbdu2Yfv27Th8+DCkpWkhF1JzKFASkWvWrBnu3LmDESNGYPTo0Thw4ADbJZFKkpaWxoULF9CoUSPY2dmhoKBA5OfQ19dHUlKScEec/yXpYygrUt+zZ8+Ev+hFKT09HYMGDQKXy8WdO3egpKQk0vZJ7RIaGoru3bsjIiICfn5+WLp0KY2jJTWOAiURiwYNGsDDwwMuLi5YsGABli1bBoFAwHZZpBKUlZXh5eWFuLg4zJo1S+STdPT19cEwDGJjY8t9vC5c8vbx8UHTpk3Rp08fkZ3327dvsLa2xtevX+Hn5wd1dXWRtU1qF4ZhsG/fPpiZmaFNmzaIjIyElZUV22WReooCJREbLpeLnTt3Yu/evdi5cycmTpyIoqIitssilWBgYABXV1ecOXMGhw4dEmnbXbt2hZSU1A8ve0vyJW+GYSpUn4+PDwYOHAgZGRmRnLeoqAh2dnaIj4+Hr68vOnXqJJJ2Se2Tk5MDe3t7LFq0CAsWLEBgYCBNviGsogEWROwWLlwIdXV1TJw4EZ8/f8bVq1fRrFkztssiFTRx4kQ8e/YMzs7O0NPTg5mZmUjalZOTQ+fOnX8YKCX5kndxcTH4fP5P60tLS0NYWJjIFhjn8/mYPHkyHjx4AF9f35/ug07qtpiYGIwePRqpqam4fPkyRo0axXZJhFAPJakZo0ePxt27d/H8+XOYmpri48ePbJdEKmHHjh0wMTHBmDFjfroYeWXp6enVyh7K0oXff3bJ+86dO2AYBoMHD672+RiGwfz583H58mVcuHAB/fv3r3abpHY6c+YMevXqBTk5OTx79ozCJJEYFChJjTE1NcWjR4+Qk5MDQ0NDPHjwgO2SSAVJS0vj4sWLaNCgAUaNGoXCwkKRtNuzZ088e/YMxcXF3z3WoEEDiR0iURoofxZ4fXx80KNHD7Rq1ara51u3bh2OHDmC48ePY+TIkdVuj9Q+hYWFcHJywpQpUzB27FiEhoZCU1OT7bIIEaJASWqUtrY2wsLC0LVrV1haWmLPnj1i3ZGFiI6Kigq8vLwQFRWFuXPniuT/zdTUFIWFhXj27Nl3jykpKSEjI6Pa5xCH7OxsAICCgkK5j/P5fPj6+opkdvf+/fuxYcMGbNu2Db///nu12yO1z5s3b2BsbIyzZ8/i5MmTcHNzk9jee1J/UaAkNa5Fixbw8/ODi4sLXFxcMH78eHz79o3tskgF9OjRA8ePH8epU6dw7NixardnYGCARo0aISgo6LvHJDlQJiUlAQA6dOhQ7uOhoaHIzMys9vqTV69excKFC7F48WIsW7asWm2R2unGjRvo3r07cnJyEBoaimnTprFdEiHlokBJWCEtLY3t27fj4sWL8Pb2Ru/evYW/pIlkmzx5MubPn48FCxZUezckaWlp9O3bt9YFysTERCgoKEBVVbXcxz09PaGurg4jI6MqnyMqKgqTJk2Cvb09tm/fTusK1jM8Hg/Lly+Hra0t+vfvj/DwcNqPm0g0CpSEVWPGjMHTp09RUlICQ0ND3Lhxg+2SSAXs2rULvXv3xujRo5GWllattkrH1vL5/DL3S3qg7Ny5c7khj8fj4eLFixg7diykpKSq1P6XL18wfPhwdO7cGW5ubrQ/dz2TmpoKS0tL7Nq1Czt37qSVMUitQO9ShHVdunRBWFgYLC0tYWtri9WrV38XLohkkZGRwcWLF8Hj8TB9+vRqjac0MzNDdnY2YmJiytxfGwJlee7du4cvX77AwcGhSm0XFxdj9OjRKCwsxPXr12msXD0TGBgIAwMDJCUl4f79+1i8eDH1TpNagQIlkQhNmjTBlStXsHXrVmzZsgVDhw6V2DBB/tGqVSucPHkSN27cwPHjx6vcjpGREWRlZb+77F1bA+X58+ehqalZpXUiGYbB3Llz8eTJE3h5eUFDQ6O6pZJaorCwEMuWLYOFhQW6dOmCyMhImJqasl0WIRVGgZJIDA6HgxUrVsDX1xfh4eHo0aMHIiMj2S6L/MTw4cMxa9YsODs7IyEhoUptyMnJwcjICMHBwWXul9RAmZGRga9fv5YbKAsLC3H16lWMHz++Sr1KBw8exIkTJ3D06FEYGxuLolxSC5S+3+3btw9bt27F3bt30bJlS7bLIqRSKFASiTNgwAA8e/YMzZs3h7GxMf7zn/+wXRL5iV27dqFNmzZwcHAodz3JijAzM0NQUFCZS+dKSkooLCxEQUGBqEoVicTERAAoN1D6+PggJycH48ePr3S7/v7+cHZ2hrOzM6ZOnVrtOonkKy4uxurVq9G7d2/hQuXLly+v8thbQthEgZJIpLZt2+Lhw4dwcHCAo6Mj5syZU+WwQsRLXl4eHh4eiI2NxerVq6vUhqmpKb58+VJmpn/z5s0BQOJ6KUsDZXmLSnt4eMDAwABaWlqVavPVq1ewt7eHlZUVtm/fLpI6iWSLjo6GkZERtm3bhjVr1uDx48fQ0dFhuyxCqowCJZFYcnJyOHHiBI4dO4YTJ06gX79+SE5OZrssUo7u3btj8+bN2LFjB+7du1fp5xsbG4PL5ZYZR6mkpARAMgOlhobGd9su5uTkwNvbu9K9k9nZ2Rg+fDhUVFRw4cIFSEtLi7JcImF4PB42b94MQ0ND8Pl8PHnyBGvWrIGMjAzbpRFSLRQoiUTjcDhwcnJCcHAwPn78iO7du8PHx4ftskg5Fi9ejP79+2Py5MmVDoFNmjSBgYFBrQmU5V3uvnbtGoqKijBu3LgKt8Xn8zFhwgSkpKTgxo0btDRMHRcfHw9jY2OsWbMGS5YsQXh4eJUmbxEiiShQklqhV69eiIiIQPfu3TF06FDMnj0beXl5bJdF/oXL5eI///kP8vPz4eTkVOmlhExNTctMzCkNlOnp6SKts7p+FCg9PDxgampaqZnZq1atwu3bt+Hp6fnDWeOk9uPz+di1axcMDAyQk5ODkJAQbNmyBQ0aNGC7NEJEhgIlqTVatGgBHx8fHDlyBGfOnEG3bt3w5MkTtssi/9K6dWu4urriypUrOH36dKWea2Zmhnfv3uHDhw8AIOytk6QeSj6fj1evXn0X/tLS0uDv71+ptSfd3d2xfft27NixA4MGDRJ1qURCvHr1Cv369cPSpUsxd+5cREZGolevXmyXRYjIUaAktQqHw8GsWbMQGRkJJSUl9O3bF2vWrEFJSQnbpZH/GjVqFKZNm4b58+fj1atXFX6eiYkJAAh7KaWkpNCsWTOJCpTv3r1DcXHxd4Hy0qVL4HA4GD16dIXaefr0KaZPnw5HR0c4OzuLo1TCMoFAgEOHDkFfXx+pqal48OABdu3ahYYNG7JdGiFiQYGS1EqdOnXCo0ePsGbNGmzZsgV9+vSp8jqIRPT27dsHVVVVTJgwocJhX0VFBdra2t9d9pakQPmjJYM8PDwwYMAAKCsr/7KN5ORkjBgxAt27d8fRo0dpF5Q66P379xgwYADmzZsHR0dHREVF0SLlpM6jQElqLWlpaaxZswahoaHIzc2FgYEBDh48CIFAwHZp9Z6CggLOnTuHZ8+eYcOGDRV+Xul6lKUkMVA2bNiwzDjJ9+/f49GjRxW63F1QUICRI0dCSkoKV69epTF0dQzDMDhx4gR0dXWRlJSEu3fv4tChQ1BQUGC7NELEjgIlqfV69uyJyMhITJ8+HfPnz8eQIUNoeSEJYGRkhPXr12PLli3f7YLzI2ZmZoiPj0daWhqAfwKlJE3KSUxMhKamJrjc/3/rvHDhAuTk5GBra/vT5zIMg+nTpyM2NhbXr19Hq1atxF0uqUGJiYkYNGgQZsyYgTFjxiAmJgZWVlZsl0VIjaFASeoEeXl5HDhwAL6+voiJiYGuri48PT3ZLqveW7FiBYyNjTFx4kRkZWX98vj+/fsDAPz8/AAA7du3r9Q4THErb4b3+fPnMXz4cDRu3Pinz921axc8PDzg5uZGS8XUIbm5uVi+fDl0dXXx+vVr+Pj44OTJk2jatCnbpRFSoyhQkjpl0KBBiI2NhZWVFcaNG4cJEyYgMzOT7bLqLSkpKbi7uyMrKwtz58795fGqqqro0aMHvL29AQB6enqIj4+XiF2SBAIBoqOj0bVrV+F98fHxiIqK+uVi5qGhoVixYgWWL1+OsWPHirtUUgMYhsH58+ehpaWFAwcOYM2aNYiLi8OQIUPYLo0QVlCgJHWOkpISPD094e7ujlu3bkFPTw8BAQFsl1VvtW3bFkePHoWHhweuXLnyy+NtbGzg6+uLkpIS6Ovro6SkRCImXD1//hwZGRnCXlQAOHXqFBQVFX8aIrKysjB+/HgYGRlh48aNNVEqEbOYmBiYm5vDwcEBffr0QXx8PP7880/IycmxXRohrKFASeokDoeDCRMmICYmBp06dYKVlRUWLVqEgoICtkurl8aNGwdbW1vMnz8f2dnZPz3WxsYGWVlZCAkJEe5tHB0dXRNl/lRAQADk5eXRu3dvAEBhYSHc3Nzg6Oj4w8k1peMms7Oz4eHhQdvr1XJZWVlYuHAhDAwM8OXLF/j5+eHy5cto27Yt26URwjoKlKRO09DQwN27d7Fnzx4cPXoUXbt2xa1bt9guq97hcDg4ePAgcnNzsXLlyp8e2717d6iqquLmzZto2rQp2rVrJxGB0t/fH2ZmZpCVlQXwz9qT6enpmDVr1g+fc+zYMVy5cgUnT55Eu3btaqhSImoCgQBubm7o1KkTTp06hW3btiEqKgoDBgxguzRCJAYFSlLncblcLFq0CNHR0ejQoQNsbGxgZ2cn3JGF1IzWrVtj69atOHLkCB49evTD47hcLoYOHVpmHGVUVFRNlVmuwsJCBAcHl5m1e+TIEVhaWqJTp07lPic6OhqLFi3CnDlzYGdnV1OlEhELDw+HsbExpk2bhoEDByIxMRFLliwRfrAghPyDAiWpNzp16gQ/Pz94enri8ePH0NbWxo4dO2iXnRo0e/Zs9OrVC05OTigqKvrhcTY2NkhMTERSUhL09fVZ76EMDQ1FQUGBMFBGRUUhNDQUs2fPLvf4vLw8jB07Fp07d8auXbtqslQiIl+/foWTkxOMjIxQUFCAoKAguLu7Q01Nje3SCJFIFChJvcLhcGBvb4+EhAQ4OTlhxYoVMDAwqPA6iaR6pKSkcPz4cbx8+RLbt2//4XFWVlZo0KABvL29oaenh8+fP+PLly81WGlZ/v7+UFFRga6uLoB/eidVVVUxfPjwco9fsGABPnz4AE9PT5qoUcvw+XwcPnwYnTp1wqVLl7B//348e/aMdroh5BcoUJJ6qUmTJtizZw+ePXuGxo0bw8zMDI6OjsIFtYn46OnpYenSpdi0aZNwK8P/1ahRI1hYWAgDJfDPzFq2+Pv7w9LSElwuFzk5OXB3d8eMGTPKnWTj4eGBU6dO4dChQ9DS0mKhWlJVjx49gqGhIebOnQs7OzskJiZi3rx5kJaWZrs0QiQeBUpSr3Xr1g2PHj3C8ePHcePGDXTu3BnHjh2j7RvFbPXq1WjTpg2cnJx++G9tY2ODoKAgKCsro2HDhqyNo8zMzER4eLjwcre7uzsKCwsxY8aM74599eoVZs6ciQkTJmDKlCk1XSqponfv3mHSpEkwMTGBjIwMnjx5ghMnTqBFixZsl0ZIrUGBktR7XC4XM2bMQGJiIkaOHIlZs2ahT58+iIyMZLu0Oqthw4Y4duwYgoKC4ObmVu4xQ4cOBY/HQ0BAAHR1dVkbRxkYGAiBQAArKyswDIMjR45g2LBhaN26dZnjioqKMHbsWLRq1QpHjhwBh8NhpV5ScSkpKZgzZ45wfLWrqyseP34MIyMjtksjpNahQEnIf6moqODkyZMIDg5Gfn4+DA0NsWDBgl+um0iqxsLCAo6OjliyZAn+/vvv7x5v27Yt9PT0cPPmTejp6bEWKAMCAtCxY0e0bdsWjx49QmxsbLmTcVasWIGYmBh4enr+chtGwq4vX75g8eLF6NChAzw9PbFp0ya8efMG06dPL7NPOyGk4ugnh5D/YWJigoiICGzfvh2nTp2ClpYWLly4AIZh2C6tztm5cyekpaWxcOHCch+3sbGBj48PdHR0EBcXBx6PV8MV/jN+svRy95EjR9ChQ4cyywcBwM2bN7F3717s2LGD9umWYJmZmfjjjz/w22+/wdXVFStWrMCbN2+wbNkyNGrUiO3yCKnVKFASUg4ZGRksXrwY8fHx6Nu3L8aPH4+BAweyOjGkLmrevDn27t0LT0/Pchect7GxQXp6OqSlpVFcXPzDSTzi8vHjRyQmJsLS0hJpaWm4fPkyZs2aVaYX69OnT3B0dMSwYcOwYMGCGq2PVExubi42bdqE9u3bY+/evZg/fz7evn2LtWvXomnTpmyXR0idQIGSkJ/Q0NDA5cuX4ePjg3fv3kFfXx+TJ0/Gu3fv2C6tznBwcMCgQYMwZ84cfPv2rcxjRkZGUFFRwatXrwDU/BaMAQEB4HA46N+/P06dOgUOh4OpU6cKH+fxeHBwcIC8vDzc3Nxo3KSEyc/Px86dO9G+fXts3LgRjo6OeP36NbZu3YrmzZuzXR4hdQoFSkIqYMiQIXjx4gUOHTqEu3fvonPnzli0aBEtMyQCHA4HR44cQVpaGtasWVPmMSkpKVhbW8Pf3x8aGho1Hij9/f3RvXt3KCoq4tixY7C3ty8TRDZt2oRHjx7Bw8ODAooEKSoqwqFDh9CxY0esXLkSo0aNwqtXr7B37160atWK7fIIqZMoUBJSQTIyMpg9ezZevXqFtWvXws3NDb/99hvWr1+P3Nxctsur1dq3b48NGzZg3759CA8PL/OYjY0NYmNjoampWaOBkmEY4fjJO3fu4O3bt2Um4wQGBmLjxo1Yt24dLXotIXg8Hk6ePIlOnTphwYIFGDBgABISEnDs2DFoaGiwXR4hdRqHoZkGhFRJeno6tm7dioMHD6JJkyZYvXo1Zs6cSXv8VhGPx0PPnj0BAGFhYcLFpHNycqCsrAxzc3O8ePECnz59qpF6YmNjoauri7t372L//v348OEDIiMjweFw8PXrV+jr66Nz5864e/cupKSkaqQmUj4+nw9PT0+sXbsWr169wpgxY7B+/Xpoa2uzXRoh9Qb1UBJSRc2bN8fOnTvx8uVL2NjYYNGiRejcuTPc3d1pYfQqkJaWhqurK6Kjo7F3717h/U2aNIGFhQVSUlKQnJyMpKSkGqnH398fDRo0QJs2bXDr1i3Mnj1bOEZy/vz5KCwshLu7O4VJFgkEAly9ehX6+vqYMGECtLW1ERkZiYsXL1KYJKSGUaAkpJratGmDU6dOISYmBt26dcOkSZNgYGAAHx8fWmqokkq3vduwYUOZtSkdHBwQFxcHWVlZ3L59u0ZquXTpEvr37w83Nzc0atQIEyZMAABcu3YNFy5cwP79+6GmplYjtZCy8vLycPjwYXTu3BmjRo2CqqoqQkNDcePGDXTr1o3t8giplyhQEiIiXbp0gZeXF0JCQtCsWTMMHToU5ubmCA0NZbu0WmXdunWQlpbG2rVrhfeNHDkScnJyaNu2LXx8fMReQ2JiIkJCQmBvb4/Dhw/DyckJCgoKyMjIwOzZszFs2DA4ODiIvQ5SVnJyMlauXAkNDQ3Mnz8f3bt3R2hoKO7evYvevXuzXR4h9RoFSkJErE+fPggMDMStW7eQlZUFY2NjjBgxAi9evGC7tFpBSUkJa9euhaurK2JjYwEAjRs3xvDhw/Ht2zcEBgYiLy9PrDWcPn0azZo1Q3JyMgoLC+Hi4gIAcHZ2RkFBAW2tWMMiIyMxadIktGvXDocOHcLUqVPx+vVreHp6UpAkRFIwhBCx4fP5jLu7O9O+fXuGy+UykydPZl68eMF2WRKvqKiI0dTUZAYOHMgIBAKGYRjm+vXrDAAGAHPjxg2xnZvH4zFqamqMk5MT07JlS2bGjBkMwzCMt7c3A4A5deqU2M5N/h+fz2du3LjBmJubMwCYtm3bMrt372ays7PZLo0QUg7qoSREjLhcLiZMmICEhATs3bsXAQEB6NKlC2xtbRESEsJ2eRJLVlYWO3bsgJ+fH3x9fQEAgwcPhqKiIpo1aybWy95+fn5ISUmBoqIi0tLSsHTpUmRnZ2PmzJkYNGgQHB0dxXZu8s/4yCNHjkBbWxvDhw9HQUEBLl68iFevXsHZ2RlNmjRhu0RCSDlo2SBCalBxcTHOnTuH7du3IyEhASYmJli+fDmsra3LbOdH/lkH0sLCAn///Teio6MhLS2NWbNm4dy5c1BSUsK7d+/EctnZ3t4eL168QF5eHoyMjODp6Ynp06fj4sWLiI2NRZs2bUR+TgKkpKTg0KFDOHr0KLKysmBnZwcXFxf06dOH7dIIIRXBcg8pIfUSn89nrl27xvTp04cBwHTt2pU5ffo0U1RUxHZpEiUiIoLhcDjMoUOHGIZhmKCgIOFl79jYWJGfLz09nZGVlWUmTJjAAGAiIiIYPz8/BgBz9OhRkZ+PMExkZCQzefJkRkZGhlFQUGAWLVrEvHnzhu2yCCGVRF0ihLCAy+UKL3sHBwejffv2cHR0RIcOHbB7927aeee/DAwM4OjoiLVr1yIrKwt9+/aFhoYGpKWlxXLZ28PDAwKBABERERg0aBA6duyI6dOnw8LCAk5OTiI/X30lEAhw69YtWFpawsDAAIGBgdi2bRs+ffqEPXv2oH379myXSAipJAqUhLDMxMQEN2/eRExMDCwtLbF8+XK0adMGf/75J758+cJ2eazbtGkT8vPzsWXLFnC5XDg4OIDD4eDWrVsiP5ebmxsMDQ0RHx+PlStXYvny5fj69StOnDhBs7pF4OPHj9i8eTM6d+4MGxsbfPv2DZ6ennj9+jVcXFzQtGlTtkskhFQRjaEkRMJ8/PgRe/bswfHjx8Hn8+Ho6IglS5agQ4cObJfGmg0bNmDz5s2Ij49HXl4e9PT0ICUlhfT0dJGFkOjoaOjr60NLSwuKiorYtGkTLC0tsX//fsyfP18k56iPCgoKcP36dbi5ueHu3bto2LAhRo8eDScnJxgbG1NQJ6SOoEBJiITKyMjA4cOHsX//fqSnp2P06NFYtmwZevTowXZpNS4/Px+dOnVCnz59cOnSJWhpaSExMRGXL1/GqFGjRHIOZ2dn/Oc//0FmZiY8PT2xcuVKqKurIzAwkCZMVRLDMAgLC4ObmxsuXLggHK4wdepUjBkzhmZqE1IHUaAkRMIVFBTg9OnT2LlzJ968eQMLCwvMnTsXw4YNg4yMDNvl1ZizZ89i8uTJCA4OxqNHj7By5UpMnDgRZ86cqXbbxcXFUFdXR+PGjSEvLw9LS0scP34c0dHR0NTUFEH19cPff/8Nd3d3uLm5IS4uDurq6pg8eTIcHR3RqVMntssjhIgRBUpCagkej4erV69i9+7dePLkCVq2bIlp06Zh+vTp+O2339guT+wEAgGMjIzA4XBw6dIltG/fHk2bNkVmZma1L5t6eXnBzs4OALB69Wps2rQJO3fuFO6QQ36suLgYPj4+cHNzw61btyAlJYURI0Zg6tSpGDBgAKSkpNgukRBSAyhQElILRUdHw9XVFWfPnkV2djYGDBiAmTNnYvjw4XW61zI4OBhmZmY4e/Ystm/fjpiYGDx79gzdu3evVrvDhw/Ho0eP0KhRI8jJyaF58+Z4+PAhhaGfiI6OhpubG86dO4e0tDT06NEDU6dOxfjx46GkpMR2eYSQGkaBkpBaLD8/H5cuXcLx48cREhKCli1bYurUqZg+fXqdncQzevRoPHnyBC4uLnBxccHy5cuxbdu2Krf3+fNntG7dGgKBABYWFnj48CEiIyOhra0twqrrhoyMDHh4eMDNzQ0RERFQUVHBxIkTMXXqVOjq6rJdHiGERTTSnJBaTF5eHlOmTMGjR48QExODsWPH4ujRo+jYsSMGDBiAS5cuobi4mO0yReqvv/7C33//jbS0NHA4HHh6elarvbNnzwIAGjdujPv372PdunUUJv8lNzcXFy9ehL29PVRVVbFo0SK0bt0aXl5e+PTpE3bv3k1hkhBCPZSE1DUFBQW4fPkyjh8/jocPH0JFRQVTp07FjBkz0LFjR7bLE4klS5bg6NGjaNeuHeLi4pCWlgZlZeVKt8MwDDp37oxXr15BRUUFGhoaePz4MaSlpcVQde3x9etX3LhxA15eXrh79y6KiorQrVs3TJw4ERMnTkTLli3ZLpEQImEoUBJSh7148QKurq7C5XBKd3wZMWIEGjRowHZ5VZaVlYWOHTuiS5cuCA4Oxrp167B27dpKtxMaGgpjY2NhgIyIiKi3vW0fPnyAl5cXvLy8EBwcDIZh0LdvX9jZ2WHEiBG0ew0h5KcoUBJSDxQUFODKlStwdXVFUFAQlJWVMWXKFIwfPx7du3evlYtL79y5EytWrACXy4WKigqSk5Mr3YaNjQ1u3boFaWlpLF68uFpjMWsbhmEQHx8vDJHPnj2DjIwMrKysMHLkSAwfPpx6IgkhFUaBkpB6Jj4+HidOnMCZM2fw9etXdOjQAfb29hg7diz09PRqTbjMz89Hhw4dIC0tjU+fPiE2NhZdu3at8PMTExOhpaUFLpcLVVVVJCYmolGjRmKsmH0CgQBhYWHCEPny5UsoKCjA2toaI0eOhLW1NS06TgipEgqUhNRTPB4P9+/fx8WLF3H16lVkZGSgc+fOsLe3h729PXR0dNgu8ZcOHjwo3BZx5MiRuHr1aoWf6+DggPPnzwMArly5IlyHsq4pKSlBUFAQrl69iuvXryM5ORnKysoYPnw4Ro4cCSsrK8jJybFdJiGklqNASQhBSUkJAgICcPHiRXh5eSErKwtdunQR9lxqaWmxXWK5ioqKoKmpibS0NDAMg/z8/Aptk5iamgp1dXUAgJWVFe7cuVNremYrIjs7G/fv34eXlxdu3ryJzMxMtGnTBiNHjsTIkSPRt2/fej/xiBAiWhQoCSFlFBcXw8/PDxcvXsS1a9eQm5sLXV1djB07Fvb29hK3FeGJEycwY8YMAMB//vMfTJ48+ZfPmTlzJo4fPw5paWkkJCTU+jU78/PzERISgnv37uHevXsICwuDQCBAly5dhCGyto6VJYTUDhQoCSE/VFhYiDt37sDT0xM3btxAXl4eDAwMhJfFJWHLx5KSEnTq1Anv3r1D165dERsb+9Pjs7OzoaysDB6Ph1WrVmHz5s01VKnolJSU4OnTp8IAGRISguLiYrRo0QIWFhbCW20PyoSQ2oMCJSGkQgoKCuDj44OLFy/C29sb+fn5MDQ0hL29Pezs7FgNL+7u7pg0aRIAID09vczWfwIBAy73/3vmXFxcsGfPHigpKeHTp09o2LBhjddbWXw+H1FRUbh37x4CAgIQHByMvLw8NG3aFObm5sIA2bVrV+qFJISwggIlIaTS8vLycOvWLXh6esLHxweFhYXo2LEjhgwZgsGDB8Pc3Bzy8vI1Vg+fz0f79u3x8eNHTJi3Ah0GTMDTtxl4lfYNJXwGMlIcdFRRQHeNpti7YCzykxNx/fp1DB8+vMZqrAyGYZCQkICAgADcu3cPgYGByMzMRMOGDWFqaioMkN27d6f9xgkhEoECJSGkWnJzc3Hv3j34+vri9u3beP/+PRo0aAAzMzMMHjwYgwcPhra2tth7zo6f98Ia75eQa6MDKS4HfMH3b21cMBCAA+7XN7i3dRraKUvOMkHv3r0TBsh79+7h8+fPkJGRQe/evWFhYQFLS0sYGRnV6gXpCSF1FwVKQojIMAyDly9fwtfXF76+vggMDERhYSE0NDSE4dLS0hJNmzYV6XmvP0/G0svR4AsE4FfgHU2KA0hxudgxWg+23dRFWktFZGVl4dmzZwgPD0dYWBjCwsLw4cMHcDgc9OjRQ9gDaWJiUufXxiSE1A0UKAkhYlNQUIAHDx4IA2ZiYiKkpaVhbGwsDJj6+voVWurnR64/T8Yiz+f40RtZdognsoLOQka5DdSmHy7zGAfA3rHdxBoqv337hsjISISFhSE8PBzh4eFISkoCACgoKKBHjx4wNDSEiYkJ+vXrB0VFRbHVQggh4kKBkhBSY96+fYs7d+7A19cXAQEB+PbtG1q2bIlBgwZh8ODBGDBgAJSVlSve3tc8DNobhGK+oNzHeTlfkeI6EwAH0k1bfBcoAUBWigu/RWYiufxdWFiIqKgoYXgMCwtDfHw8GIZBw4YNYWBgAENDQxgaGqJnz57o1KlTtcI0IYRICgqUhBBWFBcXIyQkRNh7GRUVJbzk269fP5iZmcHExKTMjO3/ZX8sFM8+ZJY7XhIA0q7/BUF+NhiBAIKCnHIDpRSXgx5tFHFxZp9K1x8bGyvsdQwLC0NsbCx4PB5kZGSgr68vDI6Ghobo0qULLSZOCKmzKFASQiRCSkoK/Pz8cPfuXQQFBeHTp08AAF1dXZiZmcHMzAympqZQVVUFAMQkZ2PYwYc/bK/wQyz+Pr8KqlP3I+Pu0R8GylLe80ygo17+2M709HTExcXhxYsXiImJQXh4OKKiolBUVAQpKSno6OgIex4NDQ2hq6tLk2cIIfUKBUpCiMRhGAbv379HUFCQ8FY67lBTUxNmZmbgGI7F/Y8l4JXTO8kI+Eh1W4gG6lpoPngePp9b8dNAKcXlYGKvNphv3EoYHP/955cvXwAA0tLS6NSpE3r06CHsedTX16/RJZIIIUQSUaAkhNQKnz9/RnBwMIKCgvDgwQMIBi7HN5lm5R6b+8wbmUFnoT7zOKTkm/4yUAIAP/09PrnOBQDIyMigU6dO6NKlC7p27YouXbqgS5cu0NTUhKysrDheHiGE1GoUKAkhtZLmnz4oKWeNIH5BDlKOzUTTPmPQpJcdAFQoUHIhwPYeRejatSs6duwIGRkZsdVOCCF1DY0QJ4TUOgIBU26YBICsoLPgNlRAY8NhlWsTXNjZjSqzTSMhhJCKoUBJCKl1uFwOZKQ434XKkoxkfHt+B4qWM8DPzRDez/BLwAj44GX9DU4DeUg1bPxdmzJSHAqThBBSRRQoCSG1UkcVBcR/zi1zHz83HWAEyPQ/hkz/Y989J/no72hsOBxKVk7fPabZ4vuQSQghpGIoUBJCaiWj9kp4+eVbmTUoZVTaQsXuj++OzQo6C0FxAZSsnCDdTPW7x6W4HPRsRzvUEEJIVVGgJITUSmN6aOA/oe/L3Ccl3xTynb5foDwn7DoAlPsYAPAFDMb00BB9kYQQUk/Qnl+EkFpJR70pjNopQaqa4x6luBwYtVP64aLmhBBCfo2WDSKE1FrvvuZh4E/28q4IUe7lTQgh9RX1UBJCaq12yo2wY7QeqtpHyQGwY7QehUlCCKkmGkNJCKnVbLupAwCWXo4Gn2HKTNL5ESkuB1IcDnaM1hM+nxBCSNXRJW9CSJ3w7msell2JxtN3GZDicsoNlqX392qvhL/sqGeSEEJEhQIlIaROiU3OxqVnHxH2LhNJX3JRwmcgI8WBZovG6NlOEWN6aNAEHEIIETEKlISQOk0gYGgHHEIIETMKlIQQQgghpFpoljchhBBCCKkWCpSEEEIIIaRaKFASQgghhJBqoUBJCCGEEEKqhQIlIYQQQgipFgqUhBBCCCGkWihQEkIIIYSQaqFASQghhBBCqoUCJSGEEEIIqRYKlIQQQgghpFooUBJCCCGEkGqhQEkIIYQQQqqFAiUhhBBCCKkWCpSEEEIIIaRaKFASQgghhJBqoUBJCCGEEEKqhQIlIYQQQgipFgqUhBBCCCGkWihQEkIIIYSQaqFASQghhBBCqoUCJSGEEEIIqRYKlIQQQgghpFooUBJCCCGEkGqhQEkIIYQQQqqFAiUhhBBCCKkWCpSEEEIIIaRaKFASQgghhJBqoUBJCCGEEEKqhQIlIYQQQgipFgqUhBBCCCGkWihQEkIIIYSQaqFASQghhBBCqoUCJSGEEEIIqRYKlIQQQgghpFooUBJCCCGEkGqhQEkIIYQQQqqFAiUhhBBCCKkWCpSEEEIIIaRaKFASQgghhJBq+T/2MjJMtXMRrAAAAABJRU5ErkJggg==\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# We define the number of nodes:\\n\",\n \"n_ext = 7\\n\",\n \"# We define the graph\\n\",\n \"graph_ext = rx.PyGraph()\\n\",\n \"graph_ext.add_nodes_from(np.arange(0, n_ext, 1))\\n\",\n \"generic_backend_ext = GenericBackendV2(n_ext, seed=seed)\\n\",\n \"weights = 1\\n\",\n \"# We make it explicitly asymmetrical to have a smaller set of solutions\\n\",\n \"graph_ext.add_edges_from(\\n\",\n \" [(edge[0], edge[1], weights) for edge in generic_backend_ext.coupling_map][:-7]\\n\",\n \")\\n\",\n \"draw_graph(graph_ext, node_size=200, with_labels=True, width=1)\\n\",\n \"max_cut_paulis_ext = graph_to_Pauli(graph_ext)\\n\",\n \"cost_hamiltonian_ext = SparsePauliOp.from_list(max_cut_paulis_ext)\\n\",\n \"pm = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=generic_backend_ext, seed_transpiler=seed\\n\",\n \")\\n\",\n \"layers = 2\\n\",\n \"init_params = np.zeros(2 * layers)\\n\",\n \"qaoa_circuit_ext = QAOAAnsatz(cost_operator=cost_hamiltonian_ext, reps=layers)\\n\",\n \"qaoa_circuit_ext.measure_all()\\n\",\n \"qaoa_circuit_ext_transpiled = pm.run(qaoa_circuit_ext)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Choose the backend\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The backend ibm_torino has the circuit with the smallest accumulated error: 0.474\\n\"\n ]\n }\n ],\n \"source\": [\n \"qaoa_circuit_transpiled_ext_list = []\\n\",\n \"acc_error_list = []\\n\",\n \"\\n\",\n \"\\n\",\n \"for backend_hw in real_backends:\\n\",\n \"\\n\",\n \" pm = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend_hw, seed_transpiler=seed\\n\",\n \" )\\n\",\n \" qaoa_circuit_transpiled_ext = pm.run([qaoa_circuit_ext])[0]\\n\",\n \" qaoa_circuit_transpiled_ext_list.append(qaoa_circuit_transpiled_ext)\\n\",\n \"\\n\",\n \" # ---- TODO : Bonus Task ---\\n\",\n \"\\n\",\n \" # Use the accumulated_errors function, `backend_hw` and `qaoa_circuit_transpiled_ext` and get the [0] value\\n\",\n \" acc_error = accumulated_errors(backend_hw, qaoa_circuit_transpiled_ext)[0]\\n\",\n \"\\n\",\n \" # --- End of TODO ---\\n\",\n \"\\n\",\n \" acc_error_list.append(acc_error)\\n\",\n \"# We choose the backend with smallest error\\n\",\n \"best_backend_ext = real_backends[acc_error_list.index(min(acc_error_list))]\\n\",\n \"print(\\n\",\n \" f\\\"The backend {best_backend_ext.name} has the circuit with the smallest accumulated error: {min(acc_error_list):.3f}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### We optimize the layout and minimize the accumulated error\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best transpiler seed: 486\\n\",\n \"Minimum accumulated two-qubit gate error: 0.294\\n\"\n ]\n }\n ],\n \"source\": [\n \"circuit_ext_opt_seed, best_seed_transpiler, min_err_acc_seed, _ = finding_best_seed(\\n\",\n \" qaoa_circuit_ext, best_backend_ext\\n\",\n \")\\n\",\n \"print(f\\\"Best transpiler seed: {best_seed_transpiler}\\\")\\n\",\n \"print(f\\\"Minimum accumulated two-qubit gate error: {min_err_acc_seed:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### We execute the circuit on simulator\\n\",\n \"\\n\",\n \"First, we must optimize the parameters of this QAOA problem. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"best_backend_sim = AerSimulator.from_backend(best_backend_ext, seed_simulator=seed)\\n\",\n \"result_qaoa_sim, objective_func_vals_sim = train_qaoa(\\n\",\n \" init_params, circuit_ext_opt_seed, cost_hamiltonian_ext, best_backend_sim\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can sample from the QAOA circuit.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"counts_list_sim = sample_qaoa(opt_params_sim, circuit_ext_opt_seed, best_backend_sim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Checking the results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eigenvalues_ext, eigenvectors_ext = np.linalg.eig(cost_hamiltonian_ext)\\n\",\n \"ground_energy_ext = min(eigenvalues_ext).real\\n\",\n \"num_solutions_ext = eigenvalues_ext.tolist().count(ground_energy_ext)\\n\",\n \"index_solutions_ext = np.where(eigenvalues_ext == ground_energy_ext)[0].tolist()\\n\",\n \"print(f\\\"The ground energy of the Hamiltonian is {ground_energy_ext}\\\")\\n\",\n \"print(f\\\"The number of solutions of the problem is {num_solutions_ext}\\\")\\n\",\n \"print(f\\\"The list of the solutions based on their index is {index_solutions_ext}\\\")\\n\",\n \"\\n\",\n \"states_solutions_ext = decimal_to_binary(index_solutions_ext, n_ext)\\n\",\n \"# Sort the dictionary items by their counts in descending order\\n\",\n \"sorted_states_sim = sorted(counts_list_sim.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"# Take the top 'num_solutions' entries\\n\",\n \"top_states_sim = sorted_states_sim[:num_solutions_ext]\\n\",\n \"# Extract only the states keys from the top entries\\n\",\n \"qaoa_ground_states_sim = sorted([state for state, count in top_states_sim])\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions_ext}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_sim} for {best_backend_sim.name}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Running on Hardware\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" Resource limit: \\n\",\n \"\\n\",\n \"When running the section below, you will execute the above problem on real hardware, which could consume approximately 2-3 minutes of the your Open Plan allotment. Please proceed only if you're comfortable with this potential usage. Also, please try to maintain these settings if possible, to make sure you don't use too much time. \\n\",\n \"
\\n\",\n \"\\n\",\n \"Finally we can execute the problem on hardware, and compare the results.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Estimator\\n\",\n \"\\n\",\n \"We will not train the QAOA circuit on hardware, as it will consume a lot of your Open Plan allotment. Instead, we'll execute the estimator with the optimized parameters and will apply error mitigation techniques later to see how we can improve the results.\\n\",\n \"\\n\",\n \"First we compute the ground state energy on hardware without error mitigation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO : Bonus Task ---\\n\",\n \"\\n\",\n \"# Based on previous section, choose the best backend for executing the code on hardware\\n\",\n \"best_backend_hw = best_backend_ext\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"options = EstimatorOptions(default_shots=100000)\\n\",\n \"estimator = Estimator(mode=best_backend_hw, options=options)\\n\",\n \"ground_energy_ext_hw = cost_func_estimator(\\n\",\n \" opt_params_sim, circuit_ext_opt_seed, cost_hamiltonian_ext, estimator\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The ground energy of the QAOA circuit executed on {best_backend_hw.name} without EM is: {ground_energy_ext_hw}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we apply different error mitigation and error suppression techniques.\\n\",\n \"The different techniques we'll apply are:\\n\",\n \"\\n\",\n \"- **Dynamical decoupling**: this error suppression technique involves applying a sequence of control pulses to idle qubits to cancel out unwanted interactions and coherent errors. It helps preserve quantum coherence by effectively \\\"decoupling\\\" the system from noise sources over time.\\n\",\n \"- **Pauli twirling**: this error mitigation technique transforms arbitrary noise into simpler Pauli noise by randomly applying and undoing Pauli gates around operations, reducing the impact of coherent errors and enabling more effective noise modeling and correction.\\n\",\n \"- **TREX**: Twirled Readout Error eXtinction is a readout error mitigation technique that reduces measurement errors by randomly flipping qubits before measurement and classically correcting the results, effectively diagonalizing the readout-error matrix and simplifying its inversion for more accurate expectation value estimation.\\n\",\n \"- **ZNE**: as explained before, ZNE is an error mitigation technique that estimates the result of a quantum computation as if it were run on a noise-free device. It does this by deliberately increasing the noise in a controlled way, measuring the results, and then extrapolating back to the zero-noise limit.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"options = EstimatorOptions(default_shots=100000)\\n\",\n \"# Dynamical Decoupling\\n\",\n \"options.dynamical_decoupling.enable = True\\n\",\n \"\\n\",\n \"# Probabilistic Twirling\\n\",\n \"options.twirling.enable_gates = True\\n\",\n \"options.twirling.num_randomizations = 10\\n\",\n \"options.twirling.shots_per_randomization = 10000\\n\",\n \"\\n\",\n \"# TREX\\n\",\n \"options.resilience.measure_mitigation = True\\n\",\n \"options.resilience.measure_noise_learning.num_randomizations = 10\\n\",\n \"options.resilience.measure_noise_learning.shots_per_randomization = 10000\\n\",\n \"\\n\",\n \"# ZNE setup\\n\",\n \"options.resilience.zne_mitigation = True\\n\",\n \"options.resilience.zne.amplifier = \\\"gate_folding\\\"\\n\",\n \"options.resilience.zne.extrapolator = \\\"polynomial_degree_2\\\"\\n\",\n \"options.resilience.zne.noise_factors = (1, 3, 5)\\n\",\n \"\\n\",\n \"# We execute on hardware\\n\",\n \"estimator_EM = Estimator(mode=best_backend_hw, options=options)\\n\",\n \"ground_energy_ext_hw_EM = cost_func_estimator(\\n\",\n \" opt_params_sim, circuit_ext_opt_seed, cost_hamiltonian_ext, estimator_EM\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The ground energy of the QAOA circuit executed on {best_backend_hw.name} with EM is: {ground_energy_ext_hw_EM}\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can use the Sampler primitive on real hardware without any error mitigation and suppression techniques.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"# We execute on hardware\\n\",\n \"sampler = Sampler(mode=best_backend_hw)\\n\",\n \"job = sampler.run([(circuit_ext_opt_seed, opt_params_sim)], shots=SHOTS)\\n\",\n \"results_sampler = job.result()\\n\",\n \"counts_list_hw = results_sampler[0].data.meas.get_counts()\\n\",\n \"display(plot_histogram(counts_list_hw, title=f\\\"Max cut with {best_backend_hw.name} \\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we include error suppression techniques in the Sampler.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_params_sim = result_qaoa_sim.x\\n\",\n \"# We execute on hardware\\n\",\n \"sampler = Sampler(mode=best_backend_hw)\\n\",\n \"# Set runtime options directly on the sampler\\n\",\n \"sampler.options.dynamical_decoupling.enable = True\\n\",\n \"job = sampler.run([(circuit_ext_opt_seed, opt_params_sim)], shots=SHOTS)\\n\",\n \"results_sampler = job.result()\\n\",\n \"counts_list_hw_EM = results_sampler[0].data.meas.get_counts()\\n\",\n \"display(plot_histogram(counts_list_hw_EM, title=f\\\"Max cut with {best_backend_hw.name} with EM\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Check results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sorted_states_hw = sorted(counts_list_hw.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"top_states_hw = sorted_states_hw[:num_solutions_ext]\\n\",\n \"qaoa_ground_states_hw = sorted([state for state, count in top_states_hw])\\n\",\n \"\\n\",\n \"sorted_states_hw_EM = sorted(counts_list_hw_EM.items(), key=lambda item: item[1], reverse=True)\\n\",\n \"top_states_hw_EM = sorted_states_hw_EM[:num_solutions_ext]\\n\",\n \"qaoa_ground_states_hw_EM = sorted([state for state, count in top_states_hw_EM])\\n\",\n \"\\n\",\n \"print(f\\\"The analytical solutions for the Max-cut problem are: {states_solutions_ext}\\\")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_hw} for {best_backend_hw.name}\\\"\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"The QAOA ground states solutions for the Max-cut are: {qaoa_ground_states_hw_EM} for {best_backend_hw.name} with EM\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Congratulations on reaching the end of the lab!\\n\",\n \"If you still have some time left for running experiments on hardware, feel free to explore a few additional things:\\n\",\n \"\\n\",\n \"- Experiment with error mitigation techniques: try rerunning your execution on hardware using different error mitigation and suppression strategies. You can enable or disable specific techniques or tweak various parameters to study their impact.\\n\",\n \"\\n\",\n \"- Redo the bonus challenge: try rerunning the entire bonus challenge section using a different backend to compare results and performance.\\n\",\n \"\\n\",\n \"- Scale it up even more: try scaling the problem to a larger system, such as 10 qubits, and rerun your experiments. However, this might take some time in both the simulator and hardware, so we encourage you to try the first two other points first.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# References:\\n\",\n \"\\n\",\n \"[1] Fahri et al., \\\"A Quantum Approximate Optimization Algorithm\\\" (2014). [arXiv:quant-ph/1411.4028](https://arxiv.org/abs/1411.4028)\\n\",\n \"\\n\",\n \"[2] Nation et al., \\\"Suppressing Quantum Circuit Errors Due to System Variability\\\" (2023). [PRX Quantum 4, 010327](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.4.010327)\\n\",\n \"\\n\",\n \"[3] Temme et al., \\\"Error Mitigation for Short-Depth Quantum Circuits\\\" (2017). [Phys. Rev. Lett. 119, 180509](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509)\\n\",\n \"\\n\",\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Jorge Martínez de Lejarza, Alberto Maldonado\\n\",\n \"\\n\",\n \"**Advised by:** Marcel Pfaffhauser, Paul Nation, Junye Huang, Sophy Shin\\n\",\n \"\\n\",\n \"**Version:** 1.0.1\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"conda3.13.2\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.2\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "solutions/lab-3/lab3-solution.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8cdbc826-5411-42cd-bf85-8f3ef3e40feb\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# Lab 3: The Power of 'good sampling' for simulating a chemistry Hamiltonian with SQD\\n\",\n \"\\n\",\n \"Welcome to the third coding challenge of the Qiskit Global Summer School. In this lab, we explore one of the most promising applications of quantum computing, which is quantum chemistry. We present you with a real-life example of simulating a molecule using a quantum computer based on a workflow that quantum chemists may usually follow. We will explain what each step takes to achieve this and walk you through the entire workflow.\\n\",\n \"\\n\",\n \"**Recommended study** \\n\",\n \"Prior to going through this challenge, we recommend that you take a moment and check out the [Variational Algorithm Design](https://quantum.cloud.ibm.com/learning/en/courses/variational-algorithm-design) course and one of our latest courses on IBM Quantum Learning, [Quantum Diagonalization Algorithms](https://quantum.cloud.ibm.com/learning/en/courses/quantum-diagonalization-algorithms/skqd). \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9e49e912-bc84-44e2-a715-94ec1e95eba2\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"* [Part 1: Introduction](#part-1-introduction)\\n\",\n \" * 1.1 Objective\\n\",\n \" * 1.2 What Are Atoms?\\n\",\n \" * 1.3 The Schrödinger Equation\\n\",\n \" * 1.4 Basis Set Approximation - Smart Building\\n\",\n \" * 1.5 The Hamiltonian\\n\",\n \" * [Exercise 1](#exercise1): Measure the size of the $O_2$ Hamiltonian\\n\",\n \"* [Part 2: Variational Quantum Eigensolver](#part-2-variational-quantum-eigensolver)\\n\",\n \" * VQE – A Team-Up Between Classical and Quantum Computers\\n\",\n \"* [Part 3: What is Sample-based Quantum Diagonalization (SQD)?](#part-3-what-is-sample-based-quantum-diagonalization-(SQD)?)\\n\",\n \" * SQD Approach: Reconstruct the Hamiltonian with 'good samples'\\n\",\n \" * Choosing relevant configurations\\n\",\n \" * Dealing with the effects of noise with configuration recovery\\n\",\n \"* [Part 4: How to simulate a $N_2$ molecule with SQD](#part-4-how-to-simulate-a-$N_2$-molecule-with-SQD)\\n\",\n \" * [Exercise 2](#exercise2): Flip a bit by configuration recovery\\n\",\n \"* [Part 5: Improve the ansatz](#part-5-improve-the-ansatz)\\n\",\n \" * [Exercise 3](#exercise3): Change the basis set\\n\",\n \" * [Exercise 4](#exercise4): Select the best layout\\n\",\n \" * [Exercise 5](#exercise5): Add more interaction to LUCJ ansatz\\n\",\n \"* [Bonus: Real hardware execution with error mitigation ](Bonus-real-hardware-execution-with-error-mitigation )\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c7df7f65-7bc0-405b-b937-0d1203f7204a\",\n \"metadata\": {},\n \"source\": [\n \"## Requirements\\n\",\n \"\\n\",\n \"Before starting this tutorial, please make sure that you have the following installed:\\n\",\n \"\\n\",\n \"* Qiskit SDK 2.0 or later with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime 0.40 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"* Qiskit Addons SQD 0.11.0 or later (`pip install qiskit-addon-sqd`)\\n\",\n \"* ffsim (`pip install ffsim`)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"723fb00e-8dfa-421e-95bc-dcabffdf3cec\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** You need to have the below packages installed in order to run this lab, of which some are not available on Windows. If you are using Windows we recommend you to use [an online lab environment.](https://quantum.cloud.ibm.com/docs/en/guides/online-lab-environments#online-lab-environments)\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8465b605\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Needed for grading now after summer school before other code is run.\\n\",\n \"%set_env QC_GRADING_ENDPOINT=https://qac-grading.quantum.ibm.com\\n\",\n \"%set_env QC_GRADE_ONLY=true\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"128fe3fe-9b55-4696-87ce-4d8d1ad1474c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"# %pip install pyscf\\n\",\n \"# %pip install ffsim\\n\",\n \"# %pip install qiskit_addon_sqd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"242ba214-1bb5-4028-8122-c89b28e3564b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Qiskit version: 2.1.0\\n\",\n \"Grader version: 0.22.12\\n\"\n ]\n }\n ],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d2d72ab3-882f-4789-a414-504e0c46190d\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.12`. If you see a lower version, you need to reinstall the grader and restart the kernel.\\n\",\n \"Also make sure you have set up everything according to lab 0 and test it with the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9c98e978\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f58bfa2-295b-4831-bb96-9e6c2761bb25\",\n \"metadata\": {},\n \"source\": [\n \"# Imports\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"e92c99a3-1a03-4fca-a476-edc4b4692ed4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import common packages first\\n\",\n \"import numpy as np\\n\",\n \"from math import comb\\n\",\n \"import warnings\\n\",\n \"import pyscf\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pickle\\n\",\n \"from functools import partial\\n\",\n \"\\n\",\n \"# Import qiskit classes\\n\",\n \"from qiskit import QuantumCircuit, QuantumRegister\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_gate_map\\n\",\n \"from qiskit_addon_sqd.fermion import SCIResult, diagonalize_fermionic_hamiltonian, solve_sci_batch\\n\",\n \"\\n\",\n \"# Import qiskit ecosystems\\n\",\n \"import ffsim\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\\n\",\n \"from qiskit_ibm_runtime import SamplerOptions\\n\",\n \"\\n\",\n \"# Import grader\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab3_ex1, \\n\",\n \" grade_lab3_ex2, \\n\",\n \" grade_lab3_ex3,\\n\",\n \" grade_lab3_ex4,\\n\",\n \" grade_lab3_ex5\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5421b095-f20c-494c-a8c9-a33ed120fa3b\",\n \"metadata\": {},\n \"source\": [\n \"# 1. Introduction \\n\",\n \"\\n\",\n \"## 1.1 Objective\\n\",\n \"\\n\",\n \"The objective of this Lab is to learn the basics and general workflow of quantum chemistry calculations. You will also learn about a useful hybrid quantum-classical algorithm called Sample-based Quantum Diagonalization (SQD) algorithm. SQD is a classical post-processing technique which acts on samples obtained from a quantum circuit after execution on a QPU. It is useful for finding eigenvalues and eigenvectors of quantum operators, such as the Hamiltonian of a quantum system, and uses quantum and distributed classical computing together. \\n\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"3123cd89-2682-4da9-9322-ee40376bec6c\",\n \"metadata\": {},\n \"source\": [\n \"## 1.2 What Are Atoms?\\n\",\n \"Everything around you is made of atoms—the tiny building blocks of matter. The word “atom” comes from a Greek word that means “can’t be split.” A long time ago, a man named Democritus thought that all things were made of tiny, unbreakable pieces called atoms.\\n\",\n \"\\n\",\n \"In 1913, Niels Bohr said electrons move in circles (orbits) around the center of the atom, like planets around the sun. \\n\",\n \"But in 1926, Erwin Schrödinger said electrons don’t really move in perfect paths. Instead, they buzz around in \\\"clouds\\\" where they’re most likely to be found.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 1. A sketch of Bohr’s atomic model (left) and Schrödinger’s atomic model based on quantum mechanical and wave nature of electrons (right). \\n\",\n \"\\n\",\n \"\\n\",\n \"These clouds (called orbitals) have different shapes and energy levels. Electrons usually stay in the lowest energy level (the ground state), but they can jump to higher ones if they get energy. When they fall back down, they give off energy.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f39f52bc-d1c5-4bbf-a4db-4a46d8cdf403\",\n \"metadata\": {},\n \"source\": [\n \"## 1.3 The Schrödinger Equation\\n\",\n \"\\n\",\n \"Erwin Schrödinger's contributions to quantum mechanics go beyond introducing a new electronic model; he established the famous Schrödinger equation. The time-independent Schrödinger equation is:\\n\",\n \"$$\\n\",\n \"\\\\hat{H}|\\\\psi\\\\rangle = {E}|\\\\psi\\\\rangle\\n\",\n \"$$\\n\",\n \"\\n\",\n \"**$\\\\hat{H}$** : Hamiltonian operator \\n\",\n \"**$|\\\\psi\\\\rangle$** : Wave function (state of the system) \\n\",\n \"**${E}$** : Measured energy (eigenvalue) \\n\",\n \"\\n\",\n \"**The goal of quantum chemistry is to solve the Schrödinger Equation** for the wave function. The wave function that satisfies the Schrödinger Equation can help us find out interesting properties of that quantum system such as energy, momentum, spin, magnetization, and more. \\n\",\n \"\\n\",\n \"In quantum chemistry, we are often interested in finding **the ground state energy**. This is because once we know the ground state energy of a molecule, we can learn a lot such as:\\n\",\n \"\\n\",\n \"- the shape a molecule will most likely take in a stable form. (often called the \\\"equilibrium\\\" state)\\n\",\n \"- how molecules might change or react during chemical reactions.\\n\",\n \"- how a drug might stick to a protein in the body seen in Docking simulations.\\n\",\n \" \\n\",\n \"In short, finding the ground state is like finding the starting point for all the interesting things molecules can do.\\n\",\n \"\\n\",\n \"But for big molecules, **solving the Schrödinger Equation exactly is extremely difficult** because the wave function, which describes the spatial distribution of electrons, is highly complex. So, instead of solving it perfectly, scientists make good guesses or approximations that are close enough.\\n\",\n \"\\n\",\n \"## 1.4 Basis Set Approximation - Smart Building\\n\",\n \"One of the key approximations in quantum chemistry is the use of a **basis set** approach. A basis set is **a collection of mathematical functions used to approximate the orbitals (wavefunctions) of electrons** in atoms and molecules. Since we can't solve the Schrödinger equation exactly for most systems, we use basis sets to make the problem computationally tractable. Instead of working with full, continuous atomic orbitals, we approximate them using a set of predefined mathematical functions—usually Gaussian-type or Slater-type orbitals.\\n\",\n \"\\n\",\n \"This method is known as the Linear Combination of Atomic Orbitals (LCAO). The idea is simple: we build molecular orbitals by combining basis functions. It’s like building a complex shape using a sum of simpler, known shapes.\\n\",\n \"- A small basis set (fewer functions) gives a rough but fast approximation.\\n\",\n \"- A larger basis set improves accuracy but requires much more computational power.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Types of Basis Functions \\n\",\n \"\\n\",\n \"- **Slater-type orbitals (STOs)**: These are mathematical functions that look very similar to real atomic orbitals (the shapes electrons naturally form around atoms). They describe how electron density drops off with distance from the nucleus.\\n\",\n \"- **Gaussian-type orbitals (GTOs)**: Easier to work with computationally, even though they don't resemble orbitals as closely. Often used in practice.
\\n\",\n \"\\n\",\n \"\\n\",\n \"The Slater-type orbital (STO) can be approximated by a combination of Gaussian-type orbitals (GTOs). In other words, we combine multiple Gaussians with different widths to mimic the shape of an STO. This combination is called a **contracted Gaussian function**. This idea is the basis for different kinds of basis sets, like minimal (simple) and split-valence (more accurate) sets.\\n\",\n \"\\n\",\n \"- **Minimal Basis Set**:\\n\",\n \"A basis set that uses just one function per orbital (e.g., one function for $1s$, one for $2s$, etc.).\\n\",\n \"In STO-3G, for example, each STO is approximated using 3 Gaussians.\\n\",\n \"- **Split-Valence Basis Set**:\\n\",\n \"These are more flexible and provide more accurate representations than minimal basis sets.\\n\",\n \"They split the valence orbitals (the outermost orbital that contain electrons involved in bonding with other atoms) into two or more parts, each approximated with different combinations of Gaussians (like 6-31G).\\n\",\n \"This allows for better accuracy in chemical bonding and reactions.\\n\",\n \"\\n\",\n \"### Common Basis Sets\\n\",\n \"\\n\",\n \"| Basis\\tSet | Description\\t| Example Use | Computational Cost |\\n\",\n \"| ----------------- | ----------- | ----------- | ----------- |\\n\",\n \"| **STO-3G**\\t| Uses 3 Gaussians (the \\\"3G\\\") to mimic 1 STO. (aka 'Minimal basis')| Fast, rough estimate for small molecules| Low |\\n\",\n \"| **6-31G**\\t | Uses 6 Gaussians to mimic 1 STO (the \\\"6\\\") for each core orbital. For each valence (outer) orbital, a contracted function made of 3 Gaussians (the \\\"3\\\") and a single uncontracted Gaussian (the \\\"1\\\") are used.\\t| Good balance between speed and accuracy| Medium |\\n\",\n \"| **cc-pVDZ**\\t| Designed to improve electron correlation calculations. Adds polarization functions. Uses 2 basis functions for each valence orbitals instead of 1.\\t| More accurate than STO-3G and 6-31G when simulating systems that have stronger correlations, but more computationally expensive.| High |\\n\",\n \"\\n\",\n \"> NOTE: The computational cost referred to in the table above is the classical cost of computing the molecular integrals. The more functions in your basis set, the more accurate your results—but computations take longer.\\n\",\n \"\\n\",\n \"To summarize, instead of solving the full Schrödinger equation exactly (which is almost always impossible for molecules), we build a flexible, approximate wave function using known functions—and then tune it to minimize the system’s energy.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"13c35f0d-e9ac-490b-898e-490adcdd8bdd\",\n \"metadata\": {},\n \"source\": [\n \"## 1.5 The Hamiltonian\\n\",\n \"\\n\",\n \"At the heart of the Schrödinger equation, and to that extent in every quantum chemistry problem, there is a **Hamiltonian**. It’s a central object in both classical mechanics and quantum mechanics, and is essentially a function that represents the total energy (kinetic + potential) of a physical system. \\n\",\n \"\\n\",\n \"In quantum mechanics, the Hamiltonian $\\\\hat{H}$ becomes an operator acting on the wave function $|\\\\psi\\\\rangle$. In a chosen basis, these wave functions $|\\\\psi\\\\rangle$ can be described as vectors and Hamiltonians in matrix form. \\n\",\n \"\\n\",\n \"In most quantum chemistry problems, the first and most important goal was to calculate its ground state energy. This calculation can be done by diagonalizing a Hamiltonian (matrix) and computing its eigenvalues and eigenvectors.\\n\",\n \"\\n\",\n \"The size or dimensions of a molecular Hamiltonian can be determined by finding the number of combinations the electrons can occupy the spatial orbitals. This is essentially a combination problem that can quickly grow exponential in size making the diagonalization uncontractable for most interesting molecules. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d354e83a-fe0e-477e-b7fa-6d1bf6045186\",\n \"metadata\": {},\n \"source\": [\n \"### Example: Calculate the size of the $N_2$ Hamiltonian\\n\",\n \"We would like you to get a sense of how large a Hamiltonian matrix $H$ can grow, even for a relatively small molecule like nitrogen ($N_2$), depending on how accurately you want to simulate it. In the following example, we chose the number of spatial orbitals, spin orbitals, and electrons of $N_2$ to use in our calculations. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dc20e23f-f68c-4fd8-9f79-059ac6405b2f\",\n \"metadata\": {},\n \"source\": [\n \"Using the number of spatial orbitals and electrons in the table below, let's compute the number of ways the electrons can occupy the spatial orbitals (spin configurations), which indicates the size of the $N_2$ molecule Hamiltonian.\\n\",\n \"\\n\",\n \"\\n\",\n \"| | STO-3G | 6-31G | cc-pVDZ | \\n\",\n \"|:-------|:------:|:-------:|:-------:|\\n\",\n \"|Spatial orbitals | (10) **8**| (18) **16** | (28) **26** |\\n\",\n \"|Spin orbitals | (20) **16**| (36) **32**| (56) **52** |\\n\",\n \"| α-spin electrons | (7) **5**| (7) **5**| (7) **5** |\\n\",\n \"| β-spin electrons | (7) **5**| (7) **5**| (7) **5** |\\n\",\n \"\\n\",\n \"\\n\",\n \"
Table 1: Number of orbitals and electrons in specified basis sets for $N_2$
\\n\",\n \"\\n\",\n \"- (#) : number of orbitals and electrons we specifically chose for the purpose of this example.\\n\",\n \"- **#** : number of orbitals or electrons when freezing the core orbital (*1s*) and reducing the number of electrons and spatial orbitals each by 2.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"79f14c83-fe07-476c-b091-456458ec9f04\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** In this example we treat the $1s$ (core) orbital as chemically inactive. This means we can freeze the core orbital and save 2 electrons and 2 spatial orbitals (i.e. 4 spin orbitals -> 4 qubits). This is another useful approximation technique you will frequently see in actual chemistry simulations. Applying this technique, please make sure to use the numbers in **bold** for calculating for all possible electron configurations for the $N_2$ Hamiltonian.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"363fddb2-7208-4192-8c1f-ee60870e925e\",\n \"metadata\": {},\n \"source\": [\n \"As you can tell, solving for all possible spin configurations is essentially a combination problem. Let's take a look at the math to calculate how many ways the **$\\\\alpha$-spin (spin-up)** electrons and **$\\\\beta$-spin (spin-down)** electrons can each occupy given spatial orbitals. For the total spin configurations we simply need to multiply them together. \\n\",\n \"\\n\",\n \"
Total electron configurations = (total α-spin configurations) × (total β-spin configurations)
\\n\",\n \"\\n\",\n \"$$ \\\\Large{n}\\\\Large{C}n_α \\\\times n\\\\Large{C}n_β = \\\\Large\\\\frac{n!}{n_α!(n-n_α)!} \\\\times \\\\Large\\\\frac{n!}{n_β!(n-n_β)!} $$\\n\",\n \"\\n\",\n \"Where\\n\",\n \"**$n$**: number of spatial orbitals,\\n\",\n \"**$n_α$**: number of α-spins,\\n\",\n \"**$n_β$**: number of β-spins\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"46459f2f-05a4-40e9-9a48-a32e2391d067\",\n \"metadata\": {},\n \"source\": [\n \"Now that we know how to obtain the total spin configurations, let's calculate this in each basis set and plot the results to see how the size of the Hamiltonian grows with more accuracy.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"74899db7-baca-47f9-9ead-3bba945b6fc1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Number of possible spin configurations\\n\",\n \"# Example: N2 molecule in STO-3G, 6-31G, and cc-pVDZ basis sets\\n\",\n \"# 14 electrons, 20 spin orbitals (from 10 spatial orbitals × 2)\\n\",\n \"\\n\",\n \"# Calculate total electron configurations for each basis set\\n\",\n \"y1 = comb(8, 5) * comb(8, 5) # STO-3G\\n\",\n \"y2 = comb(16, 5) * comb(16, 5) # 6-31G\\n\",\n \"y3 = comb(26, 5) * comb(26, 5) # cc-pVDZ\\n\",\n \"\\n\",\n \"# Data\\n\",\n \"y = [y1, y2, y3]\\n\",\n \"x = list(range(len(y)))\\n\",\n \"labels = ['STO-3G', '6-31G', 'cc-pVDZ']\\n\",\n \"\\n\",\n \"# Plot with logarithmic y-scale\\n\",\n \"plt.figure(figsize=(6, 4))\\n\",\n \"plt.plot(x, y, 'o')\\n\",\n \"\\n\",\n \"plt.yscale('log')\\n\",\n \"plt.xticks(x, labels)\\n\",\n \"plt.xlabel('Basis sets')\\n\",\n \"plt.ylabel('Number of spin configurations (log scale)')\\n\",\n \"plt.title('Hamiltonian size of N2 molecule at different basis sets')\\n\",\n \"\\n\",\n \"# Add labels above points\\n\",\n \"for i in range(len(x)):\\n\",\n \" plt.text(x[i], y[i], f'{labels[i]}', fontsize=9, ha='center', va='bottom', color='red')\\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c8b45733-4801-4e7c-9264-5699e730cb87\",\n \"metadata\": {},\n \"source\": [\n \"Note that the Y-axis above is in logarithmic scale. You can see how the number of spin configurations grows exponentially with a basis set choice of better approximation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"79a50b43-5c0d-413e-8010-ccb48e07aec0\",\n \"metadata\": {},\n \"source\": [\n \"## Exercise 1: Measure the size of the $O_2$ Hamiltonian\\n\",\n \"\\n\",\n \"Now let's calculate the size of oxygen $O_2$ in the `6-31G` basis. Oxygen has the same number of orbitals as $N_2$ but has 2 additional **$\\\\alpha$-spin (spin-up)** electrons. The numbers you need to use in this exercise are provided in the table below. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"43b3c014-fe64-424b-b045-d32dae44159f\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 1: Measure the size of the $O_2$ Hamiltonian \\n\",\n \"\\n\",\n \"Using the number of spatial orbitals and electrons in the table below, compute the total number of electron spin configurations for the oxygen $O_2$ molecule in the `6-31G` basis. **You may write your own code or calculate by hand to get the answer.**\\n\",\n \"\\n\",\n \"\\n\",\n \"| | STO-3G | 6-31G | cc-pVDZ | \\n\",\n \"|:-------|:------:|:-------:|:-------:|\\n\",\n \"|Spatial orbitals | (10) **8**| (18) **16** | (28) **26** |\\n\",\n \"|Spin orbitals | (20) **16**| (36) **32**| (56) **52** |\\n\",\n \"| α-spin electrons | (9) **7**| (9) **7**| (9) **7** |\\n\",\n \"| β-spin electrons | (7) **5**| (7) **5**| () **5** |\\n\",\n \"\\n\",\n \"\\n\",\n \"
Table 1: Number of orbitals and electrons in specified basis sets for $O_2$
\\n\",\n \"\\n\",\n \"- (#) : number of orbitals or electrons based on actual atomic structure in the specified basis set.\\n\",\n \"- **#** : number of orbitals or electrons when freezing the core orbital (*1s*) and reducing the number of electrons and spatial orbitals each by 2.\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** As we already saw in the previous example, we will treat the $1s$ (core) orbital as chemically inactive in this exercise. This means we can freeze the core orbital and save 2 electrons and 2 spatial orbitals (i.e. 4 spin orbitals -> 4 qubits). This is another useful approximation technique you will frequently see in actual chemistry simulations. Applying this technique, please make sure to use the numbers in **bold** for calculating for all possible electron configurations for the $O_2$ Hamiltonian.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"b3e26a71-1cdd-4d3c-be48-fb85eb942520\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Total physical configurations for O2 in the given basis : 11440 x 4368 = 49969920\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Exercise 1: Number of possible spin configurations\\n\",\n \"# Example: O2 molecule in 6-31G basis\\n\",\n \"# 16 electrons, 20 spin orbitals (from 10 spatial orbitals × 2)\\n\",\n \"\\n\",\n \"# Calculate all valid electron configurations\\n\",\n \"# Hint: This is a combinatorial problem. You can calculate by hand and provide the answer or use the math.comb() method below.\\n\",\n \"\\n\",\n \"# ---- TODO : Task 1 ---\\n\",\n \"### Provide your code below to calculate the total configurations\\n\",\n \"\\n\",\n \"α_config = comb(16,7)\\n\",\n \"β_config = comb(16,5)\\n\",\n \"total_config = (α_config)*(β_config)\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"print(f\\\"Total physical configurations for O2 in the given basis : {α_config:} x {β_config:} = {total_config}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"8501c714-9965-469f-98d3-d607506b1564\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# total_config = #provide your answer here if calculated by hand. Then uncomment this section before you submit your answer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"0e236dfe-a6bf-41fa-9001-9501699c6126\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations 🎉! Your answer is correct and has been submitted.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex1(total_config) # Expected result type: integer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"45e0bf86-70ed-499e-bc12-d9a005b22c6e\",\n \"metadata\": {},\n \"source\": [\n \"After submitting your answer calculated in the 6-31G basis, try calculating the same in other basis sets (e.g. STO-3G, and cc-pVDZ) for comparison. Feel free to plot your results to see how the size grows.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"757e79f6-2a94-4c6a-9ce0-43f02ed5792d\",\n \"metadata\": {},\n \"source\": [\n \"# 2. Variational Quantum Eigensolver\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"ea1b9d0d-e5be-468c-9efa-7010b4b4e483\",\n \"metadata\": {},\n \"source\": [\n \"So far, we've learned about the Schrödinger Equation, basis sets, and how the Hamiltonian of a physical system can grow exponentially in size depending on how accurately you want to simulate your system. Now let's talk about algorithms. \\n\",\n \"\\n\",\n \"One of the well-known algorithms among computational chemists who work with near-term quantum computers is perhaps the **Variational Quantum Eigensolver (VQE)**.\\n\",\n \"\\n\",\n \"The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm used to optimize a cost function Hamiltonian based on the variational principle. Although this lab's main focus is on learning about a different kind of hybrid quantum-classical algorithm called Sample-based Quantum Diagonalization (SQD), it would be useful to briefly review the components of VQE as they form some of the important building blocks for SQD as well.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 2. A diagram that shows the computational workflow of VQE\\n\",\n \"\\n\",\n \"Summary of Computational Steps in VQE:\\n\",\n \"\\n\",\n \"1. Prepare the quantum state $|\\\\psi(𝜃)\\\\rangle$(Quantum).\\n\",\n \"2. Measure expectation values $\\\\langle\\\\psi(𝜃)|\\\\hat{H}|\\\\psi(𝜃)\\\\rangle$(Quantum). \\n\",\n \"3. Compute the cost function $𝐸(𝜃)$(Classical).\\n\",\n \"4. Adjust the parameter $𝜃$ using classical optimization (Classical).\\n\",\n \"5. Repeat the steps until cost function $𝐸(𝜃)$ converges.\\n\",\n \"\\n\",\n \"As you can tell from the above, the computational steps for executing VQE is an 'iterative' process - you need to repeat these steps many, many, times. This is a computationally costly procedure and becomes a limitation for an algorithm like VQE to scale and work with larger molecules. \"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"74c08c83-f1e2-4012-b10f-a97c01eb6976\",\n \"metadata\": {},\n \"source\": [\n \"# 3. What is Sample-based Quantum Diagonalization (SQD)?\\n\",\n \"\\n\",\n \"While VQE has been widely explored since its original proposal in 2014, it has certain limitations. As the Hamiltonian grows larger, obtaining the expectation value through the variational method becomes increasingly resource-intensive, and the algorithm does not scale well for larger molecules. This challenge motivates the study and implementation of Sample-based Quantum Diagonalization (SQD).\\n\",\n \"\\n\",\n \"SQD was inspired by a hybrid quantum-classical method called quantum-selected configuration interaction (QSCI) introduced and published in [this paper](https://arxiv.org/abs/2302.11320). Similar to QSCI, SQD is also a hybrid quantum-classical algorithm in which a quantum computer is used to generate electronic configurations by sampling them from a quantum circuit, and then those configurations are used to form a subspace in which to project and diagonalize the electronic Hamiltonian.\\n\",\n \"\\n\",\n \"This subspace will have dimensions that are smaller than the original Hamiltonian making it much easier to classically diagonalize.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 3. An illustration of the original Hamiltonian shown as an exponentially large matrix in the Hilbert space of N qubits then reduced to a smaller size after configuration recovery and subsampling.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"35ea499c-9d5b-4655-905b-a120eb14cbf4\",\n \"metadata\": {},\n \"source\": [\n \"Let's say we have three atomic orbitals 1s, 2p, 3d, and two electrons that can occupy these orbitals that are ranked from low to high energy. Low energy configurations mean most electrons occupy the lower orbitals while high energy configurations will have electrons in the higher orbitals. \\n\",\n \"One of the important assumptions of the SQD algorithm is that high-energy configurations will have little contributions to the ground state. This allows us to remove these less relevant configurations so we can focus on a subspace of relevant configurations only - in our case configurations that show occupancies of electrons in the lower orbitals. \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 4. Removing electron configurations that are not relevant to the ground state allows us to reduce the size of the matrix (Hamiltonian)\\n\",\n \"\\n\",\n \"\\n\",\n \"This reconstruction of Hamiltonian to a smaller size significantly reduces the time to arrive at the solution. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"26b3cc45-85ec-49b6-a1dc-963e8e0543f3\",\n \"metadata\": {},\n \"source\": [\n \"## Choosing relevant configurations\\n\",\n \"\\n\",\n \"For identifying the relevant electron configuration, we use a quantum circuit (ansatz) denoted by $|\\\\psi\\\\rangle$ in this illustration, which upon measurement in the computational basis will give us a probability distribution over the Hilbert space to sample necessary bitstrings from. \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 5. A quantum 'ansatz' circuit will produce the bitstrings we sample from\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c1da7f72-e948-4c26-87fc-15a80308d70f\",\n \"metadata\": {},\n \"source\": [\n \"## Dealing with the effects of noise with configuration recovery\\n\",\n \"\\n\",\n \"Before we subsample from the pool of bitstrings generated by the quantum circuit, we need to deal with 'noisy quantum samples' that may affect the accuracy of our energy calculations. This is where configuration recovery comes into play. Let's say we have a bitstring that is missing one electron from its configuration. This is easy to identify as the hamming weight is wrong. We can also exploit the expectation of occupancies in the different orbitals (given by 'n' in the illustration below) to determine which bit to flip to correct the bitstrings.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 6. By looking at the electron numbers and occupancy expectation, we can determine which bit to flip\\n\",\n \"\\n\",\n \"Having reconstructed your Hamiltonian with good samples, you now have a reduced size matrix to diagonalize. Upon diagonalizing the Hamiltonian in the subspace, what the diagonalization subroutine does is assign a non-zero wavefunction amplitude to the computational basis states that contribute to the ground state of the problem. The same routine will assign a zero weight to those bit strings that do not represent the ground state. From the results of all batches, the SQD algorithm obtains the orbital occupancy by electrons and lowest energy estimation and updates the data for the next configuration recovery loop.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f9feaa87-b5cc-4c59-aea9-fcc35c3a7348\",\n \"metadata\": {},\n \"source\": [\n \"# 4. How to simulate an $N_2$ molecule with SQD\\n\",\n \"\\n\",\n \"In this section, we will demonstrate how to post-process noisy quantum samples to find an approximation to the ground state of a chemistry Hamiltonian: the $N_2$ molecule at equilibrium in the 6-31G basis set. We will follow a SQD approach to process samples taken from a 36-qubit quantum circuit ansatz (in this case, an LUCJ circuit). In order to account for the effect of quantum noise, the configuration recovery technique is used.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"02847627-1ba8-4677-91d5-92c621f87ece\",\n \"metadata\": {},\n \"source\": [\n \"## Molecular Hamiltonian\\n\",\n \"\\n\",\n \"The properties of molecules are largely determined by the structure of the electrons within them. As fermionic particles, electrons can be described using a mathematical formalism called second quantization. The idea is that there are a number of *orbitals*, each of which can be either empty or occupied by a fermion. A system of $N$ orbitals is described by a set of fermionic annihilation operators $\\\\{\\\\hat{a}_p\\\\}_{p=1}^N$ that satisfy the fermionic anticommutation relations,\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\hat{a}_p \\\\hat{a}_q + \\\\hat{a}_q \\\\hat{a}_p &= 0, \\\\\\\\\\n\",\n \"\\\\hat{a}_p \\\\hat{a}_q^\\\\dagger + \\\\hat{a}_q^\\\\dagger \\\\hat{a}_p &= \\\\delta_{pq}.\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"The adjoint $\\\\hat{a}_p^\\\\dagger$ is called a creation operator.\\n\",\n \"\\n\",\n \"So far, our exposition has not accounted for spin, which is a fundamental property of fermions. When accounting for spin, the orbitals come in pairs called *spatial orbitals*. Each spatial orbital is composed of two *spin orbitals*, one that is labeled \\\"spin-$\\\\alpha$\\\" and one that is labeled \\\"spin-$\\\\beta$\\\". We then write $\\\\hat{a}_{p\\\\sigma}$ for the annihilation operator associated with the spin-orbital with spin $\\\\sigma$ ($\\\\sigma \\\\in \\\\{\\\\alpha, \\\\beta\\\\}$) in spatial orbital $p$. If we take $N$ to be the number of spatial orbitals, then there are a total of $2N$ spin-orbitals. The Hilbert space of this system is spanned by $2^{2N}$ orthonormal basis vectors labeled with two-part bitstrings $\\\\lvert z \\\\rangle = \\\\lvert z_\\\\beta z_\\\\alpha \\\\rangle = \\\\lvert z_{\\\\beta, N} \\\\cdots z_{\\\\beta, 1} z_{\\\\alpha, N} \\\\cdots z_{\\\\alpha, 1} \\\\rangle$.\\n\",\n \"\\n\",\n \"The Hamiltonian of a molecular system can be written as\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{H} = \\\\sum_{ \\\\substack{pr\\\\\\\\\\\\sigma} } h_{pr} \\\\, \\\\hat{a}^\\\\dagger_{p\\\\sigma} \\\\hat{a}_{r\\\\sigma}\\n\",\n \"+ \\\\frac12\\n\",\n \"\\\\sum_{ \\\\substack{prqs\\\\\\\\\\\\sigma\\\\tau} }\\n\",\n \"h_{prqs} \\\\, \\n\",\n \"\\\\hat{a}^\\\\dagger_{p\\\\sigma}\\n\",\n \"\\\\hat{a}^\\\\dagger_{q\\\\tau}\\n\",\n \"\\\\hat{a}_{s\\\\tau}\\n\",\n \"\\\\hat{a}_{r\\\\sigma},\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where the $h_{pr}$ and $h_{prqs}$ are complex numbers called molecular integrals that can be calculated from the specification of the molecule using a computer program. In this tutorial, we compute the integrals using the [PySCF](https://pyscf.org/) software package.\\n\",\n \"\\n\",\n \"For details about how the molecular Hamiltonian is derived, consult a textbook on quantum chemistry (for example, *Modern Quantum Chemistry* by Szabo and Ostlund). \"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"b5e32180-3ff6-46bb-baa3-1c16a3ec443f\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"## Local unitary cluster Jastrow (LUCJ) ansatz\\n\",\n \"\\n\",\n \"SQD requires a quantum circuit ansatz to draw samples from. In this lab, we'll use the [local unitary cluster Jastrow (LUCJ)](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d3sc02516k) ansatz [1] due to its combination of physical motivation and hardware-friendliness.\\n\",\n \"\\n\",\n \"The LUCJ ansatz is a specialized form of the general unitary cluster Jastrow (UCJ) ansatz, which has the form\\n\",\n \"\\n\",\n \"$$\\n\",\n \" \\\\lvert \\\\Psi \\\\rangle = \\\\prod_{\\\\mu=1}^L e^{\\\\hat{K}_\\\\mu} e^{i \\\\hat{J}_\\\\mu} e^{-\\\\hat{K}_\\\\mu} | \\\\Phi_0 \\\\rangle\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $\\\\lvert \\\\Phi_0 \\\\rangle$ is a reference state, often taken to be the Hartree-Fock state, and the $\\\\hat{K}_\\\\mu$ and $\\\\hat{J}_\\\\mu$ have the form\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{K}_\\\\mu = \\\\sum_{pq, \\\\sigma} K_{pq}^\\\\mu \\\\, \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{q \\\\sigma}\\n\",\n \"\\\\;,\\\\;\\n\",\n \"\\\\hat{J}_\\\\mu = \\\\sum_{pq, \\\\sigma\\\\tau} J_{pq,\\\\sigma\\\\tau}^\\\\mu \\\\, \\\\hat{n}_{p \\\\sigma} \\\\hat{n}_{q \\\\tau}\\n\",\n \"\\\\;,\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where we have defined the number operator\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{n}_{p \\\\sigma} = \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{p \\\\sigma}.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"The operator $e^{\\\\hat{K}_\\\\mu}$ is an orbital rotation, which can be implemented using known algorithms in linear depth and using linear connectivity.\\n\",\n \"Implementing the $e^{i \\\\mathcal{J}_k}$ term of the UCJ ansatz requires either all-to-all connectivity or the use of a fermionic swap network, making it challenging for noisy pre-fault-tolerant quantum processors that have limited connectivity. The idea of the *local* UCJ ansatz is to impose sparsity constraints on the\\n\",\n \"$\\\\mathbf{J}^{\\\\alpha\\\\alpha}$ and $\\\\mathbf{J}^{\\\\alpha\\\\beta} $\\n\",\n \"matrices which allow them to be implemented in constant depth on qubit topologies with limited connectivity. (Here, $\\\\mathbf{J}^{\\\\alpha\\\\alpha}= J_{p q,\\\\alpha\\\\alpha}^1$ and $\\\\mathbf{J}^{\\\\alpha\\\\beta}= J_{p q,\\\\alpha\\\\beta}^1$.)\\n\",\n \"\\n\",\n \"The IBM hardware has a heavy-hex lattice qubit topology, in which case we can adopt a \\\"zigzag\\\" pattern, depicted below. In this pattern, orbitals with the same spin are mapped to qubits with a line topology (red and blue circles), and a connection between orbitals of different spin is present at every 4th spatial orbital, with the connection being facilitated by an ancillary qubit (purple circles). In this case, the index constraints are\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha} &: \\\\{(p, p+1) \\\\; , \\\\; p = 0, \\\\ldots, N-2\\\\} \\\\\\\\\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\beta} &: \\\\{(p, p) \\\\;, \\\\; p = 0, 4, 8, \\\\ldots,\\\\; p \\\\leq N-1\\\\}\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 7. IBM's quantum hardware with a heavy-hex lattice qubit topology\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"8a20c704-0564-4bc3-b24a-64ce11179489\",\n \"metadata\": {},\n \"source\": [\n \"## Sample-based Quantum Diagonalization (SQD)\\n\",\n \"\\n\",\n \"The self-consistent configuration recovery procedure is designed to extract as much signal as possible from noisy quantum samples. The procedure is run in a loop, and each iteration has the following steps:\\n\",\n \"\\n\",\n \"1. **Recover the configuration**: For each bitstring that violates the specified symmetries, flip its bits with a probabilistic procedure designed to bring the bitstring closer to the current estimate of the average orbital occupancies, to obtain a new bitstring.\\n\",\n \"2. **Subsample**: Collect all of the old and new bitstrings that satisfy the symmetries, and subsample subsets of a fixed size, chosen in advance.\\n\",\n \"3. **Diagonalize in subspace**: For each subset of bitstrings, project the Hamiltonian into the subspace spanned by the corresponding basis vectors and compute a ground state estimate of the projected Hamiltonian on a classical computer.\\n\",\n \"4. **Find the lowest energy**: Update the estimate of the average orbital occupancies with the ground state estimate with the lowest energy.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"204b5142-3f9b-4d8f-8408-d6d32e9c9a13\",\n \"metadata\": {},\n \"source\": [\n \"The SQD workflow is depicted in the following diagram:\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 8. A diagram showing the SQD workflow\\n\",\n \"\\n\",\n \"SQD is known to work well when the target eigenstate is sparse: the wave function is supported in a set of basis states $\\\\mathcal{S} = \\\\{|x\\\\rangle \\\\}$ whose size does not increase exponentially with the size of the problem.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"1dce8aba-af4c-4a9a-a33a-3d8175085da8\",\n \"metadata\": {},\n \"source\": [\n \"### Configuration recovery loop 1: Recover the configuration\\n\",\n \"\\n\",\n \"The first step in the configuration recovery loop is to recover the configuration. In other words, error mitigation is performed in this part.\\n\",\n \"\\n\",\n \"If you run the quantum circuit that is the LUCJ ansatz built above on a quantum computer, you will get a bitstring and its count number as shown in the figure on the lower left. Because current noisy quantum computers give the result with the errors. Since the particle number of \\\"spin-$\\\\alpha$\\\" and \\\"spin-$\\\\beta$\\\" of the molecule in problem is fixed, we will correct the error by flipping a part of the resulting bitstring so that the particle number is stored correctly.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 9. Bitstrings that are initially produced by the LUCJ ansatz circuit (left) and the corrected bitstrings (right)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"857741ce-d6d9-490a-9c64-44ce60217815\",\n \"metadata\": {},\n \"source\": [\n \"For example, let's think about a problem that considers up to four spin orbitals for a molecule with two \\\"spin-$\\\\alpha$\\\" and two \\\"spin-$\\\\beta$\\\". If the orbitals are filled from the bottom of energy levels, the quantum state is |0011 0011> as shown in the figure on the lower left.\\n\",\n \"If the result of running the quantum computer contains |1110 0011> then three \\\"spin-$\\\\beta$\\\" is too many, so one of the bits is flipped from 1 to 0 so that there are two.\\n\",\n \"Also, if |0011 0001> is observed, one \\\"spin-$\\\\alpha$\\\" is missing, so one of these three electrons is inverted from 0 to 1.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 10. The strings are being corrected based on electron numbers and occupancy expectation with maxim weighted probability\\n\",\n \"\\n\",\n \"At this time, when choosing which electron to be flipped, the average orbital occupancy is used and flip the bit with the maximum weighted probability of flipping.\\n\",\n \"The average orbital occupancy is calculated at the end of the configuration recovery loop. So, in the first loop, we don't do this because we don't have an average orbital occupancy, and we discard the sample with the wrong particle numbers.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"aa2b0d64-63b6-455e-999e-f35c7d52b9e1\",\n \"metadata\": {},\n \"source\": [\n \"### Configuration recovery loop 2: Subsample\\n\",\n \"\\n\",\n \"The next step is to select some from the corrected samples.\\n\",\n \"This time, we have 100,000 shots, so for example, we randomly select 50 samples from these 5 times, that is, 5 batches. When handling large molecules in batches, this part can be computed in parallel on a supercomputer.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 11. Subsampling from corrected samples\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"fe817330-971e-4b02-b1a5-eb9c89a27a9e\",\n \"metadata\": {},\n \"source\": [\n \"### Configuration recovery loop 3: Diagonalize in subspace\\n\",\n \"\\n\",\n \"Next is diagonalizing in subspace. In the previous example, the original Hamiltonian $H$, $2^{32}\\\\times2^{32}$ matrix, is projected into the subspace created by $50$ bitstrings, $|x_i\\\\rangle$, and diagonalized.\\n\",\n \"This projection matrix $P_S$ can have $2^{32}$ different measured bitstrings, from 0...0 to 1...1, but a maximum of $50$ bitstrings can be measured. $P_S$ is a matrix with only $50$ diagonal components and the rest of the matrix with $0$. Therefore, by multiplying $P_S$ by the original Hamiltonian $H$, the projected Hamiltonian $H_s$ has only $50\\\\times50$ components and all other elements are 0. So, we can diagonalize only $50\\\\times50$ matrix instead of the original huge $2^{32}\\\\times2^{32}$ matrix.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 12. Matrix diagonalization in the subspace\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"52d3b688-89d0-4451-a03b-db232442a1a3\",\n \"metadata\": {},\n \"source\": [\n \"### Configuration recovery loop 4: Find the lowest energy\\n\",\n \"\\n\",\n \"Finally, from the results of all batches, we obtain an estimate of the average orbital occupancy, and the lowest energy as an estimate of the ground state. And update these data.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"2566ce1e-cf95-4915-b986-0c1a68662afa\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskit patterns\\n\",\n \"\\n\",\n \"We implement a Qiskit patterns showing how SQD process:\\n\",\n \"\\n\",\n \"1. **Step 1: Map to quantum problem**\\n\",\n \" - Generate an ansatz for estimating the ground state\\n\",\n \"2. **Step 2: Optimize the problem**\\n\",\n \" - Transpile the ansatz for the backend\\n\",\n \"3. **Step 3: Execute experiments**\\n\",\n \" - Draw samples from the ansatz using the ``Sampler`` primitive\\n\",\n \"4. **Step 4: Post-process results**\\n\",\n \" - Self-consistent configuration recovery loop\\n\",\n \" - Post-process the full set of bitstring samples, using prior knowledge of particle number and the average orbital occupancy calculated on the most recent iteration.\\n\",\n \" - Probabilistically create batches of subsamples from recovered bitstrings.\\n\",\n \" - Project and diagonalize the molecular Hamiltonian over each sampled subspace.\\n\",\n \" - Save the minimum ground state energy found across all batches and update the avg orbital occupancy.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"157e65a0-06a9-4ac9-bafa-702824a84eb2\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Map classical inputs to a quantum problem\\n\",\n \"\\n\",\n \"We will find an approximation to the ground state of the molecule at equilibrium in the 6-31G basis set. First, we specify the molecule and its properties.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"c5c49c74-d658-42f6-a729-ec1c7687e9aa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"converged SCF energy = -108.835236570774\\n\",\n \"CASCI E = -109.046671778080 E(CI) = -32.8155692383188 S^2 = 0.0000000\\n\"\n ]\n }\n ],\n \"source\": [\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"\\n\",\n \"# Specify molecule properties\\n\",\n \"open_shell = False\\n\",\n \"spin_sq = 0\\n\",\n \"\\n\",\n \"# Build N2 molecule\\n\",\n \"mol = pyscf.gto.Mole()\\n\",\n \"mol.build(\\n\",\n \" atom=[[\\\"N\\\", (0, 0, 0)], [\\\"N\\\", (1.0, 0, 0)]],\\n\",\n \" basis=\\\"6-31g\\\",\\n\",\n \" symmetry=\\\"Dooh\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# Define active space\\n\",\n \"n_frozen = 2\\n\",\n \"active_space = range(n_frozen, mol.nao_nr())\\n\",\n \"\\n\",\n \"# Get molecular integrals\\n\",\n \"scf = pyscf.scf.RHF(mol).run()\\n\",\n \"num_orbitals = len(active_space)\\n\",\n \"n_electrons = int(sum(scf.mo_occ[active_space]))\\n\",\n \"num_elec_a = (n_electrons + mol.spin) // 2\\n\",\n \"num_elec_b = (n_electrons - mol.spin) // 2\\n\",\n \"cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\\n\",\n \"mo = cas.sort_mo(active_space, base=0)\\n\",\n \"hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\\n\",\n \"eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\\n\",\n \"\\n\",\n \"# Compute exact energy\\n\",\n \"exact_energy = cas.run().e_tot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91fa0157-366c-4c0e-b040-6a18f5546416\",\n \"metadata\": {},\n \"source\": [\n \"Before constructing the `LUCJ` ansatz circuit, we first perform a CCSD calculation in the following code cell. The [$t_1$ and $t_2$ amplitudes](https://en.wikipedia.org/wiki/Coupled_cluster#Cluster_operator) from this calculation will be used to initialize the parameters of the ansatz.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"f72b68e1-065f-49fc-a7cb-7afe3d1c3f65\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"E(CCSD) = -109.0398256929733 E_corr = -0.20458912219883\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Get CCSD t2 amplitudes for initializing the ansatz\\n\",\n \"ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\\n\",\n \"t1 = ccsd.t1\\n\",\n \"t2 = ccsd.t2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"adb2fd92-294b-4f87-a438-f42d6589cd73\",\n \"metadata\": {},\n \"source\": [\n \"Now, we use [ffsim](https://github.com/qiskit-community/ffsim) to create the ansatz circuit. Since our molecule has a closed-shell Hartree-Fock state, we use the spin-balanced variant of the UCJ ansatz, [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced). We pass interaction pairs appropriate for a heavy-hex lattice qubit topology.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"527f5873-f5be-4890-960e-4ad24c1e423d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"n_reps = 1\\n\",\n \"alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\\n\",\n \"alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\\n\",\n \"\\n\",\n \"ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\\n\",\n \" t2=t2,\\n\",\n \" t1=t1,\\n\",\n \" n_reps=n_reps,\\n\",\n \" interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\\n\",\n \")\\n\",\n \"\\n\",\n \"nelec = (num_elec_a, num_elec_b)\\n\",\n \"\\n\",\n \"# create an empty quantum circuit\\n\",\n \"qubits = QuantumRegister(2 * num_orbitals, name=\\\"q\\\")\\n\",\n \"circuit = QuantumCircuit(qubits)\\n\",\n \"\\n\",\n \"# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\\n\",\n \"circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\\n\",\n \"\\n\",\n \"# apply the UCJ operator to the reference state\\n\",\n \"circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\\n\",\n \"circuit.measure_all()\\n\",\n \"\\n\",\n \"circuit.decompose().decompose().draw(\\\"mpl\\\", fold =-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5a385896-0c00-425a-b06f-dc929371678a\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Optimize problem for quantum execution\\n\",\n \"Next, we optimize the circuit for a target hardware. We'll use the 127-qubit `ibm_brisbane` QPU.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"ff55ea1b-22ef-4082-af12-d663ba3b7d6c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"backend = service.backend(\\\"ibm_brisbane\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"11c41725-68be-4289-95ee-562763547ca6\",\n \"metadata\": {},\n \"source\": [\n \"We recommend the following steps to optimize the ansatz and make it hardware-compatible.\\n\",\n \"\\n\",\n \"* Select physical qubits (`initial_layout`) from the target hardware that adheres to the zig-zag pattern described above. Laying out qubits in this pattern leads to an efficient hardware-compatible circuit with less gates.\\n\",\n \"* Generate a staged pass manager using the [generate\\\\_preset\\\\_pass\\\\_manager](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.transpiler.generate_preset_pass_manager) function from qiskit with your choice of `backend` and `initial_layout`.\\n\",\n \"* Set the `pre_init` stage of your staged pass manager to `ffsim.qiskit.PRE_INIT`. `ffsim.qiskit.PRE_INIT` includes qiskit transpiler passes that decompose gates into orbital rotations and then merges the orbital rotations, resulting in fewer gates in the final circuit.\\n\",\n \"* Run the pass manager on your circuit.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"b77ff882-7da3-40be-bbef-cadab8f9c841\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Gate counts (w/ pre-init passes): OrderedDict({'rz': 2473, 'sx': 2133, 'ecr': 730, 'x': 77, 'measure': 32, 'barrier': 1})\\n\"\n ]\n }\n ],\n \"source\": [\n \"spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\\n\",\n \"spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\\n\",\n \"initial_layout = spin_a_layout + spin_b_layout\\n\",\n \"\\n\",\n \"pass_manager = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend, initial_layout=initial_layout\\n\",\n \")\\n\",\n \"\\n\",\n \"# We will use the circuit generated by this pass manager for hardware execution\\n\",\n \"pass_manager.pre_init = ffsim.qiskit.PRE_INIT\\n\",\n \"isa_circuit = pass_manager.run(circuit)\\n\",\n \"print(f\\\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"04a7a60b-e87a-4b13-9f35-573db129526c\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Execute using Qiskit Primitives\\n\",\n \"\\n\",\n \"After optimizing the circuit for hardware execution, we are ready to run it on the target hardware and collect samples for ground state energy estimation. \\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** We have commented out the code for running the circuit on a QPU and left it for the user's reference. Instead of running on real hardware in this walkthrough, we will just read in 100k samples drawn from ``ibm_brisbane`` at an earlier time.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"8a4169e6-870d-4c30-a07b-207a09c3f444\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# from qiskit_ibm_runtime import SamplerV2 as Sampler\\n\",\n \"\\n\",\n \"# sampler = Sampler(mode=backend)\\n\",\n \"# job = sampler.run([isa_circuit], shots=10_000)\\n\",\n \"# primitive_result = job.result()\\n\",\n \"# pub_result = primitive_result[0]\\n\",\n \"# bit_array = pub_result.data.meas\\n\",\n \"\\n\",\n \"bit_array = np.load('utils/N2_device_bitarray.npy', allow_pickle=True).item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6e1521e1-7474-4267-9364-738404e367a7\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Post-process and return result to desired classical format\\n\",\n \"\\n\",\n \"Recall that the self-consistent configuration recovery is an iterative procedure that runs in a loop. In the following code cell, the first iteration of the loop simply uses the raw samples (after post-selection on symmetries) as input to the diagonalization procedure to obtain an estimate of the average orbital occupancies. Later iterations of the loop use these occupancies to generate new configurations from raw samples that violate the symmetries. These configurations are collected and then subsampled to produce batches of configurations, which are then used to project the Hamiltonian and compute a ground state estimate with an eigenstate solver.\\n\",\n \"\\n\",\n \"There are a few user-controlled options which are important for this technique:\\n\",\n \"\\n\",\n \"* `max_iterations`: Number of iterations of the self-consistent recovery loop.\\n\",\n \"* `num_batches`: Number of batches of configurations to subsample (this will be the number of separate calls to the eigenstate solver)\\n\",\n \"* `samples_per_batch`: Number of unique configurations to include in each batch\\n\",\n \"* `max_cycles`: Maximum number of Davidson cycles run by the eigenstate solver\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f585a9a-acd4-437e-b2af-eb672e0eb0c4\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"Warning: 5 minutes needed\\n\",\n \"\\n\",\n \"When running the code below it will take up to 5 minutes (depending on your computer) to execute and will block this notebook for this time. \\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"f026c1fb-e159-47b3-9b00-062cc07f1271\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Iteration 1\\n\",\n \"\\tSubsample 0\\n\",\n \"\\t\\tEnergy: -106.27329013141704\\n\",\n \"\\t\\tSubspace dimension: 10000\\n\",\n \"\\tSubsample 1\\n\",\n \"\\t\\tEnergy: -105.43810831755815\\n\",\n \"\\t\\tSubspace dimension: 9801\\n\",\n \"\\tSubsample 2\\n\",\n \"\\t\\tEnergy: -106.6376942596939\\n\",\n \"\\t\\tSubspace dimension: 10000\\n\",\n \"\\tSubsample 3\\n\",\n \"\\t\\tEnergy: -106.61258013830131\\n\",\n \"\\t\\tSubspace dimension: 9801\\n\",\n \"\\tSubsample 4\\n\",\n \"\\t\\tEnergy: -106.63164450395638\\n\",\n \"\\t\\tSubspace dimension: 9801\\n\",\n \"Iteration 2\\n\",\n \"\\tSubsample 0\\n\",\n \"\\t\\tEnergy: -107.91084907411823\\n\",\n \"\\t\\tSubspace dimension: 9409\\n\",\n \"\\tSubsample 1\\n\",\n \"\\t\\tEnergy: -108.84006034736296\\n\",\n \"\\t\\tSubspace dimension: 9604\\n\",\n \"\\tSubsample 2\\n\",\n \"\\t\\tEnergy: -108.84150457670023\\n\",\n \"\\t\\tSubspace dimension: 10000\\n\",\n \"\\tSubsample 3\\n\",\n \"\\t\\tEnergy: -107.91290084871602\\n\",\n \"\\t\\tSubspace dimension: 9409\\n\",\n \"\\tSubsample 4\\n\",\n \"\\t\\tEnergy: -108.84031227231547\\n\",\n \"\\t\\tSubspace dimension: 9216\\n\",\n \"Iteration 3\\n\",\n \"\\tSubsample 0\\n\",\n \"\\t\\tEnergy: -108.89577578545787\\n\",\n \"\\t\\tSubspace dimension: 12769\\n\",\n \"\\tSubsample 1\\n\",\n \"\\t\\tEnergy: -108.87738057039107\\n\",\n \"\\t\\tSubspace dimension: 12769\\n\",\n \"\\tSubsample 2\\n\",\n \"\\t\\tEnergy: -108.8824313957169\\n\",\n \"\\t\\tSubspace dimension: 12321\\n\",\n \"\\tSubsample 3\\n\",\n \"\\t\\tEnergy: -108.86232234859595\\n\",\n \"\\t\\tSubspace dimension: 12321\\n\",\n \"\\tSubsample 4\\n\",\n \"\\t\\tEnergy: -108.87068148406526\\n\",\n \"\\t\\tSubspace dimension: 12100\\n\",\n \"Iteration 4\\n\",\n \"\\tSubsample 0\\n\",\n \"\\t\\tEnergy: -108.92323715242358\\n\",\n \"\\t\\tSubspace dimension: 21316\\n\",\n \"\\tSubsample 1\\n\",\n \"\\t\\tEnergy: -108.90519494822162\\n\",\n \"\\t\\tSubspace dimension: 19881\\n\",\n \"\\tSubsample 2\\n\",\n \"\\t\\tEnergy: -108.90991554555268\\n\",\n \"\\t\\tSubspace dimension: 20164\\n\",\n \"\\tSubsample 3\\n\",\n \"\\t\\tEnergy: -108.95461553055686\\n\",\n \"\\t\\tSubspace dimension: 20736\\n\",\n \"\\tSubsample 4\\n\",\n \"\\t\\tEnergy: -108.9003436502987\\n\",\n \"\\t\\tSubspace dimension: 21316\\n\",\n \"Iteration 5\\n\",\n \"\\tSubsample 0\\n\",\n \"\\t\\tEnergy: -108.96268770420876\\n\",\n \"\\t\\tSubspace dimension: 29929\\n\",\n \"\\tSubsample 1\\n\",\n \"\\t\\tEnergy: -108.96881809291622\\n\",\n \"\\t\\tSubspace dimension: 32041\\n\",\n \"\\tSubsample 2\\n\",\n \"\\t\\tEnergy: -108.96611554852349\\n\",\n \"\\t\\tSubspace dimension: 32400\\n\",\n \"\\tSubsample 3\\n\",\n \"\\t\\tEnergy: -108.96736672345207\\n\",\n \"\\t\\tSubspace dimension: 30976\\n\",\n \"\\tSubsample 4\\n\",\n \"\\t\\tEnergy: -108.9687760460442\\n\",\n \"\\t\\tSubspace dimension: 30276\\n\",\n \"CPU times: user 34.2 s, sys: 338 ms, total: 34.6 s\\n\",\n \"Wall time: 34.8 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"# SQD options\\n\",\n \"energy_tol = 1e-3 \\n\",\n \"occupancies_tol = 1e-3 \\n\",\n \"max_iterations = 5\\n\",\n \"\\n\",\n \"# Eigenstate solver options\\n\",\n \"num_batches = 5\\n\",\n \"samples_per_batch = 50\\n\",\n \"symmetrize_spin = True \\n\",\n \"carryover_threshold = 1e-4 \\n\",\n \"max_cycle = 200\\n\",\n \"rng = np.random.default_rng(24)\\n\",\n \"\\n\",\n \"\\n\",\n \"# Pass options to the built-in eigensolver. If you just want to use the defaults,\\n\",\n \"# you can omit this step, in which case you would not specify the sci_solver argument\\n\",\n \"# in the call to diagonalize_fermionic_hamiltonian below.\\n\",\n \"sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle)\\n\",\n \"\\n\",\n \"\\n\",\n \"# List to capture intermediate results\\n\",\n \"result_history = [] \\n\",\n \"\\n\",\n \"def callback(results: list[SCIResult]): \\n\",\n \" result_history.append(results)\\n\",\n \" iteration = len(result_history)\\n\",\n \" print(f\\\"Iteration {iteration}\\\")\\n\",\n \" for i, result in enumerate(results):\\n\",\n \" print(f\\\"\\\\tSubsample {i}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tEnergy: {result.energy + nuclear_repulsion_energy}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\\\")\\n\",\n \"\\n\",\n \"result = diagonalize_fermionic_hamiltonian(\\n\",\n \" hcore,\\n\",\n \" eri,\\n\",\n \" bit_array,\\n\",\n \" samples_per_batch=samples_per_batch,\\n\",\n \" norb=num_orbitals,\\n\",\n \" nelec=nelec,\\n\",\n \" num_batches=num_batches,\\n\",\n \" energy_tol=energy_tol,\\n\",\n \" occupancies_tol=occupancies_tol,\\n\",\n \" max_iterations=max_iterations,\\n\",\n \" sci_solver=sci_solver,\\n\",\n \" symmetrize_spin=symmetrize_spin,\\n\",\n \" carryover_threshold=carryover_threshold,\\n\",\n \" callback=callback,\\n\",\n \" seed=rng,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b7e28f80-675f-4ef4-bf86-2a6c696cef23\",\n \"metadata\": {},\n \"source\": [\n \"## Visualize the results\\n\",\n \"\\n\",\n \"To see the result, we first create a function `plot_energy_and_occupancy`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"bd6d96b3-cfd6-490b-bd20-4c5530071f86\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def plot_energy_and_occupancy(result_history, exact_energy):\\n\",\n \"\\n\",\n \" # Data for energies plot\\n\",\n \" x1 = range(len(result_history))\\n\",\n \" min_e = [\\n\",\n \" min(result, key=lambda res: res.energy).energy + nuclear_repulsion_energy\\n\",\n \" for result in result_history\\n\",\n \" ]\\n\",\n \" e_diff = [abs(e - exact_energy) for e in min_e]\\n\",\n \" yt1 = [1.0, 1e-1, 1e-2, 1e-3, 1e-4]\\n\",\n \" \\n\",\n \" # Chemical accuracy (+/- 1 milli-Hartree)\\n\",\n \" chem_accuracy = 0.001\\n\",\n \" \\n\",\n \" # Data for avg spatial orbital occupancy\\n\",\n \" y2 = np.sum(result.orbital_occupancies, axis=0)\\n\",\n \" x2 = range(len(y2))\\n\",\n \" \\n\",\n \" fig, axs = plt.subplots(1, 2, figsize=(12, 6))\\n\",\n \" \\n\",\n \" # Plot energies\\n\",\n \" axs[0].plot(x1, e_diff, label=\\\"energy error\\\", marker=\\\"o\\\")\\n\",\n \" axs[0].set_xticks(x1)\\n\",\n \" axs[0].set_xticklabels(x1)\\n\",\n \" axs[0].set_yticks(yt1)\\n\",\n \" axs[0].set_yticklabels(yt1)\\n\",\n \" axs[0].set_yscale(\\\"log\\\")\\n\",\n \" axs[0].set_ylim(1e-4)\\n\",\n \" axs[0].axhline(y=chem_accuracy, color=\\\"#BF5700\\\", linestyle=\\\"--\\\", label=\\\"chemical accuracy\\\")\\n\",\n \" axs[0].set_title(\\\"Approximated Ground State Energy Error vs SQD Iterations\\\")\\n\",\n \" axs[0].set_xlabel(\\\"Iteration Index\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" axs[0].set_ylabel(\\\"Energy Error (Ha)\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" axs[0].legend()\\n\",\n \" \\n\",\n \" # Plot orbital occupancy\\n\",\n \" axs[1].bar(x2, y2, width=0.8)\\n\",\n \" axs[1].set_xticks(x2)\\n\",\n \" axs[1].set_xticklabels(x2)\\n\",\n \" axs[1].set_title(\\\"Avg Occupancy per Spatial Orbital\\\")\\n\",\n \" axs[1].set_xlabel(\\\"Orbital Index\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" axs[1].set_ylabel(\\\"Avg Occupancy\\\", fontdict={\\\"fontsize\\\": 12})\\n\",\n \" \\n\",\n \" print(f\\\"Exact energy: {exact_energy:.5f} Ha\\\")\\n\",\n \" print(f\\\"SQD energy: {min_e[-1]:.5f} Ha\\\")\\n\",\n \" print(f\\\"Absolute error: {e_diff[-1]:.5f} Ha\\\")\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"f05f1766-38de-410b-ab8e-0ded552c77bc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exact energy: -109.04667 Ha\\n\",\n \"SQD energy: -108.96882 Ha\\n\",\n \"Absolute error: 0.07785 Ha\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_energy_and_occupancy(result_history, exact_energy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8c8fc4ab-1ab4-4e76-9fbb-a069ed759085\",\n \"metadata\": {},\n \"source\": [\n \"The first plot shows that after a couple of iterations we estimate the ground state energy within `~100 mH` (chemical accuracy is typically accepted to be `1 kcal/mol` $\\\\approx$ `1.6 mH`). The energy can be improved by drawing more samples from the circuit or increasing the number of samples per batch.\\n\",\n \"\\n\",\n \"The second plot shows the average occupancy of each spatial orbital after the final iteration. We can see that both the spin-up and spin-down electrons occupy the first five orbitals with high probability in our solutions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86541d66-d12d-4049-abd6-0c3469a155b4\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 2: Flip a bit by configuration recovery \\n\",\n \"\\n\",\n \"In a problem in which an $N_2$ molecule is prepared using the STO-3G basis set, we perform configuration recovery. When the average orbital occupancy $n$ is as follows, we will correct the bitstring $x$ as follows. What bitstring is most likely to be modified to?\\n\",\n \"\\n\",\n \"$n = [0.007, 0.029, 0.029, 0.995, \\n\",\n \" 0.976, 0.976, 0.993, 0.997, \\n\",\n \" 0.007, 0.029, 0.029, 0.995,\\n\",\n \" 0.976, 0.976, 0.993, 0.997]$\\n\",\n \"\\n\",\n \"$x = [1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0]$\\n\",\n \"\\n\",\n \"A weighted probability of flipping $w(y)$ using a modified ReLU function is calculated as follows from [2]. \\n\",\n \"$$\\n\",\n \"\\\\begin{align}\\n\",\n \" w(y) = \\\\begin{cases} \\n\",\n \" \\\\delta\\\\cdot \\\\frac{y}{h} & \\\\text{if } y \\\\leq h\\\\\\\\ \\n\",\n \" \\\\delta + (1 - \\\\delta)\\\\cdot \\\\frac{y - h}{1 - h} & \\\\text{if } y > h\\n\",\n \"\\\\end{cases}\\n\",\n \"\\\\end{align}\\n\",\n \"$$ \\n\",\n \"\\n\",\n \"Here, $y$ is a probability of flipping, and defined as $y[i] =|x[i]-n[i]|$ for the $i$ -th spin orbital. $h$ defines the location of the \\\"corner\\\" of the ReLU function, and the parameter $\\\\delta$ defines the value of the ReLU function at the corner. We use $\\\\delta = 0.01$, same as [2], and $h = $number of alpha(or beta) particles$/$number of alpha(or beta) spin orbitals$ = N/M$ (filling factor).\\n\",\n \"\\n\",\n \"In the actual configuration recovery, a bit is randomly inverted with a weight of $w(y)$. In this exercise, answer the result of inverting the bit $i$ with the largest $w(y[i])$ as the bitstring with the highest probability of obtaining.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0ecea848-e0cb-4b35-87cd-b4e75318352c\",\n \"metadata\": {},\n \"source\": [\n \"Note: \\n\",\n \"- The right half of a bitstring represents spin-up orbitals, and the left half represents spin-down orbitals. A `1` means the orbital is occupied by an electron, and a `0` means the orbital is empty.\\n\",\n \"- Please refer to the section [\\\"4.1 Configuration recovery overview\\\" in Lesson 4: SQD application](https://quantum.cloud.ibm.com/learning/en/courses/quantum-diagonalization-algorithms/sqd-implementation) of \\\"Quantum Diagonalization Algorithm\\\" of IBM Quantum Learning.\\n\",\n \"- In this case, one more beta particle is needed, so if the i-th orbital is already occupied and need not to be flipped, you set its y_beta[i] to 0.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"cd04526f-eee0-4f96-8f52-d437899af540\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"n = [0.007, 0.029, 0.029, 0.995, \\n\",\n \" 0.976, 0.976, 0.993, 0.997, \\n\",\n \" 0.007, 0.029, 0.029, 0.995,\\n\",\n \" 0.976, 0.976, 0.993, 0.997]\\n\",\n \"\\n\",\n \"x = [1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"95e5f030-47af-495a-a8c5-823a24d666ea\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0. 0.029 0.029 0.995 0. 0.976 0. 0. ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = np.array(x)\\n\",\n \"n = np.array(n)\\n\",\n \"\\n\",\n \"# ---- TODO : Task 2 ---\\n\",\n \"\\n\",\n \"# Divide into alpha spin and beta spin\\n\",\n \"x_alpha = x[8:16]\\n\",\n \"x_beta = x[0:8]\\n\",\n \"\\n\",\n \"# probability of flipping\\n\",\n \"y = np.abs(x - n)\\n\",\n \"y_alpha = y[8:16]\\n\",\n \"y_beta = y[0:8]\\n\",\n \"\\n\",\n \"# In this case, one more beta particle is needed, so set y_beta[i] to 0 if x_beta[i] is already 1. \\n\",\n \"for i in range(len(y_beta)):\\n\",\n \" if x_beta[i] == 1:\\n\",\n \" y_beta[i] = 0\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"print(y_beta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"9e9f16a3-d459-41e4-b79d-bc7bbd91de1f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.0000e+00 4.6400e-04 4.6400e-04 9.8680e-01 0.0000e+00 9.3664e-01\\n\",\n \" 0.0000e+00 0.0000e+00]\\n\",\n \"3 0.9868\\n\"\n ]\n }\n ],\n \"source\": [\n \"h = 5/8\\n\",\n \"delta = 0.01\\n\",\n \"w = np.zeros(len(y_beta))\\n\",\n \"\\n\",\n \"# find the maximum w\\n\",\n \"# ---- TODO : Task 2 ---\\n\",\n \"for i in range(len(y_beta)):\\n\",\n \" if y_beta[i] <= h:\\n\",\n \" w[i] = delta*y_beta[i]/h\\n\",\n \" else:\\n\",\n \" w[i] =delta + (1-delta)*(y_beta[i]-h)/(1-h)\\n\",\n \"print(w)\\n\",\n \"max_w = np.max(w)\\n\",\n \"max_index = np.argmax(w)\\n\",\n \"print(max_index, max_w)\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"# print(max_index, max_w)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"d23e1599-e5cf-467d-a58b-e22952e0dfc7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Flip the bit of the index with the largest w\\n\",\n \"# ---- TODO : Task 2 ---\\n\",\n \"for i in range(len(y_beta)):\\n\",\n \" if i == max_index:\\n\",\n \" x_beta[i] = 1\\n\",\n \"\\n\",\n \"x = np.concatenate([x_beta, x_alpha])\\n\",\n \"corrected_x = x.tolist()\\n\",\n \"# --- End of TODO ---\\n\",\n \"# print(corrected_x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"b028186e-039f-4f6d-a9a6-2a4cc85f6119\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations 🎉! Your answer is correct and has been submitted.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex2(corrected_x) # Expected result type: list\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7e08dfec-1433-4166-9b56-53e722059917\",\n \"metadata\": {},\n \"source\": [\n \"# 5. Improve the ansatz\\n\",\n \"\\n\",\n \"The more accurate the sampling from the quantum circuit, the better the results. Therefore, how to make the ansatz for sampling is one of the key points in SQD performance. Depending on how the ansatz is set, different bit strings are sampled, which affects the accuracy of the energy estimate value that can be obtained. Here, we will explore the different ansatz to improve the result.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b948384-3b96-4c37-9676-b6619d0c6ee6\",\n \"metadata\": {},\n \"source\": [\n \"## 5.1 Change basis set\\n\",\n \"\\n\",\n \"First, let's try to model the molecule in a more natural way by changing the basis set of the molecule to account for more electron orbitals. Incorporating more orbitals into the calculation will increase the computational complexity, but should lower the value of the energy estimation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e3d406e6-a664-4890-b8cc-36265ac72c66\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 3: Change basis set \\n\",\n \"\\n\",\n \"Change the basis set from `6-31G` to `cc-pvdz`. How many qubits are needed if we use the LUCJ ansatz with **6** ancillary qubits in case we use `ibm_torino` as a backend? \\n\",\n \"Note: Because of the geometric limitation of `ibm_torino`, the number of ancillary qubits is limited to 6 here.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"28aacef0-e91e-4b29-9d10-d298c699bf4b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"converged SCF energy = -108.92983838561\\n\",\n \"26\\n\"\n ]\n }\n ],\n \"source\": [\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"\\n\",\n \"# Specify molecule properties\\n\",\n \"open_shell = False\\n\",\n \"spin_sq = 0\\n\",\n \"\\n\",\n \"# Build N2 molecule\\n\",\n \"mol = pyscf.gto.Mole()\\n\",\n \"mol.build(\\n\",\n \" atom=[[\\\"N\\\", (0, 0, 0)], [\\\"N\\\", (1.0, 0, 0)]],\\n\",\n \" # ---- TODO : Task 3 ---\\n\",\n \" basis= 'cc-pvdz', ### input your code here ###,\\n\",\n \" # --- End of TODO ---\\n\",\n \" symmetry=\\\"Dooh\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# Define active space\\n\",\n \"n_frozen = 2\\n\",\n \"active_space = range(n_frozen, mol.nao_nr())\\n\",\n \"\\n\",\n \"# Get molecular integrals\\n\",\n \"scf = pyscf.scf.RHF(mol).run()\\n\",\n \"num_orbitals = len(active_space)\\n\",\n \"n_electrons = int(sum(scf.mo_occ[active_space]))\\n\",\n \"num_elec_a = (n_electrons + mol.spin) // 2\\n\",\n \"num_elec_b = (n_electrons - mol.spin) // 2\\n\",\n \"cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\\n\",\n \"mo = cas.sort_mo(active_space, base=0)\\n\",\n \"hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\\n\",\n \"eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\\n\",\n \"\\n\",\n \"print(num_orbitals)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"1afdd410-3288-4df9-927e-c00afd50daac\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ---- TODO : Task 3 ---\\n\",\n \"n_qubits = num_orbitals*2 + 6 ### input your result ###\\n\",\n \"# --- End of TODO ---\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"14d5fd85-612a-4878-be7b-52aa2b8e6f3e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations 🎉! Your answer is correct and has been submitted.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex3(n_qubits) # Expected result type: integer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae2587d2-d5d1-473c-8cbc-5112642c30d1\",\n \"metadata\": {},\n \"source\": [\n \"## 5.2 Select the best layout\\n\",\n \"\\n\",\n \"Now that we have obtained the number of qubits, the next step is to determine the layout of the physical qubits. To use a larger basis, we will use a Heron device with better performance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"13f326d6-16c2-4395-bc02-9d4499b50c9e\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 4: Select the best layout \\n\",\n \"\\n\",\n \"Which qubits should we choose as the initial placement to get the best results? To select the qubits, you need to check the errors of each qubit. In the following map, qubits with a readout error greater than 0.1 are shown in black, and edges with a CZ error greater than 0.1 are shown in white. Answer the best `initial_layout`, which is used as an argument of pass_manger to create the ISA circuit. \\n\",\n \"We will use `ibm_torino` as a backend. Because of the geometric limitation of `ibm_torino`, the number of ancillary qubits limit to 6 here.\\n\",\n \"\\n\",\n \"Select the best initial qubit layout to get better sampling by following:\\n\",\n \"1. List the qubits with a readout error of 0.1 or more as `bad_readout_qubits` from `backend_target`.\\n\",\n \"2. List of edges with a CZ error of 0.1 or more as `bad_czgate_edges` from `backend_target`.\\n\",\n \"3. Display the coupling map with `bad_readout_qubits` in black and `bad_czgate_edges` in white.\\n\",\n \"4. Select the best initial qubit layout.\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** **You need to use the preloaded pickle file `backend_target_v20.pkl` or `backend_target_v21.pkl` as `backend_target` for backend information in order to pass the grader of this exercise**. In Lab 2, we used `backend.properties()`, `backend.target`, etc., but we won’t use them this time. We have commented out the code for a real backend a for the user's reference. \\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"⚠️ **Note:** If you are using Qiskit version 2.1.x, open `backend_target_v21.pkl` in the next cell. \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"60db7fe7-01b0-42c9-8569-e729cfde5f3c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# for Qiskit version 2.0.x users\\n\",\n \"#with open(\\\"utils/backend_target_v20.pkl\\\", \\\"rb\\\") as f:\\n\",\n \"# for Qiskit version 2.1.x users\\n\",\n \"with open(\\\"utils/backend_target_v21.pkl\\\", \\\"rb\\\") as f:\\n\",\n \" backend_target = pickle.load(f)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bc9dc488-8232-44eb-8b14-a28603948212\",\n \"metadata\": {},\n \"source\": [\n \"Note: Please refer [\\\"Instruction properties\\\" part of \\\"Get QPU information with Qiskit\\\"](https://quantum.cloud.ibm.com/docs/en/guides/get-qpu-information#instruction-properties) to get the properties like \\\"measure\\\" and \\\"cz\\\" from `backend_target`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"a5906475-2c7e-46b3-9799-b29a6b6b77bd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Bad readout qubits: [12, 53, 115, 126, 131]\\n\",\n \"Bad CZ gates: [(100, 101), (101, 100)]\\n\"\n ]\n }\n ],\n \"source\": [\n \"BAD_READOUT_ERROR_THRESHOLD = 0.1\\n\",\n \"BAD_CZGATE_ERROR_THRESHOLD = 0.1\\n\",\n \"backend_num_qubits = 133\\n\",\n \"\\n\",\n \"# ---- TODO : Task 4 ---\\n\",\n \"bad_readout_qubits = [q for q in range(backend_num_qubits) if backend_target[\\\"measure\\\"][(q, )].error > BAD_READOUT_ERROR_THRESHOLD]### build your code here ###\\n\",\n \"bad_czgate_edges = [qpair for qpair in backend_target[\\\"cz\\\"] if backend_target[\\\"cz\\\"][qpair].error > BAD_CZGATE_ERROR_THRESHOLD]### build your code here ###\\n\",\n \"# --- End of TODO ---\\n\",\n \"print(\\\"Bad readout qubits:\\\", bad_readout_qubits)\\n\",\n \"print(\\\"Bad CZ gates:\\\", bad_czgate_edges)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ac01a0ea-2d7b-4225-8694-349341663b4b\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"Warning: Graphviz Library needed\\n\",\n \"\\n\",\n \"The 'Graphviz' library is required to use 'plot_coupling_map'. To install, follow the instructions at:\\n\",\n \"\\n\",\n \"\\n\",\n \"https://graphviz.org/download \\n\",\n \"\\n\",\n \"\\n\",\n \"If you dont want to install it, you can just skip the next block of code, it is only needed for visualization. \\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"b80881e2-9a28-4d16-8d8b-ca14811ea780\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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lrDzNVWNxyUgzbQjyuBh7H1pFefy5YqHvumz+vyAGg2en2utDd4IEOCJpANk/w/bz4erAVhxdOxWFRn7TB2p08bSe8PqfIFCUghVTPwA+fTsW+jA1ITy+Y/JsVk4TQmBQgpXi7Be9nwENA/d7AzPuBlf8rOKXHA91EfJwMm80PW3GcFpnsUIz7wxBkxLV2hJ3ExESkpKTg6NGjSEtLQ4QrGmmbbexSrZZAXCeg6Qig4+W+W2z4OtXvCdTvBXS+Eqhe39eTMKGu+WnAmg+t5el4sk13YttY9xuBLldb21jmcXPNSEQRQgghhBDBgEQUIYIIFsY9/2TTdpjHseFL4MBKYB+dAPlIOwBMvdMEBBa9zFShg4UF67YpwLE9uccueAZY8Ybv++T1+v8VqNEIFRqKHwe82pbouqETh7rPlp8Adz5tg0G6bJdhnkhYdXOUUChiCO+yV4GFL5jw1KAvMO4jc+MUxvHDYfjmP7WQHmF3wnaZiIgIREdHo2nTpmjauBk2rorGxo+Kfx4xLW2CEkc0cyoSnwcv3jCjhO0wnPbDdUC3R/xAcxc5okoNoMNlJgAlrQtBm6F1ERpRF126dEFmZqYjoNCFkpSUhISF2VjtDndGZ1PY6PFHE3K41nxBIe/0N+0xFZZjwravZiPsMS583iYOEYbcLn3ZWpDoYmFrGV93OqCEEEIIIYQIBiSiCBFksIjlFJ25j1vxWVQBmnkU2PSdZWScaLfwkaeya5bv67OY5ghan26DCpaHsm+JuR1YwMe0ArrdZA6TxELae9peYJOI2E6y6EV7HSlg0CnBC9tPmEHC77OdxhkTnA+KFAxwPbLDheyNjRHTrw4axMehcePGaNiwoRPgSjGFVP8DsONHe4xFQVcQ3R4N+wEXTfN9DDNuvj4z57E9Bwx7BjjrC5vUQ3cR23yaDLM2G4oh3o89LCwMMTExzoWPM3IdsNYFR0RhMC3bgAiFnD73FLxvvs4/XmECSq3mwMh/+XpDTNijqJWyK++PGGbMdUXxhO4gvnfeOS9CCCGEEEJUZCSiCBGEAbM9bwP2L7UpNN6TdwqlEPGkKFjotjvfMi9CKngWCjNAkjcDse2BM/5n7pnImr6FD0IHRKcr6CABfr4pJ9MDFtpLt8SZn5k4svp9yyKhiFLgNsKBwY9bS830u104tr05Lnz8ctSKD3eECrpRvKGo0XyUTQ9KPwxs+Ao4mlDwdimyrP+y6Nf88Pbc93Pl2+b06HaDtR0x24aOIzo+Dq4Dhj9b+EwgPkanbSnntg5tsgspbJw1BY/dv9nXqYk27Sk/manAgmdz26zYbkTBiSIVhRk+/3Wf5bRYUcDJN9JZCCGEEEKIiopEFCGCEDoVTnnRRIAdU/0UUkqAKzwL0UN2ottDtVCtTu0Sj+Ytb9jeQscDC/z1n9lIXb5Gnf7g+/hqdYGaTczRwfYmbxJXAOmHTIhhe4/PPBIGr55trUDznwF2zqSoEoZwVxjCCxFuKG7Q4ZIwj84V4NfbfR+3/Re7+P3cM2ws9a6ZQGSsOU8o7FAoGvdx4aOXPTgTl8rg7aV44oFtQt3/aCOy+Tos+iewY7r9jFpTRZ76JIQQQgghhDeFdLQLISoyLDxZ5HP6SaszC3dclBw3EJaFiBGrcGTkd/ht5U9OdkZFh44Gih1HtgPzngRmP2qZJoW1iTD3ZMb9dlx2PgGqVgsbx8uJMYWNSq7ZGBjwVyBpvblVKGIx0JYOjKJgGOvAR+32Aw3v++hu4PBW+7rP3UDjIcVfr3oDlDlsmVr/ObB1MpCWbLkrFKAo9HDtRjcs+8cghBBCCCFEIJCIIkQQCynMpBj9NtD3XnNenCy8jTa37UHW6TORFX4M27dvx/Tp05GamuqMxa0sMDB24zfAzhl525z4ejoiR01gzUeFOHxclrfC1iFmqTDY9cSPinF0sEWHU2/Y0kKXS1lAF07X64Cet1rrV3FwKg6vU5bQdULB6qergO/GA8cPAb3+ZNOAeN+xbcv2/oUQQgghhAgUElGECGJYtEfVAQY8ApzzjbWY+Bp3Wxy8Dq977rcunPH3BujRpwtCQuz0sHbtWsyePduZ6lJRYX4LhY/SwrDT9hfZa9hkKLD8dWDNB76PZVZIuwvNVcGpP97jkkP9aEsJq2ZtPf0ftAlBgYRThpiXM/hv/j0WEhFjAbyBxGmluspGTDu4zS3E1iMGzlK8qtkMqF7PXgNmuQghhBBCCBEMKBNFiEpAWKQFlzboBSTMtWBSZqVwzDFzU5zwUC/YQhHJsM+6QLORQNvzgUYDrM3E5QpD//79nTG4K1euRHZ2NpYuXeqM6+3Tp48TmlrRYFsIM05KCifMcPxz3/st9JVZJb/caqOjC8tCaXuetZ/MfMAcLR5qNLb3wR+cfJR7gFrNgDmP54wAPhmjjwuoEQ/0e8DyR0rSLkQnSLNTTdhwgl4DAIWRkS/Z1KfvFtgknjwPN8RaqjLTbN3mH+EshBBCCCFERaXiVUNCiFK7Uli8NhtlRTFbJhJXAskbgKN7LWyU3SZ0bFAEoKMirquJKfyBdytKZGQkhg8f7ggpmzdvdlwodKNUr14dXbrkulQqDC57PnRi+BQ/fEAhia01nLDDrxe/BCz/r+WKFAZHPXe+ynJHKFJ5U6dDyQJSeSyDbzmyeuFzwIZvbGpNicQUlwkmLc4A+t1vglBJJylR0Gh+GrDk5bytSScDhbt9i4EGPS2XZcc0a42iY6hhH6DpCODgWptE1OacQGb6CCGEEEIIUbZIRBGikuGIIS4bKcvWFCdcNKedwvl5SEHRpOBtuBzB5LTTTsP333+P3bt3IyMjAzNmzHAcKa1atSowwvf3hA+FLhw+5xQ/RZSWY4Fh/wCSNwLT7gb2LLBw2KJoMdrG9M5/2gSAE/cfYmJISQNjeT22soz6D9DlOmD1e8DO6UDyJsCJoPElqOS8dzWbA02GAJ2uBOIHmoBUmrfE89rREcLw10DA6UYUpRh8fMbbwIavgcPb7LVrc661NHFsdFQ9E/0q0FISQgghhBCiSCSiCFFFRBVHPCnR9VyIiYnBqaeeiokTJyIxMdFxpvz666+oVq0a4uPjK5SQwoBUOjE46tgfFwin19CtM+k6G3VcHMw8iR9krSnbpxTMFWlxWslf4xMjfquZ4NWovz3+pA0m6hxYbe4Qtr1wdDGdMHU7mWBD50uNJv63EBX5GMIsS4UBsKn77HvH9gG75xT9etL1s2ehuZ3yCz6bf7DXtvedQKux9pqzhYcjpBnIy7azES8VP4JZCCGEEEKIioTLXZlGbgghAg5PEVu3bsUPP/zgiCikYcOGOOussxAbG1thhBSeydZ+Aky6xkYT06lx0RTLhfl4KJCdnntsw37A+O9NKNg0AYCPLJC0JGDRP3PdKZGxwMXT7divxuUVF9gOc+53JoYEK1npOaOhnwOyA5ghTHdO9YbWasaR0xSFsjKAdueZUyUQU6WEEEIIIYQoL+REEUIUCUWS5s2bY+TIkZg0aRLS09OxZ88e/Pzzzxg3bpzT3lMxhBQ3Gg49jtq9M3Bgdg3A7XLEgGwfY4oZQktnRGw7oM+dvm/t0FZgyb+BrBxBgSGwHIG8/Ze82SGc7NPtxrIfE1zW0OlC1wgdMNt/PcmgWy/oPjm0yfs7blTrkIRufwlFRExOII8QQgghhBBBgpwoQgi/yMrKwuLFizFz5kwnH4XCSadOnTBq1CgniLYoIYXXPXTokONk4dfh4eGoWbOmcwmEAMMJQhR2Zs2ahT0Ls5H137PhPlod0Q0sC+ZoQkF3RFRcMbeZmddtEloNzu1RFEhNzP1+m/HAmPeAiBoIevjXIGkdMPFKa9MJlJDiTXirRLgvnYg67YDTTj8NDRo0qCAinBBCCCGEEMUjEUUI4TcUTyiiLFq0yBEuOKWHY4+HDh2aZ/QxhZL9+/c7I5KnTp3qHM9MlbS0NKc9iNdjcG2jRo0waNAgZxJQ27ZtS9UedOzYMaxYscK5jyNHjgDZIag2fxAyJwwAMks4qqaExLQCzvrMMkoqC/yLkLgcmHKbZaJwqk6gqNX5GDLH/4DD0ZsdAwrbwk4//XTnvxJShBBCCCFEMCARRQhRIiiE/PLLL1i9erUjiFA8oYjSu3dvpxDetWsX3n//fXz11VdYvny5Mx65qNMMrxMVFYWBAwfi0ksvxfjx41G7du1ixyh7u0+Y2eK5j4iICHRu0xMHXxyCXdPCysRNQZjlMeo1oP1FpQuUrcjwpeQ0ndmPAOu/ADJTT75VqPFQYPBTmdiFJSfcTKRu3boYM2ZMhQsqFkIIIYQQwhcSUYQQJYKnDLblMGiW4gVhO8+wYcOwZMkSPP/889iwYYMjnpQU3k6PHj3w8MMPY/To0QgNDS1QWPP+jx8/7gg08+fPPxF2S+rVq+eIMW3btsPhjSGYfIMLu2YF4Ennf5wxwNCngS7XmkBQGeFfBk7fWf8lsPhfwP6luWOy/cYF1G4NdLsJ6HqthfNmZWU67x3HZfN9JHQgnXHGGWjatKldTWKKEEIIIYSooEhEEUKUGJ42kpKSMGHCBCQkJDiCyYIFCxyHireoUVoohtx666244447nDHL3u4TOl1mz56NHTt2OG1DHvGlY8eO6Nevn+NiYRHuyfeYcR+wZWLgJs4wXHbQ/wEdLq28Aoo3FE5SEoDNE4B1nwAH1wFHGazrS1DJ0T44trh2G6DtuUDb84GYlkCIV4w53ze2ek2fPh2pqaknhBTm67Rs2VIiihBCCCGEqLBIRBFClAqeOnbv3o1PP/0U3333ndOiQZEjUDB89pprrsETTzzhtHywjWjp0qVOuG1KSopzDIvtuLg4J1elTZs2eXJZPHCMMZ0UK94Eju0vfVgqxxfHDwIGPQ7EDwBcZRu3UiFhqO6BNcCBVcCB1cChLUDqAXtNI2KAmOZAnY5AXBegbidreSpMD+FaWbVqlZOZ4xFSatWqhdNOOw2tWrUqtp1LCCGEEEKI3wOJKEKIUsFTx86dO3HttddiypQpReaelBa289x00024/fbbHQFl+/btJ+6HgkmXLl0wYMAAp/guzL3Aw+mmSJgLLH0V2Pw9kHHM/8DUkHAgrivQ9Xqg42VARK3ChYGqgvMW5LyueXBZPozz8vjxGlFIWb9+vTMumwHBhCOzGTZLUUxCihBCCCGEqGhIRBFClAoWvbfccgs++OCDgDpQ8sNWnSuuuMKZ3uOhfv36GDx4sNP6QceK3xkfqcDBtcCGr4Dds4HkTTb+ONsyTg2XOShiWpibgiOMmwwFoupWTfdJWcO1s2nTJkdIcaYr5QgpI0eORIcOHSSkCCGEEEKICkVB77sQQhQDMy04geeLL74oUwGFMHz0p59+csbgsnWH7hNOAvJkn/gLDw2vDjToBdTvCRw/ZAJK2gFrSWGIqisMqFbbBJOoBkB0fXOiiLKDIonHdcJMneTkZCdXh+4mrrNOnTo5jiQhhBBCCCEqAnKiCCFKzJYtW5zsCjoIyovhw4fj5ZdfdgJkWXAHKnzU1xnQ33YUETj4p2jbtm2YNGmSI6R4XEinnHKKI5z5yrsRQgghhBCivJFPWghRIug8ee+9906MNy4vFi5c6Ig2gRRQCG8q/0UCSvnD97R58+YYO3Ys6tSpc8KFxODZZcuWOROgSqP5O5k4ORchhBBCCCFOFjlRhBAlgmGyZ599NpYsWXKi+K1evToyMjKQnp5e6PXoJPCMK6bTwDOe2Be8zRo1aiAqKgqHDh1yimly8cUX46233nIyM0Tlnvo0efJk7Nu3z/leRESEM4GJbVzFOVL4F+14MpCy21q1OFEoK93GUYdHW6tWdGMgMkYBwUIIIYQQouRIRBFC+A1PFz/++CPGjx/viCakSZMmeOmll/DNN984DpX8MPiVIaFXXXWVEwRL9u/f7xz/2WefnRhX7KFRo0a44YYbnOtQnKHgwtDRN954w2nvmDFjBtq1a1dOz1j8Xutsz549TmvP3r17T6wjTmLq06ePI6rkPR5IPwwkrgTWfwHsWQCk7ASO7gWy0nKPC4sCqtcHajYFGvQB2l1g45g1cUkIIYQQQviL2nmEECVi+vTpTmuFp7C94IILcOaZZ6Jp06Y+HSWXXnopPvnkE4wePdoJDN21axe6devm5Js88cQTeabrMDz2f//7H+677z7nuhxrzADZxx9/HM888wwOHz7stHaIyg3fe66FcePGOaIaoWg3e/ZszJ0794TjieJJ1nFg8w/AD5cDn48CFv8L2P0bcHhbXgGFcDoTv79rlh33xenAj5cDW34EMtPU8iOEEEIIIYpHSX1CiBI5BObNm+dMTOnXrx+GDRvmtPYUllESHx+Pu+66yxFPrr/+esyaNctp42nWrJkjllx55ZX4/vvvnaksnMDCkclDhgzBc889hxdeeAGpqamoW7euc+xFF12Ed955x7n/Cy+8sNyfuyhfuKY4jYlCCh0pO3bscNYO33+KeBxxnbY7EnP/ZiOrOW2pRHDk9TETYHbOANpfAvT/C1CrpVwpQgghhBCicOREEUL4DfNJEhMTHefJo48+ilGjRhV5PEfXcpoO23bYksPWHQoj69atw7vvvotatWo5ogmh84CZJytWrMBrr73m3BcdBwkJCXjxxRcxceJEp52H11UXYtURUhgye8YZZ6BFixYngo0XL16MyR8sxHcXurHq3VIIKPlIPwKsfBuYcCmwezbgLtup3UIIIYQQIoiRiCKE8Bu206SlpTnZJ3Sg0CVA10hhsABm+w4n6+QPkmXWCYvkatWqOf9lXgoLZYotFGoYLMvr16xZ0/neZZdd5rQS8TF42olE5YdrIzY21hFSWrVq5fw7O6k6NrzUAPsWB07wcGcBe+YDP11tzhQJKUIIIYQQwhdq5xFC+A1zKegCoTuEF9K1a9dCj//uu+8cB4knhNYDp+swnJaCDDNOeJs9evRwfrZ9+3bcdtttOOecc5yRt5zUwtvhVJ4DBw44YgxvzztLRVRuKJzQtUQh5afPZ2LHp23gXtEacAe+7yZ5I/DzTcC4T4D6PdTaI4QQQggh8iInihDCb9hOExLi/2mDggeFEm8XCt0mdK+wJYhZFxMmTHC+X69ePee2r7vuOtx6662OG4WTeOhUYesQg2hZSHPErQSUqimkhKbWQPbHp8G9tE2ZCCgektYDM+4DjtmEZSGEEEIIIU4gJ4oQwm9iYmIcUaM0cMoOW4DuvPNOp3Xniy++wMMPP4wjR444P/cIJJzGcu2112LatGmO+MJw2tdff91xpvzwww9OrgqPE1WLrAxg2Wsu7J4WDpRDq83O6cCKN4B+DwAhoWV/f0IIIYQQIjiQE0UI4TfMJ2EAbEkdBF26dHGCZF999VWnFefGG2/ETTfd5LTu5G8VYuvO1KlTT7hX2M7zyiuvOC6Y/v37o3PnzoVOAxKVF7pDlr0OZJdTHE52BrD8dbtf5RgLIYQQQggPElGEECVi0KBBJRIxhg8fjq+//hp9+/bFk08+ibFjx+Lzzz932ny82bdvnyOc7Ny505nA4g2/x0k9dLN4pvmIqgNFDE7hObqnfO83ZTew+n3ArRxjIYQQQgiRgzzxQogSMWzYMMcVkl8E8QVbcZ5++mmn/ebKK690WnQKm6yzdOlSx4nCVh8e731cs2bNTnyvXbt2AX0+ouJzZCew9SeqKfbvyBigySnAgZVA8ibf1wmvAcR1BWJaAtnp5ig5sNa+zk9UfaBOWyCmFZB5HDi4BkhaB2SlAxu+Anr9GYhuULbPUQghhBBCBAcSUYQQfkMHCqfx9OzZE3PmzCn2+MGDBztTd5555hmsWLHCcZLkJzU1FUePHsXy5cudST0cm/zpp59i9uzZjmjCwNmbb74Zhw4dcoQbCjOiarlQ9i8Fkjbkfq/pKcCY94Fpd/kWUaLjgWHPAK3Pzpmu4wIyjgDL3wAWPGdfe2jQCxj2LNCwr401doUA6SnWysNjD20B9i0BWp5RPs9XCCGEEEJUbCSiCCFKRP369XHhhRdiwYIFhbpKvF0rnKRz/fXX4/LLL/d5DEcX063Cdh5O4WFuyocffuiMRk5OTsaAAQOcTBWGyw4cONBxwYgqhBvYs8AySiJqAY36AwMeAUILWQb8/qDHgPYXAes+AdZ+CoRVA7rdCPS5Bzi2H1j6sh0b3QgY+QoQ2xZY+BywYxoQEQN0uyHn2L0mvPD7LUZr3LEQQgghhJCIIoQohRvlkksucYJi6RxhOOxXX32FtWvXFjh2x44dTh5KUezatctp4yEceXzeeec5E3y6d+/uCDAMlr3tttucr/lfhcpWQSfKMmDgw0Dnq4HI2kBEzcIDZut1A9qcC+ycAUy9C0g7YN/fvwK4YDLQ5Rpg1TtAxlGgxRlA/R7Agn8A857ObfWh8+T8n4DutwCrP7TWHv6sMOFGCCGEEEJUHSSiCCFKTIMGDfCXv/wFt9xyC2bOnOlcfPGPf/yjRLfLQNklS5bguuuuQ926dR3h5MCBA85EIIbRlna8sghisoHkzSZgrP3EvtWgD9BkqO/D4wea0LLpOyDtYO732faTMMccJQ16m8jSsA+QmQZsnZQ3KyVlJ7BvMdDqTKB+dyD1gLX4RElEEUIIIYSo8mg6jxCixISEhDiOkauuusoJfA00HHe8Z88ex8nC+3rggQecHBa5UKqmEyUjBVj/OTDzL3bZ/H0hB7uAhv3seLpXPEG0DtnA3sVAeDRQq4V9i+1B7iwTSPLD9qGwKAumzUqzkFkhhBBCCCEkogghSgVdIn/9619x/vnnl+l93HXXXYXmqYjKT06nl98w54SuEm8XiofURCAk3JwqhG06FEriuuQ9juJK3c5ASJgd6zyGEj4OIYQQQghROVE7jxCiVNAVwsk5zz33HEJDQ512GzpIAkXNmjXx5z//GXfffTeioqICdrsiuAgJMaHDX8KZl0J3ydGCP2MOCqfvhFc318rmHyxwtu89QFoikLTRRiN3vQ6o28nEE94WW4kovgghhBBCCCERRQhxUjRp0gQvvfQSmjZtijfffNPJMDlZcaZZs2ZOCw/bhZSDUsVxATWbAgfX+Hd4+mEuSnOR5Cc03MYYO8cwsHY5MOthYPBjwNgPLfskNAJwhVpOSqtxwPEkoFosEFEj4M9MCCGEEEIEIRJRhBAnTZ06dfD3v/8dQ4YMccJk586dW2JXCsUTOk7GjRuH++67D7169VIGinCcI5y4s22yf8dzLDHDYD0tO95E1bdsk7Qk+zfzUFa/D+xdADQeaq1ADJXdPQfocAnQ7FQgZTdQrzsQKi1PCCGEEEJIRBFCBAKKHQyYHTt2LAYOHIgff/wRH3/8MebPn+84U/hzzxjj/Nfj9+liGTx4MK699lrn+tHR0RJQRJ6wWDpLChtrfAK3OVbajgfqtDdxxPt2mH1y/BBwMGcad40mQI1GwMH1wPLX7RjeBu+r0QAgdT9wYDXQ8zau1bJ8kkIIIYQQIliQiCKECBicpMPRxFdccQXOOeccbNq0yRlZvGDBAqxduxbbt29HerqNOWndujX69OmDAQMGoEuXLmjRogUiIyMlnog8cDnU7wHUagkkbyj++O1TgF532Hjijd/YpB5Suw3QdASQtAFIXGXfa3EaMOhxYOYDwJoPcsNjGw0E4roC6z4FwqoBjfqX4RMUQgghhBBBhUQUIUTAoRBSq1Yt9OjRw7lcc801yMrKwldffYWtW7c67pOzzjoLHTt2zHMdIXwR0wpoOhxI3lj8lJz9K4AdvwKtzwL63gus+wyo0Rjo9xegehww+xGb3kMSV5rrpNefgeRNJtJQPBn2DxNfVr5jYkz1+uXyNIUQQgghRBAgEUUIUWZ4CyN0qRBPW092draEE+EXIaFAl+uA9V8Ax5Ntag7zTHwJKhQ/Zj0EVKsD9L0P6HO35apkpgELngU2fJV77N4lwJzHgQEPARdMtutyvHHKLmDancDR3UDnq4CQiHJ9ukIIIYQQogIjEUUIUS5QMPEWTehMEcJf2NLT8XJg2X+A1e8BWyYCx/b5PjZpPfD9xUD8QKBORxNHEuYBB1YBWcdzj3NnAsvfAHbOABr0AqLjTTjZOdOElO63APV7KQ9FCCGEEELkIhFFCFEuUEDxuFE8ThQh/IXZJL3vAnbNtNHEdKQURdoBYPMEuxQFhRSKK7x4U78n0Ot236OShRBCCCFE1SW3ohFCiDLCab/IBkKditQFuF3ITM9Cdpb9TAh/iGkBDHsWqN6gbO+Htz/0KSCmtVwoQgghhBAiLy63r7mjQghxkjhnlmzgyE4L8ORUlNWr1iDpyH6EuENRv3pLxDeOR+3WQJ0OQFxnILymTZm1/xOiIBTe1n4ETL3T3CaBJioOOOUFoMOlcqEIIYQQQoiCSEQRQgQUnlEyjlgGxZoPgd2zgZSE3FGzeQQSt4V+RtUDarUA2pwLtD3XxtGqgBWFkZVu44un32MiXXETe0oyBWjI34F2FwAh4YG5TSGEEEIIUbmQiCKECAg8k2RnAnsXAQv+AWzjtJOjJbwRl4kpnf4AdL8ZiG6odgrhG07nSZgPzHoQ2D0nb2BsSQmNAOIHAUOeABoN1JoTQgghhBCFIxFFCHHS8CySeQxY+gqw4DkgNfHk3AGuUKDRAGDYP4D4/vZvIXytOwbMrn4XWPofIHljzuhj/67trCu2knW5xoUu1wCRsRJQhBBCCCFE0UhEEUKcNMf2myNg9YdAVmqAbtQF1GwCDH8OaDte7RWi6JyUo3uArROBjd8CB9cCh7cB2Rk5B1AYyflLFxLhRkTzw8ism4CowdtxyqWd0aZHPEJCpZ4IIYQQQojikYgihDgp6DqZfi+w+gMbFxtoqtcHRv4baHe+HCnCD0dUquWkpOw0IYUCX1YaEBppU3eim2Ri2e6p2LBnGbLdWRg4cCCGDh3qjOAWQgghhBCiOBTdKIQoHW4L+Jz/DLD6PRthXBYc22cBojXigfjBarcQhcO1EV4dqNMOiG1b2FGhOLi4Djbs5dxtYOPGjRgwYAAiIiLK98EKIYQQQoigJOT3fgBCiOCEFja2Tqx4s+wEFA90Fsy4H0jdlzM6WQg/BBXfFxdatmyJyMhI57jDhw9jz549kClTCCGEEEL4g0QUIUSpOJoAzHvKgj3LHDewZwGw9DWbACTEyVCrVi3Ex8c7X6enp2Pr1q0SUYQQQgghhF9IRBFClCrIc82HwIHV5XifGcDKt4FDm8vvPkXlJDQ0FK1atXJcKRRPKKIcP34SM5KFEEIIIUSVQSKKEKLEpB0EVr0HZKeX4srMNHGVvq1n3Wdq6REnT5MmTRAdHe18vW/fPhw8ePD3fkhCCCGEECIIULCsEKLEJMwBktbn/jskDGh3IZCWBGz9yfd1ImMsGLZeN5uUcngrsH85sG9p7vhZEh4NNBoIhEcVvA0G2e6cDqT9EYiqWwZPTFQJ6ECpW7cu4uLikJKSgqysLKxdvgkRB+JxdI8LmcfMbRUaDkTEANGNgFpNgdBqCjYWQgghhKjqSEQRQpQIhshunWTtNR5qtwVG/BPY8pNvEaVmU/t581FAVoaNoaWoQtFl4fPA8v+YQEJi2wFnvA1E1csrrngm9Xx/IZC0Dqg2UAWtKD0hISFo3qAdti06jLA98Vj9cUtsTraR3Zlpts4pDobXMMGO06GajgRanwXUaW9inxBCCCGEqHpIRBFClIjjhywLhQUmhZD6PYE+9wJRcb6PD4kA+twDtBoHrP0UWPEWkLofqNcdGPQYMPhxIHEFsGOqHc9df+74L/4XcGBV3tui+HJwrV3oVhGipLAVLCMF2Pg1sOV/nRG2uB3ch6sj3Q2k5+szo1B4PMkuyRuBnTOBxf8EWo4BetwKNOwLhIRLzBNCCCGEqEpIRBFClAhO4zm6F2gzHhjxIhBZGwirVnjOSbVYoO15wL4lwIx7zU1CKISERQGnvQ60vziviEJXCrNP9i32cYMuIGlDjktFxavwE0+OTuJy4LeHga0/u5CVFgEgogQ3Yut/7SfA1slA9xuB3ncB1epKSBFCCCGEqCpIRBFClIj0FGt5OLgamP8M4AoFYloA3W/yfXzNJuZY4c6/R0BxcAP7lwLuLDvGxbNRtt1W+mHg2N6c41wF23pSdltRrLpV+AvbczZ+C8x60NrBTu7GgLQDwILngL2LgeHPA3GdA/RAhRBCCCFEhUYiihCiRLDFIes4kLjSLoSBsV2v8338oa3A9xf5GE3sspYeVwhweBvgzrTWn5iWdvvNRgKNBgDV61srxabvgD2LbCJQ+pEyf5qiEpGdac6m6fcCR3cH8HYzLB+I63HUa0BcVzlShBBCCCEqOxpxLIQoERQ96D7xF+7YM2zWe5oPb6PJUKDfXyxjZe3H9v0QulpaAnU7Aae+AjQbBdTpAPS4DTj3e6DX7TbZJ7QEHRiiakPH0rafgWl3BVZA8Wb3bGDKrUDKTo3fFkIIIYSo7MiJIoQoEcw/iahpYZulIboh0OVaC+akG2XWA0DCvFxxhUGddLgwt4LfZxtGo/7AKS8CAx4GElcD1eqolUf4B1t3Ztzv1R6GshNSuGZPfdkm+gghhBBCiMqJRBQhRIlgvkl0A+DI9pKLL63OAvreC8S2tUknzFRJmGu5KJ7pOz9cZu08R3bkXpctE/OeAE5/E2h3voV7SkURxcFRxQv+kdt2VpZQ7GPLEN1THS9XW48QQgghRGVFIooQokTQBVKzObBngf/XoXNl4CNA1+uBw9uBKbcDG7+xUbP5C1HmnxTAbWIL81Bi2+dcT0WqKAK21eycDmz4umAwcVlBEXDJS0DTEUCNeAkpQgghhBCVEWWiCCFKBMNfGw/yPxclJAzodQfQ7WYraL8aB6z5sKCAQur3sjaf2Ha+HTBs9wmLBGLbqEAVxYe+Ln8zx7VUjnBaz9aJ5XufQgghhBCi/JCIIoQoERQvWo4xUcMfajQBetwC7Jpp01EYvlmYM6BWM2DYM0CvP1uArAd+zRYJCjJpSUCtFoF5LqLyQkfTzmm5/+YIbTqiihP/uMYiY4GwqOLvg/k9UXF5M1DYmrbmI3NNCSGEEEKIyofaeYQQJaZWc6DVOGD1B8W3SjQZBlSra4Xp4Md9H5O4Clj2GrDrN2DvIqDDZUDGMWDzBGvxaXMO0PkaayGKqmuFrhBFtfIwR4eTnzy0GG1iHkNmD6wqeB2KJq3PBjpeAVSPs/W3Y6q5WY7tyXtsZG2gw6VAm3OtvY3ZK/uXAUtfAQ6uAw6uBQ6sAer3KPvnKoQQQgghyheVIkKIEhNazfJNNv9oI4y5+872HAbC5oeuERaZcV2AuM6+b2/rZGD5f4DU/cAvfwQG/x3ofhPQ8zYTabLSgS0/AXvnAwMeKfOnJ4KcrDQT49jSw+ycarFA9xuBxoPNjZIftonR/dT/r9b+s2+xOaj6PwjEDwYm/gFITcxtZxv2D6DTFSaUUDBh/kmXa4AWpwHfXQgcXAPsWwLU6662MyGEEEKIyobL7eaenRBClIysDGD6PcCSl4GQECAixkSU/FknYdE2mcdVzG2lH879d3i05aLUbgtkHAGSNlphPPYjK4RVmIqioODx9TlARDRQrxvQYow5ojKPAl+OsZBib+r3BM77EUjZDfx0FZC8yZwpHKnd44/ArL8CC5/LdbSM+xjYPgX45RYg/Yi1m3W9zlrRVr0HTL4R6He/Oa/kmhJCCCGEqFzo450QolSwOOxzJ7B3IbB7tjlSfMHClZeSkHHUdvJ5ISxSOd2nUX8JKMK/KTlHdwH9XrKWGq6ZwjJK6EJpM97axGY+mDsOmbex9GWgw8WWAbTiLeB4ElCno+UBbfgy151C8ZDjjQc8BNRubeINR3RnZ0pEEUIIIYSobChYVghRKliYslWHrQ0xrcvwfkItVJatPaHhZXc/ovJA8SLtIDDtLpsG9c25wLZffB9Lp1SjfsCx/QXHdh/bB+xbBtTtBNRoZN+jY4q3X5sTojwCicv+TfcKr8P2NTpUmOcjhBBCCCEqF9ojE0KcFPEDgVGvWmvDoc2BvW26BDpcAgx9Egj3kWUhRGFQwDi0KdfJxKlOvuDI7JhWJn7QaeJN5nEgZRfQ9BRrMSNbJprzquefzWWyd4llonS/BTiWCCz7j4ksElCEEEIIISoncqIIIU5a6Gg+Chj7ARA/KHBnFYomve4ARvwTqF5fbTyiZO6lsOr+H8v2nMxj5iDxxp2Zm3niEfGOJgCLXrSMHgbRct2PeBGo0x5Y9zGwZ76FIUfUsN8NIYQQQghRuZATRQhx0rBYbDTACkoWmGs/AlIPFj/+uCBuuCKyEdcxBH3vdaHtedYiIURJYJBxzSY27alYKM6F5LhHsvL+iMvX8z3PfxsPsRY2Mv9pYC8n+TSy0cjdbrI8n3lPAdGNgJDQAD8xIYQQQgjxuyMRRQgR0IyUU54H2p0PLH/DxhI7GRIZxV3ZDVdUOlxtdyG0x0aMvL8fGneoXU6PXFQ22HrDFh1PMHFRuHPyUzi2OzQi788ogvC2GDLLqVMU9Hrfae07k28A1n2ae+ymCcD474CuNwAbvwVivTNThBBCCCFEpUEf8YQQARVSXOFA42FAw/7AoS3A5h+APfOA5M3Ake3WHsFdf7oFouJMeIntnImdjX7Gftc6ICwTuw7HoDH6waUeHlEK2MrDqTwbvynoLvE1Xjt1HxDd0MZ0MxvFA0UVOko4hcfT1lOvu03eyT8m+ehuYMc0oMdtFkRbL2cqkBBCCCGEqFxIRBFCBBwWjxRJ6nYE6nSwnfzjh2w3n6Nm3e6c3IooIKImMynCMGdeLPbPynSuv27dOnTr1g1RUerlEaVbf8zpWfg8cDy56GM5fpuCSI9bgdi2QPKG3J9VqwvU62Zjj1N2W9sa13FELRNU8t4pEBJha716A7ueEEIIIYSofCj2TghR5gVteHXLjWCRWrczENfFBJaYFkBUXU45caFdu3YnRJPExETs2LEDbqotQpQCOkYa9in+OLqi6JbKSAV6/NEEEE7doYDCsdrR8cDmCSYAMkyWY5CZt9LuIhNTKAbSsULRpMVoIGm9jUz2N9hWCCGEEEIEF3KiCCEqBLVr10bLli2xevVqZGRkYM2aNWjTpo1aekSpYMZJl+uB3XOLz+TZ9Ruw7hOgyzXA2V9algpFvkYDge2/AGs/tuOy0oFFL1iIcv8HgMaDgYNrrA2Izhe6qlb9D+j0h3J5ikIIIYQQ4ndAThQhRIUgLCwMHTt2dP5Ltm/fjgMHDvzeD0sEKdTeWo0Fmo4A3Nk2enjD10CqjyVFkWXWX4E5j5uzpPnpJowsfQWYfGPeliA6Tb6/AFj6KhBZG2g1DmjQC9i7CJhwseX81G6lPBQhhBBCiMqKyy2/vBCignD8+HF88skn2LNnj+NAGTRokHMJCZHeK0rH7jkmbjAMtlhCrL2MjpKMYzYiubBgWoot1erYsVnHTWhpPBQY8x5QvV6gn4UQQgghhKgoSEQRQlQYeDpatGgRpk6diuzsbMTFxeHCcy9FaGZ1HN5mhTBHJsNtxWuNpparwq8ZUsvgTyG8yc6yFpupfwYyjpbRnbiA2HbAWZ8BcV3lQhFCCCGEqMwoE0UIUaFo1aoVFixYiNTkLByd3Rg/fulG0uIc8cQRWuy/nkI1vAbQsK9lUrQYA9RpZ0WtCllBQkKBjpfbmGK262QeC/x91GoOjHrFApO17oQQQgghKjdyogghKgzMrkja6MYvL27F7gkxyN5fC+7joaaKFIMrDKgRD7Q5B+h+s41WljNFeODo4eX/BeY+YW06AcEF1OsKjPgn0GSYtfgIIYQQQojKjUQUIUSFgBkUG74C5j8FHFxrgkqpCLFgz773Ah0uAyJqBPiBiqCF03W2TgJ+ewQ4sNLGG5cWOqA40njw40CdjnKgCCGEEEJUFSSiCCF+V3gGYqvOvCeBJS8Hrt2CI267XgcM+j8LAFWRKzzr7WgCsOx1YNU7QEoC4C6BmBIaAdTrAfT4I9DuwpwsHq0tIYQQQogqg0QUIcTvCgWU6fcDK98GstMDe9ts8Wl/ETDy30BUncDetghe+FePwsnhHeZ+2vYzkLgCOLrHQos9bWDOH8dsW0e12wD1uwNtxwPNTs0R5tQuJoQQQghR5ZCIIoT4XXMqZj8GLP6XjYktC0LCgG43AEOfsSk+QvhyQqXsBlJ2ArtWH8LaTSuQmZmNqKhq6Nm9D2Jbhzh5O9GNgNBIOU+EEEIIIaoyms4jhPhdYObJ+i+Bpa+UnYBCmHux4m2gbmcLnFX4p/CGgkhkjF0YRhzS4TDmfDYPmRmZyIqphY6X90ZIiIQTIYQQQghhSEQRQvwuJG8C5j0BZBwt+/uiSLPgOaDxECCumwpi4Zs868Lra60XcbI4nl+2kWUBx/YDR3YAaQct7Jg5O9VigZrNgKh6NpZbY9qFEEKIiotEFCHE7+JC4bjZpPXld5+HtwFLXgFGvWoZF0IIUR7iSVYasG8ZsH0KsG0ycGgLkJkGZGeYU44thyHh1ioW0wJofjrQfBRQrzsQXv33fgZCCCGEyI9KCSFEuZO0Adjw9UmMMS4NbmDLD0DiSgsI9XYaCCFEoKFQkjDHxNtdM4BjiZ604sJxcnl+A5a8BDQZCvS4FYgfZNPG5EwRQgghKgYSUYQQ5QqFk22/AIe35n6PO7A1GgOp+4H0I76vx93aanWBmk2sPefITiA9pfjxtNzhjetqBc3BtcDmH+zfjmVeCM+0nixzBWSlhgAZYQDXVUYYMjhyO8LWkVosREnGaC/4B7DyHQsuLtkNAKmJJjRvmwJ0uhLo9xc44cZaf0IIIcTvj6bzCCHKFQog344HtkzM/V7z04DT/gtMvwfY8GXB60THA/0fANqeZxN2eNbKSAHWfAgsfAE4xtG0hdD6HGD0mybc/HCp5aKcPxEIr1E2z08EB56/fBTuds0G9swH9i8FEjem48jxA3BnuxESGoo6NeqjbicXGvQGGvUHGvYFwqrnGJlU0Aof62rfEmDqn4Dd84oXef2B7YcN+wAjXwIa9JGQIoQQQvzeyIkihChX0pKtyCDc3a/VHOj1ZyC6Qc5ufz7Co4FhTwPtLzKbO8UQZgm0Ggf0vB2IigN+ucX3hJ86HYHBj5uDhXZ4wnaeo/uA2hJRqrQbii1laz8G1n9hrqjcgOMIAI0cfYQ6ywFeVtlx1eoAddoDXa4BWp0JVG+gglbkXVcJ84Gfb7TzTHGtO37fbiaQMBf46RrLdGo8VOtOCCGE+D2RiCKEKFeSNwKZqUDX64GWY2z0cO02QHa67+Pr9wJajgW2/gxMutacA2T1+8DZXwKtzzbL/K6ZBcWXAQ/b6Frv7BVOw0haB9RuVYZPUlRM3MDxQ7Z2lrxsQorfha4bSDsA7M5xrTR6z9xRzUZaO5qo2tCBcmBNjoCyomzug2LelFuBsR9aS6KEFCGEEOL3IeR3ul8hRBWFWSbMnqjRBIisDaTsypuPkp963UwQWftRroBCju2zfBOOBo1tl/c6rlATaRoPBmY/ZsKJB943J/WIqlfkHtpqrqWpd+VMhiqlU4BriKId28PmPQ0cZ+aFGmOrNBTYZtwPJK4q2/vh7U+72+5PCCGEEL8PElGEEOUKQxbZjsPQRWajfHeetVX4xAUc3WvZJ/vz7e5yF9ZxmWSZs8Wb+AFAn3uAlW9b+493gcvjjycH/nmJii2gHFwDTLwKWPd5YHIqCF0t854EZtwLpB6UkFKV19eq/wHbJlFhK+s7A3ZOsxHx2VllfF9CCCGE8InaeYQQ5UtOoZnJqSfEZZNzvH/mfeyGL+ySBxdQrwfQ7gLg8HZg/7LcH3HKz5AnzVK/9FXfU3hU61YtDm0Bfr7JWnECPVabbWgr/2fup2HPWPCxqFoCCtsDl79hDqXygPez4i2g9VlA3S4n19bDx09RmY5AnkfpdKEzkGI31zTdgswBYvtQnQ42IcgJVlYrkRBCiCqMRBQhRLnCIpPTJuArA8WPD+Zh0UCbs4GBjwJRdYGZD1pWAGE2RZ+7gVotge/GW/tPdMN8d8HCICYwz0VUfNKSgJkPlI2AkkdIectydnr+GQj1EZAsKidcU+s+tayn8hYG134CDP576a5PF8uxvcD6z4FN31vOD0fG+1KZXSEmtvBc2mQo0PYCoMXpQEQtiSlCCCGqJhJRhBDlCp0ivqbwFAc/yHO8Z997gRZnAIc2A5NvADZ+m3sMRyB3uNRyUPYt9X07IWFAraalf/wiyNos3gU2flV2AooH5u7MfwZoNBCIH6TisqqQfgRY+2nR64vCMQUHtnxl5Ws99CUSR9ayVrETbj1f0KX3FdD7TptQVpLfiXSGK38ALPm35QQVFup94jo5z+1oArDuM2DTd0CDvrnnYp5Ttd6FEEJUJSSiCCHKFU7iCatmH+T9hUVItxuB3ndZnsriF80+z5Baz64pj+l7H5CVBoRFAV2vy/l+jH3Ij2lht8GMFY4+FlUjB4UtXeXVZpGaaFk/Y96T26mqwNHDznnIh+jLlsN+91srDM95bJuhe2TFm0AaM3S8iG4E9PwT0HqcBWnTFUKBeNmrwNE9vu+bLTh0WLU6yz8Rw5kgtBKY9RCwdbKdK0sD2y8ZrHxgNdD5SjvvckS9P05CIYQQojIgEUUI4cBCkx/cuftJoYI76xQfeGEBEFbD/nuyO45swYnrBmz/GX6FldC10vd+oNefLVBxzt+AvQsL7vzycTIwNDQK6PeXvMUMf8Z+/kGP2wd/TgYSlRuuDwYSH9pUvve7/Vdg50yg1Tjtzv+e7z3DpjOOWsFPpwXbV9juFxoBhFcHwmtYa99J5YlkAwlz7H58jWY/8xMT07ZNMRdHwz42dr12a+DX23OnhnHC2Kkv28j3vYuAnTPsHMnWRE4em3w9kOFptcnngtmzCGjJtRZazGPNAnbOshHJPAcGIhiKE4IWv2StTKe8CMS00poXQghRNZCIIkRVxZ0TKnjYxIkd04H9y4HkDbajnpVhH4hZbMS0BOp2ABr2Nfs2iwDmmpTmAzNFkVZj3NgxhUVIvhvwcXscU9z9ZmDLRAsHPZ7k+3Zpf//+koJ5FBRtxv9oPf+//gnofgvQZFjJH7cILrjTz13/PGKby8/i0d/jfMBid/W7QKux2pkvT3guo/hL99HmicC+RcCBNcCR7Sao8O1kyHT1BkBsWxud3nSEnQucMGBXyc9nFGiSNhScyBNaDeh7D1C9ITDpanOUUKRmS8/IfwHtLwI2fANsnWjHtz7b3CQc4z71DjsnU7Ae9RrQ7nwT5Ji74gs+X4pEIVFFvza7fgN+urrocfKlgeLMpglARiow+i2gZlMJKUIIISo/ElGEqKLQgs4P5pwscnib751OFh4ULfbxstjGw857Gmh+GtDtBiB+YMnzTdxwI6r3HoQ3q470rUX3PNBFwl1W7h5zh5Y7uSwIfBUStLb7ch2waGIhTZcNC+v4/na7onJDR4h3G0Rse6D5qVa8Ht3t+zq1mgONh1pmTlqyOZ72LrZCkXCtNz2l+Ak8XGvM7GHrmih7KGbsmGpjf3fNsjBhz3vmTVamiSq8bJ9iLYEMA+5yPdD+QjtXlEQAyDruu9WGQkL93sDuWcCWn0zcIWxhZEZP63OAlqPNjcc11e5Cc6Us/lfu+HU6Ale8YRN4KKJQiPHVfsN1VlS7mqetbcrtgRdQcu8E2PErMOM+4PT/mlgkhBBCVGYkoghRxeCuLKcxMLth3zJrgfEXFgOc6LDmA2DrJKDjZRZsWLNZ8cVHdnY2kpOTsXz5cqxetRrpw5oCu8YAmUWchhgm28syAgY8VHh449Q/WXFSFHx8nChRr6d2Sis7LCqZFeEJ5mShyowcXiiK+BJRmo4Ehj9r41zpamKuDts0lrwMLP6nFcwc7XrKCybIFMWi5+1+YloHbq2xGKYw4CnInTDPKh7oyfNB8iZg/tPAxq9NPCkJFI7pvpt+N7D2Q6Dfg0CL0Sba+vO6Oi2Qhwt+v1ptGw1M0YLrxhs+xsyjQIPeQEiETbyh2HZwtU3c8Yb/PrLDQlzpTPElolAYLirUlr8Dsx+1ke9lCR8DhR4+r953lC48XAghhAgWJKIIUUVwWneSreBg2KYv50lJ4Phg9sPvWQic8hzQsJ9vh4fb7UZqaipWrVqFJUuWICnJKh1X600Ia7cD7lUtsHmCyykW9szLd90smyDBSRJFsXtO4T/j7u+vt7nhishGj9tCEBJWhavOKgLX9sG1tiPOwE62S3S5tnAHEh0Ipzxvk6NmPQjsmAHUaAT0/ysw4K+2k8+JJBQg5zxuBXJ+WHh3utLcLDummTuChWVxWRXF5XrQYZUwz4p95mpkUBhym6DDME+Gljbqb0Imsz6qhMuKrTvZ5n6YdpdlfJzM9CUKU3yNf7oK6HWHZZFQuC1WSHH7vl+uv8wUIKq+iV0e4YtE1bEWSf6M7xXzUDhdh8/Bk5HigQINBb26nex2fD727MJbz5zxy58Dm38MTAZKcVDkoeBIIYrtUkIIIURlRSKKEFVEQGEI4PR7zUUSsGklbtvx/+EK4LT/AM1G5hZxFE8yMjKwYcMGLFiwAPv373fcKB4aNI9FwytdWPMIsH+ZXXzd/sZvTu4hZqS5seb7o6hz8wK4mnRi5KPCKio5zGeg2+TMTy1ThxkVLEILmwjF0dh1OwILnjXnCYvP/UutgD0/x3FF5xVdBes/93EDLqDteMsKmvt3YNsvlofB3zPmcJTmd3XD1zaamVkWvF9mFBUohF2WAcTAVOYVtT3fHgfvuzI7VPgy8LzA/JAUH5NxSgtF5gVPA8cSgGH/AMJrFv06UiCj2JKfI7tMxGs6HGg0ANg53d5Xim9drjNxj2uLt833juIX/52/BYlCnGfaGO+HWVX5iajpdtaBp83R5fWA+XyWvlzMqOQAw/YmivTMfuFzE0IIISojElGEqAJwZ5RjLTmtJODjXt2WRfLLLcAZ7wCNhwCZmZnYsWMHFi5ciO3btzv/9hAbG4tu3bqhc+fOiEQNhG1zOdkE3ru1gcRVLR3ZY6ZjX+3VmPDjJpxyyilo1aoVQkKqwpZ91cRpO9tnLV5bfrTWAoZ5slUnPyxQGS7KgpU79t7OAhbCB1YBDfoANZtY64gvPJOf6EBZ9T+7DafNwkcuR1Fw2grFgYUv2H0XO4LWbe4FXpgBQ8Fl6WtArz/Z82WxXtnEFL62HM9LB0ogBRQPXAfMiaLTZ/DffIskHigSRNUr+H06SOZz1PW7wJj3gV0zrI2H66h6PVsbFDictZYT8O2slSIcJfldKjk/wbFmK/HztO2Ib9oIcXFxqFmzJmrUqIHwsHAnjDtxFcoVPtbNE4CetwFxXcr3voUQQojyQiKKEJUciibLXreCsqyECsIxlzPud2PAK4lYvWu+40A5fjw3EIAf7Dt06ICePXs6Qopnx5TFJwuM9V8E/vExM6LO6D1I6rMJ6VnZOHDgACZOnIghQ4agS5cuCA9X435lxGmFSQPWfZJb7LLtpTARhe0SLMjzZ6WwdYbTV/jziEIykJlrwVyg6vWBn2/IDQZ1BBA/WyhYRDM/Y/ZjwPovLTOjNNCxcmClTaGiG2bI3yxvo7K0+PB1StpoGSYMhy0r+DrynElxjM4RX24iOu0QnoXqzTIBVyTgzqtWcUzx9xcBPW8H6nW3tUC3HfNyBjxi2VJcp1xjFM8YVpy/9csZyRxpLhWfIkpYFo7W2opVa1dj1dqVCAsLc0SUmJgY1K/bEHu/6IastNg8V4mOB8KKcYhwOhDdUIStRxR+fMHHzueRn5TdFtwrEUUIIURlRSKKEJUYfs5nZgnbFIrd1Q4Au+cCE57cgGO9VgEuqyApVLRr1w59+vRBgwYNCljOq9UBRr4ERMYAK98ubMe15NB90OESYMjTTbD1wAhMnz4dR48exbFjxzBlyhQn5HbgwIGIjIzM83hE2Y6gZc7H4e2W9cHikXC3n04PT64H37uTeUsoGvjbSsA2H47B5rQqPrb8AiRFET6+whwJzU6xMbSr3wcS5nsegDkZ/Oka4+uSuNLGdyfMDUx2BX/XKUrSzcJpKU5eUSVY4gz6nfs3e15lDVtg5j1lrx1FEL5+FE54SUtLw9atW7F582Yk1MmAK3os3CmRBdYgpzv9eEVOnonL1n+jfiZKMGOH5zr+DnCNMUuHa9Z7DTIvhefHw1tsjHF+XNHHEdo06cSEZTr+mDnFy/YNexC+qQW9f3kEv3EfWFBtUXAqEFs/6Y7pdIW1NvmCzq0fLvHxA7f9rMdtJW9nE0IIIYIBiShCVGL4AZ1TeBgCWy5ku5AxtQvC269GdkwymjVrhr59+6JJkybOLqkvsYLfYrAiP6jX7QwsfM6K7JMpJrnb2ut2oNvNnJQRis4NO6NWrVqOeMJslqysLKfV6NChQxgxYoTzMwkpgceT08DQYLabbJtsGTpO/kN2TguDywpOvvx0e8QPAJqPBpqNAGq1yF0jJYEhr3SG+LPued8UPDKZO5KvUOXjo6PFyR6JKHhdFqVdbzAn1bL/eLXvuK1QLq6A5OvDdqGfrgb2LQlw+Gc2kLgcmHSdtdmxlSSYlzhfK44x3vDlyYXIlgSOfl/0InD6m26kHj+K3bt3Y8uWLc4lJSXFOY8gNAzh9ROBlMYnrscWH7YCsa3nt0e8BGyXtTtSLNn9m60XiokU0Tg2niKix8lEOAWK45I59cYJFM5H9ZhI9D9zAJKzdjkuO4onPKfxcWUfrobM7XXy6Hh83XZMN8GwAC6gfg9zLvF5e15jPgaKP76cggw7Luq1YxAyxVEhhBCisiERRYhKzJ75FohZHpMZPLgP1kCNJUMw5Ak3Wrdv6bfTg3b27rcATYZauCf7+WkLL8lj57jQZqOAXn8G6nfPHbPJ/BMKOmeddRamTp3q7CIz5Hb9+vU4cuQITj31VDRs2LDUOSkshhj8yf/yPk/WSVEZ8LSo0KGx5iMb1+prN90biivrtgMbvgFiWgLtL7SpOtylL0lLCkURXp8CRXFkZ1mxG8pxwaE+HC0RVjym+5hmRVdBs1MtrPngOu+fuJ37t4kqrkJfH7YPTb0T2LcYZQZfA7b3nPmJf6PIK7ILhS02+d1CZYob2DQxAxM/nIc9mWsdgcI734mEV3OhxsAEHNnS+MS5ii6WmOYmXDEfhFk1fN0b9jdhd+8imwDlaR3a8AXQaizQ7z5gxl+slYbrp/8D9nw3/+A7X6f58DB0H9wOrpC2zuOiy47izp49e7D+u3TsPpLXPsVx9pwu5QsGK5/1mYkldAQ6z8VlYcn7V9g6LUlALX+neP6WiCKEEKIyIhFFiEoKi0MWsHk++HLHP8Q+TBeXJcJddLYzFBeO6RSKITm7lPzgnR2CjNkd0DACqFatZI+Z90nr/KhXLT9gy0/Apu+BQ5ut2HDGxuY8Hha8nLrCIpdFdqszgZZjgAa9fLsGKOTUq1cPY8eOxcyZM7F69Wqn8ODu8vfff+8EzrZp0wahoUXbB1j8cmf5WKIVQ7TsUyBgeC9fL+4y04bPHVy6Kup0pBvGXtNgLWBLihPSOgGY+4S5IUrqHKDYkrQOmPekBa1y1HCbc+219ec1ZOsNc0xYfBYnwvE949QTini8nicLgnDyDV1SzIjIX0By7XW+xgQzjpH1vp+wWpnYFbEEK1ZVQ/PmzVG9evUCTiyu4wXPm7uirNmzwKYGjfiXtUsFG/yd27cUSChklDnfg2p1gVrN7Osj24Bj++2c4fPYWHN48BzCbBW+/4UFbh9PCsG6H48is9eBE3oY30u+pxRm27ZtC3RpjCm/5OaDUPBZ/G9zAJ3xLrBrpp13Gw81UYTZNxk5rWyErS/MwuFkJZ436ODgeYNuJjrz8o9+J2zz4fozF5fLaZtkFgovjRs3xvEJQIKv5e/2fVtDnrS/GRRZPG12/J2go4tOmZIGklNE8f5dEkIIISoTElGEqKTQBUAnijfc7WzYx4o37w/xHlikUojgh/3qcSYUcCebhSwnSuT/4N36bKB+LyCsmo22pEWdE0r4AXrrRAtmLI1wwEKnfm+77T73WDsIcxBY8Bzn43bbB3wWQgwLpYjC1gpPW0hRREdHY9SoUU647dy5c53wW+ajMHCWGSm9e/d2hJT87hmPeLLtZ2DdZzYhxGlLyfQtErBAoyhUqxXQ+iyg46UWbsrvV1Yxha8RxaSFz9uFX5/U7bElZSUw+UYbOdz/IRM6inr9mFnBdVB/8HGEvRqBzJSiLSwsarm2Wo0zJ5N3YGlYtK2v5A22vr3hsbzO3gX2GPM8hpop2O6ej40/HTtRbLdu3dr5L9cf19aO6S6sfq/kE3xKA++DbqBWZ9laDLr157agUrZN5YdZSr3vBrpeb2IlYevLuk+BeU/kuNly4LSiPjz2Ojt/EU5x4nQwtu34dFpkhiB0aytk91iN6jERjijG6V78b1RUlPNeulu6sO1cYPnruVejA/C7803ooJuDrzmFxRVvmiDkDc+XU241RxLbetgORPGRrhCOui5wfnEBbcabSFvYe8nfPU87XXF0uNTul44lrnUPzAriWGY6pji+PratnWPpumJwblHOFIouTiucEEIIUQmRiCIqPWzbYJFMKzYDAdmywQ+/derUOVHQVDacKRbrgUNbc7/HoqH3XUDtVsDSVwuKKBQlhj0DdLjcxALuhjKjovvNQPuLbYQxHSGezJHT37DRsBQSuOPIopI5JGs+BqbfYxb27rcWPwmiMJy3hQGd1ezDOy+BgO83d5KZ1cJd22nTpuHw4cOOmDJr1izn60GDBjlrwwNbdSgQzX/GMj1Y9PjV4pMFJK0FFq4zV1CHi21aR0yrICxk/YCF228PWz6ILxdAqW/3CLDoX0Basq1RFs6F/a4fPHgQ69atw6rt6+FqdgawulGRt821TscTi1IGxNJdxPeOxSILRzpa2F5Gt4I39Xva7xSLyeP5ivtqA7bjULaN2GGY8Zo1a5xpVRRUmjZtitbN2mPdv1sj7UD5jc1hwbv0ZaDJkFwBIVhwsjymFHRRUGzlZCRenLyUr+x9o2upyzUmuPG8RaGMxw54yFoG+btMcYK/g3zf+91vt838qIKOCxey1zTD0AfOQseR9Xy6ilzhQJ+77NyQuCLnMWcBu2aZC4gToDziTmEtbTyncI1TZOFj5e8PHS2+4Lmw7z25t+sLX248X1Do5jmJj515M97QhUURpcv1QPc/2uvI+6T7kK/3jPvt74wv+D5YO5sQQghR+dCfOFHp4E40C+GNGzc6+Rds3UhISHCKmYyMDOfDb0REhBMmyt1h5mHQgcBd4so0qYW763RJxLQGYtsAnf4ANB5UcEfdA9thOl8N7JoNzHnMBBgKK/yAzVwKFirT7ra2He76Nj8VWPkusOxVE1FqNAaGPm33Q7cGw2Gd78ejwsH3mG6T9u3bOyNBGTi7d+9ep71nyZIlzvoZPnw46tati/TDLix9DVj8ou1alwo3cGyPFeN06gx5Amh+urWLVBa468wpUMytCKSA4i12rHoHiKwFDHwsb1sKf68TExOxYsUKJ/SToimJ6r8cro31WVIWedtbfzIXAMfZMvuEwkiD3kCvP5mTYeU7BQv4piNyCuXZeb/PtpKBVzZCYnQf57FwLfHxcW3x61WrVmH7ksNIn8LU3MJFFE+2Dp+3v+1QPJ5FLotvXw4Xp6hfCLQ4HUEFXXDegrAHFvg837D165c/2phqwjaus9kec55N80neZGIBz2905zHI13MeZLvg2I9MXKFbhw6+/LiORyL6YBvEFCLeEQaysiVm0jV5BTf+Lvj9+5Cd20pTGHSp8DxbnKhMocwmChVxkAtof4m1DU27K2+oLaGITjfdoR3A/KfsdeTfBP496HCZ3fbEq3y7Gil+81ghhBCiMiIRRVQq8YShemzLeO+99zBjxgxHOOHudGHMnj0bH374oRMqOmbMGNxwww3o2bOn018e1GKK21wjFDbO+8GyAliveYJW88MdQxYc3IWdcU9euzkL46an2O7u7Eftg3eLUUDSJvs3xQHCiQ889txvbZd+72JzqVREEcUDXUnMDzj77LPx66+/YtOmTc46ogDHwNnh/U/H+ucaYc2HroAIAyxsmfXCwmPI360YqQwhtCzy2fK16J9lO0qb03OWvALU7cLRq25kZWdh165djnjC945OMw/8/Y3uvR9ZC4/h2PqajsjDAtWXuMCil60Mp/wT6PcXoP+D9jt0cD0w4978obHWFlKnnWVgeJwHzn2GWLtM59PqIbTaSKSmpmLnzp1OkDEFFa6p7KxsZC5ugaxU38JOVH2g0+VAXDcrQtlexLYOjk8uKsuIbWKdrwLaXgBMvws4sLrgMXQSrP/MWjeCac3x3OJr9Hl4FBDdCEhclZtHQih2cjoMhTCPa4khrxTgmD3iLSRzOs62SUD8Y0D8QN8iCs+LhTkuvN/7lmeYU2raPQXdSYGAWS6DH7c2yvwhyPlhC1Fx4hv/PnS8zIQ1Ct/5YZsaBSe+vt6vy75l9rrTqcWWIl/XpcBFd6IQQghRGZGIIoIeFr1k3rx5ePrppzF58mSnePEXiiwMF3377bfx9ddf4+qrr8Ydd9zhjOUNViGFL0nqQSD1gBWBDMFkQdb3PtshzE84AwQb2E5j/h1fukm4E1yzsU09oR394AYgZVrBnUtPC46zo3o4OHri+R7Xrl0bZ5xxhiOqLVu2zBkRum93In58cDfSJzZE9vHArgO+pjMftCKH7VKFDHAJGrhmuOPva0e6LNpS5j2ThfTG27Dl0FJs27YN6enpBQKEO3TogM6dO2NXbA38fLO1mM16oPCdfo4Y/jYn44dZOyzE2drjuAry7ebzNr6/yNY7Q2c9cPqNtVnYG8rWDwaP0vHGdrHt27dj05od2PFVK2RkuXwWvqe/CTTqDyRvttez6XCg4+XAjPuAVe8W/ro0GmBjdfl7PDcnG8QXbC/h+mOrRrDAc4kv8YuuIYobHI1OwSRhrn2fIi7/TTecZ5wvA1IpJHtE3/y3z3eDodZrP/bxAHg+9SMklWI0nTEMkWariyPsBGgyGsW1YU8BHXn7fnSB0RnD83phv5O8jfYX2fQcBjjnH19M+Fo54cw+2vY4rpyOJuZRFeaE0WQeIYQQlRWJKCLo4e7z+++/jyeeeAI7duw4IaqUFF6PeQovvfQSfvvtNzz33HNOm09x01oqKhQ7+GGXdnXCoomBitx9zA+LQtrQna/z5X3EtrPikIUyj+Nl2h02yYGug/AadqHIwtHC/OC9bYp9KC+P0MxA4LgWoqOdFh4ncHbOXGQsbonjk7vBfbxsciu4U03hgQU7A0pLMsK3IsF1sOK/1j5WXiStd2H6/zbgeOcNeSam1K9fH127dnVEixo1ajjva9S51sKx9BUgs5CMCQ90Tvk1Lcdd8Pckki6BvwOxHXy3jlFQobAT526LLZs8amPeApwhyhyDO+dvNjaZIiSL4ZH/tglFCQuAgz4cJvydHvI32/0vjqN7gaSNwSWiOFPCfLgqGDQ78wHglBeBcR9brgePbTzYXs8pt+e21jhOimxr6/GGggffMzo7inpNfIkMvqCzjO4Ouv+YD7R7bvHT0Iq8vTCgQV8TyJoOK9xNmB+6QOr3sMlAvqCo3ukKE5rYvuYLBmEzW4VuqwJOIJe91sx58UX8IMukEUIIISojQfqxXQiD7TovvPAC7rnnHmeXt7QCijfMLqCr5corr8RPP/3kuBKCDZZnFDb8dTjwQz5dKLx4ihUW9dzhHfqU2ciX/zdn2kpO375nMgOnXYz/Dhj/A9CwLzD/HzaKlIGywZb5waycXr16oUf1c+H+dhjcx8r2CbCtgEUgWw+CEf66JW+0QM+SjjE+KTJDkD23M1zZ4c571rJlS4wbNw7nn38+unfv7uTceFxkzE7xjEgurgWitPB3jcGi7S+0DImiSNsfiuMHC/7prdXCcnI4znbJv20iVep+c1csecmEkhanFfydZlHN38EaTYAtPxb/WOkqYwtLAE6V5QZzXgp77yh88XxEBw7bS/gaUUBgGLQjfOS8XnwdKRh0vMIKfLb5UPhqczbQ5hw7righsySCAIWPJsOBMz+z9hs6jDg9rCTwfWUA9YBHgbO/sOfmr4DicYKwDbOw183JympvAqPPHJgQC9w993ugYb+Cr0WL0eZo8p7m4z3ljS1Hwe6wE0IIIQpDIooIWih2vPrqq3jyySedrIFAwwyDW265BdOnTw+IOFOuuHL60Uv5sFlcMFB2/ATLElj0ArDybd/OEhZkdCGwmA6JtOs1OcUCEB0hJ9jaoPa7sPU/jeFOKoEKdRLwtXOyRMogjLU84OhZ5uN4F5Cc3lTke8+pS1G2W09xoLgCk7vhbBugqMe16RSGOxugyeFhOOecczF+/HgnJJhuD18teHQYjHwZaHehOQ8CCZ8ni00KGf4UuZx65ev3kq8DR/QyS4jZJSegULXJREuOHs9TFLssy4htGRzny+yK4qCwkH9ceUWH75+vSS8UJ874n+UuTb0DeL8X8F5PYPIN9pvLnzXoZcdSlKIzhOIJc5vOnQCcP9Hap9gSRCG5sNeFr3lJW1NcOedgro0LfwWG/QNoPtrcQk4OEp+PZ6lSwAmz7zPMlZk1w5626w140PJHStpZShGkwyWFT2LiZDW+pnSh+BJA+b3NPwIRNcwFw+MZmsyR386/h9rIZq7X/DQaaC4YIYQQorKidh4RlDDH5LvvvsOzzz6LY8cK8RMHALYH3X333fjss8/Qpk2b4MlIcVkbjvMhvQRCijPW9VRgwMP2IZj5CRzru3Oa72BHsvJ/wOoPLGuFBd0pLwADHwEWPmcOlmCChcO6T1zYv7wc7zMLWPcp0OlKoGFvBBV87Bu/NXeSh3o9gDHv2zSP1e/53qVmbkSPW6045GtOIYmhxMxZyCPUhdhEKQa9MquELg+2uGyZCMx9IgwRS3uhcZwL4eFF/17y15aZGKe+DMS0BJa9VjDPp8S4zD0y6DETMXxlDfmisPYHihtsjXJGgud7OnTTsMBmIe9MXMn5PltT6HRgbsW6zywU1x8cFxlvJEhOZxSYWMzn78aiuyimBTDvaWDlW7lrZ+0nJm6NetWEM57HuM4Y0MusmXYXmABDJwUnZvFn8YNNaPGF48rrVPLH7cmIoljI8e8cu8z2ogOrLKyYmSmcpERnB9cnnSFxnU00YrvNybb4sRWMQgpH2nv/XvF3kCG6FI2Z+1MYG7+yvyPMbTrnm9xR0Vzr6z63trP8wjrzt7rdYIKLEEIIUVmRiCKCEoobjzzyCPbv31/m97V8+XLH7ULXS1RUFIIFFlhRdfOO2yxut7/7Hy18Nv2QjTPe+HXB69NhwuBGOk84AYg7uFm8pNmEFk6c4e6vMxnDj4yGigSfN8M7mSfjjbNzHFK0W4Q/p6PCOaYw4SrECmIWVyymPQUIQ0zXfmSvW7DodIRF4ME1ubv1LAR73gbUbmVOE1+vUdcbrEWMU2cY4knRoOVY4PQ3gEnXmpDioVFfYMy79rqu/RBI2mAuJ2ZORNR0Yfo9LkcM4dSV4uDryt+HgY/arvqCZ2zijactrSRUiwOajzKXQb1uJSt2+XvmC7aa8PXk86vRMDcQlWHOLceYiMCpQB7hgw4DOgLYzkKhM497pRgcx0wQrTNO4aEjju+/N8wTIhQ98xTzbhtZTRxHXo7zie8/BbuZ99t6da7jMiHK17jqE/cfbe/zycA1wnXKC38/OMWprOH7zN9HTs/xziyiWDf378Dif/qe4uSBgiVDZ+k4YXBxjUa2Lnkdii9Oe2eeO3Sj5bhstBoXGlTnMSGEEKKkSEQRQQenXPz73//GunX55o6Woevlyy+/dFoGzjrrrKBwo/Ah0nZdu63/IkqLMcCgRy28ctqdtltamDjDNh/mNHAChXfxwqBB7qjTteKMUQ2yhkHuWPsKSGUhwt1w5pcUFjDJ4pdBjXOfAA55tbc4hNgOMx0Y3NFmAXd0N7D1ZxOeKEBRPGARHUxjQSmkUQyiA4nhuPV6AvW6FJ63UaulvZYpu4EJl+SMB3aZIMFg0L53A9t/NWGO4kHvu619h2NWnYBkN7DqPeDMjy0jwgk83pIzwttPKNowz4GjWengoJCz6zcTIQoVyXKKcLaCtBwHdLw0J7w0ouSiV2HtFSk7gRVvAUOfBEa/Y64JFqnMtWh1Zk6IJ4tWt60filHM3Zh4hYmZfhOSO/Y3aAixSTB023ifbxwRF/Y7tSEkryOKeSJch1wfPIjC72mv2fqhS85zO9HMUjnVxvkW9joyQ4XicbDBtcm/AcwEmnKbicSEv1/eo7mLgoLy3oV2KRo3QtrsRsjZG5EV3hdud1RQ/K0UQgghSoNEFBF0bN68GR999JGTiVJeMHPl9ddfxymnnIJatfzY9q4AsPhsNTZn7GcxLT0sWLvdaAGNU/9kVvOiMh1oe2fhweLVKVJy4GhWFjQMTOVkh2CCln7u2OYfy0zbf/dbcsaghhS+U939RhOOOAUmp1Y5AZ0PZ7ydM0Z6g42fpmjV7iIgvj8w4wF7TVnY8JhgqT24K812BAprdToB2ceBI7sso8IXXB98Pec/7bUD7jbhhAIWW8goNu1fZsc1HWHi0rZfctcwnSMUqhoPsfGtTijm8JI9br6+zJ7ocKkJQJw8tWuW3S8zfjg6l2IZ2x7opmIeC10xzHqg28ZxJpXyPeJrw9v1JdiseNPWWLebgFOeN+GEv18Mmu16fa4gylYMtoew3YJ5HgyVJY5TJWecL++HY3nz309kzeBaYx7YbsM1wefrYcc0c3F1vdZ+r7ZONgGXAddsbWK7zqbv7Fg6n/ied7rKJjBxpDXbZngcz1UUjn1lovC9okAYbPlO3rB9iS4ettj5O2WoZLjhangQWWdPxpo9+5E+MRGnnnoqYmJiJKQIIYSolEhEEUEFA17pCtm7d++J74WHhyMyMtKZ1FNYACw/yHHkKcegpqSkICOj+E+SPLZatWpO5grdKNOmTcOqVaucscfBAD+7Mg9g+RtWQBSFE9rZyfrZOS7UV4AsxYWfb7SibtX/gP4PAGM+ANZ/bsIKC5GOl1sRyBYDJ5MliGDh7ARzuq1g4s41Lfyc5sFd7f1L810hBKjf3QQEZjNwGoWvlgoWbgyHpKjFQo0ZIixsmc3B0bVdrreR0LTMczec4lSwtFpQdOOu9uKXbHoTYbE/6P8K39Hna7R7Tt41xq/5vabDLTyWYkZcF2thoYBC50WdjuagYPsOf86pIhQzuNZKgyevgg6Tuh3twsfBdc5CnKIaM1hYRPP3IlC1ILMiYtvae50fOk0W/8sycpgnxMdAwa12SxsfzufN7/F3lXkybcabq8aDx2Ey6jUb/8vfV4pD3tBRwfUcTPC152Pm79jC53MFNb6GbM0Z8Ig9Z4pMfH3o9uHaZJDsvmV2LN1PHCnODBtOnKFoyfYeimRcu3Qk+RKbeb98nYNVC+DjZoYJp0fxd88Z9R3gGDEnz+W2tdgRkQh3thsbN25EamqqI6Q0bNhQQooQQohKh0QUEVQkJSVh0qRJjqjh4eqrr8bpp5/uTNJJTMzbu8IPbx07dsRNN93kjD6l4JKcnIwpU6Y4bpY9e/YUel8cl3rJJZfgjjvuwLZt25CWloavv/4aAwYMCJoPhSwAul5n/e/sg2chwZeOX3vDQtIpiLMLFz/4wZvTHFhocmecxzIoccBDuYGfdLBwzCoLvuJGvVY06DRhiw1hkTv6LdupLsx9wmyLYc8CDXoWPR61ZmMTD9g6wrwVjzOAO+EMOD3jXSuE+XO2x/B1DZY2KD5Wd07x78lHoBhVGBQD+PzpVPLVzsLX1NPuQjGFu+Z8LU7/r41Z5fQaiiZ0rjAH5PCWwI5WplhDV1FJxtmWFK6puO4FRRSKK2wT4mvDkdF0xNiDMicBhZzds+33j66dWQ8VvG22/tANRaGOa8mTq5I/bLQk7U8VBZ5PeC7b8HVuuxxfizUf2YQYtoRRXKKwxpY8uk2cyTHu3GNXvmMOKAqVFDEpyHEt8Vg6qvJDAa0nA5AbIOihwMZ2TQpHi1601sGThb+b/HvByUMNhvfEnHnHsGzZMmRlZWHXrl2YMGECRowYgVatWiEkJEhOakIIIYQfSEQRQQNdJlu3bnV2uQiFjKZNm+KGG25AfHy840bJDwWUL774As2bN8fq1audQFqOQn366adx2mmn4bLLLnOEGW94u/Xq1XOEl65duzpjUwmFmwULFjitPcHS0hMaZpMVtvwEJMwBJlxsRVl+pwlDGN+nGFAMnuux+GDg4Io3bEIJnRsUIPj94c/ZLnmwwV3aYzk5xSxAmcPBopoFFN05vrICZtxnBTeLrX73WfhlfvhaUGDas7BgawVFLU8bBguSlD2F54lURPjcWdwyWNgf6MZhiwoFq/w4jhJPXofLxBQKB8xzOLzNJowwO4ZCAR1PdE/9eIVNMQkmuFZajwM2fGnPJ3/ormfsLNtUPOGpXa62AN/dc3MFuH35nVE5t0ExYfX7JrgUcFbkCDK+xgUHAzVbZaLxjdtw6G+NgBQmF9uoIr42TsBxjrvIed4+fo8cx9Nscz0VdZyDy3J32PIVLM6w4mDbZt97zGVHYZ2tnqVt7+FtMRyXzp7Y9nyBopx215o1a2LOnDlIT0/HwYMHMXHiROf7nTp1coSUYNmAEEIIIYoiSD9KiarK9u3bcfjwYYwdOxZdunTBueeei169evl0lLAd57bbbkOLFi3wwAMP4L333nNCafkh7/nnn3ecJgyK5fc9x3PXjMIJvz9kyBDHteINd9d4X8EiovDDP+37w55x44drMpCyKaJYgcRv3FboeYo9Cg4MDW17fnBa31nce/JQ6MpxilAWbs18t+nQAeGZAMJi/9jVvm+X7RT/61TQocEdc06lYRgms1D4+jPjoyQjqX9vmLvBYqqoqUXeeAo2X0KRZ804r3VOSxWLfbai/XBZjjODwbLvmjOFLVR8/TwTWoIFPs9mo4DYNnndKGxFYXDqwIeB0W/bmF46xJj3QaHyl5uBVO9hZEWtk0LEgTrtgGYjEJTw3D1//nysCV0E1znt4f5qJJCaTzgvShQp4XHMXxnyhAl/wXg+84XzPELNrcTpaVxvaz60STtFThU7cQPW/tRkqGVoMdiYQqq9Phw1Ho6+ffs6f2PZ/srWWbbD0vnJzQf+jMcIIYQQwY5EFBF0obIMq3v00UfRuHFjZ2ersIDZunXrok+fPs6I4vfff/+E44Qf6iicXHzxxWjbtq1zG3SZ0HHy4IMPOt/jbpmv3BQKKAcOHEBw4UZ42/3AZb8Bn/cA1rUA3IGtCiigtL8I6P+g9d8HI07+RRmcEVmceIQmD3RZMDuE7Ql7l1jB7HEpBBMxra3QP57XzFUofB2YbeLLPeK4VDLMzeRpH6OwxGBQtvp4YNsQXy+G8jKzhq1XwUZUHQuPnX6PlwDlBpa+bP+l02bIk2aAoHjEySps0SkOOpvY6lNg9Cx/R8Oz0e6ybNRsHhZ0DkQW49OnT8fatWudVhF0WoWaRxoh7cceTiZPoKnZBDj15Zzx1ZVEQPE17psuxXbnAftXWCjv7t9MzOOazGIrncvEXo5lZ3gxnTm8MAeKIqev1yY0NNRxgEZFReHXX391/l5SAKM7hbllgwYNQnR0yfvlKLzy/MCLk1cUZufLYGl9FEIIUbkIrk9TosrDD2S8XHPNNY5zhILKiy++6ITX5YciyFdffYWEhARnF8wDBZIGDazJ/dChQyfyVfgBj7kqERERjqDy1FNPOW4Xb3gMs1H4wT4YbMl8nLRUT5r8E5KjE+A6dy/Cvx+D7NXNAiakUDRhgO3wZy1gMFhxJrHE5rb0lNV9MB+l3/02XYYF79Q7c1wWrpwMloq/rE5AF0hM89wsmeJgWw5Fl5gW+YJ6OYq1jY1L5jGEwZ8UUXwFx6YlWyHFaT7MEgk2KDpSKGH2CSfMeBwAfK7znrJRxzUaWeHI6UP+hueuescuBe/QjbCeW3CwwyYcPtIvaKam8Py1f/9+/PLLL9i5c+eJ827Tlo3Rf2wjrIo0cYmtdQEhBKjdygKf2RbF96kywyXASU3N6ltODAUpnv841YmOMAoUFFB4XneyinKWTHFLhxsTLVu2dBydP//8s+Pg5GbHkiVLnL/FdHzWrl27yDXItU8hlcIgW1EpNvM8ye8x04vnUgpBFHR4TuV/2QrI9ywIlrYQQoggRyKKCCrYZ80PY8w3IbGxsc4upS8oHjz77LPOB28KJRRaKJ5QGGF7z7p165zgOw/c4eROJ+EkH7YN5Ye3U56jlU8GPm+2I7EnnUISCauXih4PHkHCy27sme8qeQuPN8ytiAV632lBssE8ApTw8TO/xHuEaiDhbfd7AOj0ByD9EDD7/4CVb9sYVg8MuwymnVW6d1qdlZPV4UcbBUM8OS6a42o5jYgtVIQtAU2HWagqWwvI3oV2k3HdbDfcO7uBDgH+sEZjK/KCEbZFDP4bMOGSvE4bp01ub2CCPz036GqQjIyR07B6SyIOfLPbyaho1qyZU8RWRDHFM2Vty5YtTisIz+We4rxDhw5OEU43Q/zr5kTixJnSTmnywOKbLouhT5s4F0y/hyeLZwm4wq1Fr7AR5SW7TRfq16/vCCl8D5llxvd1w4YNzmYEw+D5c+/152nz4/lx0/c2+W3nTBNO6IzxdY6hI4UXirCcosQMG7atURCrgEtbCCFEJUEiiggqaBEubIyxLxzrdw5sATrvvPMcgYRiDP+9aVPOmAc/ofslGHq6+RpRBJo8ebKzC0josGHOS8+eHZB2SogzqWLlW8ChrSXP4aDgwHG0fe4F4gdakRvsH1i5i8kchJ3TA3/b3CU9/Q2bHrL+M2DhCzmTeLxELO6s8uf88B80uIDmpwNLXvbPjcIMGTpQOl0B7JwG7JhhrzsnoHBENl8XTzHM8FQGX7a/2PJptky0NgOGYvb4o7UGsfAN1pBU/r406g8MfQL49c+5bUyBJrwmEHf1Ruyqk+Tk73A8/Pfff+/kU/To0cMZ417R4Hl7xYoVmD179gmRnOev3r17o3///s7XLL4p4nK8MV0jC56z311fU3aKgmuIE2aYPdPpSsuQCvZzWUWB7xHzw0aPHu3kpHgm9+zevRvfffcdRo0a5YS+eyb38Pebv+cLn7XR1P6MYqYQywvzhQ6ssQlMna+yzJZgnEIlhBAiOAjSj5+iqkInSUlEFG++/PJLZxeM/dpnnHEGHn74YUdoYD6K98jkouAHQu6AVsTdWw98fZj7Qgs8RzN7+tRZfPTs2dMRgrjTyJYShnNu/MYmhXAcatrBwgNmKZwwy6HxUNvt43+ZbVGBX4oSwWKcLTYMWvQ3KNUfOHlnyFOWBTL9XmDtx76DaumqiOsSXK8nH2u9rkCL0y3wtTgxjk4TTnUa9R8LT03aAETUMPGKbS3L/5N7GxRJZj4AjPwXcOq/7Vi+L8xhYXHFwN525yPo11z7S4D0o8DsRyyPIpDw93PAIy50vK4LVq7LcqaL8dzAy8yZM52Mp8GDByMuLq7E5zRPu0VakglfdM4wkJnPiWueU634X7bIcXS1vzefmprqPM5FixY5YjfhOXfYsGHOhBeev7wJiwSajgDq9TARhb9fu+eZw8tXNgyh6Mv2FDq/mOVENxXbeKqS+6S84Lri+zd8+HDnvwwHZkYK3UU//PCD833+TT5+IBTzn3Y5IgidKKWBf7vo6pr/lI2tZjAw/07RMSeEEEIEEokoIqho1aqV8yHa35YafoDziC4UFXjh90499VQnbPauu+7Ct99+W2DMcWE0atTIKTgqKnyuLELYh06XDf9NAYW7zrx4FyAsduh84KXXnyxccP8y4PAW4Ohem1TDD59sO+COXmx7m+jAYp/FRs5AhkoFd7SZseFvxoc/sPWk5WhgxdtmT6dIkD98lxoeHRa0pAcbIeFudLolFRumZCJ9R03smuVyxCLPdKP8bJ4IfHe+ZYLUaW9iyar3gTXvFwzgZRbCt7ToX2YtFix+KbZwRC1Hq7oqwV8wPifumjPfYeZfgEPUPf3TdAvHZb+nQ/5mr11IeDX069fPaWlkQCvFEwrHbGlMTEx0BIrWrVsXO4KWp1IWqsyr2TTB3ovE5TZByWm38IJtVrXb2rrm+udEIjpHChMqPAGynOqyZs2aE+dtBoRzHD3H2XscC77gbbc+x8buHt5hrie25h3ebuIO3QrhUUB0vIknXE8893GyVmU8l1U06OAcMGCA40jhGmRLDy/8m3xgSxoOvt4HWyeVYkqcD5iXxPPPD5cDI14E2o633zMhhBAiUFSCj6CiqsAP97T+8sM0e+WLo02bNk77DndcORnAAz+cczeMBQSn9zBE1l8RhTkCvkJsKwJ8Xtzh40SE9evXO/9m0UHL/sCBA322IXnqJe5YNx5krTn8EMuCgzUMf3yiZcIVXC6J0sCQRbob2J4SqFHDTYYBIRFAyzHWvuGL7b+6EdeJr3PwvMCeIpftYtPXTUP66OrAR2Oxf2kk9i8t4nlkA3vmW7uOU9i4zWHCwscXDJNc+Ly5GeyOgQEPAw36VI71yOfA3zGuO7aVzHvC8iBK64bi69R8NDDwIaB+b88uvOvE+ZNj4dkms2rVKqe1gkHddARwVDyFVrZM+hJS+HYnrbPQW7o9GEBaVKAr22rYusXLuk/NbdT1BqDDJQUDlH0FyBKOpx85cqTfThnPCF8GFzPw2JnokpmztniTIXYuc0TgSrB2gglP/k7nzp0dRwrfa/7dzTjmxvKXqiFzWmAEFG8ohk/5o33N36/KHhQshBCi/JB5VQQVLAIYLOgPDK37v//7P1xxxRUFBAT21EdGRjoFhMcyXhwUJBjIWBEzBAifx4wZM07s4vLxdu3aFUOHDnWerz84Iy3DzCnBXVtOUnGs+FWk6GBR3/lqG3HqgcWsk+WxvPBCn8IA200oDqTnayFgEcdsD7ayZKUXvGRnuFG73yHE9vLR41OB4fSrpUuXOvkau3fvQmarTQg9faEzStcfGBTLdhC2NhX6up44GMhKs9eLLWgczVrZLPos8OiOOP1N4MxPgBajSzbtisIEg1HPeA8Y8y7QoG/B14hFLCfzUJigG4/tiZ5zB4Vliil0qeRpmXTbml7+X+Cbc03QorBVkok4fJ8TVwAz7gG+v8AChj2hwhRy6JqjI3DHjh0n3HM8d40ZMwb16tUrXfskxakQO3/xfMZzGVt/+JpUhXNZRcUzuefss89GfIOmiFzUHxkzO8KdVTZvCicNzbjf3Gul7AQWQgghCiAniggquIN1zjnnOIGp3qGxvuAHck7b4QfxDz74AIsXL3bagCiCjB8/3tkRY7hdYdN98sPi48wzz6yQeSgsgn777TcsX77csenzMbLPnP3m/goowoorFrJdrwPm/t0KPWY9cIJKUbC4n/N/vn+24B928Y0brropCL3pezTaF+kE/9LpVFTbwu+Np+2C7i6uN8/vYWRUOHr9MRyJWW5s/THwu8osiBliy1HabH2prDBol20pfK50cez6zSaUMDiTGR+e15WiC1tmmLXTZKjl+TToZZOOimtNoYBMhxqFZrZW0P3B88bmzZsdYZnrsH379ggLC3dyWmY/Cqz6n+8sn5LA35OdM4CJfwAGPORGhz9kYc3GFZg1a5aT0+J5bHQI0hXDr0Xlw5ncE9cArQ+ejf0TqwHpZftRlGPCZz0MnPWpgoOFEEIEBokoIugYN24c/vnPf54YR1yUiPLKK6/ghRdewCeffOK0udAy7hmRyZ8/88wzToaIPx/6aIPnDlpFdATMmzfPEYlY0PKxspWJu80UjCqi6FOhcdkYXo7tZY9+oNp6fBESlYWwc+bhWK0EbNliLQ0MAO7WrZvjnipN2KenPSZ5M3Boi+WM0AnAHXnmRtRqblkVDP305FP4ezcUUDhZg7kVLLw91KlTx3FpMbMotVMIpt9nLRyBElKYfUIHyoh/Bmb8akWH7wcFkkYDgUYDgF53mPMj9SCQftg0EifoOc7cU6Vpt+Paio+Pd0TpuXPnOq4iisyHDh3CpEmTnLHoPdoOwtz7q2PjV64TzpFAcDQBmH4PsGnjVmxvMh3pGcfzBMhyDL3OW5Wb5I0urHqpBtxp5XN/zEjhSHlOlFNbjxBCiJNFIooIOhjuevPNN+O+++5zBARmnmzcuNGnGMLwWAYn3nDDDU7PP0UFTgV45513HIGF2SG+YDHBwoI920eOHHHuk8VtcnKysztaUZwCfJwLFy50rPgeRwCFntNPP73QbANRNHzJuFs5/B9Aym5rQygLIYWFb5vzslHtglCsXBvuuIk8wZoUKJhjQ6eAv+8hWy4S5gFbfgC2/ZwjnqTZhe0yvBmntSHKCnDm37Q+G2gyHIhuWHwBzt+11atXO44n/k4Qj2DHwpcBoPw3RY5TX7ZJQ/6OPi4Ul7W0sH2n5+02UaUqLWnnufJ9Y1tKeI7LpAwmp/D9owOKWSk8P/K8smzJcmx/Kx6HvuoEdwAFFA90tWx7qzFcF7UE2qx12nbYYlRcgKwIftjKx/YwBhSX232mA8tft6BljT4WQghxsrjcpZ0XK8TvCMWNiy++2Amn82cJs1CIjY11BBDutPL6xbUDeeB1LrzwQqf9hy09tJqzX5/fL41IkZ1l4zc5AYdBjYc225hQTrdgwGvNxhYwSccAi3kWvb7uho9/yZIljojkyXVhZgzHN/NxSkA5OSg87FkA/HyTCSnF5naUAAbNtrsAOOV5IDIu08mEoDhBwc+znrleKdzROVVYW4MzZjbVRrsu+bf1/R9P9v9xhEaa2NHlGqDdhb6t7p72HYqKK1ascMQUQkGSwmTv3r2dcGZfhVLCfGDxP23cKF0UJRGjOCWp6TCg150WyHsiWFaUCXyf9+7d65xPtm7dhtB1bYBPzoD7KNde2Z1LQpsmocEdizDqDz0QF2dCnKi88JyVvBH4ahyQvCFwt+uEVLuKzurhMUOeBPrcpXHWQgghTg6JKCIo4bJl+wqFFBagZYUnTHbs2LEnxip7gvEGDx6MBg0a+LVr6kyJSAcSVwPrP7XRoBwpzDYLfpjztGE4H+z4tctElIb9bDwj8xGYleD54EcBZeXKlU6LkkdAady4sdPqVLt2bRUiAYLvy4HVwPS7gW1TEJAdebpA6Kzo/6CNj/YoC3Q5MWeEbg+PwMeATYoogwYNckQV7/eVj41tEcxuWf0BkGHmkFJBe3uzEcDAx6x9xBNIypwMFtZTp0512t+823foXmjbtu2JqRuFrnuKKXOBzROAzT8Ch7dZmw/zMTzClCcAlI+Dob4tx9qoWrplKDhpOZfjhK+045j59Uqsvr81MndygZb1i+9G/GBg/Hd0GumNruzwnMB8nUnX5RVVm44EWpxetLjBfCqOij+eb5ger9P/r3Zemfdk0fff7FTgrM+ttVEIIYQoLRJRRNDCAm/ChAlOq86+ffsCfvssDPv16+e0/hA6BRi66O1uoSuFAY2FuVI8v10sxBf/C9jwJXD8UMmyIuhEqdcd6PUny4UIicx28mAYrsuRxoRiDkNvPS0VInDwPWS45tKXgWX/tQ/ypWrvcQENegJ977PgULpA8o95pVDH95ZtFRRVPPB9pWjXrl27HNHO5YSOTrnNWngCkj3iAmo0Bob9A2h/gRvZrixH0GHop6d9hzD3hJlCJVlrfA0pQGUcs5yWg2vsv8cP22vJfBaOpWVIKkfhMg+EOShayuVPxlE3Jt3gxrpPXIC7fN4A/i4wMLjHrXIIVHZ4LvjhEmDdZ3m/3+8vwKD/y8n3KYTEVcDX4wq2AfHv4/k/AfuWAl+NKfr+ea65bC5Qt+NJPAkhhBBVHokoIqhhawGFlDvuuOPEeMxAQAcAJ1QwN6VTp07O91jUMsCVI4Q97g8e16xZM6fAZW5KfldKxlFg7cfAvKdsQsDJtISw1YcZFvE3bMTM5RNPTLNgbgYnEFFIkYBSdrDdihNSVrwJbJtsIoA/4gVzLOp0sF58tszQaVHU28Q1zIBZCinM+jkx/SYy0mkp49SSY2tj8MuNLuxfGfi8FuaODHgyDcmtf8Oy5cvytO/07NnzRPuO1lrlhC1hX55hAbblSYPewPgJls8jKi/cRPh4MHBgVd7vV28ARDfycW502bhvOvfmPwUseNbcbXRmxnWziVQcS1+vG7B1cvEiCkJshHj7CwP9zIQQQlQlJKKIoIdFJvMaGDS7YMGCE0VfaaHD5KKLLsJjjz3mhBx6F4u8bRa2LHDpSvH8+nhcKZyqwkBXfvJjNgWtxcteMzElUIT23ojMsyYhKzIFdePqOgIKp2yoqC0fOKWEOTac3rNrpuWmpO6373M5OK0pkRZeyLGzvDTsW/LRmnQZMYOEocHeTpD4mNbI+t8YHJgX4JRRL0LrpSD7D98go94up4hhK9HQoUMdJwyFQ1E5YV7TtDstENiXOMddfBa6XNt0ER3ZbgHGvsTE8JpAjZxjGXp8mMfuLVx4ZCvX2V8Bbc4O/PMSFYcDa0yk49rxB7rTOJqYbs7JN+aKe81GAaPfAiJr5Yz7jvZPROH5mW2LAx8++ecihBCi6iIRRVSqUMRXX30Vb7zxhrOT729wrIewsDB07NgR9957L84///xCp9vwvhhOS+GG7Q4e0YbHMtiVxWZcrYaYcW+I079dVNBdqXBlI6TbFkRfOQdnnD8MTZvlFXpEOcD2lJzwWRaF3F3lhe91WHVzc4QzEDi05KNn87essVWNYZ9btmxx7it8+lC4f+4PZJdl34MbIZ22I/uSCWjavp7TvhMXF+f8RGut8pKSAHxzNrB3YcGf1e8NDP4/oOmI3JYbTq9a9CKw8i0LOPZAh8Dgx4Hmo3LHyVJAYUsjp7L4FJVdQOergNFvq42rMsNRw9+el9MWWQw8l1IoYTbSF6MtiN07eLpuZ2aUAbVaAKe8YK2NxTpRAHS9ATj9vyf3PIQQQlRtNOJYVApY2HFE50MPPYRLL70U7733npMZsmrVKqf1xpdWyOvw+9xl55SR8847zxFPOGqzqLBYXo/TbziOk6IJW3wo4PC2tm7disR9B9Fk87nY+m6jwAsoxB2C7OUtEf5BPGqMq6ai9veAwkjO7jlCger17BJouA7ZpsXA4KVLl2HpF4lIm9OjjAUU4kL2uqaot/hcjL29DqJjtM4qOzxFMmvCu1D1brU49d9A7TYmmuxbYm1pXa8Hhj1tE6HWfGDHRsUBI/9l7RVLXgH2LrAWnS7XAYP/bq6UFW/4egDW4sHJZbwNUTnJPG7tOMXiAtqeZ608C54pOMmH62TXDPu6TicLqvaXQDpDhRBCVE0koohKRUREhOMmeeKJJ3D77bdjw4YNWLRokdMWsW3bNqctgu0InGDD1gSGwnbv3h2tW7cu0VhgHhceHu5MTmEWClsumJWSlpaGrO11sOXd2sjy2pkNOO4QHFwShdmPAaf9x/rDReWEa40ZJJ2b9MfKH9PhPux73HHAyQrBoV8bY99vNi1HVH4ObwHSfUx5ajocqN8DWPg8nHOOpyVn7yIL9Ox4KbD+Mytk4wdZ+9qy14HfHsotmHfPAy782Y5d/R6QZZnYeWBrEN0wElEqLwyO9biTioLTcziKOHkTsOrdwI6YZ7ulEEIIcTJIRBGVEu7gMyeEAgdHsRZHaXfZvV0pLVq0wOxfluDIR4ORlVQN5cGm72wsJIP1ZBSo3C6BTV+HIGVj+awrD8wfWPEW0GSYBRuLys2hrTkTjd0FnSih1SzPwjvTJGmDtfFwbHdIpIkodGRxohgnMHk7DpI3mgMgLNqKWF8iCqdgpSWW4RMUvzs8jxQrYriANuOB2HbAL7cCR/cE8AG4gOj6Abw9IYQQVRINExSVGooc/lxO9j7obmnbti3aHRqP9JWNcyqRsifzGLD4JSDtYLncnfidYLvExm9zC1hmUnC3nmGKRcExwSyAo+oXs/vrKvyy/VcL0hWVH+b6+DodHlwHpB+y9gpmUXBdsBBuf5EVxWzvyUixY5M2AmlJNo6da4/HhkTYNBRmBXE0ty+3C8lMs3YPUXlhMDHzoooisjbQ5Ro77+yYGtj75/qu3TawtymEEKLqISeKEAHiaIILmz6LAAJoO/aHg2utwO5CN4pk0UrpQqGlfd/S3O+xCBj3oblEOP3Jl2W+1ZlArz9b6CKdBSyEF/8T2PZLXjdBowHA8OeAMB+7w3QaTLrehJR63cvoCYqKQyHnrp3TgIUvAr3+BFz4i436pkDSqK+tp4Uv5LpXGBzKMbR97gYumAwkrgCqxQGN+gM7ZwLznyl6LLei7is31eOAWs3t3FIYzUbayOulrwIpOwN7/xSWmdcjhBBCnAwSUYQIAPzgv3uOFaoe6BLglArmDBRmR6YNPqYFENPSrPC003PiRYaPnVreXq2cY1kEH9pix7LlYtO3QIeLi3cmiOCEhejxJPMOVqsNdLveRA2O9/QZyHgBcOrL5g6gAMLvNT/VJl1MugHYOjH3cE64qN8TOLytYIsF3SvMItg5A+j5JyBE040rNTwf+RQxQoCsNNvFZ6AsnQKh4bltGWFeXWYur2NrNLaxyCHhJtLR5MJWn8IIjQDCIgL/vEQFIsTGE2+b4ltM4zmn7Xj7G7fus8BmoZC4zkDNpoG9TSGEEFUPiShCBAB+0Nvxa94xn8yRGPsBMPUuYPW7Ba/DSRdDnwRanGF2d2dsbhaw+Qfgt0fyTsngzt2QJ81d4BQuPDYb2DrJjmWRyzwBiSiVE+78NzsVaDnGQjsb9iv8WE5C6XuPCSI//gHYNcsK2qanAOM+Bvr/xdaqRzChKMdxo1+fCRxmJkY+uM6O7DQRR4GflRunuMxf2IYAnf4ADHzE1hKDZZmNElUH6HgF0O8BYMS/gB8vA9KSrcVn8N+APQuB2Y+YAEjRpf0lwICHTNz7/kI7X+UnMtYuonLD0dcLn/O9Bnj+orCbtN4ugYQCH0d06zwmhBDiZJH5X4gAkJ0J7JxlX1PkaNAH6PcXC1H0lTHA3dlhzwCtzzHR5OcbgIlXARu+BlqfBYx4MTfIk7vDFFDaXQhs+xn45SZg4h+AdZ/a+MeRL9lO8N7F5fucRfnBUE5OR6HNPSLGppgURr0e5lLZ9D2QMDdXcNsxDdj1GxDXJdfOTocAnQVHE8zRxOPyXwjH0rJAFpWbmFYFQz+ZX0ERhTkmFFC4ppiPQiccxx3T1dR4kI2ZpZOEx1Kgm/N/JrowZ4UupyUvWRA2z42FtYYxlDaaOSqi0sK/h3U6mJjhKzqMzszarW3yU2aARxFTzOtwqbU7CiGEECeD/pQIEQA4UYK7+X3utQDFGk2sGMjymk7hDQvipiOBjd8Av/wxp1UDVpBQYGk2Amg8DNjyg+3KUSzhz365OXf3bstEc560Ggc0GQ4cWGU2aFH5YCG6/E1g7cdWeFBQG/6s72NZ0HIqyq6ZeaejUBChiML1Ur8XsGcBEF4dqBFvbWFcd2y/oLDCYNAjO4DsdLsu2zN4EZUXp1WnqTnkeC458f1QcwcwvDplV8Fga7pSKAaztYw7/dHxJrgd2Z7v2DS73fYXFz6SnffthNGKSg3/bnW/yZyUFG+9YQg2nXcUfdni6g90gDLc2LudNj9cmwxGrtf15B67EEIIQSSiCBEAmHlCNwozKFhUHFgLxHUC6nbxfXydjpYRwJ1Zj4Di2fHfPsXcKNyNY8Fcp525UTZPyGt/5rhQhjqyoI5pDRzZZXkGGnVcCXHb7j8vhCJHYbAQ5VqkCJIfFrbchY2qZ/+mU4rTMiienPWZtY1xlG3qfnNF0XJPl4qoGlBQYz6Ot4hCIY6uE7pHKLJ4ryu6VtgORoHl2P6crKZNQPxgy2/i9U4cG2FOF67ho3t9F7kU+OQSqBpwjXS8HFj+37xB1xu/tktJYO7YV2OKPobrtNcdOa2zQgghxEmidh4hAkDGMSt0+YFw8nV2Wfd50RN15j0F7Jmf9/tOGGO8FS4ca4ucySzznzQXQd6D7Vh3JnD8YI71WZMtKiV0ifhLtVggO8vcK/lxvufKcQKE2I4wRZSazYD9y4FZfwVm/sVGi/a81VrFeAyFlfxtHqLywbDYDpfkfa/ZmkMBl+2FAx8FGg20ljK67brfDLQ+21xPzK+g827TBGsvZIZK4yHWQkF3StfrgXYXALvn5hVpPHANOi0eokrANcY8nQa9yv6+uAnR/69A3U7aZBBCCBEYtOcjRADwjBbmjppHx/DeXcvPzul2yX8bbMvh7hx3cJ08i5yRobzkPRhoPBjofBVweIeNDm3Yx3ePuQh+6C5x3ls/RDIKKDzO+W8hOKGy2WalX/46kLgK2PiVtVyQ9V8Ao9+xINuWY209ciqQqOS4gPhBJn7QEedpA1vxtrlIeG4a/73lo9AxQoFk7wJg9qO5bRlrPjAXXZdrgHO+yXssWy5+ezhHIPa+21Cg42VAzcbl/5TF74Nn0tMpLwCTri165PHJijU9b7M2MiGEECJQSEQRIgA4IbAnIWBUrw90uAzoc5eJKXP/VvhkAk4W4AfCPvfYji/dAwdW2/QV7bJVThgG66/LiNk8LEp9jT+mA4XinqcdiMdy/fi6DRbDLUfbTjGzCTQ1pWpAsaPXnyyo2tNqSJcbHUobvjSBha06/Nn+FdZSmLovbz4FJ4Zt/BZoMtTaKI4ftik923/xPe6dImHXGyyPR1QdXDmi3YiXgF9vtwDtQEIXXbcbzfHC/CchhBAiUEhEESIAMFOiNO0O7M9ufhrQ914bXZswD5j7uI0sLnBsGND0VBtfy35yTi/gsdwxDgnNyVARlRJO06EA4qtFJz8cR8y2DBav+R1MXCN0oTBDgHDNcl2x8PVM4vHgyd9hlgWLYQozomoUti3GmDNk2Wu564LrhtN2eCkOBhInzLFLcbA1iKOPazU7+ccugg9uGjA4fcz7wLS7rMW1KBenvzD3iX8re/5JrYhCCCECjzJRhAiQE6V2q5Jdp1odYMjfgHEfAtXjgOn3AN+dZ1MJ8he0LKCZR3DmxxbuyF3hb862kcc81hWWO7ZWVL6iluJHYWNh87NjqgUMU2jz3tlnIcF2MbpQducUt8wJuHKZfT8/8QMtm4ftYhytLJdT1YHCGnfvm51a9vfDtp/2F+W2RIqqB88tjfoDZ38O9L4DqFa39M5Oir3M1hn3EdD7Tjvv6dwlhBAi0OhjixABgE6QkoQihkUBgx4Duv/RLPLfngcs+0/BrADCUE8Wu5wssPkH4NvxwJJ/553qwxafwiYBieCHrTQcI+uPG4TjQfcuBNqdBzQ/3SztXB89/mjCyObvgWM57RfMqGCwLIuN2PZWcDCEscUZlreTtNFGG3P6k6hieRWNgVOet+K2LLKWKPB1vAIY8Fe18QhbcwwgHvwEMH6CnX/opvPLAReShci6WWgyDBj9FnDmpyYAcl1JQBFCCFEWqJ1HiEAQYpkkS18uevysB4bCsoBY/znw659tNHJhMDC263U2IePX23y3dNAOzSwDUTlhIcDJJpz+lLSu6GM5nnj+08DpbwCj3wT2LzNhhE4lCiwLX8i1y9PJtOpdoNMfgHO+tvG0FFLiutmanP+UjdB2Mn9ElYPC7Oi3gSm3WYthINosCNdjpyuAwX8zgU8Iz3kuLBKIH2B/95gLxoB1tpDtWWTnNo5vZz6UK8SNzDr7kN12KzJj96Lj8LYYcn5HZ5KZhBMhhBBljUQUIQIAP7Qx06R+T2D7r8UdbBNPWKymHTQru6/QUOajMDCWroCw6uYeaHt+wWOZq9L5SsuuEJU7d6fX7cC0e0wYmft3H2Ovc9g6CfjuQmuVqNPBMk+WvAyseMMm7XigUDL9bpsUxTVJ9wHX5Mq3gLWf2HXpZlFRUjXh+16nIzDuYwu7XvUekHHkZG7QxrKzVYjCsLIqRFGtXhxJzPXX6UprW+WkJ14opDAodt6KNVi4ZD7gciO1fggianWASycrIYQQ5YBEFCECBHfru95oha0zQrYQ+BmPO/1s6el5u+VX+GL6XcCBNZaFwWKj+82+j+VHRtmWq0bLWMc/AFsnA5u+B/YvLfxYFhwM9dy32NYZ/51xDHBzFzcfFFjWfWrTVCjEsUDh+mXGT/8HbQKUqLrwvMLpYcOftZHXi/9lzgCum5IQVR9oNdYyL+p2tiJZCH/WHzO/SFRduxD+LWxypBEWLrU/igcPHkRqaiqqV9cYHiGEEGWPy+0urIQTQpSUtGRg0jVWkNZsYj3dB9cDx/KN9WzQGwgvpkUieQOQssvcLZxg4Yvo+sCQJ3NCbSWiVHp4tj6wCph4peWZlBUMPR75MtDhEolzIu/6Y24TBbr1X5iYknrQWgzzC3QUfqvFAtUbmvjS5hwThCXKiUDAj66JiYn4+OOPHfEkOjoaF110EerVqyc3ihBCiDJHe0FCBBDPFJ19S4HDW4EjO3wfx/HE/lJYsczAPYYy1tRo0CoDawPu4lPgmHRt8fkopXVUMauCGSyqRYQ3XA/MXuIIZLYZph6wlsN9a1OxbP4apB5LQ0R4JDp17oi6zaujdhtrCWO4sef6QgQCCiV0ncTGxjoiytGjR5GUlOSIKEIIIURZo+k8QgQQp1WnKzDsadvNL7P7CQXanQ/0/BMQyskWKk6q1Bpj8OLY94EGfQI7GpaTeka8lJNXoYkporD2Cl5CgOr1gKbDgZYXpiKz/0Ic7TELqb1/Q/trjzkuJoaDRtTIvY4QgSQqKsoRUTwkJCSccEx5X4QQQohAIyeKEGWQXdH2PGvt+e2vtlsb0NsPs53gU14oW6FGVFxYwFJAOetzC/xka0X64dLfHjN14gcDgx4FGg9RXoUoGdnZ2c7F7YxN4fqUYiLKnpCQEMQ3isemtduRlVgdu6dGYNkaCz/mRgMn9dRqBkQ3BGo2tVHxEvOEEEIEAn1UFqIMYFHKyShsjZj1oBuH2daTffKf3kKrWU4Fc1D4wVAfCKsufO9rNQdG/htofSaw6CULki2JmML1FNMC6HoD0PFyILpBWT5iUZnzKTzxasqjEGUNl1rGUSBxOZDwbnuEz2sM156aSNgXhQR33vXnOKYaArFtgKanAG0vAGLbKptHCCHEySERRYgygpNOOlziRla9ffjluZ3IntsB7sOeyQElLDRCbCet7z1A56stY0C1iuAa4KjP1ucCzUcDO6YBm39wY8vsIzi8PgKujHC4XCFwZ5k7wGn9cQNR9cxx0uJ0oOU4a8tQS5gIhIhCd4CEFFFWZGcAu+cBS/4FbPmJYko04M4J3fEBJ5Md3W0XBiEvecXynjgZr27HnL+jWq5CCCFKiEQUIcqQzKxMbEmbj/RT1iGk63JUmzMcGQtbIjvdzxug26AZ0PpsoPsttoOmVgtRmJjSagzQcGg6Er/8BcmbElANtdE5dgRqZTdyXCds/6rd2hwstLpz/LHqXXEyUDvJTLNilbhy/idEoNdZ2kEbsb3stZw22RLmnXCNph0AVrwBbP0J6Hc/0OkqO3cKIYQQJUHlmBBlyI4dO7Bhwwa4Q7IQEp+EoS+ko1YSsOk7IGGefShk+0Vmqn1IZBsQW4B44WSLVuPMgsyvFfQpisUFZGSn4UjGAWTXSEFG+HG0vTALTZpILBGBITsLOJpgo7Z3zrQWssMJsUg/dB5CszJxPDQMP31WE/W7AE2GAfV6mhDMkcdag6I08G/j4W3AtDuBLT8CWekneXvZdnvT7wMSVwMDH8lx4wkhhBB+IhFFiDKA1va0tDQsWLAAGRkZzveaNWuGjj1bITwcaDHaero5AvnoHhNS+MGOfdpRcUDNJkB15lPkFB0qPoS/cN0dOXLE+ZojQHlRe4U4GZwpJ1nAoc3Ayv+xZcxGG/N75gagwtvQOV0xXnb3FmD3TGDpq9aG2Gwk0OV6oGFvy+HRchQlWXtHtttI950zctZcgMhIMVcLg2hPedHGd2ttCiGE8AeJKEKUERs3bnScKCQyMhJ9+vRBeDgzKuxTGkd/siebFyECxeHDh08Id9HR0Y6IIsTJFLHHk4Dl/7U8CbpQii5kc6rQnFYLCsWr3gM2fAW0vwjocw8Q2z7nSBWsohjYfvPrnwIvoHjgba792ASUYc+YY0oIIYQoDsYMCiECzNGjR7Fw4UJkZdmnvvbt26Nx48ZyBIgyZ//+/Se+rlmzJqpV0xgKUTrojktcAfx4BTD7USBlZykLWTeQfgRY+Q7w3QXAxm/KpiAWlS9ElhkobOEpy/XC9iCuTYopbFcTQgghikNOFCECTHZ2NlasWIHExMQTboDevXs7LhQhAhroeRRIP5qTqZPz4X/f5hSEZIXDHZqJunXrSrgTpRZQds8GfvkjkLiy5CGehd0ms1R+uRlI3WeTxrTzLwo7v+2aDSx/E8jOLPv7Y0vtvKdsahkzyIQQQoiikIgiRIBJTk7GkiVLHDGFBWy3bt2cYlaIQBQWWWnA/hXA1knAvqXAoU3WMpFxzDopXHF9ERnfCplRh5C8rzESIoB63RXsKUq2zvYtASbfCBxcGxgBxZtj+yzU0xUGdL5KE8dEQTjBbtELwLE95XefSRuAZa8DQ58GQkLL736FEEIEH/roIkQxxQRhCOzR3TZWkbv+JLyGJfpHxwOhEfa97OwsLF68GCkpKc6/Y2Nj0b17d4SEqHNOnKTr5Biw5Scbz8nJTmyP8Glx3xFrF7ixYRaw7W2gYT+g2w1AyzFAWHWJKaJomHsy9c9lI6B47/z/9ggQ0wJoOlJrUuRl9xzLQSlX3NZq1vV6oE5Obo8QQgjhC4koQhRiO0/ZDexZAGz5Adi7CEg7ZIVs1nE7JizKClJO02k8CGg5FsiO34fVq1c703konPTs2RO1atVSS4U4qbXI9TfvSWDbFJsk4R8uuDOB48nAtsnWmtH8NKD/g0CDXoBLup7wQWYaMP9pYPfcshNQPFCYnvUwcE4nILpR2d6XCB6YUbLha+D4obzfj20HRNQqPoj20NbctRseDdTpCNRuZW1BSevtUtiYZE6g2jnd7kt/toUQQhSGy81qTwhxomDlyOE1H1rIHEMV/erHdtnozurdEpHafzqON9iG+Gb1MX78eCcTRYjSwA/66z+3HXt+uA8EMa2AwY8D7S5QHoXICz8N7PgV+PZ8ID1fAVtWhIQDAx8B+v1FbT3COLYf+GIUsH+51zddwPjvgWanFn3ddZ8AP99k587q9YEhTwHtzrN1RujgW/4GsPBZ+9oXrc4Ezv0GcKmlRwghRCHoI4sQOcUDxRJOAZj7N2DfMji7+P7fAJCVChyZVxeupWcjqt9mtLujGqpHVXduWztaoqTQ8cQP+7P+aq0PgYJizC+3AmkHgW43AiERWp/CoNNu6auBXW/+TGBZ8SbQ6UqgVrPyu19RsdvJDq7L9023uVOckOP8uID4/kDD/kDiKpuww/Na/4eATn8ANn4FrPnI2m67XAf0udscK4tf8u22YvgxW3cpwgghhBC+kIgiRM7u1OIXgUX/Ao4nncwtueA+Ho70me2wdDsQlexyPsSFajCPKAEsAla/D8x6sPDd0pOBLgO6W+hEYf8/ixBRtaHYywJ1x7Qi2nhcQGQt29Vnm1igpqawdXL9F0DvO9RmJkxA8bW2Vr7l42AXUKcD0GqcraFlr1lWVFxXoP2FQMIc4Nc/A8f22uEMTD5/EtDlWmDl277PrwzpPrRFIooQQojCkYgiUNULB36I+u0hC+xkHkBgcOHINmD6PVawdr8FCKsWqNsWlX1Nsid/9v+VjYDigUXwnL9Z73+T4XKjCGDrZCDNh4jMtoZGA4AefwRqt7a2m6N7gTUfAJu+s+Bt0vosoBOn7RTRBpGSAMx93Nomvd0onDbV9VogsnYZPDERVHDaGM9H/vSaU9Qb8oQJJ3P/nrsWG/W3vLKFz9s0KA/Jm4FdvwFtzrbA7e1TCt6mEyRfjlOBhBBCBB8SUQSq+hjFeU9Y2wRHxwYaulp+exSIqAl0vkZjE0XxsM2GLpGUnWV/X7wPul3O/c4KDlF1YRG67WffLpTmo4DR71hhy4kpzI6KHwiMfssCj+c/Y0JIzWZAsxE2ujg/PPcxiPvA6txpZt4wxJtZGBJRBDce6MYrFhfQ/hKg5RnA1DuBJK8WIIooFEPoPMmzpjm+exHQ4RLLh4IPEYVrOcMG7AkhhBA+kYgiqiz8kLb2E2Dpa2UjoHjgNJXZj9mOf+Oh2vEXRa/JVf8D9i4sv/vcswhY+T+gz50KUqzKpOyyS34ofPS934rPHy630bP8muezcR8Bve6wEO7kTdYese7TgrfBFp12F5pjgO0Wh3f6dkZxakps27J5fiKI8PNvJCfu9L4TSJhvrTzecNoTW4IoSufnWKK1pFWLLeL+9XdaCCFEEaj7WFRZkjcAcx4vycjY0sPihO0Zvj7QCeGBffsr3sodo11ebqzV71ouhai68P33tEJ4Ex4F1OsKJG+0MdkUnLk+mZ+ya5Y5R2o0tWMzU4HUxIIXOlSYd0KBhSIhsn0LLUkbyv55iooP15Q/gm77i00sWfiCBcV6Q/cn3VU+M09SbDODAqEvsYQZZsWNUhZCCFG1kYgiqiQcf7j43xYeV17s/g1Y/5lZ4YXwlYXCdgoWqycIyR3NWRQsQNkiUSInSQgQFmWtF2yxcAJFRZUlLdkCNfPD3fzjh4Bqdaxg9cBd/FotbKJPqlfmRH4iY2yEMdf3vKd8CzWE50VP+Keo2nBdFReIUqMJ0OEya9fZzja0fDjiicv3+ZPnSq43ZwqVj/sJjwZqeK11IYQQIj8SUUSVgx/mWTRu+qb4D2qBhLu3y/5rtnUh8sMdfoZ0sh/fA/Mlhvy98CkRFE/iBwGDHgdOfdXaJZqf5p/w0ngwMPptoPmpVlBs+DJw01ZE8MHzE3fu85OeAiz+F1CzKTDqVRtFzByKEf8EGg8Blv+3CDE6J7OiyTBgyb+BI9uLeADuwgUWUbWo0853bs4JXECbcy3ThO4mX4Hwx/ZYALKvjJ2oenae9RWiTMJrADEtS//4hRBCVH4koogqyeYJNiXCA3fk/bHvRsYCNRrbTpU/cIRsREyuZTh5PbBtSvmKNyI44DSIA2vy2tF73gZ0vjpnDeWDrpNuNwJnf2lTUxr1A7pcA4z7GBjwUNFCCtfw8OfMDu+EK3Ks6HrgSDmE2YqKCc9VvpxMFFaYN7H1J6DFGcBpr1ugbMfLgUObgVXvWRuPL6rXszXM1p81HxbjwnP5f14VlZvqDWxEcWFw0l2nK4BjCTnTdXz8PeW5lO06dTsW/Blvm06VA6t83379ngo4FkIIUTQSUUSV3HHljv+JD14uYNBjVhz43P1yAXU7AaP+A1z4M3DBJODCKcBp/wXqdi4igM4F9LkbGPueFcSEdnmO8sxML6tnJ4LVHcXJJNypr90WaDEGGPGSFa2Fra+4LsDAR4HUA8APlwJfjbNL4gqg55/MkeILFhYD/mrXzy/iFOkUEJWaarWBcGZE5IPnrmHPAC3HABu/Bn66GvjxCmDZf8ydMvpNG3tcABfQ9gKgVnNgycvF50HRVcXiWQi2gDXllKdCPqFS+GWwMVt58rQ/erFjquWdtRyb+/fX0yrUbKSNOt6/ouD16F7hqG4FywohhCgKiSiiysEJEEd25X5gatgX6HApUKe9798Ifn/cJ0DnK802vHuuiSEdr7Bdf187ZtzRZZFK6zuFFt6PB05e4QhHIfKHD4dEAGd+Apz9BdDh4sLdJCwuuLY4lpjjZSnMHdlha2vO3ywYse15PkRBl01J4WXjV3l/xNwLCjkUdETVozCHXbNTbb1s+8UEFLZPUEyZ+mdg4fNA/R7mlsoPM1TojOLUnl0zi79/ulQ0mUd4/n62Ox+IrFN4KyIFv50zC3c30YnCjKlWY4G+9wF1Opowc9p/gBrxwPLXgSwfDqrY9tampil6QgghikIjjkWVggUiJ0CEhAKdrwLq9bBdJ4bUcYqEL9gywQ/38560bABa12l957SJ/n8Fet0O/HyTfZjjjlfrc2yaRauz7Hr58wIo4rBgLSznQlRNuFPPTIiZfzG3CIsEOpk42SQ/tJo37AMc22cTUrxJWmeFKwsNFiHMBvDAgnfAg8DqD4A98y2Y8QQMWpS4V2VhYUkhhS06+VsbKAJTqPOedML8nE3f2znQEZJZdHoJcC1Ot/Mgz5n+TH7imqa7QAgKGPW6Ay3PANZ8kPdnFJob9bev9y4q/DY4gee3R4Co+kCfe2wUN8VnZqEsfsn3KG6uYYrXdE8JIYQQRSERRVQ5ju62bJMet5uFnQWCd5inN9yZbTLc2hxoSXfS/FlAZFgh2uU6oPEw+2DHYNBqdS0DgA4B7qZxCpCvdiI+Bu26Cm+4VjhumLunhIIc3U6FiSi1WppA51mTHijQ0dUSPxAIi/S6Tiww+G8mvMx/2n7u6zGIqgnPVxQ+HFHOSwxh+CbFZ1+5PFxf1E6cVh2v6/Cc2mqcCS3rPvcvA6phP8tQEYLQhdfrT+ZiOrwt9/v82/vbo8D8fwCHtxZ9G2z1mXAx0HiQOULpIE2YC+xfbn+v8+NxVRXWRiSEEEJ4kIgiqhZu202lG+SHS+zDEsd2nvGO78PDa5pDhb3X7K/2Nc2CrhbPhy4Wrz9ebgUJhRRah+kqyPcQnHGiQnhTklBNtulE1gIOHDLhJf+6pKOFEyY8a89TkDDbZ9J1hY+SVbBn1ab56cCifwJpB3K/xxYx7uq3vxDY8qM5+XjeY7tO52ty8ycKBIN2AQ6u9W+MPEXolqP9C/cWVceN0qAX0PN2YNaDXgKvG0gpQQA21zIdU7wUBdczR3HTjSWEEEIUh0QUUaXgjip3R7kL5Qmky0gtfAeerRBfjcm5rlfvNUUS9lpHN7QPZyxcPbtkbKUgHGXMDJX8Igp3bgtzvoiqC9eSv3D9URjJSAOy842l5TrN4vpy5Yp7zLWgQ2rBs8COab5vk7dXLfYknoAI+qI1rrPlRnDctcc9kjAPWPoq0PtO4JxvgJ0zgMxjtmvfoDew8Vu7eMN2CAYkr/24oPjsi5qNLYRWDgCR/zzX9QbLN1n9btmNYOffaK7vVmdqDQohhPAPiSiiSsEPSCXdbc8fXMfdUoZ6DnwYOJoALHrBdmZLgve0ACFYwHL3niIG2yeKg6IfHVUc9UknlDf8N50qzO6hfZ0TVIY+aZMoKPh5WiacEZ4uW4vM56HrqnpDBSpWZVhMclz29l9MBEaO4Dv3CZv6xLHGzOKh4MZzHzMnVrxZsKWMzpINX9gUtOJaxHgsC2VmsgiRH56feP5iXtPGbwIvpIRWA7rfbCKKrxHfQgghhC8koogqB9tsWASU1A3CMNn4QRZSx/R+FhWz/lp0uJ1PQkrmOhBVA64JBmty57846KQ6nmRrmesSR/IWwrSmp+43h1RMSyCum+VWnM18ihw8rRMMXKQoyOBQHiuqLhTQmJXD0G1mQHnEYU4xYRDn5h9s/KwrzIra4xRPfExH2TnNLv5AUabLtQXFQCE8azKqHnDqK5brtOZDc0IFAp4ne/3ZQuLVyiiEEKIkSEQRVe4DWa0WVggUNo3HF/wQ1/deG9mZngLMewJY+T8gdV/JH0M0HQd1S349UbmhINKgD5Awv/ggTrpVDq4Hmgy1tem9lukqoRhCcY9uAub0LHw2p4/Mi9qtgbbxJgYy88dxwhQyUlRUHehu6nc/sG8xsDNfyCyzUXgJFMyfGPx3TSoT/gkpI160NrIFDJXd4VvA8+v2wqx1beCjQOszCx8lL4QQQhSGRBRR5eCHJ7Yy+CuisMVh9JtA01OA9Z8D854GkhmuWMoPcAzLo4gjhDfciW93IbDyLcvSKQoWsgz5ZBhnp8uB2f9nziq2RrS/xFwt236xHVtOsJj1UMHb4PQUjuNmy8XKd4Dzf1Qrj8g95434F/DjH4ADq/2brlNSeA6kgNJkmNadKB6uEbpFut8ENBsJLHkFWP+Z/R33928x23WY10PnXdfrgRqNlIEihBCidEhEEVUO7rY3HpwbLFscfe6yMcdz/g9Y+trJ7cRyx4utQLIOC1806Ak0PRXY8kPxx278Guh0BdD9j0B4LXMOcG21Ox/YPdsKDA++Mns8hQfDlikQ1usewCcigr5g5Xo47b/AlD/aSNhACikU+SigdLxMbTyiZFAIiW0PnPIc0PNWYMOELCz+dB+yj1RD5t5oZCbTVpKjyrlMrKvRBIhtY8GxzU+z/B2O4RZCCCFKi/6MiKqHC2h3AbDuMwvfLM5u3vos243dNMGCP/NPMOF0FIYs+lNksGWj9dna/RK+Ybgrgz0T5lieCUd5JscWHGNMju0Dfr7ZQhc5fpaCCh0sWydb4CczUIqCYiBHfdMSz8k9EXJHCS94jmI+ytgPgJkPAtsm504hO5nbrNsZGPJ3oOU4CSii9CIfs6DqdAA6xh/FwmpfI/NwGCKOxmF451GoFlLLWWsMpWWOCkU7RzgJl+tJCCFEYJCIIqok8YMtJHb7lKKP444XsyK4k3XBZN/HUED5dHjxggzFm7bjLYtCCF/wA36L04D2FwHL/2siiUMhAt2BVcD3F1kgbVRdE1aSNv4/e2cBHml1vfF3kk3W3d3djVVYdHErpa7Uqcu/LW0plAp1gwoF6rRAixWHRddZd3d3zUo2yfyf3z3zbSaTb5JJMgmbyX2fZ8o2mYze795z3vOe94STLolg1PE/RkrDPmnyeJ9ceCSCNQHpceXfbQrPonvMY6ei08iCKVAD3iuN+arUok81vWCPOocDBw7oxMk8FWUXqVGPiAbeVF+5ucW/9/uah4eHh0d1wJMoHnUOBFV4ooz8vLRztlSQJ617zEZxJiYHTKDAB6Us6S8mn6hREoEqgJGytO5QwW3WzRJWTO08PJKB9XHebaZ+2v5m+fdnbWEOWxl0HC+Nu828VDw8ku6XzW2CSZ8bpJV/tSk9+5ez70UdwQfHZ7lqXMbKP6Nm5N31YmnYx6V2I01B4BNbj3QgGo1q9+7d7r+gVatWys3N9evLw8PDw6PaEYkGp4+HRx3DmTzpta+YkWeFK6sVBInD5B9Ko7/sW3k8yge7MhNzXrwl/X4UAUhop94vtRvlk1qP1AFhfGKPdHCVtOKVPVqzcLvO7Gii+qdaqXnDNsrKibiWx9ZDbHoU3ipNu0rZuX6deaQXhYWFevbZZ7Vq1Sr3/ydPnqyJEycq4heah4eHh0c1w9fEPeosUIhMuF06sNwUKdWRqAbKAjxYmAbgCRSPVEAOAMnBVKhpt9q44spOgyqFLKnDaOmS33kCxaPiwMcEfwluu5tuUX6L150SoEvP/rrioutVr6GU08jvdR7Vj1OnTunw4cPu3xAn7dr5WdkeHh4eHjUDH+Z41GlgHHvp76W2Q0sq0dM5SaD7xdIFP5Nym6X/8T0ynEgZLV39sNTvXVbJT4ciChPaax6W2o/xBIpH5QFxcvTo0bOtFM1bN3XG2blNPIHikT6wvCCQiwqk00elg2ukPQul3fOlPcvzlXfklFQUUaOGjdSsmT9kPTw8PDxqBl6J4lGnQRLZZph0+Z+lV7+QXkUKPhNM4rnoVzYdwCesHhUFa6Z5T+myP0o9ppqxJ+09FW0/g8xrO0wa/hmp/7vN5NOvR4+qoKioSHl5eWf/v09gPdJNntByS1sj3lAYYR9aJxWetN/ZfZrptN6tRr33q9mIPB2a21zNJkXUoJXf3zwqDtYVU+3ydkunD5mvHYQw6joI4sYdTcHs15aHhwfwnigeHrHD88gmae4PpLWPSflHq/Z4HLjDPiGN/orUoLU/dD2qDqqxTEbZ9Ly06l+WUJzcJxWdid0hWGOxHZ1xng3bSi37SgPfL/W8wpRXXiXgka5WiieffFJbtmxx//+GG25Qv379vB+FR5WB4mTrNGnJH6W9C6WTB1L7O8hhvHiGfFTqfa1N1vPL0aM8j6e8ndLOWTYI4MBq6fRB6fRhqeB0jERpIEfMMQGvw3lWHKMo4WM7D4+6DU+ieHjEwJXApJP1T0oLfi3tW2z/P2VEzAug8/nS2P8zU0X8UPwh65EuBLs1I4yZ3oMq5dBa6diOIh06ul8nio5IOWfUqVt7dRvaSu1GRNRqYHErkF+LHlVef7H2iuPHjuuxJ/+rvXv3qF69err55pvVtWtXT6J4VBqsK/a0uXfbOZzKqPYwkPh2mmyeZ10vLHu6nkcdBHtYVDq+S1r+Z2nNw9LB1an7jmXVlzqNM7Kuz43Wqu23PQ+PugdPonh4JICDlCrEpuek9U9Zi8/xnbFKf/why/+PRKXGecrpdVBdBrfQyA80U5cLpHoYK/pD1aMm/AIKpTP5BZo2bZpWrFzu1uXFF12o0WNH+4TWo0oIogPIZMg6iLujW00BdfL4aW09skrH8verfptCXfrO89RrfEsndwd+6XlUBHieQJy8+XXpCOKmqhppR0w5MO5b0vBPS9kN/Jr0KD4z1z0uzfmBTRlj7VUGrKlul0gTvit1GGs/82vMw6PuwPPzHh4hVSykmwPeb1UG+mNpnTiwSjq2WTp1yDwmGrSKaluDGdp5ZqWizU+rw5Tz1HPy+Lf75XvUIRCwoXbKycpSToMsRSNmlpJfkO8MPz2J4lEVmfuRDUYmr/mvdHSzdHJ/vDqvvqQRrqybX79Irz2VpXkdpd7XS32uk1oPTo8ZskfmozBfWvFXaca3bY2lBVF7rBm3Syf2GpmSg+mx3xLrNCiQzfuJtPR+8z+pCgpP2f7IhMdJ3ze/Mb/neXjUHXgSxcMjCQi2qKq26G03PCUSKxqzZ2dr5/TDInXdvXenCgoKnLTdw6MmAVkSv+7y8yupg/eo82BfI+lc9oC08h+mQCnbbDui6Olsndgtd8MIlL/t+w5p1BfMGNn78HiURdat/Y/05jfNzDPdKMiTFv7GWm3HflPKzkn/c3jUDuCtA1FHC89ZL7GqIiod3SK99kXp1GFp+KfMj8yTdR4emQ8f2nh4VAGdOnVSVpZdRocOHSoxrcLDoyaRk1OcHXgSxaOyo2R3zJCeukGafad0aE3lppUd3y4tvld6/Cpp3WOmNPCNwx6JYE3sWSBN/1b1ECgBCk5K838prX8idd8Ljwyb9HTC2ndQoKSNQIkDCuVZ35VW/zv9j+3h4XFuwpMoHh5VqP43b9787GjPw4cP69ixY66NwsOjptciJErQvgOJ4tehR0VAcsnUp+feJ+2aU3mfgPjHQ8Xy0qcsgUX67uERj/xj0szbpWPbaqaNY9Yd5unjUccQtXYxFHJV9tpJYY2xf/rj18Mj8+H7Djw8qoDGjRurZcuWjkChlWf37t3q3Lnz2/2yPOoASFKRJx/ZKO2eL22b31P1NtRX4bFcbYm01OO/znKtFO1GSO3HWlsFRou+tcIjERAmECiYep7Yk97HRmEw94dW/R37Nalew/Q+vkft3b+YirJ9euXUTpUB3maLfydN/qH3rng7z6zjO8ykGqVb3i4pP0/KaSg16iC17Gt+Sk27SI3amv9clZ4zKu1fIc37mXTmuKodkHSzvidd87DUoEX1P5+Hh8fbB0+ieHhUAVT/27dvr02bNrn/v33rTg3tN9okw1FLWBmvmJVr//V9sh7pmCxAELrqn9LG56Q9C2Nmn9F2ktq5oVEnJZ0tuEYsYWg/Rup1tTTwfVKTLlJWFYNTj8wAe9XWV6Xp30g/gRKA5AUzx8btpSG3+JGzHmbqufRPUiGbVQ2BvROycOgtcqPfPWrmzIKkhexf/bC05WVp35IYoRFJINBi/x+itc0QqevF0sD3S636WQxVmfgJBdzCX9egAikqbX/dfH6GfswXLTw8Mhl+xLGHRyURXDnrVmzSc4++rjMb2qjxse5qvmeoTh2KKFpgxrRNu0ot+kjtRkqdJkot+1gjnSdUPCq63vKPSsv/Ii26x8zsSAoqApJX1uPIL0hDPiLlNvfrsK7jyGbpqRulfYur/7madJau+6/UYZxfd3V9L9v4jPT0O80vJxlym0mN2kmnj0qn9pfhZxKR6je36TtuglRZrWNZ0vk/lMZ+w6/BmvieIfyX/F5a/lczrK7ImWVTEKUB75VGfTFmUl3B7wyj60cvtlabmkTH8dIN/zM1jYeHR2bCkygeHpXt918nrXlEWvVogY7sOq2io/WlAsr74ad8dn0LCDtOsApF50lSvUY+kPNIUZK8TJrxLWnLK1X3l8huIHW/TJr8A6nNUL8G6ypIYKd/06aX1JThZu9rpSv/KdU3KymPOgiUCS99Qlrxt/BWnoZtpZGflfq9S8ptaq1gh9ZLc74v7ZxZvFZJsjlHx35datXf1AqQKKv+IS37i5R/JPz5u1wo3fg/e2yP6vuOt0wzjxDUkhSVKgu+55b9pEnfk3pfZ7FUKmCdTL9Nmv/z9OxvPC/EHusRYq8sfxUm9NzwlNTzyqo/r4eHx7kJL6r18KiEDJngb/EfpMMbOEjrpXQp0XKBgR63zS9Kva+x4I/+X99a4ZEMwcSUVz4v7V+anseEhNn4tK3FS34rdZrkZcd1kZhDVr/6kZqdWELrEJJ+RiB78q5uAqLjwIpwAoUk9fy7rY1j+xvStjekxh1MjTD1funpdxXvg10vkq74i+1dm1+ysxm156QfSI07G+kcNomF1pIjm6S2w6rwJqIxdeBxUwhyQ+3AOQ9JjYIit4m9HxQydWmt8xkQI838bnpaBFGvHFwlvfRJadwmacTnrOWnvM+Udbb9zfD9rc+NUv93Jd+jlt1f/P9RFPe9Sep/s9Sova2p4zvtPbKXuXbaEBJp3RNSjyvq1nfv4VGX4EkUD48UQcCE+mTmd6R1j1e8lSIeVMjozSY5nvwjqd87vdGdR/ia2z3PqrZMOkk3aOF48WPSlf+QOpzng706hah5FOTtLOM+EVsTTq+agmY1IOLKImXO5FmCgiLF73l1EFFr66CNLAzdL5X6v9tMZ1/9opETKBEOrpam/Fzqc4ORKCS2533dkuln3i1te93WHeQFZMuQD0sbnrIzNhGseQjkyqjwuBZQcB1cKW34n7RrnqkEMUiNjwlQItC+BlFDa0eva0xNkeneaHw2tJxiUs30pbRPv7lLKjgtjfumFMkp+3viO4Z8CUOPqVK/m+wxE7c2CJIArL3zvimN+Zp06oC0Z5H9vMsUe4wZ35EW3RuyP0JSL7bRxw1bVfINJ7wflC+s8bP9A+zPWcX7biavKw+PcxGeRPHwSLVqu9gqIXsXpqlyGzVfi1c+K+XtlkZ8VqqXokzVo27gWGx9QN5VF3jsVz4nXfOI1KJX9T2Px7kFpmRseDr8dyQOJH7tR5kKgP2JCVAQemGyfO5DexhmnQWnbOrG1lesEhwG5P37l9vje9QtkP+RuJKQlkJEGvRBqSBPmv8rI1Dc3xRK6x6T6reIqT8ltYhNcUEJ4NQGMQKDx135D1uP3S6RdswsneCiEji2veKvvfCMPdfS+2x9nz6SvJiCOsEpXjaa/8uCX1prx7BPSR3GZqa5svO6eVaa/q30EygBWBsLfiE17SwN+nDZKl7iK76jMLToLe2YZQRc4nfIHhYALzvMsFFOvXCLdHi9/Zy966qHpJGftzUIyZeIE/uNsKsKieJa2dbZhCEKKbwninBFhUYkNukUm2g0yK4H36Lm4VFzyMBt3MMj/YEBBygV+72YL6bZRYiAcvb3LKga8RmrYHl4IBOf/X0zxqvuEaB7FtgY2ovvtVGTHpmPXXPDpfb4NI27TRr6CXN3OnPCkleSQpLHuXeXlK+3GSZd+nubpuFIk4h5P+2cJb38Keno5vCxx7RqkKD46mndA34SYYUITDhJBPctt4QRcg5lCaQHa/Wtu0velySStpzEJNiZy542soIqfRjRQeuP21cjKRqkbrcxuai3Tu6r2Pt1r3+vtOLv1so75OPm+UJrSKasfz4jVB94oHB9VycgRmbeYXtPhzHJ7weBVWoCEHtcQ1MJQYid4Lssw7C44zipQWtpxrdLttPummNKp2GfNvI4jERhnSQjksv7LPk71FVM4du31JRObt9NXC+xKZD4CFEE6fMOqe8NUrOevk3cw6O64UkUD49yDrMTu6U3vlo9BEoAKm6z7zL3ecbQen+Kug3WHdUtAvYa8auISmv/a5Jz5PKZEth7hIM1BTkXVi2mWj76y0ZyzPmhJX/sS/hMjPqyqUhoYwBMRLnol5ZEzLrTkgr2LgiYUV+SRtxqyUeiLwVJJc+PNw8JTXWNAnfXTpzk3a/r1HC2XSBGMqT7cwvzKQHNetiaYqIL1X8miDFNjPWCcgkFwtbX7Lul+FBw0ogWJu7EJ8KM0q7XoJikiFbgNSSCNcSaf/XzMSVWFdp4eSGouub9WNozX7qQa2dAZqxLFGoLfm3fU02ANQKpdvmfbc2E4dSRuHbEODTuaD41qEpQbqAA5j6sJzd6OQb+lt8fXicdWFnyMbg/ZCD7SjKjW/Y3HrNCrWKnpS0vmRILE+VS6zQavkYhGbntnGvtkkM/Lg35qBFAmbC+PDzORXgSxcOjLERtnCwu89WtBkCGjBEePdTNulfvc3mc24BUW/w7qeBEDT/nPVKPy6WcRjX3vB6GwHeEhJFEi758R3IUGdFAQti0iyWHVR2Rjm+Bq9JGw0kU1HCzfyDtmm0/I9mg6n/to1LnySbZJ5nsdpmZErNH4gsQtPqwdqng4gHBWgqT1NOWUZAGEiVIkHg9fGa0XfLYJBSouVD45TQ12TsTXNhfUTdUBzlQm9cdrSoki1TUaXUhmSQ5wyS1URtrf2g9RGrQsuqfHWspDCSsTNjpcr7U7WJrN1vzH6lFT5vUc/lfpGffJ+2YbtN6WMPsV7RWQErwXkiQIWBQVLlrpazXUM574PFQVL348ZgnVZpiAK7xzS9Iz++TLn/QFBW1eS3yOaFsW/9k9cdJxU8qbXrOPG96XhX++SWbCMQaYd9hj8Jgln0KsgLFB/sYJAb/n/U//xdGDiWO4kZtx9pDbXIk1mJW6vljHiYVUZ/M/ZG07EHpTGXboZgcudZaqngf5//EK/48PKoLnkTx8CgrMJhjDuw1Nb3iwCo7xDGbzfZtPXUSLnCfLe1+q/hnJAOReiWrZGEgcaAqRgBGoJ4MeF64EbORmLQ+dt/dC02B0POK1GTuHun5vmkt4PtGfYSEGwKApDb4DpFl8/3zndGiwMSHzhNNsl2Z4JgEAYVJKUSKE8/ECmiwBwaviTXU83JLLjDJjk9YSMKfvtmqtMm8EbhPqmqAMhOPfeZPsfpRac88IwMxr03cs93n19QIlG6Xmpl3pwnJE/q6AL7LfcukTc9I65606j57DC1c8ckwxAaTZmjr6ny+1O8dRqY5MqoSQDlAEptYpednrHWq5y993JJy1gikHp48F/1GGv0lS5xpGaG17LI/Sdf9137G68YgO9gDMfVMltNDqqUyvWraZ8zjp7raKF/6lHTV3y2Rr60oypdW/rNyrStVASQsLYb438SbVBcWFiovL08F9bMUjTYJ/e5Zz10vtpYe9lzaezCKvfqf0mtfstYrR2qzR8XvU1lSuxHSpLvMmJjx8E6lHAKum1TMs1lrEIKvfdGmTFV1X3SPyYjpV6Sj77Xrhs/It/d4eKQXnkTx8EgCDjIqAsd31dxzUk1d86iZ61VmcoBHBqDIxg8HySeJKK0UjMokoA+TkzfpIg37pNTlAkuCUTKsesgMDeMTFRLJzpPsvi36GE9CorH+KVt3qKE2PmeGjH5ySg2QJ4esNWb5gxaIl0eS0VpIlZHvCjNBxm4i2W7WzUiNlJ+7KMlzYQz5nE3OYSLF7DvNgJbKLS0++AdsnWZrkESXVgQ8T5CtD3ifkRIoP0h4UavwepPBER2Flf/sSJI3PCst+m2sNeh0OX9TYJ83N5Qqax6Rul4ojf6q1PG8urXeMaVkHaEg2vCM+X2UpSAgSYbo40ayt+6/RlaM/ILU4zIjb1M9q7gf5AtJa2DSGQBCjrXJ98N1ESST/Bdj2XHfMs8U9jj2NdQcz75HGv4ZU8lwP9RT+I5c+CvzkQh7X/WaFSqnHQ9OH0b4C2cvZMIMhp7Vid1zbRTwZfclb0s51wF5sunZ0p81Z1fjTknUZkUWWyVTWzbvZWuEQlZZpMKeRVFtX3BSDXsf1/79+7Vnzx7t3btXx48fV/RYDyl6Sam/cfvu09L6J6S1j9njczaifrrqH9KY/5O2T4+p9c6+GVOfsN/SrohqCrPg+T9L/vogHbmVBTdFaLs07VYj0dOq5GGi5Frp5U9KUx+Uul9csXPCw8OjbHgSxcMjCThAXe9/3KHmTOrKU6VEYgdVilJO7uvuF3sexvLxvJAoHnUPZ05aBSlYGygPSFCPbgrv7ydIvfwBSwjpR2f9IN9lVCg+O8iRg2S1+yXSFX+zSu3eBTY9oP1I6eLfmrSZKT073jQCp2HrGn/rdSqJpQqNCSMqiopWHrk/Pfq0Xqz9jyWX/W627zWlZDbYo0JAssrkiwl3SF2nmGIFEoX7k1RStQW06UCkkMxe8DNTJpBko5YZ/FFp8EcseE82YtRVRStBEpN0MKFi5u3m4wOBUxngqYHSgWRp1Bel0V+0tp9MJq6D8byr/2Wm1c70txJJGwTYttekXW9JA99ra4WEN9XPDh8T2oMSSRQIO9QFrKNEJR2tWTwvXhZU+HXK1iSqPb5DyDvnhVMgdZ9q65AWk9JncFRFnXZp1pYZ2jG9k3r16qV27dqpXr16ikQi7sb1tfj3sbVeA+0peAmxf0Nu10Y/NKbchKlQGraRrv5XbApXwufId/nsB4yUTQTf76TvWavgQ+PKNvKFKHv2L3OU13ehithY45BdWF9ZDQsUPVky1eE52XfjXxPrZvvrRioOfJ/5QAUkCuus20XSpB9KbQZL296Q5v3U1E/JieCoGraJqmG7qKLRLLeuwgCZ/cbXYp9DNa01YoJXbpWue8wX5zw80glPonh4hIBgjMM0vpefShfy9dX/tt77UohYsNBlipnhEaTTY46fSph0nkoLFX+qyDwPcn76rwke1z1hxoxITj3qFlgzJJgQJ5AhfW+0pAMSJQyDPyR1uVBacp+RJlT2WH/XPGzjF6nKIksnwR79FXvsFz5iQRsxJ1MuMOejN3zFX82YjqDVkyjVA5JYVBD0rNM+UZXAmQQRIgXyCzXGhO9Kuc3LD5JZA8mq3ozKpNXFmc8uttaiJvtiZN57LHFgWhmJDkQKihhUIE9cIx1YbqoE9q5RX5Am3im9eIslTImgQlvRqij7MiPmX/6MkVBJp2pUACgO8CE4uFKa8ku71jIxyXDqnePSnO9LS/6YnhG0jJtd/hcjb5nQ1HZE2Z9dNGZgcybrhOqPPCRN6ywVFv8BpEpgZIxaJT55Zk/jZ9wHFRKkzQV3S4c32XS7gIh0Ce/Fdo6i8ih1fUFED1yvnfu2aNf+rZo/f75at26t3r17q1u3burQoYOOrs3Rkj8amVITgDjCdwNj74qQUedS23NBiBIMtQbfG8a87npNUDc5BVQCmDLDmYfPSTDiusznZ/0caayiwqISpCykRbOuUrTnSR1fGTL3N2TfZc2wJ9M+xj4WYNCHpAt+bDHdK5+V1j2e2vVzov16vbVmq3oX9lLHjh3VoIH1SgaECs+39E9WNKvulnHUXTO+I135d9v7a9Ma8/A4V+FJFI9aBxI/V6UqssOOClS6QSIaLyMlYRj+CWngB6yHNpFEIXCjisRoUJIDZO94mhD0kdy8+oXYqNoY6Mm/8BdGpBDE05fPIbr0fgvoeXwqzcjMPeoWGNlJInfeNyzYcRXWJNUuqrIktiS6KE5YS+DQOmnJn6SLfmVEHSQKky8w1qQChyFfsLapUhEUdr1IatpNis4y00ZaNTzST6AQNKOiICBPF0g2Fv1OOnlQmvIzI8bKAvsmLWCJYO+a9H2p1SDp9S+Z1J3Em1Yy9r7JPzTVASScC8Jj42MxxGaSBIAQZooKrWXsc6hVwkgUEuVkUy2SJWuYhzJq3k0ASWPVFjXLGlQt+dLF98SUNxmWZLBGZn5HWvaAJe3pAt8/ZyXfC/4k7UeHf3ZFRUU6cuSI1q9frxUrVuhQo9OKtH+nojuL2dr8I9YqNPYb1k7GOkItB6ELAcx+GHiU8X6a9zYCmSQUco2WDNR2JL1M8SF5T0SkfqHqD9mtgkjEkTpnzpzR7t273W3BggVq1aqV6s+crLzdPWrUGArVAy2YY75Wu1ouWEvJFE0QuhgRz/qutPIfZT9Oow7SZX+0cwciiQJSKiSKw44OanFhSzVq0kAtW7Z0yiLIsCYNm2vm/EZau6r49dEKdsnvzTD59S+XHPNOSx/Pj99O0IrYbpQ04Xbp8Ebz6cG3LiXyNrtI+YOXaOGijVq+YpmaN2+unj17qk+fPmrTpo0jVA6sjDhPlcqq6SqE2MS/NQ9brOo9zzw8qg5Ponic+9WzPJNsMl4QIuLoViM5+B2j6ehTbTNE6jjBAnMC/qoaaGG2iQydBICJGIx9Hfih5Pdn4sP475i0/aVP2OskQRjwXmnMV6xC/PxHLEjk9U75qSXKyOMhZUh6zrvNfAeo5pK88PxUfzMtmPcoG1Riqdo9824Lpgkor/hz+H0ZLUvCh6S9RFUvGjPZPGYy8QW/suuIBJ7EIl4qD0mD0R5yZhJglltZXhYelU82GVmN/0E6CZT4yu6qf0q5jaXz7y67NSVSL6pmfQsUqVdP0YLiO0GiseeQkNIqEyjxaK+gBQSZO+uJBAfCASUC6xXSLR78DIVMh9G2tx3bmvgKolKHAzp6MqJm9ZsoNzc3qdz9rPHiJkt69i9TtYD1jzdQ/ZbW3pZJE6r4rlA6cP0nThlJF4JiAe0bTJez0bJRZ/J5+PBhLV++XGvXrtWhQ4fsD3Iiyh2zUtFnJ0mFxT0sqGRa9C32eMrbaQl264G2vvGxACTYJKCX3Ctd829p5xzz3sDzCU8oihFh42U7jcvShPderAMntztCBw+NU6dOOZKH/+7avlf1poeTAlxTXF9hYH9NVCewtzaKtS7RpgPZwGsLe13O9+UJG01bm1SAhSfNYyQMtAXyviEgUtkfucZp6SKuwwg1VWRt76R33PBOtWjTRDk5JR35+79T2vBEsWcSRTiIH9ofIX5RX/J9UCjDsBtzWdR2B9fYa+9zncWBEN/saw1DzJT53hM9mXJ7HtGZ1tbjlJ+fr3379rnbokWL1L59e/Xq2Uv7HxymY9vZaGomyIOsgWzHS6s8ot3Dw6N8eBLF45wEQTMBPL3CEApMDOGgc8lfSHBDsEIVqt1wqfd1Rl64QK6S/cUFMeLmmv/EzBJRvGQbuVIKEanXVdb/SzUOqX6A+dvt73GBb97DFAG9rzfJ/LyfWwsGVQ0EBLzfd71mbRwE85BFVNxqU1UqU0fOotagAkVLDQETP3OjP9uaQStmh/Tg811VlfQiwOM5ggSWiRNMagkDcmXUKChPglGvAVAzETQFUyggWeb+OHb9RE2Z0qSj1HG8Be70/1NRdsRlDY5WrgvgM929QJr5batyVtvzFJoZdot+1k5T8jXQmx/VwYMHtWHDBm3OPaTs5lNUcKDY9ZE9jr2OpCjRkyJIEt1em22E8LEdtucmqgH5PSQECXsYYZTVuEC7ms/SPx5ar86dO7tWih49ergqclaWbdrxpAoE0Zy7zHuh2o29H5Y6jDHzyEyYZsHaQ3m26DfVR6CUMEn9DiapUaf42LVr11nyBIIiHo2bNFKf92Rr4/windxZfFCTqL78afNcoS0HEgJVJmOzIfbijUj5/7T8oJKigMJ5ieKBayBsoo5rNftclroNaKduaqvhw4c7gmfTpk3avHmztm/frjNbWiq6gwwzYSPPksZ/Sxr2qdIxCJ8xrw+Po/jnGvk5a6kkNgjGSONhRELuTG9DiCj28tpEorBPJDN15mx005WyTB1EQQrlB4QXRaL4Fha+xze+ah87n9f1T0pNOqT2GqInc9S8aUsl8CcOeJm0H1OslHN+N7+Tul5gZDNrjBYxlHkQKG7M8N1G0qFMwesJco6JiRO/F/78r38l5p8Xt//1u6KROl43WZu3btKWLVt08uTJs8on1tmeTUeU/XpvKVoGWxsswTSq7nivxNVDPuYLdB4eVYUnUTzOLUQtiWSyw7yfmWw7mXt7PFzAX2BGclTxaYsZ/ilpyC3WY1vRw8IlDMftsCWoJrFgYg6V/0Rw0DbrZYfvvoRRd/l5VoWhooZUHnVKl/PtPTJZIF4WSs8qJAuqFgggZ9TG7zMgkK9tIJkimN30glU+cbiHWODmWmsIarKsZYtWLNRPfK993xH7rltWPkBxo62zUpMM03Pukt6DpX8XkD3ch3XnJMNFxUEe0yBQHZDsnjpsiQdrjt9VpM3Co3xAJCBph3SobpDQoDyiIk9rBYv12LFjLnAnmeW/LqAvlBr26i8d6Hn2b/n+IdvwdoJkw/skPiGi/ZCENiBUdky39Y5Sj0kvQdsZqgGeG9UTLUaJiDTP05lOW11CQfK6detWJ2+nQovcHWIFnwrMPkm0IJWp0teEySfv7a2fGPHdqhaPnQ1A0jr3h8nVAukE39W6x6NqPnG/9nac4ZLH06eLM2yIMdpl+vXrp8GDB6tZk+ZqsSFbc35QMhF3Xit/ttHZnK/8LixRZ71tfVXaMdPaNCAziBfCxruzr/W/2bw2bG+OKDs7260zXtOwYcN05MhRvXFnnrYcLS034W8gyzkDSpmhRu26iMeIz0jjbzevs+m3WVLe/93SoA/Yv9/8RsgY8UJTpnYar1oDClXJCj3sGZD8VwXf4xkrNrAfoowivor/XgNS5ewZm+pryE5eMCPuwjh631Lz0gHsa0+/26Y6cQbSwoMCBqNp9k5UdO51RG06E+djWUg8f/G5G/vZBmrZf4gGDh7gJgWxz6F8om3sxIkTim5rp4LtLUNVKE27W/GNwhvrjeINJs7xrUeotdz+VEacwTpj/cW37xEHMLWv/3u8556HR1XhSRSPcwYcWFRn5vxQWvWPyhvfEUAhm515h5m6nv9jm0BSEUUHhw6PE0iHUR3QLhRGohAIMeZu6R+NCIkH0k9UJxzQJCgEehjU8j4TfVU43EjWST44+AkuEtUFHtULgjgUQEvvsyqn+z6TfQdFxcE9QdTKzaaaouo18rNSzytjQUoFyRR8dCBSCkISgVKIPbarMCdWR2MTn9zEigRChv9P0ktSjHltryvNo4cEhL5pVzlNxySQ06aowVcjWM9U97meIJ7Spd45l8F7xnOmpiZ9ANpnFvy2UEPu3Kl1G9e4AJ6KO60VASJZEeWO26SCxT1UdMa+AEaOrvynGcJOfcBaKzA6pspPwhH4G0Aw817wpwjGIfM7ElraFanAQ94t/HWIEiUidbv6tI61b6bDRwrPtlKQWASKgMaNGztCpW/fvmrfvJsW3tNC+UdrbpHw+bGfc3bU5tHHtC7gs7F3ac09J4rRRQ+c1umbtqugnmXIkGH4QAwdOtSpjpo2bWqKo6g04lZTYJDAJu5TnImp+EUkI1nigboIP5+wcbuQO7SUtW7VRtE1bUKvU/YprgMIFDyBygIE5LBPm1kx90XJCEiEae2BeKSdN/h5AMgDCjFOgVpLpvQkmrDGo1F7S+RR56LAYd+g9ZrWZ1qcMXFlXHtV90XOkqSti1lS72ukfjeZSikgeiFSXvuSxWisCVTGqOvi1yCx3WtfrNhroajCiGRIDtYV7UUo7LhBHKIE3Lxpi5bNaKWj+QnSmYgpXy76tfmXBfsseykk0OtftRHeoP+77HMsa53QGv7E9aUNfCEd8U/zJIqHR9XgSZRqDp4DqWPg60EywUbJpsghyuFD/yetKJmcSJQH1/O+UZr2aTOESz42LnUgAcdE85l3SZf8zqSaqRIpkB3W0536hIxEkEwwehRShL5b+n15XKS6SFcTWyYIeKl4cKBTvXHrog6viZoGRASydzwrCLAq45YPCcGIYKo/VD0n3GmBd0W+R1RI9RqH980nggSVdc5+4giVuPWKwoT1fnpfrOIZia3pGLlCNQoQhBGQXXqfNPILdv216KXK73n50tFt9lnSHgQRdWy7vVaua1QuqMOo1lEBRE6NzwbqnUxonyiBqF3rTDApL8lL9/Ouefak1vR9RmdyS/YgktAGaoBOjftqxmwbd+3+rEBadK/9e/inpUt/FxvBXijl7TED2TX/KVY0QVY/FzOcpdo79v9sHyM5eu3L0vqEEfGBT8KkT7dTyyHvcVXZbdu2uQrtgQMHVFBQ4CTvVG650XbU9GR3nZr3zrSHKyR/XAthZw0/J+mDOGL6UG0F19yyP9s1mRSRmHl1ks+ilOogy77j5IlvRPkrOir3gq6KdNvoVEVDhgxxo4QTp5Pw3Fz3U35uRQY3MrYappSwz1z0GyM3ypweVBjm32Mg2YbgxiujPNBCwjrHGDeeKDm516a7EBvET/6LB+1MxItuT68F4Jxp3D78d6iJF99rSXuguuFs5b1f9ZA07BNGMFd1ChLXaFmxHaQ9pA1xGmrlYO2y34VNTqwsuDZox2a8exi5AVEHOdyifnstj1P5BaB4BnFLzIA6i/iVHKLPO6TRX5Im/0B66kYjpja/ZMWbsOehcDf0Y9LuheEqVfYFlFPEGh4eHpWHJ1HSjCDpRjqLjwfVR8bWkki4IDr2ezY+kgnYamTXTMagHcAx6u4OqlOfGcH4Cx+1vtV0B1GQFy9/yiYH9Lg8tYSWagIHbyqtRGHBOaZoVGepftErywhGHouAA5IkrHLG+3Y/i5gSwbUhpbEaVR4hVNfWXQnFxClp4T3SW3enx/STx6MCTDsakwA6jkudSGnZP+oqRKf2l/8HKJwYLUnQlUiiYDjLGmb98zteA0QilT/60ePXHQoCAnyCUcgNzGwrgkDtAnnE9I8NT8cInpDgmDVOEHhkg+2PK/5ihqaDP2zeBozEBJlAIPJ10F7IVJlk4BpH+YOxId9Bmb4VYZ9Jkus6erSBoqu6ScOX27jPZs2c7wjkCYktFVJ+fvJLETd1ItiP2KeY9ISJJ1N6IPsx9yQBwRQzcX/m5xAprEHWTx5Gs2tjwXvIeFkSjLbDI8rKznUjZbt06aKxY8dq//79WrdunVOi7N2716lTwIn5nRTJL94ISZaoLGP2WBa49liPYcCbgWoxI5xZf2Gg9YokpraSKFyT+IpQnAhFxKrdva62745KPAnuhmesIh8PYhUmLdHmxVpFRQnhQUJKIlrqufOz1XTVRF3wuRHq2r2zI+6SGQfzY8iNy/8iTfuM7UVhj1kpZJnyYeqfUjNph7hOdv2xL/E5YLrN+c5rZp9n/XONB9cF5z+FEz5Pfs7e1mawFUaObjHVTVmkgXsNXIu1hEShAEjSDhGX2EblRkyHgHVDUbFlP/tMq0qitB1eNokSrDFMiNmrHBGWblVgROpzozTpruIiXPhriTiiLsx3DH8prrElfzDPvOA64PXyc3IEiDzOT4zjuYWpf676p7R7vjT7juQxLOsWtayHh0fl4UmUNActHLAw6/gLIIN2crwkoNrAIcON+9PfiJldn+sr5+NRW0Gy9cb/VQ+BEoBqEOZfV//bPEfK+2yzGxepSY8iHV5ZsUuEdh9G0/a61tYC74tkOkjMqXTxbwIt530Rh4BYI6AgMSb4ct4YVQR9yBzaEHkkz3i0YG5JwMO0ASpmVOoIaILpRnVl7QECVjwQSBydUixNYC2TpL34EWnqg9bjXBYpRgWeloadB3Ypa2RU2tynXFaLpIdgFLIu6DV3iFjQBRlDSxvKAWTk475tAZhTTsWrVhrbmiQ5wmi2olVQlAdMykAu7Sp70Yp5IJF0Y8rI9BrUDEwPyJTpKCh+whIzSBMMqVH/QFyRBCCxpp2GNpl4A1oSgIt/YwFyIiDsIUH4DkqgIFs56wao+zVnNGBob0dYNGnSxPlAxIPzJiCz4j0JWAuJLYdlJX5UNhN9IUq18Vxs6o54xRFtHVRoO3bs6MaSjhkzxkneIVS2rt2jI1t6qiBafB3QXoOMve2Isl/Tgl/aRLfEtchaRyE25KP2OScjUZxa6zkzXU40zq0N4DukWh2mgGIfwhsEdQZjg/meSfyafEHq87w07bNG6AXVccZmc03SYktSRnzC1DlUS+ydYUqXgvXt1LJIoWafpV5PxM6eK/9mpp58L4lETkVBsaLXNVa9x5sjlYIEn1myYkNAorCHckZyfvIcrP21j0pzvm+kNvsobZKcJSjs8GWj1S07dl/agWbcbj4Xoa+hqPrioOoA3x3tSS52iSNReM94yEBGJr5Xp2iKWHwc5l9TEXBOOIKsnO+X56N19cp/mBrIEV9pUDwHe/mAd5tRrVPllBM/oboKI45QZbGH0lIbTyRyfkDYsQ+VtRfxO0ZkQxwy3S/Z/o2SDHWoh4dH1VALQ4NzEDHXdao+SPAYN1rRzZmAwY3wXWz9o0gPg6kwmQw2cybUkGhUd+BA4jjj29LVD1ngGI9gFCOGizt37tTa1euV162jtGqEFE0h+opIPS6TLvipubzTvoOvBuak8UE87xd5P9UEgvlS4xDbWpJLKw/mjJUlMwgEeRz6ZzHnY21BnrhgN16xEPf4HP4EPZhEMuEINULGtVckgEBm+YPpJ1DOImpVJCqs1zxiZFX8dxqsO4w/16xZ4xJHRm5Gu3RQFuzWsbKZBKr9tM2M+Kw05CNmqMxeRPWcn6EcCKYSYFqMomDg+y25wqWffYokCfKWxGjVY6d1ZsRKHTreVa1ybVJKeaNnCUbdqO43qnYNE0wzvnbarVZFoxUumUy8tgCCaMfscEVG3xutMsp3SLLOdwHJQCCOUmLGd4r9IEjg8GTCcDORmCdJDk8gIipa0UPnD+ihDkOTX8gQZhPvMGUQppbVsg/HVA/sjyikQu8SibgbfijcUKhsipzWk+sIUyIlEoo3v2nrNhEkcpAe7J20pYV97sjt+9EdlMLeenSjXUN8H7UNkGuO1AohBZr3thYa1s1LnzBlAOfReV+3/aH/a9LiP9jfYkCJQgyiYP4vrRUDQpbvkhYDlLYodhIBuXtgdeANUf7r5T6oni74sV0HtILgD0KxqSJAfcf+R3sZ33NFigKQIsmSVF4b1wYFmYW/NdKStcz0HfZaCBJ82PhMiS8ad7LXADFMUszaZGogo3UBE4jCzhzuV9t8eIgbWg+Vds8pucYoWhEP4wsTr4joNNE+O+K+qpIoEFYdzktxjWWZSojXhUpj3ZNVP/dZF8NvtXXQIGRPKstzLxFbXrJrETKd/Qlik/XA+8Nrjd+d2Jf8cbtMsQEIqE3LnGQWraZ4x8OjjsGTKGkyT8Sk8a0fh/cfVjSpIyAhmQiqZWykmQjnfL7MPrsa8QuIWhUIUoHJPYrYyE+IE6ZDYGrINAESWn6e3f+gIm8Mlk6WP6qEBOGi39oh/dwH7TAMI9JIinB7p2oB2eJaLWLAHwdZLGRHNFKkVgMj7jMqK4lNtoZI3Ob9RNr2qqlaSgTSSf5NssCNqjSu+VSMabHIVGUKny0B3uzvV39AgWIEAu/yB0n+7ENnMgmE3erVq533Ax4QAbI67VFWz90qWhr06SQHxEm3S6SJd1lwevqY1GWyjTZmKkTQw097A4o32sxueMqSHxKUNkMtQNsxI6qlr27VocmvaPPD9TVgwAA3sQJDyDAyhYSCx3jpkyXbg6oKvgv2BCYRYLCH+qK2rr/j22IBcQJIWEd/xQiRZ95r5LkbO91duvY/0rBPSkv/ZAqdswlcofTCLUZ0JCIZaV90OluHVkkdRpX9OtmLLv2jKVrwL0ir1D0itexj06DajUj9u2S9ndjcQNHTIRNZQpL2gJhCaUgxA6IwEVSiWf/sz/gMlAcmC0EG1EYShcJMslYezh/WGoQlhq7B983EFKrYLSF78X0tknpeERsx/aPiscG032IafPmfLcGjBSdxzUAqOAUCP6/A9QuRAYkPkQJBjLJ3y6tWFHBKkcS1nmXEB3ES+x9FANZBZdS8KArwLAv73CDl3PUxo+R4YmKYG5818gkFDdV/iBxez4K/mRFz8Jr5W/ZlWqg468PazVBwJBZYznXkNpYGvEfau7BYlcRawQyVlhHUhav/bUpJ1svk71vMxx5XpdatiKl+2R9T/pOY6gkPsB5X2jSe/UsrOP47IuU0lLpfboosCp4VUQ27MfEh9+c8DkhD3hNeU6ioOKNRE7/xteQKLeLHcbfZBCLit/I+V9a6h4dH1eBJlCoCdp0kjE0rnYkYkng2TCTdo78c7ihf28GhhVojPiCp9ufk4L4vqrYXHtWR6A7Xfw9xQhsFpobxKGq7X/VaH1F0e7syHxMZPmQDh97zH46NLo4mf8/8nooU4w4x3oT04EDtfqkFsEvuL9LRXnO1cn1DN8kACX4qRAqkAEEKCSiVslBPghRAwIdqZ/o3pU3PW2Xc9RzX0kQ2Gfh8Zv+g5NjA6gLJCGO7Vz8cVY/3HnGE3YoVK7Rv3z7l55eM3qjCd+/eXY3f30iLGSd7XNrwlJkOh6kEUJTQ502g2m6UVTHpvWdfYsLQ2TUQNcUNCRDrr80wWilsr5n7A2n5w/nKu3SJoip018PChQsdwYOHBlM1MMSLbwWht98RKKur4wOz144S75J7wlUHtQGQkmE96fVi1XLUQZBb8Uat+KdANlDJdiRKxEgOklKURBVROXLJHt6Uwv0gOvpZUjz9G2YImxape8QS2wt/HqsWV7A9kWp/kMyXBz7PST8wkgklYOKIcLw8UDmguGF/JNkuD6guqloYebtw5qSpHsOSJzw9+Gw3Pl3yjIAMfeyqOLIi5s8VmFKXeJyYSras6Tm0DFRm0gzPhUKq37ssSab9gf0GZcqRzcWJpmtB6iK1jRHBnMEQEJX1EuN5IeFcG1gC+GzCyGLe484Z5vXDGuP/c82zb295seR1hDoIco8JLKG+U1mx1qNaFpkTA6H6QdUJqQRo/UKhCBHO2YRSg8SeZB+TatfCHZs0U1nQBsnI6Aq/XkiQRkb84JO3+Xkboc5IdsgKt9/EK3ZZT0V2trbsb/5ikGasObfeKhgbUZgqT2XOZ8q5x/1QraC4QUVHUSQs1yB+ZI+FQA7I96SPnWWqZw8Pj6qhlm3V5xao8OMDQNJaGQPSch//hPUHc+gwMrI29mWXhWObpXWPlQziAl8QNw61jMCZSg/BSVmGZASLHHokH/GBDEnLa/9cod0tZ5UY+QkwwGP8Ir35fXv31+YtzbXygbLfB8EeMkpAIOWk4iFwh9saq6LSZsPhz3dMFY8KKdMwju+IauW0vTowaqFeftkSWSYb9O/f3xlDJiNT3Hjo3Ra0MFUiHcoePjcIH8bjIf0m8K4tYxfLA58XpAaTdGoKVOhm3XtI8yNP6dCx/WeNMxMnpjDWlX/nj6+ng9PM04RRjGW2DK0yeTiBEfsEZFrY9AfWG74lVHcJ0Pg+qfCiXul2WbbGfqi/lq457kw+uTYgUxYvXuzajHr27KkRI0aoXbt2OrU3xwXBid4q6QTXLLJkJgXR2sO+UNuA0gTpdhiZyvVK0odKI/ASoUpNhZrvLm9HcTDdAuPPIxZM47sEqc73RtW7rLOHPTRMCZOUSOlrFdq2I6Vl98f65iv5/aIEoHWBBArVQ2VI2FQLE6wNlD2s/5c/Wdocmt+P+pIpBlH+QBxGU7xmnZKvFoIzIKxlgNYwEnj2DNpKkf/jn8Sa3D7d1B8nDsWRmY9Lnc83ZSzqRogA2nnw8kGxkXR0d6TirTjJkt2cbpYw498TP7I98NZIGzDgnmATjUrEJdmWPLMeUI+WIBhjr4dYxN0KpKNbrSBS6vNnetmZ5JOQaJ/FX6Q2FizYu0Z9MapXP0/sEDmrwHz2feazBdEFDsYUKpCZyWI8rl+GAUAgxHtDJcZ3bnpWv8p/Xs7Uu7W1q6FMo1ULIptzjb2PPZb7cJ044+z+tpfRiuTUJJV8XpRt5Z1n7O0U5djrud4oZo34jJGJmH7HA287VOuQjfHKsqTvmzOllhpme3icS8iwtLyGk7BnrG+3OgiU+CBy7o8scO6RYUns+qdNLh0PmHQOxvk/L65oJIJqDwcKFUcSwkQwPhAyA2IiMM2DWMCzhkCR4OXQKx0Uvak4gaX6z+QKEkUMDiFSQIuPSVuetUQEmST/TQyMIGogUvCioMKUDKiVAMnQq1+ULvyFNOxTRpDhlQLBMv1b0vHOa1XUKE/RoqhTKrz++utaunSpBg0a5AiVQJkSECp8lryuVz9nk4Cq2mOcCEgnEvSpD9joxkxYgzjjkyTW6NhZKpFbG+nE0oYq6lakSFbEGWp27dpVAwcOdOqTRo3MA4XvNruNVdYPrDIiqzwQ4JcyF02CeNkwwMR40p3Z6jhuiAYM7621a9dqyZIlbv1BpuTl5Wn58uWOTIHkyX75Am1/o2m1ESjx7wnVACQlE8xqW3LhEqVo+L5O+ye+ErR4EfiSrFMVxTCV36EmChIrAnj2tan321nAfoNCAgIWoj1oswhDRSdfsL+O/7aZ3tIqtv4JU0GVOSY3LjjntXFWsbfxvfGzSic5KXoyse9CXtMy4EaYJoDPlZGfTN/i926aVSqIjfRNF5xpKYl0gd2cSgOVR6wlBQVCutZ4sscJ2kUYo37F34ykJPniex/wfvPveOXW4tG8fKYkfRCZVLshUSD/2Dsx6HStaKFvtnrOCvd5VZNXF4+NZ4azo4rbc1nTl99vySojZiEAAnBdQrxwvTKZis+FNdbvJotnduITEiML+NwxQeW8DhulDBGBmrB2Iqpm5+9SoysO69jTAxQtsC8fNRSKJ6d6qqA/VlJErKiDl1e6iouQdRAy3LpPLeOp03B9Nupg33WJcz1i1xlFU9YXnwHnBDc+Q1rtrvuvXYOJJAokKNOzKDrEt4gnA2cKJJ+Hh0fV4EmUSgZCBBiMrU3HONTywAY68zs2/qwivZ/nMpAAOxPDwpLqEgJdZNZOjp3MwPVyadCHYlNAEkCVgMCw43lm6kqwQk8ukwVwz6f/VdGIsva1VccmfdS2V0NHnpDI1q9fv5T3AwHP0FuMyGK6D8FbonwZo6+HUhhhi7IjAFXA/73TqjNNujLOVtq7xA7Rq342WBv2F2jVqlXOJwN/lgMHDmjGjBmOTBk8eLC7tWzZ0h73RMSZUDJWNt0ESgCqMwTMHOKYyNW2ZDbx+sUAL36SCIEYzvhUnsoiViDMSDZIeKmQlaWWImAmWCL5RL7Mf6PH6ytnbX81G3Zcffr1cgqjoE0mUWUUBPRMxuCzr662ApKq83/MWoeYkxo2bKjhw4c7TxRIE8iUXbt2OeXM6dOntW7uHumRmpsgwR4776cxUrRF7Vp7zqgyRLbNZwepi6cM+1270ZZ0sg4hzfDsgJQF7Dkk/VRM90C+/MTWKD44tARiVvz0O5NMW4iUNtEuD+7zZZLFKOnie6QxXzXVFgbFEKoki2d9lmj3qGctFShoSBoZPwzRQ6W1qt8V12S5o9nrWTLlvHTuLU0aUcVl7CjnzdIHKtamRLtIVadEBcQJ5AMmzLSkMCmNNsL8PPvOkfejEEEpQ4tAm0HFngWV/QydQWlM1VniPTWyfazzJGn9U9LzHzTylc+azwmFJC3EKM34rPB7GPg+I10xj+a84zPl7/HugURJRuK5KV+16HoFXE+sY6bKBASoa815xVRV478Va4vcauoYpqHwvaE0PQbxGbViBr5rfI604OHpQlsU7RjEI6v+bdd5CURsYlKjNqp14GygNfrl117WkZGnlLMzoug8pDvVU3Gh3RF1LGdXOlFTZwskBp4/8Z44EI54oDBRiniS6Z7xcOo5CNjE/Sti1yf7CEMpUiHNWw8xEsfDw6Nq8CRKJUBfJ14eZY5zTDMIvJb/RRr79cyYmMKBEJi3UXllmgKHBwlFWJBLpZxeZWTF9KKGfQYcIkwLQDoKYcLnhUoIYgLTVwzACKTxHDizo4nGdLxCfS/NKXsCScSc9wl+NyXzOolWTo3E37iq6dziitb5P46o25DW6hKd4hLZZcuWOVXAoUOHHJly5MgRzZ4926kCUC8MHDhIu59qo5V/y6pwxbmioM8Wo9Ir/moJXW0FCSyydZKaAF0uNLLizW9YApsIqrYkF8NiU2z4rOmfnv8zafeCkv4LDVpbaxbmjfVb2e94LlRTyx6MKLpksC7/UV91Gd2w1LjZRLDOqWiSwPDZh3kcVAWQPIwA5TmCayq4Fho0aOCUT/jyYLxMW8/OHbuUvXyw8vc2UU2C63b761LvFHwsziWwViDTEsl25OEYuZKIcpYwwQOSjeoqSryrHjJiBNKC/XD1o2Yo6EYfHy5WCBB0QyCgvCOJSwRfpRuVXgnwtyR+waQT1j4qOhJK2r/4LyQRBACjtBmxndMkveoDpxgph0SB6O5+ibT2sdJeABDzE79n3wGKJkgRbgEZB8nAd0HLVdgezj7HvlxZBBV1khsS6+O7Ym12Sd4ThBv7B+chLQaYutI6UJnkjvfM30KAxyN4KEhZ2j+D30Pyz/2hKb5I9DHC5rud9H37jF74sCmf+KwgR4Z8TJr0PTOzZMJP2Pnj2rhqmXKR9cE1xXkfrAm+R1q3WY+0fWBIyu8g2FhjKLYW/76YWHZFh89L5/9Iuux++6yDqT08Lp9zIrkFOY+3Wm0z/MRLbuXKlXrzzTedYlG5Ur0bZqpJVmcdmtss7WQ7ZPolvzPiqjYR6qWUNFOlJX8obrt1Cum1tpdiPAwhHJjDsiZQBgLMexPXDTE05DZxarlPnS31uKLi5LqHh0dpeBKlEqDiR0KUrhnzqYBDfNVDZgjZvIdqPUgqkTKyoTPOucv5MfO6rPDPFak2VSCCzGTj/+jzxi2dQ4bWmSDZ4L8EQFf90w4nfl94OqJTuxo4Y82yDuJg7CIJNsQPiXN1tDAQ1I//jtRjaswXRtlq3bq1pkyZ4iakoErBiBQSharP0aNHNXfuXK1ZuFXRv1yvglM1cCJGpc0vWmsUUv3aGsDQ0oWhr5ObZ1uSRK8xMt4wA2e+j1FfsBYHAhXa+EiuUAJA7D3zHiM5AUkZ42Kp0FJpxHvEJceXWpXXja69PVfHlucq+7zUXi9rfvCH7Dlp96LqW+XANGKJGu1CTMJIRsxCqNBihCoFxdbqOds1/RedpKLkXz5kJq+5PF8jp2LItc+3vPuS4K3k+r22dpku8n1DLCQCNR2J/+pHTOEWqNR2vSWdPmptPqjtSHL5bObcVfox2NeW/9XWRodxCUaICaOFqwrnTdG45qeGYKZIIo9CLAxcbySe/Jd9KfH9U6lG5s46o30y/u+49iGt3hGbAEMLaeIaJEEJNQAtB1SMOd8YE7zqHyVVDWUBIuLEbrsxyQWZPgQW1ygKkorsuZwptOokkii0MrIHuvaTBFN3TFv5GeamJFn4JqAqgsDc/HKxCpO1t/oh82HoeqF9nokkCkQL319tPCcgN/nMIb+C7w1VK9OMOj9kxqKsLSbpQZJhkJqoUGUy3tPvMlIKxSlGqxhJ4yFTSsGcXaSO1x5Rq2G06pbjOHqOgKIOE+YWLFigOXPmnDVIp914ypTx6nhDY2dOj69aOtpmOVc6X2DXcW03ug9Uph3Gm/lwAMh09v3zvmHnBp5t7FOsRfY5CoCox+JBPsB1DjmXiocUe5ormtSic9TD41yFv4wqERzh4o0pYAA2OwztkMSGjkmLzXt3zvFNTcbLIZrsYHGu3M0tuSOgptWDx0X5wqZaWZO+c+kzJJDjvRGgMALQjdGtZ8kqREIiCJAxY+WzpM+ehDQRfF4kmjum2+PHg/5jAnEmRcDqE/C4ADLF8YutBpl3AdNInHN/GokUgl2XeENMZJdOYmnbmThxoiNTqPjQ0uOUKUVRnVjaVkWrak4VQKBM9aQ3YwU71c51CKmxf6XU/122lpzp3bDkIw757lGW0B729M0mxSfxRxGFmgD10wsftTVBRYjHRWHExJygxQKp/vVPGAlK3zLmeja+OrXXDCnR+3qTmtPOgSEzpqUVXocxo0YeC9KOx0sV9es3UGR1bxUeTL6OaS9Bak1rAEkkfho755Yet8jaYTIC9yXBhaCkys3kimSqrt3zLcnDiLW2gAo+7W+J7Q60vrDf0R4R3+YHgYwnBZ+XIz8iRnA5Uirm5xQPEjsSfxQjrKXE1hcIABLZ2oqmnaQ2g5NP8UC1QYJBcorhYiL4bCk+uLaSOPD/8QRAIYDJJWd3qUspElXrYYXKbZ6laLTYg6o88B1te9OmHLFmK3tWsNeyj7z4MTN/nXBHxVQpuc14/VFtfY2FUfxHeIRRxMhtHiMk4+IQyBCIZJIxkn7nZ5Nl1fJEgolWJM7RSLPw1+RMkgeqVoKYDg+Y3XNLekzgiwYp4CbwhZGWKh13rPxbefeNql6/3dra+1lNn9lTEyZMcMR1quvt7SJQaO2cPn26a/cMDPoxRL/00ksd4c6bvuIvFi8wfAG/mMSJWakgMHYd+nEzj4YEOIc/mpTBHkTLFwRlkAugzp72GWuvpW1M3y7eCxhIMOvO0v5oxCecoahQyh3TzBj4d3g/FA+PdMGTKBUEiTib2dmqS8Q29xG3muEYbuRhHh7nfdOqEQQlrhVggfXV8ljxwQlEwJj/swSDg5yEj755xpUSJCJZRuZb2/qME+GqNrGgwo34jFUa8t4Vfn88aAKjO1oQQh8z3z5bgsDEQ9bJbutbYEdSUhgpmbyUBx6PkbCoWZA5kxxW2X8kYr3lSPJpY0qmsAmCKSo8Y8eOdX4oa9as0bIFq3VoxgipsGb7u1BYbHzO1n1tBAlBQKhhCMjHi7EiRF4pRKx3mXWz4JdGoLhpDIVWvWbiB54BVIMItvEtgkzA0ydYr4B/Yx5KIgRxAWnAtV2RiTNuekp/6dLfGxmz7IFij4JyEzW8TtrYe8F3qNOk8OukLPB6d78VCSV/SdYvu8+qrhBHkMSYEJP8zfyutawEIKnnOoJAIaGjRYkK45BbrEUF0+UwIoX7sQdyzdSmIBr1G/tF/HcEkc6+H+Zx5cY5R2JEfVTq/x5rSaEFYDlTQ+JANRNSGII9TMnDGud7r62gHYoWTsiEsPeHtL9hK0tUwzyDWIdUwxPPSyq3kC887iufi5FTJR4/KrU4qs3dXtScOV2cdxEJIigrueVMgOBEXUTrTjrIds4pvvdD6+3aL2saCcktQBWwZcsWHey1W5EGExQ9WaxuoHjA++5/s9T7amnNf4vfO9dssx42Up22Hcgl9ir2C4jgeENV1ABc9xQVwpK39mMrp+I5F+DO+0Gm1MOPKtT7riLfbRn3zW57QrruDZ3KPqiFCw9p7969ToHaqZOZVpxrZAprDCXsa6+95jyzgilzvN7LLrvMeXydbQltaQpiWrUhUzY8EzM/T+Wzi1hxEp8YyAY8miCTz7GPo9LgfaBQhdRA8eQmNhVZTvD4lXadc9ZREDy83m5h067WPiptejaVokrUPebIz0WqzZjZw6OuwZMoFYQL5GPSfZKQNkMtKaEiFrYxYRJHckGCTNJFAspGxsaJsuF/N8WUDbH+7Qt+Zq0rECcw1M172wF05d/svlSwSTyQiddmhJktVhUEh5i9EXTTu+zGr8Y+V9oASE6c3DFWFaroawhGgF72RxtDSMsQ6oTKtHVRWUHNgSmdq0qn0DceTOSBTBk1cpS0cKDe3N6g1P14XySzEAOJ/bPxICghSGGEbrL3QBBMorZjZvGoVIgqEgXaCGrj2FkCEYKV2XfFZP1ZFujRB58IiD1IEiq3iQokiEAqlZgFQgygkoBgwIBwX+JkgZiiwE3mKDSSgP9WNJZhDbLv0IaA5Bw5+c4Z0tbXLcEhkHKqj9gEC/qr2UNIjrg/r7OyLRk89sG1Ia8pWxr5eQsIMW7GH4DPGGLpwl+ZZwLtU6gF+KzxGCL5hZQiOaTiDTF64S9NyYJBMv4RYckk17SbaFLFIJBkl++U5IjXyv/ntfHZogLk+kyHMSrocn7U7Rvm12EPSBKLMTFkmPNpWmjXFYkqnyXfofNgipHMfGd4pfAZOi+uqFUg8Xni83NtBwmg/QNyNqxFrbaA7yQgDMNIkq6Mlo/Y/pQMYV4dQdLPvhc6dYivadg6HcnZohkzNrtqe69evZxHUNu2bZWTk1MquWUNQZbhhZFu7yIeGyPiaUxJe9DOt8S1SXJ77NgxZ/CJlxbJ+JmTUTUc0Vn5s5Ejxf4gKi2+1/a1Kb+01h3WHwovxhaf3GtnG5/N0U3Wusw1DPG5/EFrA0IlhXLSPdbvws1rUerVNj+UeLDHEItBZs66I/Vx2xUBBOeY7xVpa+scbdqS5QiJbdu26emnn3aKFCbysdbOFbDGWFevvvqq88kCeMpxbVx88cVq0aJFqeuCeIQ4+aJf2/pCTc3eRkGCvcvtv/kSXUzsc7mNbX13u8xaHiESavMeVhYolNLOTk7BsIEAxG7cyAPKA+di+QXBqKLtD2jgV6SWA1qfc8Sch0dthSdRKggO1NMHTQqPoV3bkUZoxJtUlvAy+LAF5C9/0vodg7F/VFQxx+r7zmIShd7ivjeaseULt1iQzUGOygUncsia+b+0HufaTKK4RLCBkRuJfcRVARWzJX+0zxUZKaodvpfOE81Ii0Avnq3ne6kM+Du+E0z/1vxHWv0vI28IssrydXAJWgtLNul7ZSwnn0NlUJgf0b5ZjVQUogogKL76IVtvryYhUQhW8Hmh0vuv8eGVf9YehpVMGHji2lg7VQwE3fT5U9GtbXAjMmPGig5ZsXURdt8sIx7cmMH9JX/Hd01VDZWTU7FEzfiTWyL4nCBO2T8OLDdio6rvge8QdRu3YZ+27/DkgeKAivVGe1tFvRSSgcdP9FcIiDaIXogjRr4HnytqC9R2EMD8Hu8A1hsEJMTAvJ/bHufuS+J2r9T1PzY5ACIlrKpGAudIogqSKBTo+TueZ1vM34HKHpMz+MxItB3p1MxUR0zd4PqkCkr7ZGUIFRIOZO4HoluUff4JacNgk8Bx/SyyzwqF4jWPWBDNXgjBzvOt+Lv5DwE+Nwgn+uSv/a+pHVl7SLJ5vRjK8niJQCmACrK2x8sot7h2lqHCKSqZgPBZsSZoyakIIAjcWR4zdUxEVoMC5U7eqLzs6NnKO8bKq1evdqPIMf3u2LGjm+hGQsL6ghh77SvpJ1CKX7QRbm9+Tbr8L0b28dpIvCFP8M3i9TGWPFCkcJ1kn7dG9ZZ3V8GxeiX2b9oNMZWmRYIiD9OgWFtMAnTeX4Gh6q9sbeLzNOUXRgZzvUAav/oFI+ETgeKt03jVevC5cNbTLkcrRdompEVsnzn/bkjCJhqSf5Xmz5/v/EVQEeF9Nm3aNO3Zs8eRKRROKpL48vVzDkBS0P4B2e5ayPONWEcBBznRoI2RFqmM1madbd++Xa+88oojUgDG6BCLF1xwgZvqluw1BibOtHdxo2DBvov3D23drK+s+lL9plKj9nZOZMIAhZQKc/3M5wVzZqfSSTuiirY9pKKbn9OKrHx12naZunXr5okUD480wJMoFYTrj80ywzUCWJIKEuGwyihJDioDGGaqSEFSQACMaRxBfZOOMYOniCUaHDRUcoPkwrUNPGZtA6gLFv2upB9LbQUBoJsckMJM+4oAvwlnBPpFY/j5rKl4MJmB1gISYVfJJjm2tt1Kwf19b+m8r1tlHUXCjllWbUYpxCQNAlACFqpNzXqaooMEkqST77wqZxhSazfZ5+wLsrVDxQcD1GSj/3heqt2DP2rmeARXYe8NoshNCvl0eDBDQk1LWrpIlLNeDtHikaDBdxOMXD377yrC+SOkWiGNSA1aWPCZWIlkHZGEBR5GyT5vyNHJP5Ka95HmkvAutAQ3ncZuNWH8yXoOSzppQUTRRAtAIhnFfkmQjAqGthNeJ+sW1UyiNNkRipEkygAQsUSgoqa63J/XgeqF0aIkFckmWfEdQ6w4A7//GUFB2weta4xNTyXZcM8ZjTrforfeesslt4XdcpXTu50K1jLqJeL2flQ7KMVo1yGQ5pqFMMHzZu1/iwlmzgDuC1FAZZwqLdcfRBMtYkyaSvSc4WzCq4eJObUd7Gucf3jFQJjEf1fPfdC+j4oaV0J6/3VQ8rXUZVI9XfjpqVq/bZX7/vbv3+++01OnTrlWyvXr17v2BVor+/btq+jxhprx7Ug1JUHF4PXSSrnkD1GN+nKR9h3c6wzHeU1MRjlLnjgPo/pustag6wdr0ZosbXkh/oFsjT95g+3hnEm8doiRREKdRBzTXchh2glpn+KMYwTrqcOlyU4KBYzFxnMlE8C+xDkImU4rL9dolYy9s+zsveBuU81GsiJqVK+RJk2apA4dOuiNN97QwYMH3dSbRYsWOcLiwgsvVOfO1htVVvLL68rbI216zvylUDKgLHKWOLFz1Zn3R40YIj7FFwxVLGOdk5HFkMGseQgUCDv3udSrp3HjxrkW49zc3Aol5bwGzgxudR18FpDdqDZf+2Jps+cqP37T04q88zUVdt6lAwek5557TldccYUjg1EReXh4VB6eRKkgCBoI2F79nCVPVHkv/YNVLRNBMMKoXZIPWPf4TZNDlL8nwef3BB5I3PFOSBzTSBLCwc1zkKzRI1kRU8pzEVQamndPP4lC4kEVlwkqzjgvlnhRsUQmj3klCRTVDnq/q/IZur8lgW5mhAPTWlgbEBzOLyU2/YVqlvOfyE7fd4bBXbznBm0QkEYEJZh6hpEEJGpUMEnCCITDWmE2iLkAAQAASURBVHFQT3EfpiVBpJCUQwglgioXlfyqrkP+nsotbW5UQWlX432RIEEy0CZH8Iq5JFMi3ASdKrYQYe7M+w97X2Gg7YYEPmngHA1RVMWmorDmMJrlGqaCHKhUIBCro6WtOsF35T6LxJ/TElFQTJLEwynOcqUmXYyMI8B/8/+K2yjY99jTICRHfdn2A1fZTtLbzbVbESsCiBqm+qCIwbSvIq13EBNUnvnOSEogYUnmSTjL8qQ4ceKES2wXLlzoKsoOuflqfMUiRXdeqsLjduzyWtiPaEVx+0NWTJod5xcV/74xs4SM5zN1LWGnSpMnZ80Db7TEqDa3UwTgs6Z6fd5t0htfLUm+JSXcykM0OfHCuTDp+xG16dxcrTuN09ChQ7VhwwZn6r1r1y43kYSkkraLnTt3asH8BWqz8SLtnNWzWia3JYLXveC3UW1v87p2nFzp1ls8mjdvrj59+jiFABPeSHYb3hHRoVUlSSj3WKesPcy1iJUB9j6UZdzKAnsARQVahWpzfJII9mrGPnMOMXGJ9jmIuIp831yLqEQHfUAa8nFTg8R/Rqg6IOQwkmdc8MaNG536Y8eOHXrqqac0fvx4Zy4f1t7D3kxxjdarFX+x4p7bR8rzuJpn5scQzBA6Iz9nhZ54fzbWO2b2M2bMcCQiwPh28uTJbo2dS+1GtRWsDfZszkLORzfavihN7WJfz1GDy/rpjek73V6Bqu7ZZ591BsCsN9adh4dH5eBJlArCBa1Rq86cNTNNEsjxO6dAiYHqMzfGFva50YLnIKkKTE9RSiRWvAmaSDSpLhJA1+Ro5eoChwWkEVMMKuPYHvqYLaUhH5ZOHZFW/bNk+wXVZMiOnTMt4XNj4arYUpGIwFOhJvp3SUbjjW2pnm982ogaCCO8EBJB8sHYxV0NTQpPVTsRHNxUKJHZuhGgl4a3jnG/s6qeSpzBSMepZGIk6vqj18VUDolTDGL/n+cgEWeKDkEocnHnQ1SJQJ0AkeswfupCUvA+99pzQ5iWQJatY67z07H9AHCN0qZHyx9EFNO8UEK5RKUo5qU0uPYlGW5915cKE/qvqZxRmcZ8kj0s+Fz5nCEXE72I4vu3If5otWvc3n4//TYjjJMBUi81JUjMy+B2U58km/iTEqLWBrTgNzaC+KJfmp9QIkFBskFizbhPEp/AcJEktmvXrjrv5sFadyZLyx8oqYRhf09VSZHKfZk2Nf52a5fMFPBZc93jibP491UgT8oBaiDWpFMdxXlQkbxiLsv3SkJJWwMJCWTK/t1HdOQ/9ZOqm6oDJ3ZHtP3Zhjo53i4mEiGSb0aRBya48VVmWo8xJ6b9Jtm46KqCPbrPdaZCqY1eWamsQQg2jOAhVCnU4HOHkTZka9h1yVlQv5XUoqedCb2uMiIlGbnJesNz58orr3StPShRTp48qePHj+v11193rVqQKRBlgfKDsxSDcXxbIEUqvA6j9vpREhIf0WqM11LjTlHXWjR37lzXasT+BvA9wfi2X79+XsmQRlA0It6iZfOtH5vKsLLtY+z9KIuY8NP1omxF6g1WTv16bg1BoqBae/nll933i5rOEykeHpWDJ1EqCIL4xDGSqYKJO1Rpggo0lcWgquZaAprZSLyChMMY0gSZPJssCUllfTTcY8UknY6YOWCtRlRoA18AEkUqrRgh0rKE30O6HdFdH3e0SG0uO6SsvzdQ0QF6ENLwBFGp3832+gkqeF88LCQA/dyofvBD4Gfdp8amYNRSuKQwbh3SK88N8N6oQicCufbM2+3fkAKoTRLBWlv4a/s3gXD9PyT33+E1OEIvu2Lrj2ombWkr/hwywSLx2gpa4AqlY1vsxnXD1BHMYFH/OGVKBZYPyX3HCVGtf4oHLvsPIYkIkhlTi3rErakYUFbQpoXSKajQEhwP/6yNrN6/wpIW1AbxigGusQ7jVOvA50z1NHFSBWQFxp8X/crMsiEtuA8tPFyP7CtunwvZN/ECYX+hTQAPBTxCqKIiRQ9bCyityiPtWGO07Lz8KXucdJHOfIe0YT77PmnqAzH1YSR6tnVn3rx5Tq0QJBskOSQc5513ngYOHOgk7+3virhWTUa2VwcZzjq95F5Tb9U2kq48cN1AcKDCQ11U0Rae8oByceL3zRA1cT/hu2zQoIEz0OzZs6d2797t1EZr16xV/tIuim4ruy/BrdlI7DtPh1qFtscl/dVg9Eq17tHAKQJQnzRubP18pcw9s82gFwJlxnfST6Tw/lxLwi9r97maCojDuM640TYMsYeSEgVlQKaQxDZqY62z3I94CpUOSOW6xF+E9h5axpiCc+DAAdfeg8Fx0N7TpUsXRc9kadmDRqAkenZVBjwGrYMQ2RN/dlpLdr6m5cuXnyWEIXiYwMNze6QfgVoJdTvqYr5bPBIpQpQ7DTJmOo9H2pCPWZGMa5H1xoh2CFbOILx2Dh8+7EhgDII5r/B4guz38PCoGPxVU0G4kX2VDIIW3WNVaRQQTPTAl4IDGTd/R2ycifODSICrXMS8PKhuV9zk0B4XOe/GZ62Kspu58rSeoKYJgju8NXLtdbmRqJeaVwuV/1SrwMlfg70xqikEA6vWrlHR+GHS85Okoqoz4SRuyFlxgb/ir6ZyILgb9H47mEhoSXZp5el7U+1OMgIfnVCksj5TXcNl3C9Vf4j49Yd/w5tfN2PlylZu+TtIMvwkmBAx9uu2VlP1qsg/k6/CfluU3b6DCneXbRrBdbH1NVsvHcbE3PJjnwnKMGTrKC+CSVBUfKni4fvxwkfCjeIggFBs1MYkNn7qVTxQfgXeASRSJIu8d2TifHaQtGHyZP6O6xQipd9N0sX3SOO+ZSq9UtMwIlHX9mNeMpHk7WH7rbc8nQRK8RPYRK6XPyNd9Y+omg085ZJpKrVU+II9Dj8KEtsxY8aoWbNmZyu2GP2yP0FOr/x7GsakB4hY5ZERuIyOrs17WzLwnkgKmChDO+iie9M3MaVpNzNZpfUu2ah5ew2mTsFUllGuwweM0TOPFunQqXpJrxnUWOwLfOecv6hT3fSu2D7S9WKpaTmjgGkdcT5qZ9dzRAVbW2pUzvWaeHOzlDwpeF/sl+xb079l6sV0gOsRXx9MUstqdcskBO+RpBVFbdsRMWKrqLjFlVitsu10wTqDtIOIpb2HccLsL7SU0d4zYfxEnXl5uOb/tF7KrakpIWotlQc+cUx5125RUU6R821BTQeBQouYNyWtPjgT3piKk0IXvnWMMN41zwoMFAhQwrPWWH8UdyDrIE9QdeK9h+9b/NoLvi/W09VXX60XXnjBEXOnT592Hjz8F28biBT/3Xp4pA5PolQQtEpAJoTNa08EmxgVCZI+bkjeue1+y5KE6x+3tou3fmrjY7kRaCVKYQlS2BTz8+zx2DArAg51grcVf5VWPWQbcVnBeyC3Z6Nedr+05mGbQjTq87ZJY5ZaUbgpFQcOOPJk7dq1Tp4KIiOWK3v+EGlPK/c6aesgWEysdseDJMkpTULaMfBDIaEY8lEbH8x5QDL3ymdNehuJVeRa9VOtRlBheDtBMJ5qKw/BP2aZtGuETXipDFifC39j3iwkpiRC5XlVMP6TKRtMPYiOHSU9c0HZLrMQP2/Y9QNBsHO29StDZCJbh1TAbDBoV+l5tX0ukJQYrpLsJV7LtXU0NC1gGBe70bsJ5Br7IUkt1xhtVsF1ymfAhCcUYOw5KKAgnhjVi/eTSzqKbH/EMHPE56xli+AwMUHOaZmvY603as+elq5dgcQxETzOvJ9KG56p3rZHFHwvfilPWbc8o937t7v9DSCLxrAPw0WqyKWqexFrXWIaA1NnINbjvY0q6/HT9wZpwh2ZqUApRaQ0s/cKWcTZuW9x5f0DWGcUCGh/aj8m9YkgJBp81zknWypvSfh9uPYv/LnU61prVWQ9Q56iXnjj/8wwHoz6gtTr6rKfj70T8/KC+DVdmKUjs9sq9/9S/85JzlDasE4Y8b5jehUUPZFir7Ght8TMtTN47ZWFwPw83R5ErDNIC4xAMZ2FrOUcO5F3Um/9Y68KHorqTDoJlACo+Ra0Vk69i5V17TT1GtBFF110UYWnBHlUcYplrPUXA2A8bvJj+wgFV9fmXC82HbBpaeIk/DEjzqD4mmuu0YsvvugUdShRZs+e7fxuJk6ceHbimIeHR/nwJEoFQWCEPBMipDyQYF10j8nxghaJACgiDq41bwYquPgqQLBgRkmCFm/gxkbKyE3IAP7N46YKHpepBozno3JfmWCTZHXHm/b3/WJeD6kG6yQXSN0hT5ggEDi7A4LQtn2aqMHFZ7T9kVgryW/sVhZ4Lf+5NHmLyRKM3x4t9lngcwtIGdoGIINqYxIbD5JTDs14f4maBNLkZl1TCxpRc9Df+9qXzWslnSBpZkoJicDUB0ua9UGcBOQJVTwUAwQNQcIbGbRW9d4arug+9OfJFzNtYIyYpdJ67aOmxGAf4Frd9LwpCuINo3MaWpIXlsRzH0c+1cIYhQSTSUN4UsQTHCRPtO2QKK5/oiQp0O0i+73zIjojtR4gXfWQTftglGqJ/SimVnJVtpDPrrDVQc3b8byWPFrPERS0L9BaQQuD9XRHnPoECXS1+1NEpX3zchXt3UCF/QtdpRZPCpQntO7Q+lEeCcpoWXxkOBuo/Lo9qgIqR1QOjDlmBDnte9U5melcA2dmv3eZ/8uqf1mBgLa7lLxvInYNkpxAjPLZVbYFhbaHsIIK1zkmnbxG9gc8kWj14PVe+jtrW4NYpHCC6TFqrLDHYLQz7Zmc4ZzlicBniesu2TS2MEDkUuFmtDZtZUv+aMWLVApDwd+z/2GEiZoWj7dMMDA+l0F7D8Qs6ieUA0c3R5T/+AQVHqxGU9eiLJ2Z00cdujfShZ9orSZNGvjk+m2Cm76Hl10a/Pb4DllHV111lV566SXn70SrGEboECqMq+b88t+1h0f58CRKBYEcG9M5HM3LM0QleeS+jJIjyItXV1DVpSqFnwBJA0kJQRXBDfJQKp0BnFRvuLT55ahyex9UYVYTZUfLl++SYC/9kzTre8Ujk6sCXiNGuExRueR3VrkLewlB8gp5ggkfyStGVonmaSNHjnR9mnnj6uvppfa46UKg7IkHgfKku8wcrrYDRRKqgK2vvE3P37BQ2d0PqaCgxVlTsrD1iLoIwrE6CJSzz1HItSHNuM3aQXKaWCaK2gmPimXLlrmRkfHjP1Ex9B7fUac2RLT9CfMtIQELa1UhsaUthSQNFRPVV8xUVz9skxDO9qJHTYkBYVAKsRHEGBJWxoj3XAE+Jy37mJdJAN7P0I9ZYoVSB7lxQPTxc1oRnBdRrC0B3ycmXSx/MDZiO2YcjL8NJKf7LhLJwUhUuZPW6nj0jE6ePOOmpTC9AgIF5QeESseWPfTW3fV1+lANBX+ncpT9+njl9turQaN6OQIl3vAxFVIKVQ7y611zTJkA4X4YhR2TkGIm5g6R4ulekOyQWZhlo2CoraRcVcF7Rn123jekIbdIW6fZOmP0Ouq0ElOOYgqWVoOs7QmpPGu5qi2qB1YnKaD0Mg802hZR3wUGkZh3QqT0uMzMzTmjUIOEAUKWM5Y9BiImrABSkGfXW0VIlAD8zbBPSP1vtnMEApK9mnY1R0IGn0uszZeWSdqSiFFo8XUGqWmcOOdRNmgJhDRulNVKz344X4d31sAM6WhEB57prJ030Ormv+hMAWdUmzZtXGsPipQtW7a44hLxOq09l1xyiZvAVOY47Ti/OjeBdL906pBZBBCfck6xZ3DOBTGP3ys8Mg2eRKkgqMLgsr78r+X3YzPRYfOLFuwO/4z5dRBMoZDA+IkgifYaiBQCJKarjPiMNPZrFshApLToJU3+kW1K66ed0KaOz2nXw0VuWkDv3r3VtGnT0I2OAHLBr6U5P6jiZIpEkBTPl577oHT5n0uPMiRRJWElcSWBJZENkldeZ7t27ZyJFc7uwSZdv7805RfSix+15LQ6gIKH9gumImVC1YxKLN4aJJw1MVYzEdHWB/Xqyv9o4b4m7rskkeVQThx3yNp+8xvFiXW1vZ4Cq+a2Gx1V9/ce1Jq1a5whHiNmA1M8QNJNws31A5F3uHc9PbUo4qYWcUsGggTG3PJ5o2IicHDGunGJDf9eel+SB4hIw/EjaKpaDchf9q7Xv1o8IYXACeUXSp2p91vCh8yYSVlthklvfK3YGwZVD4TyuG9L1zxqSWXeXqndcKu6Q/aiUkkkUWgD6nNZC+2JdHWtWEwVYF9hf4GkReXW+tAIHVp4YcWcjquEiIq2tlOfTe/RBV9spNxG9Son2a5vY0XZSyHaj2w2hQNEAOcFZDwEAOQdsm6IABJgNxa5jgelQRsFZyo+YyglMWFHVQFBQYGC/R4C3Y1sb2Rr2Hx1qg7Uo2GqKRRCTbraOFyuj/g9Yvb3pPk/i00jSwKSjwt+aufhvJ8lb79FnRL/+JX57PhciGmIU/jseF0QnaxF1h6fGV4nmDrTOsb/z4QztDaCeOnQzBY6Oqemzv2Ii3MX/ELqMbVyZJ3HubuWIP1RpGA2i1qXWGn16tVOmQKRgp9XWH7BvnBkg7Xeb3vDyNfgrArAHst6Ic/pMsWM2CGXU22Z9PCoDfAkSiWAwSR91Bv+V/b9SLJICKjYYJZIFZqgrkELM4NiA5r7o2KSg2keBEwkGNc/Zgmoa9tohoQ+qm17N+p0vz3avbvQObQz/g4lBzc2w0ARQFCHYoQxaWklUOLNFddIr31BuvJvFtRHo0XO8ZuEBvKEf8dXUEiwIU+YS5/YV0tA1v0SUxG89qX0J9wEzfShc0tX8Px2g4OIcXhLfm+mnTWKSFTZY9cp78xxndx1zBndQYhBSvD9QqiwHutl19OKv0bMjLUGQELx1h+Oa2HhizpwurinhLUG2ch1MmjQILcWg2uFpPSCn0iv3GqkZ7nPccpuFQHru+tFdl3X9pYL3guJKuQv6gmHqE3ngVxiPOYFP7Mi9ol9ZiLMfQO4lr17pDMnpCEfsalZEC4kvfhE0dpw9nGD58yWBr43okk3D1N+YT/t37/fBXybNm1yJBkS5KKCqI682V5Fp7LKHtHcyBLSlL9DWj+a2usObXcoytLuac2Ut0PK7ZviY5ZhJsh+zw0Fo0clPsP6UkNura0FtrqBEqT0C7ER2LQY0ibD1BrON4gIiiO0vOGrlIwY4XoY+gl7jJc+EW5OHYDHcIqbNH92FG88zj2cPmp7bXWN+E4G1itts05JWceJ20xCMMJ96tSpTp1L7A6RwvkKkXLppZe6NtUgXme/QTFOyyytzPgSluU95mwLVksrHzIbgp5XmX8ScVemxOIedRt+GVcCBOKYqZEcnjosLbnPDpiw5B857/9uMs8AerAJUGBt98w3n4j4ahSbEaNf2XR6X2+jZTFgxMhx15yoev4wT+uO5DoDKDY6ptxwwyiTPvyhQ4eqRYuW2jsvS3O+H0n7GMNEIJue+d2oJt1zTCs3LAklT9iAIU94fcnGL55V+NxozPXrX7FDu8rGkBGrno37jsmWCRIzJgCITeNoN9paAGoSDdoVqsXIEyqon3tWEYDvCJJQbhAqOPl3bjxQy//WR9HCmis95K1trOwFbaTB25xPBZUUiBNuGPQlrj/IKNYdVWsMYtMxJjIezicFH4TfxyZ7ZQCYboWnw/MfNqM7ADn81k9MoecmjUQswEpsqXP3PWIeM7QpoDBBVZW3x6ruYV4mtDJiOJudG1FDNXTjNbmNHz9e27Zt0/r167Vl8SGd2RCeNUNOMAGFKUFNOlrSyf7C89NeVFZFF98bSCGInbk/DL8PRsns6Ywx9ahbSOat5cbCR80TpecVRhoCzvWhH5emf9PGNIetPUzPR9xqU3xQv5UF9i8mW3lkPhD0EjcSE4YBZVroeozanpdoIIyvGfFoMLaePThZ0Q1lIK2qEOgokTwyB8RDxGwoT/BCwRuF/IIixXPPPedMjYmdaJMl18ETzRG70YophSGUF/1WWv+4NOKzMWVu8wyKyT3qJDyJUglw0dOTTtUVE1R6scsC5ArmgRAFJFWuhzAYKRyy2dACRI8yFVjkv/zNed+MaOy7x2r00V6uTQHJXWDSyn/nzZvnWmh6dxugvD9P0vEdNVPy3vRiVLvvXqAjXeerKK63gU2XlonBgwc7U7RgxGcqFfvrHpfm/cQMAys7wpLPjsca/22TEWYi603A5CbGzCr9OSUblV3h+4Tcr8cl2br08xfqaN5wV7Fg4g1qFCoXAEKF9oota+urcEMNZ5aFWYrOGK52k/dqwLDejjwJWt6S9vcyzekWU3y51pOd6ZFKs56Z1jMFNVrfzAkWeB9U1zHORO12luyMmu9Nqt43tCGU14oAYYNSyCWlZ5/fPkgCP1rJaM9afuCMpu0ig0j4kLPMwBXShyouLTJ4UeElQvvQc++31pkw0AIy+Yd23zIJaUaCvmLtJJnyHXukhobtYmd6vF9JbBQzN7xX8IOCDOE+nEkYy06809pi473PAhBXsBehyiqvEEIi7KbieGQ8WD/b3wzfM4lvGNGNZ1ciiDVZS/HDDRp3stiI4h7kCwoDiGJazXbNDT//eG7Umnj5eGQWOFMhUM4//3w3UY58Ao+UHTt26Omnn9bEgVdq9U87OMuBsiZ7louoGc/P/K6tM2IjFCr+3PSorcjA1LJmQJKOx8aBVdLm51P4g2jFJka4jarAnqfXVbSiRJTTIFvtGrTTlClTNGLECK1atcqRKYwORg1w8uRJrZq5Q5FXa67yX5CXpVMvD1Tkg8sUyTnlRo+iiKF1guQ1FfIkHmymSIkv/JUFBEza2TErdeabwLXtCJMMYl7JuNVM3aB5XwTp9LKv/lfxzyFV/jGy7ASVqtOT18WqVCeTt8cwQQVCK5i4gppizFciqt+onto2autaYzAIxkQYRQCECu0WZ04XKrKyj6IFxd8/agD8H5CBhr6felLrgTb9ijHaqARQcp3cV/bnwISTRm3ixtruaquheodGjYO8i6T0OfLcVNnc6M87pa2vVbxtp/gBzaOBijMKCjclKsOAeoT9j6Aaj5PqmIbToI10/o+NsE7Kf8VGzR5eHr7ntR9hVS8kyK41Yqe99sEfliZ8Txr7den5D5XeW0iMISgxu00FPD6VXh7bo+4A1UjoYK9IzJD6QWlNbPIcIAlZPkYa+w1r2UokUWj9RRmHObVLZssB6y1TFG4eZYM1lMyEmDixzXAJ+6/EyZGQL/EqaZTUF/3a4krIXxRPLXrb+XfFX6Wn3iEdXFn6OYgTeGxPomQuaOkJxhwz9hil8b4txzTtgVM6PTcN6vAYaEfDD43Y7rI/miVApsbpHpkNT6JUElzwVDQv/Ln0wgFp97z0G30RyOOEj+lq/AhGkgbICja7IUOGuOTVTSA5cEhaMFTREzU7v7dgU1u13DNKg96TrQEDB6hFixZVHo+GZLTrFKnjBDvQmfqx/Q1p31JrHQgmVxA8UEmBeMG8ioS6/ci6IxOEbMBvB7IhCMjxbgidMpOgeDrCZJQy72QtFoHZL58zwT/tFQH4nlEacevYsaObUILx5+o5O7VmTbF8ADLr4l9LJ/ab0ipx0gQVVSTsY75iRocQOFTXdr0lvfHV5BJmKrYoFQgMN73ISG2p6HSW9s9tpKIPS1kVSGp5PqZPXPUvm5TC6E8+x1TVUFyvGEJCbJGA08aTiQqoALTeYSbLe6Q1Jh3eDPGVUlQgA9+f2md4aG2S1ogBJlfHawrCm/ugal96vxFc9GZj2ppo8glxw3dIywW+LeUBxQCtYPGKGY/MR9th1k4Tm5puYI0dsuuB8yo+8WDfQ4Hi4gfIDzjmYC+MxFrOupjCK7H9IgxMmmPP8ch8QGIcXBP+O/ZIKvokpq98tuzHwUuNAhNFh5c/aXsXZxdE8EW/sQIUXlaJZzSJL6o91Ct1Ibaqq2A4AHEc/50xbZ6KXpmkU291ldJEoARgfeGDNu1W6cp/2BRSv648ahsyOMSvGbQaaOaq0z4jbZ+ePqaWQ5HKPV4KVMfDNhcSWDwfRo0a5ciURc/u0Oylbc+OHrM7WRLhRpCVUdGv39IMFKlYhI1ShKzAFJPAj+COxDoYIRnNz1Kz1ZM0ciCJVRp3wdjkCpQl3IZ9ypJ/jFQhUnidkC0EkUwvCqYN1LWNGPXGhb+QXvioGXlVV+vQkI/ayNpkSW3QW9ujRw+dmN5Da/ON3MArgtYPxtdCooQB3wAk7hA7BHAoj7pcaONLkXw++944vxLWRQOpWU/zumFErEuQ40DFjPVZUWUAawd5PO09/W6yaTyM/uSwx43eXd+xdRYk7VwXmEC60Z9X20Qa17pXB9YhhBdTRKhk4onivqMqkMkogkhMp/zMSNFUnPzZ7+LHx8ejsMD2CfaIoJUS8P8hBdlHEskf9jgUMPgPLPuTNOzj5b8GlDiVbT30qL2A7GMSD2vlLKK2H7EHhE3jgvgG7lqJO2s56xiLzN6HQqDc6yimRPSTcuoGONuTqUZR7TGwoLziCcUKzD2zc0wlFbSLsUeu/5805mtGIGNuzWCDUoqWoNW1DpxtdRm09AzsOUKblvXUltktXJt0tSBqsRV+dLQ5UhTz8KhN8CRKFUGiRHJ41T+lOT+0McZVDabZSGgFIIFs2LbsZCxQfOTm1lfRsp4qOuh+WqJafMP/TBr86ueSPEa2VX2ZOvTfqaUTEiYJjf6y1P/dFgC6w3SHNP9X0tpHOdgjrp+WA7Y6RuAF79/Nms82BZCHSk43uswkupjypntMNAQKioBJP7DEM4VX5MgQxtZe9if7vkgmkpEvVHJpuSg8Y/4BKI7ArnmW0A7+kNR+jLT5Bfs56gGuN6ZvuMcNMVZkRCfXIYFlZdcb6i9GYkOMoO45eVA6ilnqIUuaIYgwKqWCwjXL/69r4/v4rNgTRn3BxvRCpKA0qsweyOfIOmMt8L2nTEJFk5PXO2eYig0Tu1P7pd0L7Hsd9Xm7bkgk4iv+fIfjbzci7cU7jWRJ6SXQrpnmSp3HuQ/OO0a/ogSMX4O045CgoixZ94QpUwLClbYJCgH8TTxaDrBrAI+1oH2yLGAK2u2iNL8hj3MWjJ8OK3CB5t3tHIT4wAOO/RPChWlQjliJEXLs1e1GWKss4+ZLPP5RM9zm71GOJpIoAINkCnKYdXtkMKLSuv9ka9ujyIKr+akKLY8gfiDvqQvFJ4/MgSdR0oBAmkv1lGSWyRN7FlZ8vDAJIRNXMEIkOEotYY0hivFXpLh6FbEgi1GJVHYPLE8+/peKb5/rpDNUORI2sHqNpfN/ZBJOKvIEhxywVOkv/q0ljcv/bKMeCQqZQORR84CgIDgnSJr+LQuG0tFeRlsUSe3Yr1nymcoBF6iF8F3Bq8VNAWglDfpw+P2RIbcdau1I8eOQ8STBP4XEmv7tgESB5MNnAPURrRiMXUyEU16lob0iGP3JDdVFyz5Ve7xMBQE8RNcVfzHCFjLZEas74kYDx6+dWDUzGEXb6xpbvySSVEkrAje6OIniCGJ3zl3SpfdJF/zcJgPxXUKWLP2TVV+LH8iI4r43SK9/TTqw0pLalF4DbYXeD6VO7rv932UjP+NVgJz/GKOjaJt6v7T639ai2O8dpjBddr/dJx6dJlgMgKI1lb2bc7vNUJ90eFhbF2txwndjqrts20c5h4nPFv7WiG3OYopiECSJI9shkzkzOVMrFHt6ZBai0uGN0qJ7qsfrLAzsjYvvMQ8ybzTrUZvgSZQ0gipTnxuMAIFwoAKFaReHFVWBxGop0nUOKw4t/oaqFZsIxEZFN5FTB6U8pJYyXwcYXQw6248Kr9SjcCExbj3E7s/Bm1iZAKgJMC4lmaVdJKgwM575Hc+ab8Cqf5qKINmUC4+aAUEU02CYBDPn+zZ2u7JjrlkzrI3x37LvvyKVJ6plrHfaX+b+yH7Ga+rzjvD7oyzJaWqS+MRDG2IFMhJviwAk5ngGAJJc5MmlXkO04iSmRxpUKU1tD+t2sVXT2RPo42fcMeRXwWkj+hg9jokm7ZAkgig/qhI4JTPXbNlPmnCnydMh3vCuggxjMs/A91r7IuoZ9uZ2I6Vxt0lrH5fW/id51TcMECjxvlUedQecsxCAmKAHawYCeNYdRqwMv9XUKoBYYMGvpEW/KRkPkNyyVjFq3rOg/OckRhjxGYs5POoG2DeTtW5RLODMIw7kbDy23QxgR35emnCHtSwySZKiF3s0StFEzx3WI+uTuDRoOUtETsPM9vnysHW09r/lt4alG/tXSOufsCKyh0dtgd8O0wznqdDClBsQKjD7exdaVfP4rtghFbHgx/VTDzY/BWTBJK6VTSR4nqC3n7GgJK1shsg5CfISAXnjpqA0swM1WeWB5IP3Q/IRX7nYu9ieE6UCj8G/yzUq9aiZ9rJ+0tQHpS0vWoV0yzQL6lNJCoMJOQPfZxNMWEsV7rmPVExNgLIJkoYEIhEk3qhK3JSlxFGi5YDExKPmEXgTNesuNe1mJBcB+lmvpphvEdXSdFWc8KWI96lxT5NtKipIYhJakggqXjwnE4Wuf8LMjDe9YGTdpO/b3696yHykzpIzsf2a94Ok/tSB0s/PdVIdrYwe5z5YZySrW18pmXiwd0HQoQSALGRtQSiyzyUWVDDtnP4Naca3jGgs7/lQ53WaWD3vx+PcBHtMMrUbZzzqElop3SRDFRvx085NOyPEMC2H7IGhiBSfsbTtlPp1lsWs3g8ls0GhFBVdshZZ8hSKIPz3xL6YT08S5Rz3cZ6F9c2T0a2rJPdl3XH24n2YjMTz8DjX4EmUaoJrA8g1PwhuyNXZJDjEXI6RnV4TVFpxgk1vS8wIEzAh5Lr/lr4/FdjnPmCvAaLkmkfCq1pU0vCAoE92+V9i5rRZUtcL7FBHsh+M0vXGiucGWFOYFPa61kziINI2PmutMhB5JIEQYqwXWmJotYGkIBHF/8Mpk9pV3t+DShXKglSBOTDXw+mjSdZ1kV1LbgpGChMrgoDPJ7VvP86aPVez+SVtkKwhWsni12HnSZa0bnymeO1A5uCZs+F/NtkK+TCJB14BqFTYC88+RratO9o03zNTWvOw9OY3Sz4P7w9TYW/wWYeJ675W8X/5UyXVf+yxJLdh/hKJSJrcJgB1KeuWvduj7gACBf89Z+6q0kbqiaONAaQeE+56X2PnYXD+E+uR2MbDGSE3szguzMCWogSt4b7VIrNBO+7RzUmUzldJo75kbc2cd6yTzS9Li++1QnEAzmLUd6hKWvWP3feUGfXTJpRMtX5ki61jlKweHrUBnkSpQbCRZFdToB1JCMaCgMwFdCHML4lpYCBLgloiKYjDnkUmU2Yc6DUP2wbLQUuyjYEpZrpnGWt/uJ577RXNbHoNaieSSBJKSC9aXRw5Ud8ID6oFqXqelPu8WdZmQ/KZSk+tI3SSmNWhaOE1cZ9UkwyA14Zvr6g7YDoQbWH7l8b9MBo3eSdkXaNwcrczZiA7+67SFTDW0Nj/s2QEfx+Cv0Q1FMkNZKVH3QV7HsrTw+uluXdXXyshKsMLf+nHgdZFoBKFrKVFPDG5bdI1NjURsi5uf3KFu/zYPldk9zm2w9SmEMbxPj6okVHb0YYZFMbiwe8psHhkLlgvGLGHFURp0536gJ2XrMGjWyy2HPIRI9eevrl4qEHn86XL/2z/5r6087YdbgpnihX/uyncPBsDblTvnKe+KOFRG+BJlAwBRolO3ZJmcOhCnLD5oUbpOC7WrpErbXquWDoK/Hiyc1ydwljgbnarbjDml2pXstGz8cD8Dk8dJKKJQCHjZKN7K2aUC2nkjT7rDlDFoRY5sKKY1EX1h78Ehrf4+pDgBu08tOtgVkxVF6UWSe/S+0o/Lu1Io75oJMr8XyRXwYS1THrULaD+G/3VYv+JdCszm/eWLrlX6jTJEyh1ESSVTDCp37J42lMw3vj6x2zdkZzGT+djnyN5pdX69EFrRySpRUnMNEbaGAM072XJMAq9eHIlQJfJts96ZC5YQ5x1iYUCVG/DP2PKzOc/KG15OVaEayBd+Atp6MeMZMEfkcLciE8b6fb8h0wFGqiJJ//IWh97XmnG7qXAePiVdk77+M2jNsBzfRkCWnKqw1GdCQCX/sEqGS9/Uvr7COnhyWaO1/UimzzAoc5KwijSwwMQjGFenAoI8EhiIT4SFQNUXlnXEHkVCTaZdOEnDNQdUI1lFHZ8CxdkypI/SofWWvsDk4NGf8n+ff1TllxAnBxaU8YDl0PcQfDhT0FV16OOI0ZUj/+OdP6PbQpKWh422/azq/9liYonUOom+N7bjzUj7nhQqNg5x4yxabVgUg9nH60/tJi16GXJbaBScSTJPlPYQchR/EJhwnRJkmT8MBIVpDwegw/8mZrZwDsvrF0MMo6WRVQqTA+LN9BmiAZ7VNOYfxiqZgYB7J4vbXs97r755tlDfIZyKpnS5Ogmyzc8PGoDvBIlQ0BLBlLM+MpClZElDfmoSdrf/IaNaQySigOrbGPtc6MpVGCbSZw9PILpEfRhQ7aVByofjMdm/dDvH0ynQMnCFBUUBYkS5rIAedP9Ui8HrWsgcGO/Ys0FQRj7Id5PqEkw4oQURvV0co/0yudsYk9ZZsVOiTfHiJgwkNz2vdEnth4lFX8YebKfzf2hJR2Vbe/ByHPQB6x6i8mxX2d1G0xyHPZx25No0wkSWSbyMI2HdcekKFRQxIRM4sFcfun9xfsceyJt2BPvML88lKCQzygI5v3cDGkTAXGDx5pff5kNzs14lVM8UUe7K546rLf4FrO2Qy1Gc+RL1CwEWF8Mm4gfRkE8xp7IOsSTMdm5y98lM7X18DjX4EmUTEHE+ghheivS9pCKYR6b4r7FJR+XjRSjUqqwkCkQLVR2PTyCA5PKFVWtMGlwPKh6YVp82X0mDZ3zA/Oo6P8eacC7bZz2viWpPzekHu1EHnULyIVJNre8YhPRAuCTgooOg2+qZEiWkbyn0mpGG9l/Y+Npw8i6cd+2ljMPj0RlFP4VbR+xttdlD9hEu9NHyk8QnNl3a6n3tTYhjXY0P1bWIwB+dCiSNj9f/DOMsiGLu0+VOp5n5MnhjVacoAARb8hO8rrsfiNTeCz8ddxkn5dswlRAzgSgmDH6y96ovS4A03WKDIngrKTgEABfE9oLae8fFGvvYcodIHZjElQAYrGWvaX250mDP2jqFNRQyeCeP005jIdHdcMfzRkE2msgM8KY5EqBKRbbrMKLNDnefRuVCuQJ7twn91riSpLi4REQcEiEe1wurfy7rSWqHMkMjNc/ZT3ZY78m3fB07IdRG9044zslKxqJh37849JWMfzTPumoq2jSRTr/buvFjifvSFzZy8LM7CoDEl3ULb69wqMsoAaADMZ0lnGzTKeATKGF8fj2qPILT6mw6RFlNzutpm0aqHOvtuo4Lssp6ThP0zkG3CMzQPv0hNutsJAX13px6qBND1v7qBXVXKU/2TjZQlOc7Jgea+ovSqIMiEj932XTJT0yH7Rz5YZM6UzE+O+arwlxFqoniLr4qWTxOO8bpijmvijyIFHKKmCgYvZGEx61BT7VyBAQaEF2MNKT1pp0gEMVhpkDdMzXTPaJrJ3NEBn7oA/b/9+/3CqybvPz8IhTBoz5irT9dRuZ9/AkU52EBWsom+b/XNr4tFU3SFKplNFXm+xwBigK/ntZjKTJl/p/1JzhfeJRN8H3znjEC34ivf7l8CkTVQU+KMM+KY36gm8Z80ixxaeh7WuYEFN44IY6YPmK9Xpz1ms6k12o7gP6acpVU5XbMMvvXx7lFigmfU96/aulz8ey2hMT4e6b7P54i403X5XEqWUemQnGWDdsV/79Zn3X1Eyth0iD3i9NvNNisIW/La20o6Vx5T9sIhTK9fHftjU876fh3idNu/gimEftgV+qGdYvi0s21a50jVhc/YgZfg58r3TDk9b3SHUMszKkyfRJUhlh+oUP/DwSwdSScd+RXv9KyUlOYYBggZDjlio4sN00gojUcawpWZiS4VF3QQA24L22JqbfVrJaW1Ugkx/6CTMP5d8eHhUBZ2ROI7uxQJseylKk0WkVFRXpdFGelF3kz1GPlPY42igwiH3rbmuhSCecf8UIGyrAhDKPuoFgzHXYemDNOdVvUXGctul5aedM6ZpHpKEft7ZFVMMUGtx9C03Bzm3zC+YPde1/zLsM03fUU4lgQAV/7+FRG+DraBkGxnxi6BlMOTmyQXrty9KKfyT/GyoZGJPNuqM0+XLmmFV0n/ug9TGiRqH/dvEfpCdvMBnfmK/6flmPsoM9Wmyqzdk/IjXvKV34K6lZz2p6Do9ap4LCkPOqf9goz6qOf3cTBbpIF/xUmvxDa5v08KgqcnNzlZ1ti/PUqVOK0p/o4ZGiagCvEvYjvJ7SBfZKDLiv+rsZynpSr27Fa0x5wmQ4HijoLv+z5ReJgEyhQEZ7P2sS8o37hrWAMUTg6BapUYfSzxGc2+2GeyWKR+2BX6oZhnqNrPK/Z6F0eL0ZhiG7KwtIi9f+N/nvkR3TIgSTjBv3WX+LImnIR6zf28vaPZKBwxJJMOuIKQHx7u7pAKqoS34vdRzvAz6PYrAn4RPFOOPFv5dW/k06hhqqInlqxKTsKO1oaSSpyGKv8+vMIw2oX7++I1HOnDnjSBQUKR4eFZoE9WmpZT9p5u0W91Vlsgnjjgd/xHws8MHz52ndAt93lwtsHZzclzDi+h3SyYOmdI8fgU1BAVXmse0W40GEkBOQV2B+zGjjs/dtZj5RkC4YvIeZtXec4NedR+2BT30zDGw+bQZLk39gG1I6AXFCAsxGyUHNNKDxt4czyh4eJSTsjaVJP5AmfNcmT6QF9GxPkq74mx9p7JF87WHQCYl300s21hNvCrc3BoFaJOEWa9tpM8RGhr7jOZscxUQCjPc8geKRTiVKlmPlvBLFo3Kg9YGpPNc/YT4prQdVXHlHItzzaht5fMGPY6bGfp+rk0DV23VKyZ8dXGPG2P1usolObiR2A2v9QQ3VrIe04WnLDzDN3rPApov1vsHM/rkvU6CYngfht/HZ8GEBnSZKLXrX2Fv18KgyIlF/amckYIqX/dk8AdI2rScAxmZjTLJHouHhkSoYX8cYRczGMI2tlColYkkwJmWjvyQ16+oTW48UETWzWYLCg2uiWjp/lU4cKpRO5apdk65q1raRmxLVqp8ZdbtkwpNzHmkGRYj849L+HUf07P9eVP7xQuWqicb3v1RN2zR0yQmjs6kIozbwCa1HKkAdfHhTod54aLV2zJROL+mknPymKjxZz9QDUSNYWFMkto3aSz2mSr2uMiUnSma/1uo2yAghOZ7/YMkpOv3eKV3062LTf0gQWlzZq5i489LHiyfiQaBcfK8VIw6sMsuAJp2NoNk5S3rxltIeeex1eKsw0dGvQY/aAk+iZHjCyri7N7/JOMX0PCYHcPdLpIvvkVr09ZudR8XBjgOxt+Y/1mKBBJm1mnRKQAz0yVIxo7ViyC2x9p0svwY9KrcGC86c0d//8Q/t37ffqQHedfO71K1bt7P6TL+uPNK95pzR4iqbQrb9TWn3gqhOHbIQzO1liihaFHHnLMlJx/NMZdDzipjvRcSvS4+ycfLkST388MPau2ef6tdrpPE9rlbTEz3dmVtUaK0Z+FdQ8cfE0xmx+3PUIw5nTkgvfEha+3hx+yv7U/vR0oD3SS372jo6tk3aMUNa82jClKiIKTcHfsCUJyiRyUEgUFY/HDLimFHa75Yuv9/u64tiHrUF3hMlg5GdI/V/jx2UM79rbDFjYCsFqv+tbfrP6K+YEsAfuh6VAesGieewT0j93yXtXyZtekHau1g6sdsc2+mjRQVApYzDmoOVsdoYm1HRcIGfX38eVViDBYUFKiKriFCejSq3QU6VDWg9PMLAMtu3RFr8O6vynjoQjPcs7iGDYAkqWvz78Dq7rXvcCBWIY9R37IseHslw9OhRHTx40O1pOQ0j6nNhU7VJo/GsR+aD6WHn3SbtesuIkkDltHuetHeRtfBDqhCnOc+TxFJ81O5HbOfumx277+lwTzLGGuPDk+NHaXvUMngSJcNBDz/O2lf9U1r3mBl74pCd8gjkiNSondTlfGnk56z67z1QPNIBDmF6azEy63y+rUlaLZCJRguMaEHiGRzYvgrrkU5g5hlv5InJp4dH2lV3R6Sl90lL/mCTKSoKDBo5s2d8W1r3pDTxu1LXi61I4uGRiK1bt6qw0NxlW7RooZYt/ehEj4qj7XBpwp02nTNeZQL5awRw+UjlvvVbSpO+760BPGonPIlSB0DiiRR42Kesr5GxxCufPaKtr0sF+xsom5E7hdmOaeaf+NxhVoYnAJV/ehSR8fEzn8R6VKf5rJNyenjUEIkSJBu089Sr549Dj/QSKHk7pde/Kq17ogoq0BjwtNg1S3rug9K4b5uSj4qxh0eAgoICbd++/axBMe2JgXGxh0dFQPv0oPebd8m8n0pnjqf/OfBMGftVaeD7/Fhjj9oJv2zrCALyAzKl7zuj2tBojk73XalG9Vqob4vz1KXRYCe3I4lt3F5q3tvuG6gAPHni4eFRm1HC/ct5ohSoqBCjCik3J1eRSNbZ+/j9zqMqYB0hg8dskaJFqpXbVMDoUQzjUeyN+UrsjPbr1UPS8ePHtX//fvdvRmd37YrruodH5ZCVK439uhEcb/04wfekishtLo3/ljTyC1a89fCojfBLtw6C6uuBQ/tUVC9fx7VXnSZFNchL6Tw8PDIUriVirfVoM37xwErp6J4Wyj99tXIan1R2m6je2lhfHcdIbYbadB68eDw8KkOgnNxvMvitr5q/SbpReFKa92OpfjNpxK3eH6rOmWKfNF+d/GOmUKLQld0wqv0nj+rIkSNnW3m4RTzD5lFJsHSyc22MMVPrZt0hHVob7muS8mNmSS37S5Pusik+3h7AozbDkyh1EMeOHVNeXt7ZakW7du3e7pfk4eHhUS1+FNtelVb8XdozXzq+M/gl/0P01sP935OSFr8kLZYZd7YfJQ3+iNT1IpsI5fMQj1SBn9PCX0sbnqkeAiUACfTcu6XWg6WuF/o1msmg1frkATMn3vS8tG+xEXWsgYLT5n2HirioYWvlNL1KTcdsU6s22WqY09Ttg35teFQWrB1GYve/WWo7TFr4G2n9k9KJvRUkU/BXbC/1vVEa9UWb8OO87jw8ajH8iOM6Br5ujMcef/xx5efnu0rF+973PjVt2vTtfmkeHtZOETUndxJg+nCRxWPImLfHemhxcmdsXjOm0eb6djOP0qA6u3O2JZm0U7ipABUEa6vbRdK426ROE80TysOjvP2LKXhP32xKgZoAptw3PGlG3B4ZqGraJ636t7Tqn0aiuNawMqP2qBtZ3LBtVH2ui2joxyNqN9JaMvw56VHlMe1F0t6FNtZ44zPSkc1GHJdqWWTqXb0iqf5pNex/WIMv7qAB785yhrVM6/Fr0SMT4EmUOga+7iVLlujFF190/79Xr1667rrr/GQKj3NiDOjBVdLmF6UNT0sHVsUIlVNGqnBIc/gy3piElrGfva6Wel5lxsfITj08qM4yCWXez61aWxXpcTDanb7w4Z+2EYw++PNIhvzj0jPvkTY9V8V1VwEgh7/wl7Y+fWU3s4jgjc9Jc34o7V8SGyVbUUCmtJaGfNSq/348tkc6QNZIPIZHCpPDUHkeXCOdPmzrlFgs0jxPmxpP04lG21W/WZY+dMv71bxlM39+emQUfDtPhielbGpU9Bkf65LQrKh2rzmueqqvwki+WrdurZwcX2L1ePtAZYODeMl90oYnpWPb7Weh9y2U8mPB5KmD0t5FNrabtotRX5Daj/GjP+ssotKpw9LM26VlDxr5lo7HhIiZ9V3p6GZp4vdtLLcPBD3CsHOWKVFqikABqKxoV+t7k5nCe9R+cLbN+4W09I/270qDNqB90vxfSLvmSBf8VOpwnt+/PNLglZJjBF3nSXYLVCrEaBS7Cgpz9Ne/7lHRoeM6E6mnPft2qUWrZm/3S/fwSCs8iZJhgCihd3b7G9KWaWYClbfbpMUFp2CII8pqPVy5zbqqsPERFam1jvXKUpOOsdYIf7h61CDOnDBZKM7vlTUsO7FHWvOItW1gsjjicz7RrasqgJnfkZY+UPVxsolg73SPWyhN+bmU2yS9j+9R+0EFln2IgkUYSCyYeNeilxTJMVIOX4Ewsg9FCffFzBGlCe2M7HMYioYBs2Qk9j2vTO978qhZkIhCmsz4lrT8L6ZGScvjFkrb35Re+Ih06e+lLlO8askjvSDeYo9Tdiy5jNRTx44ddejQITd6e9euXerXr583OvbIKHgSJQMQNGQd3WQVqVUPSce2SoVnSielZ9CnH8T/hFtUy2ZL639tVayhH7PJFN5jwqMmgEIKqfLie5MnBykjaknGnB9I+5dLF/1KatzJr+O6lMAuurd6CJQAPO6Kv5oXz5iv+fYxj5LI22XV/jAiOLuBtVSM+WpxSwVrdv0T0ozvSMd3xN03Vxr0QWsha9q1uDiy4X/SjG8boRKWJPP7Hlf4Pa+2T92BCE4ngRIPFJ8vfUq6+iFTbfq14lFdgCzp3LmzVq5c6f7/3r17nQ+jtw7wyCR4LjoDcCZPWvaA9NhVlpQeXh/rny23qh9R0ZmIC/6W/F564mpLQp3rtodHNRMo079pTu9VJlDiQOC57jHpxY8ZkehRN7B9uknWq4tAiW+dwGuFqq6HRzwwwD60Lvx3kCLn/9jO1le/ID37fvN96v8eady3SpoW93+3NOUXtke+/hXp2fdJa/8j9X2HNOEOU4yWQlTas9A8CjxqJzDnXHqftPIf1UOgBDi8ztYV69U7InpUJ9q2basGDRq4f+/bt08nT6Yx2PPwOAfglSi1HPhHzLpTWv3v5DLiVEAvI+M/5/5Q2j3XemcZnegrFR7pBq0RtO+4als1JL2s5c0vSW/8n3TJ76RGbdP/HB7nDk4dkub9pOamoZw+JL31U7mJF/SEe3gEVf6w5LdxR2nk5yxpff7DlsQCiDjMsbtcYIqTIxulRu2kkZ83soXWiwNWxNXW16Sm3WxKVPOe0qE1pZ+H9U9BpH7zan6jHtUCSLC3fmJFsZrw7mEM9wU/sdYyD4/qUKK0bNlSTZo00alTp5SXl6cD+w6qcb0Wri0WUTwToxif7FvLPGorPIlSS0EFgaDs1c9ZRStdIAjc9Lz5qEx9wBIFT6R4pJPgWPe4tV5UZuxs6k9kz9NqoDThu1JWrE/XI/P2QaY57ZhZs8+7Y7o974D3+v3Rw8CoT9ZCYnW/82SpRR9p9l2mEg2A98XrX7aR7RjAg/ZjpdaDpPk/N1ImAAqTN75mBEoyshBTZTeNyqNWThRD4VZTKmDO4ZX/lPreKHWa7Pcwj/SDfbBhg8Zq0bitjmw/o8iu9ppze66Wnzb1McQJ0+4gmdsMkjpNMg8oT6p41CZ4EqW2mo8dkF77krTx2ep5DqaevPQJ6ep/Si0H+EPWIz2gnx+105nj1f9c+AQsu1/qfonU+Xy/hjMRkL74lFRFhVcZYAa64i9Sv3d6bxQPA0RIIoGC0WL7UZY07JotNe4gtR4o5TYzFem+pWYKa3eW2o+0Nb1jltSwrREqKEtQie5bYreyFH4YdXvULrBmdr8lbXm5Zqc6MbWHNvAO4/we5pE+uD2QiYtrGBoQ0YH/XiJtyVJRfj3tzg9POTHPrtfQfHoGvEfqfa3tfz5m8zjX4UmUWggq+HN/JG18Ovko2HQAt/83vyld/qDUoLXf0DyqBiScBG3xFdbqBoqqhb+1sY5UODwyCwdWxCWhCaCa1bKf3UheSUQPrpKObCq9b2L8SXLbaoAFcxh94m/BBJVke+zepdL+ZVL70el/Xx6ZAUgUVCiQfO1GSRf9xtYia4yf0aYz87vSkQ22Xlv0MzKEtTj5h6ZSYd8qOG0T97hvWCuPA8mL97iofYhKq/4t5R8p/660P9RvYZPIko1w5z45jaUzJ8tvl930gu2HrfpX7qV7eCQSKJydxHkUN5zvTlHjlHIablunSTtmSEv+aCbcva6xtexzD49zFZ5EqaXydTeFoqD6n4/WHrwr2NColHl4VHbdMj2KFpvqJP5KP7G09RVp9zyT1fvDOLPW1PYZZsCZCEiRYZ+URn/ZRsVS3ScZPbrN/FNW/M2USiC3qTT+O9Lgj5i8mMTDjZXdKi34tQWEmD4mgnaMnXMsOfbrygN1iTsj44gM1kX9llZVZdoOE3Q2PWeJbt93mpIJU9mXPmZqlQYt7P7nfUPa+Lyp9njMPteb4Szky/MfDF/z9WLVXI/aBXxsUjKqjtiawV+HIhrrKB6QK4M/bOccXk14RaFUxpSYdqEwuPs8Z0Sz38M8qlok2zXXBgbguROcrxUF5OCe+dJLH5cGvt/MtGn58evT41yEJ1FqGeiZnfcz6UySQzHdIPlY/HtjhKnS+o3Mo7IgUDy0tmSVFiNFqmpJ13PEElrMYTmk6fkvq7oW3JcEG7kyU6pIOJjY03G8lO1N9DIGrIP9S8PNPLtdIk3+vrRvmflOkKi0GSKdd5s0+UcmNd4Z81FhQsqoLxohwnQMWs4w1T7vm9Kku6QDy606lgiIFdorAtLFo26D0dellCARI+9YHxDITEUJ9q8tr1iy2+Nyqd1oW4/1GllrBQTKK58tVhtseUlq0FLqerHUcYK0+YXSz+/IGm90XKvAOUVL14k95d+3zWBTJ+GLAymX+N1f/oDU40ozKEZBh5qJkdcdx1nrd9gUPPZOBgnwu5xG6XtfHnULbgT709JrXzT1Sdqmjj5oZ/Vl93miz+PchLfvqWWgb5ZDrybBqNiVfw+vxnp4pISoHbLxKhSS2punSQPeHf4njPIc9CHpPTOk978lfWCe9M4XpJ5XS5EE+pdqLiaf737D7vt+7vuy1OcGM5Xd9kaxeaNHZoAWh2QBW7932PqZfpu0/kmrkC37szT3bks0u11k7ROoB1AD5O2RXv6UTTmjiob6BCNQElcqu8lUeEjh3Th5jzqPlv2NGC6BqLXtcEM5EE8AYxbLz3IbS826FicOECf8PL5dg5+zf6I0YaJPGFirVGw9ahGiVlgobzR1g1bS+Xdb0SER7GMoUHpeZR5g/50qPfMe6T9TpdX/smo+SqZkoG0xmVLFw6M8ENNtfEZ65db0EShnH7vQim/4M2LK7Udye5xr8CRKLQK90pAZiW08BG7I18tst4lV9EsFeSlukmsekU7W0AhRj8wDrQ/4R5xVoHSQRnxWatHXqq+lkCUN+Yh08W+l3CYmSd7wlNSspzT1T1KPqXH3jRiBcsnvzbuHii+3Jp2lS/8o9brWfFhQI3hkDqiihk4jiVjSoZga6SxihtwEYpBugPYd7k/L1/HtJe8brBd6spPtrXjuuHGNHnUezbvbdInEsxNlE/8tpaCDYDll65Hfu/vGPHjCiDlIQ/4mWTtkm6GmSPCoPSBJZKpTWWCv4qxsO8LaEBMBEYwRJ14Ui+6R8naacTv7GX5gmA33fUeScxaz9801M1bZI0NNkedJb37DzsLqeRJT6b35dV8I8zj34Nt5agui0v7l0v4VpX/V80qruM/6rpknhgEJ6LjbpNUPW8IQgIMZJUCQVISBKsXKf9hYz343p+G9eNQ5ECjm50m9r7PWMEZntx2W/P5UZkd+wZLkZ99nJscksl0vlK55RBr7NVNlkUg36WjeF6xT/AJ2zbHHYGTetf+Rxv6ftGWaESllPadH7QLJZOg0kqgpj5CyD/+M+UrQMkab19CPW9V352z7e0gWeq8xteOxIPi4Qdz1v9l+toe1lyRxLSD5qEmPH49zFo07SR3GSIfXFf+MggcqqIEflNqfZ0aygVcARY2O50mnj9r+yM+5L14+/Bw/skD9yflMWwbJMSa0YWoEEmkvd699SahLDBO8dOLR/VJp2CekBb+QTuyXRtxa8veQvKiTIOsSSWUKX7QKQbBBLB8P2S85N8NaIj08ygOt0jO+XbJNuzrAWc1+uPh35hdVVr7i4VGT8CRKLQHn6/6Q3lnMxEZ8Rmo/1rxSQhExyfrADxQnmAFwZR/5+TIml0SsIktlf9ubUh9k8pVQs3jUbZzYbQknvjoEdCQMVM6adAm/PxNPWJsLfhU31hMj0TdsDeNvAiHCZBaIQFqDlv6p5KQW2jLwsuhxmdR+hBmFemQOSBiT7VuYYTftKg3/lNTzCiOXUQkwLnb292wKACB5oF0xAO1jXS6Q2g2XmvWQFv6mtIFjPNzz+8TVI0aKYP7KWQkp5xAzgmcq1MjPmrn25peM9Oh7o9T3JpuQ4khiSdtes/0OIoU2CxIHHgPiGbUd5ODOkHZePHyYQOZRyxAtm8Bo2k2a9AMj15bcL/W5rvR9IOpQkrAXYVgcD/x1UKrQBpZsr+QsrlGzd4+MAApMJvDsiHmLVTfYUxfdK/W62mI+Txh7nAvwJEotAQcdShT+S6W03QhLSAnCul0a7tbPaEXuQyWDvtiwTYcA718TSicC3JeZ7Rf8RFr3pDHNtEdwWNdnCoGHRwXgRjLGTIpJcIOElT7vMKAi4dBMdHkn2ONn3S8zMgbSpNNEC0QhTEoEpDEZKP3gbYZ5KWimgWoU7VthYIwsRoyAlgn2TPYuJvWwrzVqbyReIlr2sb0V3xTk7ySmkCnJxsryOImJi0fdBGdmlylSpwnStteLf45PAIaL5/9YuvQPsRaziK1FFFGz7yw2/aRg8dqX7dylldEpC6JmJIpsfubtMfVTHFh/FEhQ5HnUMkRiE5VCVCgoTCbeYfscxG/i9x6vJKFVFqKtw1gj5Xg8/o6Es0knawVLtk/hHeULYx4VBXvVkvuSj9qursEaxJAX31NG4dfDowbhw79aApLHwxuL+/gvvtcq9Y4YScLIMgoPGTH3cZ4pISCxDEsuMagb/CGriiGHJ6FFTYCc2JMoHpUBy5T1ww2UNWEK1QC+AMdDfEyObbcKWzCJokUvq8aFJcXcl+CRhKWyI/c8zt3Kf9MQJVNOU5tigaJk1p3WiognD8QKqjv8BU4fkl6Lm5QSgNGh+AhAjuDJM/xWadL3pBduMXPQRODR46XFHgE4G0d9Sdq7xNZYAMwRn3m3kb8o6CBNmMpCS2JiC8au2WYMioKOiitr1N13WvgUF+4ziHPeJ8K1DiiSkpnFUvii/fWVL1gBLRnYl0gsu1woXfQbqfNj0rEtti56XWXti6gGEr30AnA2+uliHhVtQ2N89pH1ye9D3MWtsKD8oRTO1zE35gd1uqwntjHxtG+3HlTpl+/hkTZ4EqW2IFocbFFRpbIFE0u1dMLtZtCZiMV/MGkxwMuEvtpUwGY25mtS6yHSU9cXGyyidvGTKDwqg9ymVvFSilWLBi2M9MgPUVg51VVEym1u/8VMkfueLuO+TK7wkuXMAoE/bQwEavEJAmqSLudb5X7JH4snX+AZsODXUvep5h8x4ztSYbZ5BbBOHMGXZzeSVdojURZwf5LjRBKFRIc2MvZLD49gTTAlBZ+xJX8q6ZeDImX5n+OKHmVMmsAc1JmI/r3s+7KvTfiu1Lh9+t6DR82B5JGCAf+NJ/lREI//jrRjlnRolU1+AsH0JVRH/AwzT85IxrM//yEbyz7s43bWMuqYKWMoNV1RIklyShHCGWx7eKQIzkqM/sOIORRU7IFdLzK7AczcKcaue8L+HQ/WPa3ZtKnh64Niiol36x+X9i0L3/dO7DMFPSO8fSutx9sNT6LUErCXBAQGrO7ut+zfHH4nD0otQv4GCXogQ283OvXn6nyBVbbW/NsSkQA8v6/me1QGBH85jcof5RiAwzmYWpGIoC3NyUjjesrLui8tHbS3eWQO+G4ZP0zPP0qTAKjuuOGDkkj6cj8UAkjcIV9QBWBUjPfJWz8pGbSxVlHpJVP71W9lASCJs4dHANbVuG9Jh9YXe++UQEXGdJZxX4ooqF7w/PFrsHaCvYWKOqpKWhUCsD/hF0a7YudJxT8PCNtx35ZGf0V6/UvSqn8ZWbftVWnPPIsJuR+GxRQvKKAxHjZZOyvP79XFHhUBCl9HciSAou4FP7VWbYoRqIPxIRv6MfvZCx82ksQhYj+f9H07r5kSxX7HJClUoG98TVr735Anj5qCD6W9V4F6vN3wJEotATE8EyOqG/TnIpVjKsXCe0pWL2CY/ablURngK1GRahdKAPq0SZATQXUDkuVUTC5P8EkSkey+TtFyXGrVrwpvwOOcBCQISQB+OAFQ7LEm+B1eEse3Ff+uWTcj9PB4glhz5AtTny62qm18a0XLvjbV7OCa8ClArKf2I6v5DXrUysQYU+OLfmWTn3a9VUHiJAWQdAz9pJEoER/F1WqgZsNANp5EObBKmvap0uRt+1E2YYzkEm+w3QssZhvyUTv/UN4FymGAvxMqlDX/sTMwLOlFMeDjOo9UQXELwiOsfZp2RdrQOI+Z2sNahMhj0uLwT0rDPiVN/6bdFzsCFO+crS99IjYFL2oTGC/6tRGF7J3xxu8BGBJAqzfnuYfH2wlfv6gtyDJj1+qWr+EjQHWVhILpAPFwZovezMmjEqA9hx7tVMEhSaJAEpsIFCV4CgQVDdo0kC+36F36viTCEIH16hdLoT0yB+xHgz9csqWGoIt+bdbJxb+2oAwSD6Jkys/NGwVzY9pzUAtwX9p/Jt8tdT5fatnPppBd9FvzC1j2YGn/HpKOwR9J7jXl4UGr2RV/lbpdkl6ig2SZQsfkH1iC4qdU1G5QXMD8PD62I0Fder9NnIu/bZ5mSSzGxcvuN6UxCswO40z9xFS7IKrHa2XUF0yBsvY/4UQeMSUkiodHReByg2jpM7HHVIu3MEJm4hgkCgWLt35sJB7rMyDsMOCGBFn4a2nD09Lx7bbu1zwqrX9SajXQSMAw0BYUTzp6eLxd8CRKLUFWlgX36a5olXiOHGkIBop50sb/leznBo07WdDm4VFhRKTe16QuO9/6qv2Xdo34KpmrnE2xCRdBqxljQVGbkADHG+SR4GK2d+qw/RtZqUeGISL1ujaWPMRAEIcZNkQJCcKNz0kfXibd+IxN25n/C2nVP2MmdqekuT+QNj4jDXq/dNML0gcXSlc/ZATenB9JK/9eulWM52M9e3gkA+QGvhVX/9Ok52FKuYo9oMUAl/5emninJd+eQMkM0MKAJ0RlwH7HqFlaF1kb426zyv9V/zLimP0usSAW+FFAQOMJ5eFRETBkIiyWo4UWH8bDG0r+nBiO+7tx2lH7N+orvMjwS4nPa7iPK6LlJI/ZaFUL88Dz8KhpeCFobUGWydNJIsOmRKQDjP5kHDIJ7P6VJX9HJQ1W2FdePSoLqv0Y5h1IWFth2L/UxhcPeI+09RVp62tG4A3/lNRulLT4d8U+GDzerjlS3xulLS9Jm1+Q6jWWht4idRovrX/KnttLljMPJJFUXMd+3YwVGfcJINle+5K09D5TQKE+wdiTgO3g6pKjsFE0vXiLjcHGrI5k9+gmm4hxeFPpyQIkr2P/T2rUwSexHmWD9dGwnXT+j8xskaorUvdUvaHsQaRmXaU+N9pkKYxA/SSeDCPb+plnBORvWb5zJK+bnreqfTyY/sT+h/KEdgrOOlpi8ZVgOlnYdBTaILmv99PxqChowYEMiQdn6oJf2n6FUpg2M0gQFMCQyKwz2tCCtbjoXmnF30u2nwHOX+I1SBJUxmGgnTtxsp6Hx9sBT6LUooMWEoNe68AsNr1PYOPwGA+6+t+lD102RAgWnzR4VAasG1oq+r1LmvP98g2KkWoybnbqn6Sp95svBZ5ATKGiFxwSJaheQKagGLj8AavEkSSzXglM6bOFZOl/c428TY+3aW31uFwa9knzcQqCK1QmexfZrTxAvuycabeyQNsYz9PzSr8XeqQG1klwfkLqQvguf/KIVr+xX5F9OIo2UDSvvhS1BQVBgv8Y+x17JuRJz8tN3o5prUfmge8VzwiKALvmJlccQ8DF+z8F4Dzd9Jy0Y7r5QNHeyBSTxGkoASjGjfmKqV/8PuZRUaD4Zd0kEimQJwEYz43nCZOkGrSxyWTxRrGszcT1SXvZed8wVTH3pZAWBvzyvBeUx7kAvwxrEZC/0cpAj2G623pgf+mrzdsRk9eptKEZI489PCoLKhGoQ9z4uqXSztnSm/9ngV8YGGP31DukQR8wBQsH7uqHrbKWWL2gpcfd94NSm8E2sQpZ6ZZXzBfDjVf2yFigkMMTgMoVoxSrY4oYyS2jGMd/2yvyPCoOkg7UdN0ulfY0X6PlXV9XdlF9dYwM0cAmFzuFKckBSqemXYwEhkQJFHQ+2c1s0C59wU+kZ95rI64rA8jgQI2XDCSftG1T0PBryqMywB8xkUAJa+2h4AVR12mSqZ4Yu73g16WLtJAyva6Rxn7NlKMb/ifN+JZN+ElGAuY2Tt/78fCoLDyJUovAgYeZISZhwWSSdG6KKF2o2iYaNjn394/YiFoPj6qASsP4O6SXbpH2L7NbUkSlPfON1KOy5jwsToePMua++xZL05fbgRyMR57wXan9WB8sZjr4fuu3NDNYVHXrnzDJb9oeP9t8C5gawPP49eRReUS1a9cuKRJVYfYpdZ2Qo1HnR87WRfzaqpvgeyfZnPJT6dUvJleRpIMI5lz08ZxHZdEcE/9ySBQUU6juUFm1GSpd/S9p5OelzS9Zu3YAFHYT7jB1J0WQVz5n5rKJZu7xwPCdNkkPj7cbvhuylgGigwpC4OROxfXgKks246V0iaCygUw0L4mjdaP21iaE/0TiKLyO46TeN/jeWY+qgzWEISfTJVKd9ESvLRUJ1ncogRJ/3wK7L60c/d5pHipUdz3qRhKC98kl91qwVmUjzxjo6+bxLr7H+rt9kutRFZw8eVKHDhVXQTp16uTOc9aVX1t1G5xV/W42stZNk0vjeqAQgcfYxfdaEurhUVngy5RoSEyxtefVUscJsR9ELXYjbqM4i5cPRTT8nQJ0nypd+x+p20XS4nulp663tp+yCBTAOd+kU/rfl4dHReGVKLUMbFT0sjLiDtKDDerNr5f/d2sesVsy7JotPXx+6Z9j2ohM3k828UhnMDf6qzY1Z8nvbapAuqttva42abSfPFA3jWYZV9xpoo1WRO1UmTXGOmVM7XnflHpfb2OyPTyqiiNHjuj4catUNGjQQK1a+U3KozTZgZfE9NssAa2qqg713IhbpTFfleq38GSdR+XB2mnSxYyJ4/15yE0uf1A6vF7672Uli7oogzmD8SsrOFWsZrngp/Z3mCLTeh1mgFz6BdjZ7pVUHucCvLagFqJFH+n8u+1grE7Qiz36KzYm1B+6HukEpomT7pLOu808ANIFWnnovcWM1lXyPOokIDz63iS94znpgp9Z0EVyUp6ajt+zhjpNsL9j5DGVYU+geKQD0WhUhw8f1okTNmKvdevWjkjx8IgHLRCMJ77+STPnpPJemWidvSyo9tPC08C3InqkASiZul1cckoYE3v2LpRa9pW6TIlbrxGpRW9r12FCnpu4E7G2MhQts79nCniUKzxe/C1MiYWCGdNaD49zAZEop7pHrUPhGRvfiflSeUZilSVQGKc48XtS/TTJ4j08EkG1Yv3/bBLPgeXlt+skRcTMGEd9SRr6MWvl8MGiB2BN4SFF2yPj23H8x/fp9MkzOlV4XNGsAtWrl602nVuow+gsdb/E+rRdwuHLDB6VBJEVrYV4WwStiJGcqJZumKO3ls5QNFqkYcOGaerUqcrO9j2HHuFriJYIqvsYqpNsHlglnUHIxPmWGL3TEpZlUxw7jJEGfsCGEeQ29+ehR3rBOnz0IhulHYAx7lf8xdQmqx6SDq2zuKz/u6XmPUxZxWRFCJLrHpe6X2aed249h+Ctu6Ut00r+DAPaq/7p1fEe5wY8iVKLwUa17AFp1p3pNSFDGYCXxLjvSA1apO9xPTzCwA50ZIO07C/Smn9btSJV+TIBI1W6HldIIz4ntR3qx4B6lA2SktNHpC0bt+vVN1/WmcJTatO+lW5853Vq1KTh2/3yPGoxML4+vkPaMcsSXibpnT5qPyfhzaoX1emsYzqZu09NxuzV4MvaaeQlvT1h55HSOXlityWmtCgeXG1V/T3563Wy4W5FGp9Uj259NGhsT7XqLzXvlbrvmIdHRcGeBimy8LfF0/Aovva+Vhr7DalpZ5tihz/d8V3SontsKAaEMtMSr33UVCtl4c1vShufLv7/TDe76iEjUjwp6HEuwJMoGZAQbHrBNjMqrZWu5BPhZRepQad8TfpmAw35aMRtgH6j8qgJuF0IMmWztPl5c3DHCDn/WJGKFHWHdCQaUVa2uS8SHLYdblLlnleY+ztJiF+vHqli69ateuKJJ3Tq1CnnS/H+979fjRr5RmuPisFNAiu0se3LHpQ2PiMd317eWYx23VrMOo6PuBbEfjf5yU8e5SMxYn/uuee0fPkyt6QmTZ6sSZMmuZ/7deRR3esQEu/xqyz3iEcwpr1BayvwHtueRG1S3hqNX+tZ0uAPSpf+0ZODHucOfM22lgPmFxPNNkNs/vrqf0sn95c/fqwEIlGp435pwhLVv2CPerz3WtVr6Ht4PGoOLuCjd7aXNPwz0uBbpPyj0tr5u7Rw7hKdOZ6lZvXbaNjAkWreLdv5AlG9hejz03c8KoN69eopK8vK/wUFBSoqqjQD7VGHE4mT+6TFv5eW/smk7akVMiLujKaau/0NI4yXPyhNuFPqfqlX03kkRyI50rBhLKOMSKdPn/LkiUeNgHXWrJu1/L/8Sen04eLfQZiglCoXFchT2g2Txt9uPj8eHucK/FGdAaAC36yHOV0P+Yi06l8mJT6yKW5jC/pngwM2KuU0lpp2l1qPP67tPV/SsXrbdfBURKtWr9D48eMV8aexx9u0nnMa2i23x2EdWrVShY0L1aJ7d/W/abhycjxr4pFeEuXMmTPO9NPDI1VAluxZZNPxdrxZtQkqSN4hUp7/oJm5j/ycSdc9PMpDvDEx47M9PGoyVsMglnbs2d+XCswvO+1o3lOa8nNrUfNpice5BE+iZAjYWJgg0W6k1HaEVcQwfkJmhylZ3h4zuUMGh7M2btmtBtiYssadG2vmrK6aPXu7SySWLVum/v37q2XLlp5I8XhbkZ+ffza5zc3N9evRI60kSrCePIniURGwVLZPl179nLR/efoeFxXp7LukvN3SpO97U3ePipEotCZ6eNQkUIaM/Lx5ncz/lXQmnYMuULt0ly76rZ8S6nFuwpMoGQg2Gsw2G7WXul4YkxfH5wdmK1H8X2W5KQGrVq1y4xe5LV++XJMnT/ZJq8fbCtosguQ2Jyfn7X45Hhngu1OYLx3eIG1f2EDRhf0VOXhG0YJczd+foxYdbIQ8fjvsn0Fbhd8GPeLX0b7F0rTPpChZr4QqZckfzS9l4p2mGPXwSLYWG9RvqIgi7t+nTuSrMD+qSFbEjIrPxngeHtWHeo2kcd+SGnWQ5kACM7GnijUJ1m/7sdKFv5A6TfDG2x7nJjyJksEIDs/4We7J0KxZM40YMUJvvvmm8wZAjTJo0CC1adOm2l+nh0cYIE/iFQKQKJ7U86gMigqlAyukjc9J6x6Tjm6WzpxsqIL8KcoqikhFES16OqJIPSmnkd3aj5b6vlPqMVVq3NEnIx6GvF3Sa1+KESjVJF4qypcW3Wvy9eGfTO0M96g7oHXMTelZLm1b2F5NNk5R0bEGOn6iuZ74lxXRmHzCtLrWQ2y8rE9CPaoLnI0oUoZ9Qmo7TJr7I2nrNCtYVAYY0g6itfHLNq7bn70e5yr8dB6Ps8jLy9Mjjzyiffv2uWQVUuXiiy92sncPj5oAuxFtZ3j55B+Lav6CeVqyfKFUr0jDRwzT+RdOVIOWWd5M1iMloMJDdbLkD9Lax1KZmlLauLvNYGnYp6V+75QatPIBXV1GwWlp5rfNxD0Y61mdwAvgmkeN0PPrrm6DsxGz9R0zpRV/kfYstKknEG5nPe8CxP5/vYZS025Sp4nSkI9K7UeZasCvJY/qxKmDNmERs22IvlSGXXDWNu4kdbtQGvoJqcMYbyLrce7DkygeZ8FSWLJkiaZNm6bCwkLXa3vzzTerU6dOb/dL88hgsAPRT4t/D+NB9yywUcfHtkd1piBfRbmnlNWwQA2b1FerDo3VelDEjTbucr6U29xP5/EIB1WwDU9Ls++U9q+ommqAAK/HFdLkHxqp4qu6dRM7ZklPXmtJQk1hwHulK/9ma9CjbqLojLR7nvTWT6QtL9t5WVHkNpP63CCd93Xzw/PqJo/qQpBV0poI2bdjZlQb5x3QnvV5OrOzibJPNlVOdo6yciLO86TdCKnjOKnzZKllP9+G5lF74EkUj7NgKWBM9thjj2nHjh3uZ4MHD9YVV1yh7GxO3LhdLXHVxP3Kb34eqYCdhyra5helZQ9I216XzpxIocIbM1Fu0demUQ14n/lXuF/5teeBYuCktPC3JiumepsWMIK7j3TJPVI3xtD6JKROofCM9NLHpZX/qL42nmTS9usflzqf7/e3uqrMXPpH6a2fplbRLxPBWNq7pP7vtnPUw6Pa47zCIr3+2pua/9Z8qShLI4eM1cTxk5TTKMuRw3iP8V+/v3nUNvg+DY+zoIUH9cno0aO1e/dup0ZZv369tm/brvbNuuvQGmnvEmn/MpPIB+OTqXAw7afNEDNkpMrBBCC/IXqUdbAeWCm9dbe04Rkp/0hF/lgqOGXr8M3bpFUPS+O/JfW8yswYPeo2zjAl4KeWdKR15GLUJp29+DHp0j9Kva7yipS6BJRyEL1Jk9iImcDSQpF/zKqw6QCqF1rROo73+1tdAzHWzNulZQ+maT1FpaNbpFc/Lx3bap4T3rjYozpBHhBVkfJOHpXqUSErVMsuDdS4Q8TnCB61Hp5E8ShFpPTo0UNdu3bVls1bVHSsvub9/pQKZpkk3iW7if23kra+Yj/LbSq1HmwJ7cD3Ss16FE+48PAITPFQn0y/zYiQKj1WvrRnnvTiLdKIz1tQ2KBFul6pR200kF3xV2nez9NMoASISsd3SK9/xYhiJMg+EMx84KPDSGM8dcKAEm7ox6Tul1lSeuqwtO1Vacl90ulDJT1OzrtNym1S+jFO7JNmfscImJJPbm2O478jNWqb3vflce7i9BFpxrdNpUk7TzqBOg+VHhjzNale8ZRkD4+0g2EVeC4CVO2NGjXyQwI8MgI+vfUoBdQoo4aO16HX2uvMa4O1bUObki0WYZW42M8IAHfNkXa9Ja34szT8Vmnox6X6LXyy4WEEClVVpluc2J2+xz11SJr7Q5M7T/6BX291Vd3E3jPn+9KZ49X7XIfXSTO+JV37qLVb+LVWB0iU18NNiSHTLn9A6nqxtHeRtG+J1GaoNOFOqWV/6ZVbiz0sWg20NgoV2V4Yj2PbpKwkSpPjO02512hKNbw5j3MOhaetHbE6CJQArMm3fiw16SwN+pBvT/SoXquA48ePlyBRPDwyAZ5E8SiViBxcFdHy27rq5EtdVHQKvXolMoQiMwdFiorqYMrPpLYjfLJR19fWpuek174ondhTDY9fKC273yTv59/tq2t1DVRXZ98l5aWRnCsLTMlg+sDYr8uNRvbIXJDI7llU+ueYczJJAgJl0T2WlKKAYorT1AekfjdZIrxzpt2fcZ14XDz3fptakbh/QQaHIVpghttdPYlSJ7D1NWnBr6qPQAnAWpx1pxl7thtZvc/lUXeBNcCJEyYNZdqnJ1E8MgU+9PM4C6psu+ZKr36OgDErLeZ5VFRo9Xn+w9KFv5S6XeKJlLpKoNC6M/1b1UOgBCDoJGlp1c+SG99KVnfWF94629+sOdNPWsmW/NGUBXhCeWQu8vaEGxQ3bC0NfJ90cLWRKIFPGEQeY5BRZuKR4pBlJArtPUc2SSf3pf78qFbw42Gd+/Mzs0FbF2RcfBtYdQJvFPyjUFN5fxSPdOQRtDOe2CudPiid2C8dOxRV/R29dOb0EdVrXU+R441V1NrHZx61H34JezgQnO1dbNMHkA2nGyTQ0z4jXfEXqdNEb8hY1wCZNueH0gFGzVYzaOWY8yOp02QzO/ZJR+aD5HXl38o2X0QtwlpwbRTlEC0Ed+xR3DeshSO+zWLlP6UJt/s9LdPXV9g6YDwnE5sgUCCHc5pI2Tm2bra9Jm2dVnxfFHJMRiFJZp2iVmGdYZKNIqC8qWR4ZKDwlG+7yGy15rNWzKpJbH5B2jnLpo7589KjMusWBR7qOtq1d78lHVpre6LtmyhPrnFnZEGbM3r52Ry1HWYjtztNkuo38+enR+2EJ1E83AZIP/a0W6uHQAlAJe2Vz0rXPSY17+0P67q0vjY+K21+vuaeM2+ntOAX0qV/iKsEe2QsDq6RdswK/x3Jau/rpTaDpXqNpKObpU0vxNopEhJjvHR6Xyu1H21VWUxk9yy0lkSIwESQ+PK7EZ+RGrWrnvfm8fYD1VEY8cY0OoJ/Jp4M+6TU9x1mHnt8l5nBMlUlUJxAojTtZuvxot9KHcZI9ZtLhzdKax61VsRAyRL6Gs5g0IinQPW9T4+3F+wxaZvEUwGw7lb8Tep2sSfpPCoW29FquO0NafEfpC0vGZlSmnCOBftFUv7eXO3eK+2eZwWI9qOk4Z+2vZPz2ecFHrUJnkTxcAc2ppx75lf/c+1bLs28Q7rsDzYa2SPzgTJk6X0hUyeqERzi6/8nDfu01Gl8zT2vx9uDra+GT+NBJXDZfVKHsUaIML2n6fukkZ+3yRerHipWANBqAenW9UJLKk4etGkoBHarH7JWtLAkd99ia8/wJErmIhsiNiS4b9jWgv5BHzSDWUxlmeLTepA0/nZbdy9+3FozsuubcqVJR+nMMWn9U0bMdLlQmnin1HaI9Mrnku+T2Q28+WemA9+bVNWarK0eV0ir/20FqnhA7GFujBKzcUdrL0MNDHGcTPHEuj261UhAD49UCBSKVW/9xNYgpv4VewDLPVBAoYJf95g04Q7vnehRu+BJlDoONkI8S6iElScnTgtIbp+Qel8nDWBKgUfGgySTg7IsZOWYtJ1KXFntE+6+9ez+5d2XxGXtf/0Y2kwH+9aO6aWVArTvjP2aJRtz75bWPGL35f/jzzTxe2YOe2SD+VWMuNU8m1b/S1p0rykImnSVJt0lDf6oJThUiRNBOwZqFdaZR2YCgoQ9JxEUAjCXbdLFWmG3TLPKbOMO0iW/k3pdbRXW5Q/a2sNglkR14a/NMwA07yVd+kep383Sphdt/ZVCxF6Dl7xnLjjLdsyQTod47ySCiTqomdhzaJ2IJ1Eg60Z+Thr5BVufJKrB2G3azhb+JqasSgBJMNPNmvXw56VH+WsVUu7VL9qarWruQAEEUvngWumCH0s9r/J+KR61A/5IruMgESVhKEtGXB2j9Rbfa4e6R2bDKUKeKh7xGVbJJdi79PfSFX+Vzv+x1OE8S0wSgQx+xGelS2L3veBnMX+dMg5bPAlOHUjf+/E498BEk7xdpX9O+0S3y6QjG6XFv7P/0nax7glp3ePmT9Gyr903p5H1Z0OovPlNG1V7bLu0a7Y05wdSYb7U711JXkDMNNkjc4GBLCRGIoLpKbQqbnnZCBRA5X/R72xv6nieRVqnDkrPfdBGYwcECgjWJyRNlwvCn5/ftR5QHe/M41xBwWlp39LyE1KUceNus3awMHS9SJrwXdvrnn2v9N/LpRc+aiaf478tdb8kyfOfSO35Peo2nH/iItvLMHJP53o5uEp66ZPmq4Jq1MPjXIcnUer4ZkhljEpGTYNN2G3ANTRJw+PtAVU1pJphihEmmlzziBEnkCFUwIbcIt3wlE28iAcy+Kv/LV3wU6nL+fb/kdBf/4Q05CPJn58WjgOr/TrLZECihLXyUE2l/QFCLr6qxc9pjWBNBuRe4/ZWtSWJSJyaQqsOZDOJdFZu+GvgPh6ZC9ZQ54mlfw4ZwjqCcEtMJvAZY93ge3J2/UWTT0jhd7lNw9UmrGPIZY/MBYqRVPaRvjdJ/d5pqpEw4OlE+9msO8wwdv9Sa5Vg/DuKFHxPwooUZ/e6EJWKhwcgjqLQgH+i8xSrhrgKM9o3vmIKeR+3eZzr8CRKHQYTBDY9X1oRQiDX61qpZb8y/jgidZmSvHIGmFSA8/aQj0mDPyJ1nmzGjYBpBBuf9lWPTAfVLyqtiSCpGPM1kyPP/YH0+FXSk9dKT91oqoLx35FaxSqvVHNHfcnW2vyfS49dKT15nd2ObLaqHL3fYcDX4qhPcDMaJKphVSvabGjhwcxz3LfNI6Blf2nQh011sv0NG00Lju2QHrtcevMbCQ8SMU8dlCqH1oXL4N1z5aX/fXmcO4DY4LxD3RQPPFA4RyGAE+Xnzbra/WnfYd1wDr7rdVMKJKL1YMtHDq8LJ5z5Pc/hkblgjaBgKgv4RYz7hrT6YWnjcyF3iMSIOJUeye0mQLHIymjVgUD2MZlHMuDXhJeY80+sRoKDqXfTbyse6+7hca7Ckyh1GFRvt75WejNEFXD1Q1KPy5P/LYaLU++Xxvxfkt/TE36vdOMz0pSfmwfBTS9Il//ZkprADJKD3SNzkX/cDsREkBCwvhjlSI820mOquvTXLvyteVF0v9QCvqZdrEd270Jp/i9tugr3pRK34FdSo/ZSzyuTBIdFRrRU54Hv8TYjSVJAMjD/F+bBNPpL0jtflt75krWOMS6Wth2qXkEVmMoa6oH4FgqqthPvsn0qzA+l+M7pfUse5xZQL3EuYlQcD6bZsS/1mGrkCH4UVPkxGR7+Gdv/Njxt9z2x2/x48N7B7DNQSFGsYLpT/uHi+5Z47mypz/WWHHtkLpwyLkRRFyC3uTT5+1b0wswzbFoY5xwVfNQkFB4wiaVw1aKvNALT4qPStteTEyX5eeV7knnUTbBmMJDFsL8m1gheepzfQcukh8e5CG/dU4dB1YFZ7kGljV5bgrwJd5rcvRSQweeajwAHNIkwo0UTQWDItAGM8hhhtuKvthEO/rCNgUS6/Mb/mfEnf+96xj0yEqgBwoI9jBgJ7vYusPucRdSqD1T2O19gY/NIOBq2kra+XLrij8qFRAWV04Ikhnn4/VDN8F55mQlUImGmnySfyN4x94Skw/QTsqTdKKn9GGnMV6VXv1C6fYeF0qSTJR3DPm4JyZtfl7a/nvw10LLhkdmghRAF04FVxaQsfku0TTAB6sq/mzqAn6GwazfSjDwhigFjQDG67v9uMwbdOdvOSsyM8eeZ+yNpX4i3DvfFj8ebymY4YvFVGNjfRn3BlCgvfDjcAyoAQwIoVI39uk0ao4iBKgpl8Kw7rcUnGerVL1up4lF3wTpa8OuaG78NUbP2UWnAe02F7M2OPc5FeBKlDuPwhmIjvLHfkIZ9QmrQ0rwBwuTxbGQX/9aSWu6XzD2bJKXfTdYq9MZXbcRtsAm3G2HBZYPWVhWhv9KTKJmLZFUEfk5lg8ptIiDzCBpRoHBwskaLgvsmHKT1GkrZOaZcCU0yIrHX4JUoGQsMP8Oq9LTvjP+u+VU8/yEjbFlzGBRD8g79uPn1zPtJyfUE+UsCApGy+UWr+mIcm1TmHimn9dEjI8BeRFvqmv9Y200AFJVPvUMa/WWp3TArQHDWvfZFa7sIiF3Owde+ZGuuz7VSzyukMyeMNIaI2fhsaRIYIpDiQ8vePonIdHDmsZfFq+ECQLQxcWf+r8xLrjyyr+uUYk+wkwfsbGTsNurOjc8UF88Swd7ox2h7hAEC+HCSdVNdQHXFZDOKuxRLPDzONXgSpQ7DmeLF/n1ojQWHAKIjrG+bwxi5MYc97TwD3hPyoBGTNmNghgIlIFDc8+2WXviIJcl4ZRQVSXkxOb1HZoKk1LU6JMg/USOx/rpcaKaeQcsPa6PHFVY1Q77MesKvIm+n1Pl8S2xRFQCSFVqCuF/9pkmSjKitRV9dy1ywVlr0ivVpx6HzJCN7l/6x5PQclCdL75cGfsAqtQGJgpqEscd4prAfvniLqVfi97BQRG3P9Mh8NOtZpH6fPqL5dzZW0bHcsxVTvFFe+kSx+TBqlLBWVdSXjDde8Rdbb0xk4SwMN/OMquGQA2p5VZ6K1EXZ8tltJoMiAepeTPfjQUsO+xLk2+qH7H7ullt8DnJuovjkv9yX1rMZt0sr/2FKTMgRDNjH3y5NuMPWaljrEIqoZObZHnXbvH39k+FtPG7Ee2crJDRoYb4+R7dJx0PMtuPBmkRZxdlcSg0aIGrFWB6rqoUKCnFcCzwXMSUxKCpm4kaKMFxnTvXcxvZmr/zzSAWeRKnDcJMpYiwKIz+5gWGfCidRDiyXZn6nuMqLC3wiOMRRmrCR4vTecbwpTfg5ngNIms8cs/uySSUbfeuROSQKBxLJQzyoti3/izTpLvPJgcAj6SCppf+fwzdIXiFQlv1ZmvJT6fIHpdWPSvlHjFTp9w5Tqpw1zQuB8x/wJErGgu+268UxEjhuDbggKGJmeIlg/ZwNCGNrY/itZv5J3zfKgLIk84nBYJthaXgjHucsotGou23eskkrG74qXdJfen6CdLq4jwzFW3nGoPZgFsxzK+tOOR1PqvDKV/TSnF06Eh2n0aNHKycnRxG/mWXsWdl6oCWr8fsYU5naj7YpOyiBAwT+POd9Qxr0AWnez+zMRDEM4bLiz5b8BgUsPJ1oR+t+mamNE0kUN0Z7cHKFcSrgDObspkUXvynUzlwTtOFGcizJJlmFLKK44TyE/HI+p8F3igH7/hWlf+eUch+Sxn5Tat7DTLZRMkFSLP69tOT3SWL8iK1F/BKf+4C0KcwkOQb2SXx8KkOi8NohF/GuQknDNFIm8Lm9NxKLEVizsesNAqXdcDMRZwoWEyS5Lvwa9UgGT6KcoyBgS4Z0BVFUMtK9OdBT27iTHdDDPyUNeD+ujUaigO3TzYfAyaHL6AH2yAxAoHC47k0gUcDS++wQG/oJ6WL8TM6YymTR76ShH5NO7i8+3Kjc0rYDwXfhLyxQg4hZcp+NQ+a+YS07rrrX3StRMh2MvUYdh7opAEEf/dsomyDhAiKPNdftUqleA2n3PFs3mBPT3rNnoU3oSST9ygJJjltjHhl7FhcVFWnZsmWaMWOG8vLyFDlvnnIONlfR9GFSNP2bS07zIjV5z3ztb7FV0dNFmjlzpg4dOqQLLrhAjRs39kRKBoJ9iaITyrqg0AQgI7a8aP+Oj5eCJQDp4WK5LFORcOad2CcVJrTSQiaTPBKjhbXsQAazl1VmaXFOc37j/0NCjIKPaWYUOCCrSa55fSTd/BeFID5mGMJD6rD/xr8nj3MLFEBR1yWi0wRp8t1WxGJqD2QFZyGtZ5O+Jx3bYuRFPFjfeEbR/phK/M/aoYWNAkeqrWYubowamcjgAiaBnj6aoIyJEX7xQKXC2t36usWh/d9pBuFMivTKFI8weBLlHAvWCgoKtGvXLm3fvt3dduzYoZMnT6pevXpq0aKFevTooU6dOqlr165q1qxZlYIpfEnSnVxyoCONo0rCgf7G14z9hUTBpHHIx6XJPzA5KRJm9xo8MhYEZlQQEiXKQVDHtB0q/6wDDjRaxiBdRn3eKgaBWoBDmqk9a/8Tu2/UDnXaezArxpCRwzYR/J7n98FZZoPKZvep0qqHism03XOtqou57NX/Mn8KkghGFg+5xVp2kLsDkgdIGNrOrvhrOCGHsg6Pp3hJM3scj+8np2QuTp8+rXnz5mn+/PnKz7e+m8bNGmj89+prx++kDU+ld4IE0viJd2WpxSW99dobm108UFhYqOXLl+vIkSO68MIL1aFDB0+kZBj4OiEWMIElGQ3AxLo9C0rfn4LC+T+W5t5tiR8V/yYdzQel7TDzFAtGuAetOiS4B9eGt5q1GpqvlgNICSqWLdKSxmvERJlYjzO8PA8yFDOc++uesGo/RRPaw5nq6Jf1OYZorB02UvJ7RaGBfxikHPE8Xjvu9xHz3HnHs1Kva6S1KNxjZybKE4gzvmeItLKmUcWDMfGoR4knUwHnPAW2xb+z66GinniQf6i3UNNgFn7eN61Y51rDPTzi4EmUcwBUuQ4fPqzZs2frkUce0ZIlS7R+/XqdOGE7DMFSoEzJyspyJEq/fv10+eWX64YbbnDESmVkvsgq016hjxiRwgb71o9NQRAAZ3hk7/hYcKBj+NiC1+CRsaDaT2sYY2bj+/4hQga827xQNvwvzkwvYi069HnvnGWHb/2Wdl/UJgRdrJsAHMgksNw3rP8WAgW/DI/MBqZzBOKu4nTEfkaigKrk+C5bPxf92qpJqFNQoMz+npl6AmTmqFjYt5DzhsFNAErYL0lWaGv0gX/mgTP3+PHjmj59ulasWOHOadCmTRtdcskl6tatmwYOiqh595JKp8qCc5N2isk/4oyMKJLVSddee63eeOMNrVu3zj3/1q1b9fTTT2vKlCnq06ePsrO9T0qmKTcHvl+a+d3i84ziQFhLYjDVjkQ0UK7QOkNb47hvSZfcKy28xxQhrNGRnzeCjkljmPrHI6tBoY4PnaU3ZxRqxMgRrmBX3toiJOX5FvzCWnNPHaz4+2UvPrDC9un1T5mfC15WVWkp8kg/3DmZQESgImncwYpdTFk8+/uoTU1k3bKeUY/Etk4X56FG5xymcNEyYWR8Mrj2x6PlkyisSUiTGd+SVj8SPq2xIqBgwnt5/cumxpl4h8Wj/rz3COC3qrc5SKOy9eyzz+qee+7RrFmzzla6Eu8XgEAqUKkQXP3mN7/R+973Pt16663q3r27I1lSBQdq0852yKYLzssizw7U3W+V/B0VCuSeHJL4VNCv22pg+p7b49xEz6ukOT8wc7AAHKLIJAFVLNeOI6lpV5uAQQVt15zi+9JqkdNU2jGz2KuCNYSigL5rDuZE8HdUt3zLWN1AxwlR9X9/oZbeB1Ni+yBrZcZt0qLfSs17m+8AhnKoSuJHNa58yCqjqfT7B2A9MiaZPmqPzAJn7sGDBzVt2jRt3rz57M9RgE6dOlWtW7d2RYuGbaXz77Ye+vk/l3bOqUTgHjFDWqbwjPyC7YEWpEdcMnvllVeqVatWTglz5swZ19bz3HPPacKECRo1alTKBZRg/WKmiAqLfRPiEM8A9kiI7ea9pNYDYsaK9XyyUOOI2AjsVf+MjdIuA3yXfHfxyjgUUQt+Zd8npPI1D5dMRN+6W1rxt0SD0KhyBu3SkfZLNX/BSa1es1rDhg3T8OHD1bSpSewS1xdrCbXBK581v4qyDERTAa97+xvSM+82pfKgD4ZP7vN4G4DqN8S/CeXT9NtszboBEXiMUETNscIC/92zqKRKDzUx96GtC7I4VRIFopC1XubLjFrr27Rb41QxaQI5zZI/WoGG1nM8hfze6AE8ifI2BmmbNm3SXXfdpSeffNLJdCsKJL47d+7Ur371KxdU3Xnnnbr++uuVm5ubcvWW4C+dJAobK0mKU7mE8Dmw0gHRAhMNq+uR2aBaMfC91roTBFuQJmtjFbOp90trHpHqNZaG3mKtGRyEgWM7hNyaR82EduoD1rJBkMi0gVb9bWwo0stEtB1u3hfeD6VuIO/UMR2b/KaiSzopMnuEVGQbEAkD1Skn602G/2fvPMDkrKo3/s5ueu+990o6SQgphF6liVgQFSuKIFhQREFQpOifolgQEQUREKmhhAAhlISQRnqvpPfed/7P7575spPZb2bb7GZn9r7PM0Z2Z6fe795z3vOe9+TlV8uKgki1o+p2ZZ66XFxFkRy/wLIJQaECAmXz5s3HksiePXs6T5L4NlqXEFQ12ToTUVaNlxb82yZFHZs8FxbMx5INxs62G2sECnsZ7WHxwTnPU716dZ1yyimOuEEVQ6xAsQV/lq1bt7rXVKdOnaREimt93GZJKqo//AUO7DQS0akDWff4aVQ15WA1PAuGmcE3hs3s3z5hKB/wORM7Dbheevt6KS9F4jj/n6beSPSqQJWCyg6yBKUcxQbM2amkBwqBeEAGN/3sCq3OOejWAuorVNELFixQ//793boPyJT4IQNML9sQr0BIA0iCJ95oypv+3/UFkAqDvOQqjQAojpnMSdxFbE+b4+y/Jv5RzK8koTWoMKSwiDwG1FUoRpxJbRrXZADI8UX/sTakU3/tRy57GDyJcoKCNHqsb7jhBk2dOvWYTLikgEyZP3++vv3tb2vx4sW6/vrrXVCVCgRhGzZs0I7eyxRpOETR7anvX1Qc3iet/dDUB+3PsMM2qHoQjLUcLu1caUk0XhZetpn9iEaOqNZZy5TzRg0dnd3OTtCoVSU46yBYRt1r96W95+3rrP0n/wGsCkD1ggrVqLvzA653fmAETCJoB+r3bfNE8ch+Qpq97J133tGaNWsUOX2Fquypq+gnlLnSn/1Fc49KQz/RrhFrtPfgGNWrXjpvKo/iIyAG6JXfNCOWIC4zlQVfOS1+VDkJ6J0/RDupeoPCyQDO0kWLFmnixInavdt6JChKoPg4+eSTVbMmM9sLIhJTk3S/Uup8kSmdeE0YG25fKO3fZn32eIPxWpr0tokreIeR5BZmmIgnWq9evZwyhdcGycNrpc0IUuW0005T8+bNj1Oi8hnhI7DsFemTP9lrSepBkCcdPSId3W9tScjuSYIa9TZiG0UfShW/zMseKCi7XH5E8z5Yq3XPtlDkQLgkg0JUmLcJoPqPeb8z8E8BSIoB16L0HKCVn9bVjBkzHDFHTIriCbUzsSVkCi3krP+dKyJ66zppQ8JI+XQBAmXKHVZgY/KLN/Q8wWA/rV/43SCEu1xiKg2IWHxPiL/CClzFBQrSVIQaZDD+JbR7l1YVlQqoYWY/Yi2/vfza9ODyiKYaA+ORdvBxf/TRR7rmmmvc4ZRuULX64Q9/qJ/+9KfOxT/xuZEDY1aL7wpKmEMHD6nquLMUfb//sYQDw7KxD5mJImZhYWDE8RVvm3z5hYRRxwSJl7wk1Wxm8tGVb9jG2v87FmRO/pX97JKXraXIB2bZCdYbkywgCllvR9bWV86/LpE2NchPbiNGrnHYusrGihSjPyNSrWa2ZsCulUn6sCNRdTg/Txc8kaPq9f3iymaQSLKPvf322y7oB/Tyt686WFvvGa2969L8/efmKXLyfB05d4Ki1Q669o5zzjlHDRs29ERKOSDvaMz34WlTVWC4eSyRTIxkYl9H9XpWGWWkJmMrMRBODH4DU/fp06drypQpzkwW1KpVy6lASCIhKIr6HRc1qirukuF17tq1yyW3FExY/6B+/frOcLZr165GpEQjrmXng1+aWqE03gAoVFCsMm2jxdCiT8jwKD6CdUjr1uT3P9LRd/sp8uYIRQ6lX5LBNYAx6Bl/gmC0Bct5jYHxrFmz3DqL9+Jr2bKlBvUbohW/66IF/0RSrDJF3fbSRf81stFvrScOxGW0bWHUmuo7x3QVsqV2c6nrpdbqun6q9PJn81XFASiI0QbZ/1rp5StSjzjmSVsMz9OF/8tT3eZVQvdg8pAXL7bCWnkAG4LPPO+HFnh4JUq5ggNp+fLl+u53v+ukkmUBgr/77rvPmd9973vfO2YOBnlCsjF79mxnTsdB7RCR8gbOU86c7orujOnT2CjzCgkEA1leiIiGdp7Xr5FG3SWNusd6H9loYIvp12W07fBbPYFyohB8rzD29MRzcxXKHKlabauMOvOsnJIH+gBVwKRJk1zLmftZk82qMna68p4+/TgTMnwrAp+T1A9sh2TqgzKq6t23SJfM1N6jg1Ut6pPbbATriQSS/Yy2BiaYAfwhhg4dqkEDBmtDD5OGx0+6KA1IJtufHVWdr23X7GVHRP7KGqeVEiIl8MnwSD+cqmKXeUVMf8AI1EIn4sT2GPrYV70prXlXmvUnC+67M1Wilu1trKUDBw64vYoEMjgbISbGjh3rDFz5Xovz3ZbVMuA10E7Eegt8UjjzUaO88cYbjkgcNGiwNrxfRW9/L6JtixL9L4oPPufVjKxdZNM1IKLcuFq/1MuEQGGcNWTeEfpuTpmu6vsb6ujbFLnSB74/Et0x/xcotOzLpPCG4ooWHgofjPWmvQdlCsW33TMa6tALnZDjqaxBHElb0nlPGhHqcYIQMcPrRAIFIgTvJNQZgSqKG61j+C01G2CtikxfSiRRiott1Rdr/Hvz1a59WzeZDNVdYFvA/jTjgYiLY8sLePbNeVQa9VvfLl7Z4ZUo5QiY/W9+85t69tlnS93CUxjYZP71r39pxIgRWrVqlassMCoRMiUACUfr1q3Vq3N/Lbmji1a8ZAcjhyoVfzalZKoATL8wwMMDJZnXAPJfpNRN+pipLGPS8F9hxCgjR0nWPcoPgfEW1dvVb5kZK4fb0cMxCST9/Tk2sg41ET2uHIJ8hxyWRQUJLQEYAT6VLUCQxjSLk3uP0pxfttSyFyOlDu5D3qGqddiho5e9roNNV7vK2ZlnnunHgWYZOLJYY/Tts86CPY2+/ZEjR7oEgPYH1hfBzke/tpaGxIkUxUHt1lK/b8b69OsecmpCWjKD52atYTjKvuvXWnoRfI/v3yKteP14Q+CSgIpp10uk4bfjP2EGsig7li1bduxcZgLeGWecUaBFpqKRiLQeQf4QWwDWfecqI7Xp/wZr96r0v+5azW2sbu+rLInySB/Y0yBQiNUChVGbNm00os/Zmnd3E9eqUNq1H3ig9P2qdPJP7ftMtl3xGrg2eD2ss4O7j6ra05fr0Cf0yJbPHkdb7jmPmdluZdtWUd3RjgcJHBDGfB7E57TLlFcrCXHj2knS8xcdf4YS/1/2unksoVSJj+coOJz2gA0EeHasDQ8oqRIlkpunqte+on2tFxzziMKugNyFyaTRVS008er62rexfBdIvY7S5eOLbo7rkZ3wJEo5geDs8ccf13e+851jUuGyxrBhw9zUno0bNx47lAGbEAkt7usc0vw3/a2vXGHtFGWFaCSqKl036JyHaqn7md5HoLzAFQ5Zwsg3KrmQWW48YhGufA5spPA4/Xc4x7wGkn1trPEtW7a4QJAR3UFCQh81cvhBgwapdq3a2r1WevdH0pL/lX4E3TFEpEY9omr1/Xmat2/8seS2adOmLhmi7cKvt/KFmwaSZyon1hueEICpH5CwGLMV1xOJ44qqO/4n8WusWbNmTjXAvpb4PePTRPvgzD9Im2bGiOEirH1eJ2QyI9nx12k+MP/1sr6oFjNRLX6tMXaeBNyvtfSA9YNUe8K3pS3zwpWPJYGN2Ixq0F3b9PGa17R2nVUCIEw6d+7sPEbwH6no3yPrn+II7Wz8m7uroXKevEh5K5uVWaJbu5WZgXc8x3sCpAPsafv27TumhOI7Zd2RIFIEYB0e2hXR/H9RcY+psBLMYYsCTIsb95SG/ETqerH5TBQFxI4b1m/Q1Me3auUdvZV3sHzZs/ZnShc8bYae2X5eHt5jZq2r37ZJhBQo+ZkzgI5YgYuWGcyH246R2ow0RXdRv8vigrWIEmnhzJWacV0b7Z3T8Ni+QpH0i1NMffLCxbYuA3BuXvicVK+d9N+zTMVWUhKlSoODitz4pPZXKyhniURyVGvBQB18fKxrYUx1ljfrb6RUskEaxCR8lvU7GAmEdyPeUMl8pLjPmN9ZYcXvg5UXnkQpJ2AEx+QcjLvKC7C1V111lRt9DJC/ESD27dvXJZW0+gRBIkqEpS9Jb34zf9xsOhEla2m3TnlXjlPL3jV1zrnnuJajih6kZjpQmaA6oRrPyOCSBF/BAdPxXGn4L02ZEn9oBFVRWtQgUOInTZHcMj2iY8eOxyTx7DhUVxgJihkYRoalAcFhu9Ol0XdHVb/HUc2cOcO1eATJLfJ3gtFOnTpVyKpyNiEYo8oeQoWKkeZIe/GxIIABVetY3zSVHAi6tqNj5pqFjFRlnZEoMjWFf0GQbJx++umuvSHVhBKquOs+lFaOtwAV02tHJroHyp9SAnHY8mSp9Uip49lSo17h7QtI71HCkPwEo+nxRqHVwpN2pQff2Yap0qtXFW6QWSJEoqrebZuOXPmiDtbc7M5DyF7UmzVq1MiY74/rgkRn4uuTtfL+jjo0pYuUV7avHRPJz7woNezs5ezp8A176623tHDhwmM/x9uGMyuYuuSi9Kgl2PP+aePYSfBccp0qgs8xQ068G1AP9fhiyaYtHdod1bgvSctf4r8ioWdwrSaWWONThvLYjaRN8tqcwXIb+3fPp0ZsJ4tN8NO7dJzUakR2qlH4blF0M9lr7j+MQCFxL8wgFSKCEeudzpP6fM3OrHSMJWdNQp7s2LHDGVezLvn/deedon3/GnGMrOC8HvYLaciPbBCAI/hWm4qZokPPL9pAgEk/LqigKg6J0vmSI+pz51pt2rHW5VGc/Zy3roCSF1HVceco+kHflBsRpNPnP5AWPSu9c33B39dsIg29xQYXoFQEXFu8rw9/Ke1aFf64+GzRbuYn9VReeBKlHMBH/Nhjj7lWnnhFSHkAf4DPfe5zzlmdAJGkFslvWIDIIYZh37s/tKQnnchttltHv/SCjjRd5/Y6ZNJUbX2rRdmBCsGMh6Rp9yYxYC0uGL/YwTxuul1uh2igDAhGIgbEBWusd+/eTg2Ft0DYd8whBbEz9W4LHIotVc6xyhpTnjj8gukbJLeYNjPFIvDKwCAS40UmXAQ+QR7pVw2g9KBiuvQFUz8d5uNPccJQQeN763S+fYeMVqXCk7hcCJgw0uQ7DUg6vkcI4VNPPdV9v0XdR9jnUKdwfRDAMyIUspFACHk7N/4/r62wChP7Ob4BtIPgqwFY76ifIKz93lYyEJXsWiG9/DlrPywzE8ucqKoPWq2cz03Q4JF93RkZ9NpnEqJ5Ub1/21FN/U2OdLTsiWKuiz7XSGMfKLsqeGUAe9mbb77pvPI4S9kvOKM4q/AnSRancZ7TIsG5uX2xtV4f2G7KTlo+ajayfQwvC5QcLQabgqGkpsB46/x7eHjBgwlYw34utRllZAqKQ6r4Mx+0pDX+XCd5RtE69Kd2drOOjhy0SVCT77D9uAAi0im3ScNuzT4SxZ2Zs2wAw4o3bDx1sZFjKp2eXzC/J0iMknxOQTEM7xtiOZSeKKSCFLHq/oaq9tgXdHB1/iRP1hjTEokHWZe0HXF+A5TG7/0sfDpPUUkUlM+QFIyQj0bzXGxHTMdrxKZg3bKt2nnH+YpuCpcp8TxMQDv5ZosvmFLG9Md4QDKOuNNeC2bli5+LmS5fZpPWMOZ+45pwRQqE5Jdnm1m5R+WEJ1HKAbTvXHDBBa6CGg+CbPr3SQyoJIUBKSeVMRjhlSvj9HIxTxNM78LGLrIZLlmyxB3Cf//733XppZe6xLYwsBGufN36zzfPSUPwGpEadpWG//qwdrSarikfTT5WtUWJgo8AvY1eIZD+MYGYsqH0oMqRTiDVHH6b1OurR7Vm7Uqn+qBlLNhKqMYPHz5c3bt3LzQhCVQp6z6Q5j8hbZxmY46PHVjxwUDUDkUOLszKul9ho7QJGhKDQ5Ju1j9tH0HSTXU5mLTBteORHvAdEvyy1vgOnUdStGTrCtPKQTdKDTrlExjsF/TlMzUlIMX4LiHoBgwYcMKT3kCFxVoj6AzUT6hj2J/93lZ8QHK9da2tp7IcWQkiVfPU5Su7dM6DdVWtRm5GXn9b50nPX3i8pL6sAfl58QtS61HZl9yWNTgrGSUMgYLRf0AKn3TSScdI4SI9Tp6d9XhVuJbJo1bcgNhCwUFVPR2tBiSWJLuJ7XS0P5z/H2uVIG6E8KzRyKb+8O+E70gL/51///ZnSec+bqPI+TkV/g5nSZ0ulBY9Y0roMMNo/Nk++2Z2+fDwfVG0/PC2mNKhlLE233PzwUZOoO4s6mcVeIwFUzv5NygIuMeNRFwe0r1bD+V+cLI+/lWN49qwUZaigmncxxSmuz+1lvGNM1KMVI95qnDm71hqMWDBN2Qx3tl/D1d6uBHvzx7QO9+oqSN7Ci7yzp+JkUptpboMsagizfpjQRKFePILH1nc+eIl+aQPI+sv+p8Z5P5roL3ORHCdXfmeTZDyqJzw03nKAZhycYsHSdyNN96oyy67zB2asL5hoM8fFcv111+vf/zjH8f9Dsn4M88848iYRC6MxBHiZubMmc4E8fLLLy/Sa+UA7ni+1KCr9PG9xiYX1UMgEVQ+Op1rMrnGvarqaN5gVa9RzcnfIZbwzxg3bpxTpNBy5JON9ADS5OP7zAPCSWrTDKpeH/w8qrW7lmlpjdd14MD+4/wEWM/4QxSlCs9dasSUCFSoCCZICDiwqGbRAkJgWKWOVKelkSeMl2vUI6YUSPIUvBbUV/j9QF4SsAYTOAgYUGiRfHulQOlAEE/Lzns3m1FxaQJB1hVjFKmwMtWr/RlR7T+4z7WIMYUnUPFBUFCp5futCKoiXgPVY0hq1hryfIw+x48f7ypnkIkV4XVmCjjKqEySuJU1geKe73CO1jzXQBuutGQt07YEPiOSMaaZlCeIC2b/TWp9KhdB+T53JoNYbdOmTW5/YHIdYO+AEKZgxplVnMQZ0/fiGL+XBFtmh/8cAgSvKKYuTrkjFm9EpOXjpM+8IPX5irUecS4QDw6+0f5u/DXW7gkWPytd9JyZ2NN2xPmfCIj5/dutZSgbgAqXhH7yr0pneJ54FtP++MbXzNQV9UYy5RFrMGglI/dAuUubTLxSnjVJi2yfPn1cMQAi5UCfiNZPtBbx4KzHswVFFLfigHY0bslArHfyT5Ir3ThTD2+onVTBzGdMKzE+j3Va2d4e+jg1TEmCIghVagDWG4p8ciKIolTvw5MolReeRCljsFGxQW3YsOFYckeCiT8KbTZh7T0kdkg5SfRuueWWpIcqIzW5QUSQZMSDhJFNkedHbk5QzyZYFBBEcpid8bDU4/PS3L+bMSNBU1H6NJHgISHt81XbuPDT4DGr5FRxZrYkrxjhkcxC9jAiFPk7SVHw/j1KfpAu/p807XdlQ6AEQE68/E8tpKvrSrX2u8rZkCFDnMqD9VqS75ADHwUC/atuhHbcmG0ezj1kMUZr8hog5yATX3/9daeWIamFVGTt4dVSHO+DYzxl/L/Bn8b+rUxLl71g1QRpwrUxQ+p0aBrzrIr1+leiGnjbLq1q9IZT4AUkMe1/7BUVzbw1IO0gxxk1u3v3bhegUmlmzRGIphqTe2zsOJXlndZbTkCH1B15NCM+UVyRRLgKY3A9ZCFoGSDBIDgvL7Cf8Zx49DD5IpNwcJe0+L/ho4ydCiHJOkk8y1PeNy/8+obw3L5UatRdWYn4Pd9dm7tMRcH/rxprRXQ+FLH6T2HXJPsYseBrr72mzZs3H0tWifW4FUUtfCLAd5yoQuE9NxtoceGS5+LiDbyMPraf12xmSTCKBKY0thhqpApm0QFQ0nxwq9S0f3JCAdXG/k3ZQaLQOjrnEenD20vYvlMIKEQxLQf1Bl5xif513BiFjtcJuQn/P74IyxqkQEs7NkWx+HgOz5vR90jjPm8tXmUFrqsRd9iaSHVNcUYky0lQRjHaHpCPYMIbBtYcpuXN+kmtTpHWUgxi4t5wa4Nj7Scje/jYWL8eJw7HxeVxa6W84qOKuWNnEWgrYHQiXhH0yj/xxBNOskkrC204wUEaj9tuu01f/OIXHUFC1TV+LHE8eAyC9kceecRVNZJhxYoVxSJRggVIryDVAUYSI9Fb845VDwjwA/koC7dKDSNOkM1hxghxUq99TEoaSV61xUyNNiZuJBu8T35XkZKjTNtMti02E9kjNlm4DBHRoVV1Ve3NUWr/nVk6ZfQQ15aVjoq7+/pj45ZL/1gR579z4YUXHpNOc01COqKGYgpHYN6XcrrRJjtMUchQ3cDvg/WPMR5VDPpuudG6how525cwn8mad01+vYsAI81NoYzinnJHRHlfOKRo46hyciv+1BSIFMyLzzvvPLfWGA8KmU27JnsbBGPY9UFQvW2B9Om7RlYT0JF0MOISfwFIE26Q0eyr7U+X2p1hMuOwPTbTgbJp3eTyf1732c+18y6TQAtkWJBPInLa/dZqkQj2LgwfAzUA5/fo35ksPxEkx+//PD8hSVQI4GtF0SXb1iHXJYbG66daYrVphrR/W8wANdZaCuGGMhI1DuumyUnJr0kSVc4fSFYSV0BBiRbTgQMHVugW07BkHyIJk9vlr5ovSzxISFHHbP4kv52Yn+VWlda8bQl+vV5GDmNCiwqA6z4ZSJRp8cuGc3P1BOmDX5YNgRKA1pR3fiBd+Kx5zwDIfAqrtJ7S6gzJH0+eULil6ER+QrEiTKnLf+KBg9LlzW9ZLpDus599Cw8cxtCXak+B+IyZFQf/hgEvIfZC3tP5T9l+ytnLXsiam3hDmjwFPdIG9p69G803DbUQ6x2PO/ZklO20GbpJSx1NRVSWZ5MnUcoYJGz0GAICafr6MUhkgzr//PPdxpUIfv/iiy+6/z948GBXoQgD4zzxC1izJoUmTnKVj8BLoLhg8REUUGlq1E066RtW+aKHMag8QLZwYBbVuCy+akuygRqFDR11Cu+HTbyiVmQq+sbyycPStnyT/zJGREdmdlD3/W3Utm3FbY3hdSFLPffcc90aI4DguqQSA5GCsiEsMSfR4LNc+B+TrzIZ4dhhmuDVwn/TQ1u/s43+7P5Za4nj2shG8LlMvKlsCBRDREfW1VXkf2NV4+rX1GdERw0fPswRzxV1ncWrn5jQQ7JEGxl773vvveeC2PhkiSQNmTxmd0wMYvR3YSN88Z7BP4hWvVbDpX7XSm1HxSYKVNyPpchw5ubPxKaOJAEkJe18TPfYt8WuS0iElCrJWPDfpK+ZWIZVvAmYaS1wUy4ypLOUHIj1cGzKVBwgRjBqZm3sta6RY+DsjleucB/WE59torkn30WYygXgX4HqAFPLSMXlAIoMN8XroBEms/9qRDFtUsF0nGR7IVM8mPCBrL/v100BEBidA84bzGNp9ws8utjLaN9BnVvR451kW/zGj/P/P0o5iCTMbLteakQ4itjgfIRs4rOt3lA6/992P0goKvluguBvrECR9AVkgXsja4lpL2WelMd8kqbcGdWp9x/Q5p1rXeGI6TbxuQCxOIVapkHhz4hKPn5qZxjYGyHxz35Uevt6aeuC9I2eJ/EdfqvU+ytWNCgMLkHOTU2SFAbeqjOzz5Gq1bbJY3x+EH3sfazXpM8RMe8hj7JH4J8IyUX76gaKByh2txfcG/g+mfhI0Yl4HH+chl3M+Drd4WPF3rnLClEONXMyJ3BCZeGk01SWa1plOZi9DnNeGjOrYPwgmxKmg7/+9a/dz9m4qKwOGlSwme7JJ590N/7mhz/8YSiJwu/4e5JApO1UP/kXQoVEESPaoFWITZMAvtSIxJi+hnZLR9UWIolWC6q2vM6gasvnUthm7lHQXJCpKOUZbOQdyNX8R3PV8wpjgCsqWEdcc/jvIE/l+ggC21deecX9PPBxIWFAEsvIvgVPWqWswGca8t+M9eVGX/LsR6wVbuB1ptDKlKSsKKCySP/75lll/UwRRVe0VL23LtPQb9dRzZqZsR/wGtu0aXNsb8P/gH0aIoU9efDgIYruq6KZf4xo1h9KNgnNtdONk1a/Y+Z7TMeg6pLp64zqkltXSfYwktTR9xo5wHXK++X6RH035+92poehXgczKGzaV/pHryRtA1GrhtOGAJmQCeD9kniGEUgkGCT2GFfSqlQAcZ8xijoMFvFB45bqvolAvQP5FUzlyGiD7LXStPtMYVFcLzj2ftRMeEPgFTL051KLIWwINlks8EsCtL+OHj3aTRfLBBSlxQ0C5Yw/2VpiLaBcQrEZgGsKsu6UX0qbPrFrlmuYz4pxuEx6GfeF8GuTlqlMHyPLe53zqCV/5RWjLXs5qk2d39OWup/YSOC4+BulO+uPgiaxESjq+UqxtO1Y87JhCAUeVqUZYMD3y95+6q9N/V5U1GtnSvjStH5CyJ/1iH0/479lo6bZC2j/Gflb6cy/SHvXS+s+DHndsdfgUXbgu4Csh2hl2ihKtsJGu3N/CBZutJzOfMjipAHfT3+cVKUyfhlUGVa9JX06Udo82y7+oJ0qGqgvaknNBkltx1hVgeDLyapLEMOHGaYWZShS0L8YhoBEgTh5+umnHfFAoE4iSKXjrrvu0l/+8hen7KioiUd8skHVlmSD94GRJK+b6RvJxjHHI/4jQtLFhkpQB5ONXBT2Mf85lZ3Is6TKVbOTgI2DnlYOC8fe5qW+LwG4S1K2xCTMSRC0IjAOriJ/vqwjqn9MTeHfGTNmOHIRcz+IlDPPPFOtW7bR0hci+uDn1r5TImPLqFV+Z9wvrX7TRkJ3PDc7fCy41lhn3MrnCSPa/mEDLX7KDsBMAWsNSTR+PPgfBKZ9H374oQ5tqaodjw52CVdpfYto+5n/TxstTetGm9GZvcZQlGAoHQaqSvh01e9iVes1E42gHPoz6ZTbpa3z89tT4kExZNgt1k5Q2LELeUoinSkkCrHLvoIdwQ4kpSQYbqpEIe+bKRkkuNuXFD/BYyJQeRgAlyU4E9d+IL37Q5swU5r3Q4xJiwtEwck/O6rc4fM1cdI7xxQAtI9yBmWSBxwKS/zIUsUMK9+U/nuWVKeNxQI9rpTO/Kv08uW2RllfxGK07rx2db6JJ1Xlc/9lcTat4CjFEsE6xl8lU8G+A9mJqrU8i1yHd0e0471myjsbxtna6Ym5aZvHLDbwhCvJGgy8E895TFryvCkqIYiKpQqJTe/s9WVTcFHALs5LgQAhzi8NidLlEhtaANnMWgy+n2Uv2zkAwYKyKoxEQW3Gevcou+uGItPU30jz/2VdEMW+fmKPgXqXfXn4L216WJWi+3enRKUhUVCZcBFQkeGwZBJEqi8DNnzFODMnokIDmdLv21ZZcBL9Yphb1q1bt0ikSXHA4zJKllad+++/31U8IR6Qi99xxx26/fbbnSHjyy+/7KoeFbXflvfRsmVLp6SBSCHZgEiZOnWqS3AZlctGHwYCnT3rjQiDbdzwkVUyA/kxbDlVEcxKYZUximLTRX6XAXFLsYABpVOhhAQ5fAY9PmcHFRI3J/Wcb+zsmknH/w1O5QRArHXGwrHOkeXO+ZuZF0JSJQI5LlUniIKK3r7CekOJwgQh1hXtdVw3eBO9Pm68Oqy/WIv/0DgtclvWJ2tz/NelYbdKfb5mwWAmg+9/1p/SN1GgKCAom/sPC3bqtc2cthXWGr5VtJFRhcYPIXd/Pc29vYUOzUreIlFc8Dh4DzCVYewfpA5nF62tsqIGTEnHXX5OatSLyWB2jjtiN2L/nv6QJWAFSJSI1OsqMxYk2a/bPvVrYK+jRYjXkglnBO89rJUnmG7B7yHBMfUk4McUlXYdfnbcfTvZOuL902aB0pTEBFI+Xk2QbE9Ic3hTruB9L39FeueGmEF2Wh7UPuf3bokq95LN2t8Fh2i5tlHaRzt27JhR0wgpIobFy8QL/BwyGENoCChu+H5QiOzyGTPrXPpizOfpsMUp8VNQiB9oo2OaDL5ix6qZcSBuqZkhxGYoYhPHaD1MBtQ7EE3Er07VkeqaisSmE+bEYrKk940oOreT6p3RVi1713KtY5D7xTHUTwUegrbBXl+yc4fcatF/TJ2GesPt5QmvjfdYq4XtT/ieoERiXy7JmYXCECI9fj0VF+R3vETXlhT/WqP2PgAFxbB1ydpONbnHo+SIxj7/iT+wgklpiXr2eQoKmC5T+B3yEyuylxZVKsMXQXULiSpz6YubHLlEfa3J+le8IfW9xsa01SwiYwrzi9N1uoE0j1YfyBlmuwdSPUxk+RnjkL/85S87EgXDT4iUigo282bNmjlFCga5tCRBoEybNs0RKiS88V4IBIYExPMel5a9Ym0sgdlbGJCHM6qZIJJ+915X2zhdAsVMCJSLAmRrroqYACo/g2+ShvzIrgM2I6YK0NPafLAlXm5cXUx9QsKBXB6jJipLHGztz5BO/6NVNqf9PrzSQPLCqLmKTqIEgFRkmhDr6t1339WB/Qe1b1pLzX22jvLSTBCQGL73U1ujkFO0CGYKEZC4l66fbB4ISREzpObAImAuChgh6CYxpSAVmNjD2mV9ZtJHx56FOhCPlAkvv68ND/XVwVltpLz0vwsSwLeutaouRpeZuLft2xC+j7NGul5mZzHV3GPKuFhy8vLGgsQAQCJOsMTZTyLWsxAShTXrVHoZhGRfM0kK1+Kou61ijG8Zye7mOdLHv7XJWsE1h7oHST0TMagMc1aSzHG2EjuteC1FEJuB6+w4g+yJ0ts3mElhunF4exUdfW64ql22R/WGbdGZZ5/h4sFMUJ/Eo+lJtn5ci1NcMnzGn+16eef6469b2uQ5J5DQs5YA/lmsIYi8RLgKc6zwVQARU9g5JUyGgmuJqYlh1xBkCARSt8stxiKOQo2HwpDW4EQVMNdn32/Yd0LMhifP3MeMSA87Q6Pb6qjj5gt1+rm1VLV62bDrvI7azaVulxpxRvy4c5n9CwnLeqDyX72RqT5oqYAAKW28iAql84X23kuq8ME8mqsRA+5lL8V9R/j49LA9IkyhB+lF61G6FA0eBe0JGFyAcXk6QQGQls3D+6URt5Xe0yarSRQ2lI0zpLe/byqF0lb+DmyRpv/OmPaxD5hRVmFnIYclsjmStZKau4YBooR2hLCf0w7DNB7korTD4D0S9DxWVATmnyhSIFLwqoAYmjVrllMKjB071n2Gh3ZFNO8f0vT7i2AmmAACAAwc8RHgUB56s9RmjB0AGRbTFAATPcL8AKhADrjOFBGvXRVzU4dBHyGd/4R5KTAJgyoRbPugG6yC8PJlsRF2ETPauui/9juIK6qVYTJ4vAkyyWSLawMT45o1a+ndvy/T3udHKW9X2bBAbNyTbzOvJaShmbjcCOYIMsKks1xDTKYgoCE4IllD7YR6ySXGKdoIBl5vPgIBmRcGrnOqlVS8MvHDq1OtgepNOk9rZuWUCYESfx2+e5NNZcDoMdP2tWQTOOrHKo604FJ5bTPKiAH+P0ZzKBETz3f2s1G/NcKd8+LUO4v2Go6U42jl0gJyyakBkpAoBPrcB4NPkhlG0vb+svnDvHKlfW7uvp1iys0a9llB/GLE2+cr0tl/k175gvWih8FNo8nJzEAdZQBTTMqCQAmQt7OGcl8+XQPPOKy2betnHIECareSWo84vo2TPb5JbyNJSEoo5ASg9Z31B2FCPOHGHk81IoR1teS/ceRARGo+IDYNaVm4coGJZJkM2nu3zS/4c65P2lSH/8JiBM5Mftbz80aqUDXHaDv4TBr3kc5/0hQ73BfFCgpjSJjXv2b7YwHk5WjblLqKEh+WQ8LPPoJiFONvh/jvM80tzew7Hc8zE+iwuLQoIPaA7KFATk7BGmdtU4gY+lOLX4h7EkEbD+rrTNz7Krwidb004dr0EyjxpCZ+dLRrnfyjopkYJ0NONn8RVMdf/ZJ9EemSTrPxE+zzuHirFAU4XyOhSyeoZtBvD0GSCNoVkIoGY/QGDBjgWooqOuLNP7t3736MFGKW/euvv6G1s/e41ohJPyldH7YzKZpgJmaQYmEtKpkGgsEwWTfJBpI12nGoVPOZcVv3vpFJBNXOjRzzuHpS/U5W9Yd5d+qAoyap48b0JaSPYWAkHN9JpiESyVH93V109N9nKG8baq2yC3Cp2NGKgJQwE+XvSHPXo0JJfO0RMwa8dJwRdlR0qNCMuke6+EWbiJIMKMIG3WiBdWGgMoESIdPAd73spYgW/jtX0SNln0BB8k//v9JNLDhRSFaZJGClYsv5S486hoaY/vH/L39LGniD/T7+cVhXEC2M5y1OgJ2TQZVFZwjbPPx3JAO895c/Z/5MqGnfu1madLPt5Xw+gRks/jwf/EJ6+Qpp+u9NufPBLTaBK7emNOSHx3++8eDMyMREgsLB5F/lS/bLDhEd3Vxbs+9qoJ3LIxm599OywYSL+GSDM3/RsxYTUIxhGl3QqtH7amu/Y/rYxpl2f4o1xMx9vmqFBKae8Li0gfS5xnyd1rxT8LlRD1MMykDu6RhQf9FynQiIJopTqEmev0B68RK7vXqV7XUn/yQ2eS12rVP443rDzPW5s6QXLpJe/4q1lIz4lX2eYdi6MEmbZBmC78vdcuJuaf4OeTzUhh3OLPljQEa9c6N1KYz5P+mq6dLVc6Tz/mXqPMYfc5/jkGMTyfDp8iiDffn2cA+adII8cPp9prIsDbJSicIhxWb99nXS9kVl8QS24fP4Zz9mgVr85kDij4oCNciyZcs0d+5c9e7d27XapAsQM7TsvPDCC7r22mudWgNAntCDDxmBiSFtPKg7MqX6ERAp9A2jFGCmvZuiMmuDtty7X3um1k0bIYYvDmZSsNCYDcWPJMw0HNoZThQimyTYceaCceC+9GxTeWw+0Kq5VDVQk9CfimKCqjbgoKjX0Q6ZZMkIH9uBOKlvpuDwbmnqb3O0b235ZAHbF1uSMvaPmScDPbTDiIywQPDkn1pv+4TvmIKJhIvWm5N/bNWccV/MJ19Qn7Cm8LDo952i90KzvnhsFAkZVVVZJ01/oHTTC4r1nHlmkEclE8VZJu1pyQxdSQ5QVFBxJaAd/03bv0goSODYvyHYnDGgpI7nWyI39e5YBbyoiFTsKWOJgAShKu1GcCYUFWgHCDU9HWctyez7kE38bOFTIfc9YuQKsvym/U3xEraGm/ax7ybTQDFsyXPpG89aGFi3n/xZGnmXJWeZBPaQLhdLsx6Om8oWleY/LrU42Uh0fAOJEdj7UY7RkvL+rdL+TflqDApgY35vU3z4PUQvykViMAoMie32ObUPqeVlO1S1fmNFozkZE8eGFrlCrh08FtnziEMhvwNggEkxi5Zr2qhQfzIFhv2PCWLzHssnRbieUVh3vdgKFmHJJypl5wmVhSaotEcPQM06seB49gAY3j57ZkwVFXJeLv2frWuKjo3pMKgqbV8offqeEVyJhSNIPc6XTFTFVvR4aeV4I/zLw6yca+iju6TmQ6Q6rUoWK2XYVl60L4HeaKotZV1hgFl//2fSOf+wIA/yBC8PTCohTmhJYVIOQFnBiMvgv0sLWnnmzJmjyy67zHmIPPfcc65dCNf3n/3sZ87E8F//+pcbj0zLQqahdu3ajkipVq2a5n60TLlvnK7d05qmPeDhYCMwgCVnvBqBYiae0xAlYT2hBDC8NxLX4xCx6i7Bd/A7SCWUOcN+IZ31NzPb43oieKrf3iqVmC6GgacuqgdGhVKrvWfvs7wc8zmwaXHp/ZXMS3AZBU+VIBH0aEOkTL3LpjQFZB4tBFRrUDuRmAZeE6gGBnzPEq/ijI+HpHEtfBli+hkAL634ALk8QDsGffIkOJniUwQIZNiD8QWIB++B7xzVIGpEF9gSHH9s6+rCZ8y4Gek7rSlM6+F8pvXTSciJdGI8Kf8/jHQA+GQxwSxT1hevkzYLEtf4Njs3EhaTyoMFP0s+Q+TMrmpNdTjXKtkkWomJHtc7CsegqhzmuUUAmmmkAB4cEBrlqUJlvZEcMIWkkQltMwo1Gxv5NuG7VnwAkB9vfNUI22YDrPhC2xh7Dz508S0+YO170kuXmr8R07IAahbuW8B0NSdP0WGfaOahqYp+eJIbmkBRsKRECucG6/7ofmnvJnttzp8lz64FyHlUNBSWUNykbQ+I2h4VpgxkMALxZ2IbDqa87vo9kH/9MtYdNQoK+3hVCXEXf9/jCiOyQiv4keL7QWYSUL8O/oH03s/CJ95heszaSwXWXyrjX0NUOfUOqt3XN6tep1axQyVDDosKjijXyVZT0ZbnvozggiICe1tJvsoMO/qKdlBxOBLMlwdWvM7opai6fnm/Vq1e6VpPIDUCZUh8+w0z2d9///0ijTCOR9j9duzYoRtuuEH33XeffvKTnxwzmUW9AYHDz9auXeum9FRkU9nCpqgM6zdam/7UTxsdgVI2mxWH0OxHrKJ30reKl9hVFLi++JARhCQSVBR7fskM9NxBSuB9qh24BApBTz2fA2Pd6DFl3CCsfFDtpCWOCTzJWgT4ZgLZaaaAhGHOo6UfM1uSAJ5pM1SZMmlaT5hx57EvPzYtIP4Q4r8JRvEiiv+Mce8P1AEYHPf7ZtGen7VX3pLk0oLEdMETSa6bwrazaMiI8kYWSBNsOP+QFMcH5OCeW0ytkQlgL4JEoZ8+0SSb98rZTivctsXH/w5fD9YmSRDJEAlao26mvkOhEnxGLUn2I9aaAiHInp9YuUSF5yaYZRCaDTJlV7xKjKQCg2HaVifeeDzBDekJcb51rpQX87U479+WfOEfF0+6oGQkMUZBF9YuygS3VsMzh3QChFJUnTkbiwIS62p1Qvw6inn9Akb9Ln6G0ceZN0GL/YdrC3USZFAA9nfOUd6PIyfzUhv9Q4TPeCDWShZNft/cDpt0ZORU7Tu4R5MnT9aqVavckIF27doVeSxvEDZDKhO/oD5a94FNcXRkf/AQEPM55qMEKUk7KhO9gvG1pV3fieawAfAZggChCAYhyf7HjXYn2qw/+UvMdJdxvj3tdXItJsKtzYh51yRDWDtRtoCCDGa7eP8t+HcZFvSqHVHkokmac3SRak8f6og9hodkqkKqouHTdwsZXFAG4Nqc+3fz/3JTmCo7iUL7DiRKeVXFqdR89PBuTTv6onYe2uSUKAForWncuLEzeEWJwubPJJ3du3dr7969+s53vuPMUlGNJMNjjz2mcePGudG/ieCxrrzySud50rNnT0fcLFq0yLXAbNq0SRdffLGbeJOxF3g0ooX/qK4trzYvc8ktVTxkXS2Hmsw508hlV8GtWpAQWDfFqrFIDy952Taoag2kdmPsPRNI0AoUPAYeA/hTMP7YjUyWmYViXkZQ/vJnC1aXAqQ6wCsaCK5wZU9qXBWxBIJWPRIUklaUbQQwYW1TyGRRXPA3sOgENRzooaP3otKq8SYt5f6ZAre2Qq4LnPGZ2tPzKlMI4JvCBCgmEdEiR6Ut3jCUz4UboOpfrNdAIhfNnOsTw+dANRGAZP/MvyRvXXGISlN+HVNJRcwXAA8L1lhuFUtouba5TmktDUtCAiNtRwxnyOfFZ8M1l0ii0KpzLIlPfK+x/+bM5wbRxmeOIoVbAEeORGwsJdceU36OQ8xEO5nHSEUFCT5TUPD3CNQ1GIhTfe3+WSPPSR4hjfl88VWAbMIkHDPPYIwxiRueFCtft+uV8+Dkm220LD3qYe0ItOTRupFpQB1WFEIWc+ZzH7fP69+n5Ju3cx72K6TgglktXgvxLQSQqUy96//dQq7/CgqIcYhJ/NUcCRVciwEZUlQfpmi4EX4A/Nn6fDdPqxvX1caNe12BkKLgSy+95Mb0Bj5/qWJbznj2jflPWNsR7SzJxoEHICbifqhFWdc9Pm/nGv+/xL4/sYl1YSNy46fSQW6iqIPgpIUHUnPuo/m5TLBewhQlbmpSrBWR1xkWo6BuyWagpht5t6mLMIJNdw6YWz2qWpfM1c7ec5R38IgriJPHDRs2zOVxHqUDa3bhMwWVk4lgz8VHCWVyYeqi4P6o6SkGfPJwOKFJTsMZ2e2K4sdKWUWiEBAgIyypS3NJsWdJTWlRVR1pa98OLSgYyfbp00ft27c/ttkz5vLqq6/Wn//8Z0e2pCJPAmzdutXdUv1+woQJ7pbomUJbT8Ze3Iy4WmAJGBLM8gDS1I/vMyKBwDSTQOJB1T+RRIHk++BWS0oIqDtfZG078/9lQSQjLflv0OUSU6jM+pOZaQWPRaWX4IMKLsE6TvxhBxgV5EwCLRZhPbIEIWy6tAQEk2YIgthX8Fgg+Yg/oNtionq3VXSDII3vgg2eHnA3fi8BBHckv5lEojjFUjT8vWB0BzHAyEvWE+8f+TcVyzmPpPE1JKhdKnzF+xNLUOMRtNCFEUiu9a6p/RuYOJLYn/O4jb+kioovDNe78zwZZkaEiUQNYN+kfx4jx9K4z5cn3LjPC03hGd9uw/5FcsP7diTLooK+Am7K2H5T/ix9seBjj/yNJb9vXGMqlQL+C4xRviTz/D14vezL7EuBHJ02zil3SKc/bNclZ+mRvUYIQyZxTS5l4kRMQs19z/yrdPof7L4EqHgwEHjO+6dNxkq89qnaYQiaaeMJCKLdpKFo4WsRvx3ORPb+SMJ5h0IlLLHG64prNdk+hXycdpJMJFFIMFDsjn1QeuNrZpia7lZYFK34aA28tqX2HbpY06dPd63rtKtz++ijj5wqZcSIES6+Rn2dCM5s9pCpv7XCUXH9Ffh79tkPfyUted5eT2CsWxJCmv2e6zRVYs+5SbyL+qXtaGuTG/uQ9NqXbf0FyqUwIihIDFMJ2wu0dGcZ+F54j+xhkFC0aKTLhwwStd93pXZXN9fEKY21ceNGHT58WNOmTdO+ffs0cuTIQkk9j9QgFmeKbmHgDMNuAJVlUUgUVGWn/9HWBwM2wkgUCp9MaSIHKm77c4aFC4V/CbQjJG6YbvOIpCZXYL6pmpIQFLbhcnA27GGtEy54PVRFkel9VLvrNnXp3smpTmjfQebl7h+7sGrUqKFbb71VS5cu1RtvvFGkVp6SoEGDBrrjjjuc1CxTL2pHiD2a3CiqTJBnDPbGb1mrSyYBoyvWMGPyEtcq1RXGec3CzBRzwAPG+mLwRrAQ+DU0Pcl+DiMbT8Zw8ONgjUmo6+UOqai0GJx57Tyf5nfWHQeUOKc9aEnHO9ebAoWNGyXA6PusCheM46VaO+ZeqT6eIL+1jZjPhwQXBQAmguM+X7DqGUwPY9POlEuU5D705y1sTCNtIxBuTu3E1IWzpE7nm+oEf5TSBtsEoTXqK2Pg2k8WFjy0MWymqh32vXMdn/9vIz9QkeA1gdcHFXHaMlxl8ohJ4QffZBMZqJZ+SNtKCEiqqczVzBASBTA2EnIx3gwbMpj2G/as0+431QW/b9RDOvU3tl8hfQ8CorCe6qD9CVIrII4TP/s2I5WRaNhdOukbNo0niF9IIpn4wfqA4OUs4GdIptnP4/d4JrUdu29fu9ZWTcj3jCpQHaySp+ZXrFXDvgRXQZk9M4DBKQWTVOCz4rPoclF40D37L3YtJoLPDZXJkB9LH98TTtJz/tJ61ZgYMgPBvoUi7rwnzB8FYjddZv8Q7xQvIOeq1IiobvW6Gj16tDp27Og8BVFkEzfzL6oUWuRPPvnk4xJY9jsUepzHifFQsZFnRPj4b0j9P5GG/iw20ruYyx3iCVLuUBiJEsnfl4jR+G/OT4gq1iAEKe8nIOPDCnyQejwG6rOw74Ln5gzJdjgipbl9dsRx0+41pV1JYw83qruLNPxWqevlEeVWa6nPNP+M3nzzTTcohKEX2Cfs2bNHZ555pho2bJixOVcYXIqKaiw21ZO9kOIMREPgaZeTm57R1VxngW9e2H7MGmbEN3lIUQlBigDEB9w/sZiViI0zrHjAHlRpSZRNs2K9gXGgf/yCp60H+rWrkn9BjBLD+fq5c8IPvnjgFXHOP62i8OLF7hEUmdtVZ/Vpp06jayXtkeNnTZs21QMPPOBaed5+m4wrveAi/vWvf61LLrnEtRNlKuhhXfxcwQOB75OLlsWeDPSWIl3kAE16uOfY6F/gek7z8oN1Km9UnzKpIsmh2+40U5jEw3lOfMsSDFpIguSCKiIJAx4DkAKAPuFkyTLMPhuqSz4SvRpyTY2RKdVuh7yQsXUx/xeq1ayNiT8wWS/vFw8PPqfLx1ubCgQI5FKb0darDElAO1hQaeJA4PBlHTUdUNA4Luht5mDKFFNGDqQqtfNNBR0ipnRA4TT7z9KHt9t1x55KInzBf6RBP7CWAiZAlQaQhASCmRKjuLHfq4suZacyTQLB3kYyzOcMsd98gK2VeIkyf89UhlNus8/EVTnDZKprym8qULoAMYnxMi0k8ZVbet0ZzYsi7tJXjQSg6s/UJhRfRaliJQN710nfLFlPdEVB769Ka941zwq3R6PmnGcEW1BB5zML9WeIGuEHOVXofSNRRfot1spWb2rCO+01atQo1a9fP2OSB5KqwiTjmKQOvcXUPZB6iaoR9u2jR8Njw4HXS5/8SVr+cvLHp5U0k8FX3ThG+KLOZBJUaQgLYggKMex/LpaIjd4OvE9QnBA7M1Bh1qxZrvpP+zr//emnn2r48OHq0qWLju7PdaoqPFfS6XXGnsyYcFRbo+6xeKioOHr0qKLNtyqnZkNpV9Xj2wwusaTNtdbFtSvyWTI5ilgEohjs2WD/ho3VRTVGfLZrTfhr4AzJNHV1adYmRFf/a02FMONBOzvDYtfkD2Kfc7fLpH7X2mAFa92LuCI1E1AnTZqkefPmOVJv5cqVjtQ766yz1LJly4zZC5OBtUSLGDHb+qn2L+cDP3PESqx1rFF3U0yhBuX6ZWx5Sd66a69fdrw5ejxQp46+V6rZ1HKdorTWkSsO/4XFCOT0heVzqF3JjyoUicIHwyGM6RvSWb4AKrIktlzQfOB8EXwwsEylXXf08x5LmvmSG0o9rrQJBYE5U9gHzQLgfi54KOTLQd5/6l3mgRBv8JS3t7p2zaqu3LGp3wcXFx4pf/nLX/TjH//Y+Z0kmtCWFLD1t912m/NJoaUok0G1LLFaxCZ22gP2+VIZSLYh4v8Bg49rfDCm99hj5FhiO+A660HlIRhlNvOP1oNKcIRPBn/HxJFMAck/ctPF/zMFRQASqA7n2HUI6ce1SDLKaFnUE7TtBH4VVJQIPEiKN8Q2TT4PDhOIAwIJp7QISXo6nFmKnuETAN5z4AUTDxh2TBkhXZ35adwa43on+KVijbcA7QUcInxm7D3xCR+bMZ93x3Ok2i3CXwN7IfcrTkB2IsHrZJxpvCEj12L70y3oI8mlEgZYNxxKTEuhZYzPrLQkCoRCEFBmBPJivepFANcOSgI8mSZca+QHYG3RXkf15zh/nUhsFGOOVdaTGReyxjJtapYzCfy6Bb7x44khuEmOUIFxZkPqoUZhXSXzKooHRCetPgUSjYglv5wZmbSHxYPrsHYza1ni7Iofy8m1GDZVKwxFuW/Vtjt1+Jz3dKTKPi1cuFDbtm3TmDFjjlPfVmSw76fyxiAeZc/iGpz2e0vqiwIUeQTtxC0kbsnakPmMw5RQmbjmiINpZYVomvmgXa/JqsnJ4hYUjL2/LPW6KjYxMBIeNzOx8ZRTTnGEyocffugGOKAEoLXitddeU69evVX9/ZGa+VCNMjGLJ5aC7OAcwhcmlccIxAnqBHxc8CjcsG6TcvuNlcbnj2Viv2J6HQa2FGUoaCW2rrJWggo6vmPs80zpcW0JsX2dPQufJ4j1sIEa/L7NmPwBApUFnCONe0unPyQN+K6p8Cg8oG5njRLPBqo97ktcTM5Yr6O1cxPXkAM4A+SEtYjyaezYsW5NQuTR2sM6fPnll92EVHKxTNgLE8GahGTGD5Gzkpa2sDgZ7Ntg5wyts3x2xGY9vmAtsewLxTlLeV4eL9kZzl6MtxEFdB6bQRmFKgmvtOLe29dJg2463h8tDMTi8BQQkieWRIE4ybOAjwt62StW8YVlcgFl9HgCgwSND58qOmyTM8gsQf7P5sJcdR4fhpykDwmrm4ARYgvCl05Fq8lJFpDRJ0wwmgpsQsg08X4o0PITLd4Yy86dO+vRRx/VQw89pD/96U/OCJYDoSSgTYj+UFp4MDnKeBY01lISP9ECdQmBbteLrQ+Xt5jYDQXJwgVAAE4VPEwZwYF19t9tjZEM8j0SJLU8xUgXnhcJPEE5h3umfJS8TibqMDYwfsQdvhuoKWgvwYOBg9opJEZIS5+3JDe4JqlikrD1/470mf+ZsouDGnl3YMqEvPv4J5Y6nGu+DZkEgmg3FjoRObHRgofCg2D+hiAbthplCvsb5qGBUWoA1hckHQlJsqk2yZ6jIpMoLYaaoWn8Ph4NKrOJ74WK2u5g/nXpn592s2SEVEVE8LkUBVxj/b9XcOQ2gbvzo4iBoI4blXIk77SaJarPChgXltP47nQC+e2IO6N67atHtW9tTC8cM+Zksgq34gKjT26JIAahLYqYIKMRMQn7mX+K+b7gj5Lm7574bOhduVpTp4UWLd7pksUgeSD2wPSzatWqpY5B4tdsus9g9qlkwTqqQJQknGfjvlB0f72ABCVpe+u7KSaZZeGkFMgESBRaoGnnXPGqtSJS/aUtm+Q+mIRDssrZSEyOWTbqb0wig1aTwr5rElPIuosuusgpUkhgMfc8eOCg5ryxXpHHjihahsq7gNSGwEYlEp8oEr+TTG/YsMENeMC3Zfv27cfa9mt0XKScmp2Vtz+WdkVNoUnLKzErChp3XsamlPX9mhV71n5gdydhpVW2/Rk2EWvth/Z5QaB0+Yy1IyQSMYB4hdbaTCWISwM3fbK65XncUMVS/EIhCpEZkG2sRwzFKZJTIAsMo1OtR/IupkVBpDA9Cr8epqZC6OGRQqsZ3QBF2QtZOwcOHHC3Xbt2ueEj+GYyWZXuAp6LWzr21qTjvw9aTvDx72Jm9cWI2Q7tMgKV4iuDLIb8yMgUcuaivFw34CKJCgVQkOQGWg43A9hUID7CFB1fHIoxA39QhDcRU4AVF1XSPud5mzFYMKVUIlMxwiQYbmThAgsc6WGEyeIwatDV7lPU9ULFLWDAkeTSbwqDGFSOE+EurL6WDMFyFernEJF6fM4mlSAzHnVvwbsUPmM87uEiJgvD/BVp2B//+Ee9+OKL7gIqCpnC33Og4FJ+zTXXOPVJvXr1Mp5AAZBtQVsW5BasMBcFh02YGSOHMpMFmva3A5lka3+IFy+SXC4svnuCTPrCAYwzxo3DbrUDC1klhBwKjkwCSe7A66OO/LBqYsTJ4/D1oL0EaSOVbuTvH95mm108y8z7/vAXVvGAdHEkUo60ZbY0+TZpyQsF2xBghft/O/PMBTkkwy4VSCOq23xWJFfxrX0ENiSwBI2OmIU4nVZQYcH6IsAiQEQKGWYsG7yGTBpzSYKBRwDj4ILDhj2fw63NqRZIu7GpMfKzKr4oZ9uZsHVRKZ87V+r22YyyXnDrC+lpmIdQ4nqh5Y5/p/8+dfsNxQHkxW4S12HrlcfHKxk41zKpLTEfUdXut0XVvzpD+/82RNENDcvky+dMQL2BWjULjk5TWo6Uzv2njSsuTmEn9QNbJQ8laMdz6qjX0bPVvEVzTZkyxSUPtFe8++67jlChvac4JotB3z1BNLEgZz8JDmcY7we/B/ZeSOkG3fKLbCX9vvAFC70mIlLHs6W+15ifSVFHIANIAP6OAoYzNU5FXrEvZJh/WJFaKGpZkYqzc8QBi4c5S/esN5k+8Tjxtpt6191U6K7KX0w/BdYVAxMg7Tp06KAPPvhAqxavU+7bp+ro9rL/YGmzpHW31alRFw8QrzPgYfny5VqyZIkriMZP6QTE6q1OzdPODw9p55z8xbfoaYu18NGh6EvyyjnA50ixi7YwVCpBjjP9/6SzH5XOfkxa9qLdl5yEIuNHvwlvc6flmPg5G/a3kiJ47664FTMnjydrS/LZBDnYoEGDVKdOHWfPgPqIvfCdd95x5B5+PRgfh+2FkGuQbkxZxWNl6tSp7v/HF9T5O4gUWtWGDBni9tbTTjvNTX0Nfp+uvJ28dt5jqcmMInkIzZLe/JYp22nPI2crystM1/qkw2XEnXaG0GpYnEJlSYjGKml1PJ9oyRZsVHHldCQvyCDZJDiEYLKQuhXVrJLAM/iwOMgYxeokz32ki2OjWuNBkv36V+1DQ55H0JFqVB+JOT2yVPV5fYzSKvAaDthroAerKOAC4ALj4nj44Yd14403OiKFYITJPTDaXJQBkw0L2aRJE7Vq1Uq9e/fWpZde6npB+Vkm+58kAjIsIEE4cJn/TvuXqyCFVHaRPdIP7sbQ5SZPQqgSweDzHdI3HhACjBzkoIJMadrX1u/2pZk1StUQVcuxB9X4M5u08ZnW0tHcY6QU/bxMZIAZ5nN0h20IV+fY6Bfs80EpBghmnfw5ITDk4KZ6DhmZaQc0AV+Y4g0iicAGQgDJ7ke/Nq+YOi1tT4KoYq8LrWRGLBAaeINNQkIK+f5PkxtaEcxnksSW75hkEzkx/joOUfMQom0Jwy+IJxzTCe6oRKDyYt0FVYSSgiCQYDCTwF7kxuoWogagYNDlUpMaU2lMhdl/M8VYMJ1n4PctIcRDJczngednnWUSOO+2bNmi115/TZvrbFDu5zYr55lzFV2PMUX6NhoqtBAoXS/Lriot74VqHX5EjMle8r/S+VVAGFNQICBGoRHJjahabjWXPBB7EK8Q+KNKmT9/vksog/aelHEJxMlea5/lzMHI9iBt37tj8WP0eFNBlEIoIkkaIag5dwLvjOKAoD5s34UkwoSQ6wuiuKgqHq5z5OUE75CahbVD8X54H9kIzgjIduI1YmZuZfM85pVCLHzeOedrwtQtWjaPD7V8AhGIvhl/PKIWX12qxcsWOl8WyMT4YRGsfVQE7dq1c8MmmjVrpsV7aujt6/NjTxK9V79ksQXnKoUC1j4xx/T7LRY5lktFpRXjpPFft7iLVgWeDqX/jIekVW+GK/oGfC/1KO7KjNLGrcE65PuF7IAMYf/DogGCGSKFLgHUKvFgrUycONFNauV+nHfJiufcl8dkKtU//vEPt68y5ZXCOWurtEQKRRgKrQxlSVfr7+E90uy/Wk4P8V5Yi4wjy9OgBIWURW2EUuz1rxRvUq8retU7QSQKCRZO5VPuNEPQ0oAkGfUIpo4wWsN+YRtBYevEJTWx/YsN6kBskwo8WFL1qwfeLakO3VN/bT1b03+Xotc1WnKXci7Ak046yd0gU9iUCUyQdXERQbZwHxhIDg425GwiTuIBCRIEfTDzL19uFxlSOxzhwyoDb37TLiBIN6Y3QJgkAtkjZyyESXzCgUQcGSrBGe0KJDLlPSa7tODwZq1MmjJJm05aoZxVpylvSi8pLydfLgdxksKQN/E7SFUR5/tA/YNqLBMr3ST5BLK0QySC3ln8E/CNuei/5qdEIgqxB1FMQh/4yMTvEUxRwZwSomX+k7ZXJBpdx4PWlLBWw4oMiDX6i7lGAv8T1CdvfN1aHUlumCbg1tsuq+gycSDZ4YwSCsIyVR89ZB1VXqrRmUTWoTKiep5KicJ1hIcTh/ei/4QUH2ISeNeWg8/MovzxvgTUF/1P6vlFk5jvDFlrkFqZNDUrIFBeffVVV0QAVdpvUfcfbtanf2jkpNilbVHhM4e4gkAhEcnE/auoo2gZ7ch7nPkHadP0glPCUgEFCCpe9kHaBVij8dcfVVh6/1HUMjmFKjxECt/bK6+8oqFDh7p4JlGCHkjHUX3O+D87e1O9LtZ+MHEJZSDqGozSO19gU8HwpSrOd4hcv2rN8DYeijYoNF2sEAP7OaQLRBJt6olFQojKXl80xWZRlT8oMTzSg0Mba2nri+0ULUfvJ/bi+f8+qrlNPtCBKlsLqGQwF+3Ro4dLeFFlBbE6JAlkObfA/Hn3aumdG0wVx421RTtYmMkmeQqEI950xA/O7H9DuAKFNY1BN+rjTDo3MxF8vxAatJmNHz9e69atc2qkwAQZUhnzbc439snf/va3ev7557Vz587iER579jiPHToYnnnmGf3kJz/RhRde6NZcccHaYZ29+0NTmaej5brAWn3RCDzGTjM1Kdk6dMRyS7tvcceRxwMFF2pdYs4N0/Pj66BIwn8faz1PiCOcJ04Jxs5XSQeBQpUbAiVsrGBJwWN98mdLXBhXRdUo1UZAUlQWwRCJOUkD3iqvfM4MJ1GuhL6GqkVXoaQCrCXMJrfEMcjZ0K5TGIIxWoBE/libVCSc7CLICgxkaSFItg4bdbO/30kgngCCc9YP5nzueYtoxFeREg9YcMzWQO5Z76rGrsY6OK9l2vviFfNfGXNf8mshE4AXEr3biZ8PQQxyXdQ4jHLkPZKgQugx4pg9KSAQgoAY53A2cDx1MCPEDyrVYcCmzp6SaZczr5cWHbyJ5v4j/7OjH3bclWbKBtnBZ8g1RaUtFbGMKoxKeVLfDtbaWKn75zLvs3LKpL5mVodcNgy0zJGgMkWFscaJgDge9nNbT0zAiMfOVdZ22PXy8PYAAhJMkgNFWSbsY1Tc6CkPCBQM0oefMlyDB3fTltEmz6ViVth0lWTgs+DzHnaL1KhXBq6pEijuaIfFEwGFGAQxRDBtFvg7OX4vTnHpihXtzH8M0oBrL6jOJTP8pNpOSzKFnY8//tgVfQj2qbRu3rzZ+QYgd+e+QeD+8d3W8u0M/4t7PkXNv46R1+zRJ//MjEmDqUKFgelzEEzBVLqA8CQpJd5DCh4PF9PlSOc/ZdfbS5ceb3rPesKknf2wMC8UwH6QUQbZFRxr3o4ph8MQm9TCmqYQgmkmZ3doITSS743BWnJtSLuTn1+Ht1dTtcWdpV5bXZGT6UFdu3Z1xCLXQqBSiAdJGgVZ/E3iR7gHBrKFjWENQIEipbosIrUdLQ2+qWQekx7FRzB9FVKD1h7IEs40/HEgUs444wzXrnPTTTdpzhwMPEsOVCv4AX3ta1/Td7/7Xd18882OzC5OfkguNPUumwKVbgLlGKLmswLRPOZ34WuRz4hb7U6HVLVeNR3aXjJxAMU2Ckq0UKOYPvtv+b9r2MWKAmf+xeJSrEMS92qmbKYyi06GUtEOVBfnPpZ+AiUAmxdBNlUAmKxU89mpFrj+8zSDxOikb1mbUbxhZxicdDvNQVllIE0SQSBTFuNyOcA4rOIT4AD4hPDdkTAHfdiZgGC82ltvveUSEJCTm6Meg9qr15j6+vCmNPbFx4JsKptj/k+qnWHKgES0GhEbDRtfwcqxyiZ7DSoV114RUxIwfQEVCp4zBGOBAmXsA0bIvH+LycCLMpGFKhE+IpmInGpR9f/RQa2edkS75taWopFjhzIqwuKO70xFsiCxH3lXZpJ1XBusFyZOJCNRkLujFuEMDQuKKWDStoMak2An/pwlKajRxALvsD5m1jDnVyZco+xjTHlBgbJ+vRkRoWBgGsfgwYOVkxNRs4ER5wcA8cl1Bing3ndhSXhsX8f4EgNHCIJUsUS2jvxsf7a111GYoF1gZ+A9csDOW7zkuN4gE5yXTrWi9rJH3HdF/z/JI+QJpD6qFJIFiBT6+Nu0aaMdSyNuWgJkcyr1b5HASNdV0rs3mhqOdiN3fkdSr7O8aJ6ajtqnVRPqHNu7SKrxCUOxk4ixf7Aq6StXWuwQH3w7/4qzbCIeCoGiAJVLccdoeoQDInDJ8+F7AHEkSdWgG6S67WNjuw/ZKPQPb4/FRdH87xE/RloB6tIVlGNqZBT2sx4Oz2+ihyOqurS7Blwhde3VwSnEuQ5SKcR5Dai7xj5kbTmp/KxKA84d1i3FZ4/yA3sh3pSMOqZzYPbs2Y7woLiJ7+UjjzxyrECQDtAu9Pvf/97tsffcc4/rVChKzkjMxQh28vdS78NFeK4F/7Jzl5HR5BCBOAAjXVQ7+AktW7pSh+tfKG0v4fSAqHlqoWyErMmNU5XwnNzIAYnPw9p3mw+0Iny5kihMEsCMpiwIlAAkvYufyfcaCOuBdQdjzmHV7HhYeh8qKT3REaw0RqR8ObxHRia5n8f8FAg2GMuGMS3S1LLq/6xsIIAjGGIUWToRVLuPhm0asUQu2FCopld0IBdE2oeUmlaeoHJLrzoBbfXqNdTgCemDX5r0v7TXqWtZ+Yo0+IeZNSUlVTBD21f8pA8qkrjiU7197eoYcRq1TRdTVZJZ3L5dT3PEqpAExSTBjHgs6oEE6UI1NNPAXsvBPXXpOzp40V5V2Xm2jqxpcCwZSScgH1D+sK9masLLPsYaCSbHJQKfF5JazJzDyKR9m02Fwlh21DgkasFYbEbythpmE6K4X9gaCzNVr8gKlIBAYR+DQGEvix8VSaGENkImVFDNZbQ4HhYkI3yOwfUHOco5QusGago+Z1qrIMczdS2lZVIFwWU1Wz8oM9P32Gay2KlTJ1cRnTRpkpYtW+aIFL5TpvcM7DRKK3/dW2s/YLRe+p6b62H2X6zFcvQ95k0S9h0TsPNaSGw2Rg6qWuvzdOjTWLUk5i2RrOXwaCMj7RIVDOxRqEqYzhZvQp4MJOv4bWVKkaaig+s+dKBDxFpnxvzeCGxatJjMhfqTItC5j0svXRYbBR6xVlz8cKhS42vD9ww5xrAB9hG8hQq2pEZ0dFkLDe7aQo06Fr16TizR4UwjOSb96HhFSrr8kCg6UwyqrHvdiUQwjpsWHhR4qPMYdf3UU08dO9/SnQc88cQTbnoPRArPWRgggqfeU7SCXzpwcKepD9uOjkp1zONl6dKlrgDMRCM8ZNiDawxZrCOrmpconuT6RAXO9ZuIi18ypcmrX7TCS2KrHOQJ13tJullKRKIEkszJvyof7wiCIzwKqCQRGNpriLrFQ0DPKDG+kI0tkRBcKB2ukj5FBC06NaURt8f/wn6GuR/+G4x0o08W01KP0gO5a1lUaqjYHpuYkQAkyyQxHLj8e8zLoAKCtc+mM23aNOfo7TagmK8Oo9X69Onj5KWAAA9ZG4ou1FSMbi6u6TMBH/I4RoLTxpMt8lCIMiY+keAGrTdsxFzLfb8pDfmJ9P7PbE0gh6etgmk7tBME+0MwwYn1Mvy28OfB6yLee4UKL6arJem/PNHrDpk+bWPst9G6UdX98puq/p8LtHd5rbRKQkl+aZEi4M1000+CecYQOz+POKCwhBwhwNgc4s0DUKdMu88CABICetzxZUD1CAG4bbGNxkR2Ho8qtS0xqOieO4ECBQKFilQ8gYICJZ5ACcAeDglArz83vHjw1OFzcT5OyPJr2RlSmDLBI/0JBNVQ2ns4n7gdPHhQe3bs15R7Duvo+2XzvCS9VDtrNrJ2nOCMYn3x/ATrqGIw7CduzKmaq1p9Vkpre5eKAGY6C0qyla8VrQUYz5Uul/g1mS6gRApT+bHv9b/WvpPx3zCyNTh7OceH3mx76Xs3216KWoUWLVr2iZHAnL/bwAmU6IuetZbLROzflOPIs+ISkrRaQq7TOvTeT61YU1pTT/Y8PMloF6J9wePEonr16m6CFDHTo48+eux8Kwsw5eexxx5Tt27dXHsPiqhU+TteJaU1+y8uNs+J6s0/LNGe7h+7KW685njkVslV1S6bFWlw2LXKlQRuCEbIz11xJWqxVtg+TfzeZmTJ9uWSsQ1RadEzxRsBV1ogO532f1Gd9beo9h3a5YiTFStWuEORCgOHZaRmLVVtv0l5S9PTVwNbRTU6sbWEJBxmCwaZDZpAl6C/MvRYlwcgNEj+3Wi3NFasaMGgjaI+E0RCghsOMebHcxjhZVBaBKMbIWUI8mB9nXSaSmA9ex6XIBZjxB/rnP5KKn1z58495uhN9e/MM890I/8S5aQ45WNeyQFLfyIkAJI3Z0YWYobsnNwj1jtOFZfpFbQFZJsEPjD1JMHF3C0A1f6m/Wzs7Bcm23fGe2eU8dvfy+9bdsahXaytgpHGyYDSJZ5EIfBGOpxJ5ADrjvHrr7/+utt7+W/WWYfhddX3glx99Atp9YSSG2sn+niM/p21O2XSZxQGrhec4lEsMukp/vOh/5bpC1TSOd9SKT5fvNhIEfZF1FB4czHiEh+GAhXwSFRNxmxXh/Pq528wFRCsoe3btx9HoBD8MXHOWniK9uXzFiFMfItExQFVUb7H5s2ba+LEd7X77XY6+mHZyqI4YzGcbTog6syt9+3f63wJOCcJ2lHFxK+ZZhdu1ObpvQv1oeB8QN1C60hYjDjtXmnFa4WrEN0o82vtXPVIDyjihpkSQ04wSerTiaZUC4CClIISbTvNB1k8yHlDazLejgGB4h57g7T0f1LbUZZghZEorqUMcnx0yc4GJvJc+Iy1DOHx4/byaPHPTM4FlPo9P585HliVAew5Tz75pObNm1fA3zLdIA/GsBYPKhScydp6yEMW/qdw0i4YSY7VQTIPskiuFb0pVlDI4LGTeQFy7a2ccFQHa25QNCd/s6T4iykvKsbWTTpq4qyqzrsr6XUQGwRTHANa7ptsf3b78rdLbgdSIhKFD4qxlcGYrnJBVFr5zmH9988Ttb36EmdeFn8ogqr1j6p6t+3avyw9ZpoEvPGbaoBqjDk9bLIgJ9POkfp904gUj/SZV8551CbnpAvI4qlA0H4Rv4kg4Wo90kgFyAUSY1RGJSUMAqUWPbdUGLhhYsdG5JKoHFM4cXBTSaUa3WKwrZ9USWMge8e0ispasCnTa3766ae7gDV044yRNARvjIumLW3vpvzkHqNdErmgwkubCSQCQQh98lR1sok8SSTPGBP73s/y9zOUOrQpLn7WPgcOCAhTRnHG98NDrjxfhOke8UF6bq2jGnDjUdVuTqUgMz7UQC2AAgUCBaAQ6N+/vzuwq1evpsb/tPG79JA74+YS7L9UBOlh5/tAJp8ta471wSGND4QrPMQ+GyqoTC8qFLQazJPe+4ldizzekYNx+0ninTuu06ZBr+u9qW1dIhuYelZUAgWpM0A9F0+gVLTX7FE8sEd07txF+2Y30cQJNXXkYAnmERcTFLQm3xHVzmZzNXfFVCcVj48TqQ4zMaVv375q07KdPtlirZipEgpGdSZDcL4XBW1Psyk+flmnB4Q/roUv5Lsjjlr+SkyBkrBHUlTiO+BvSa6YGsKeCjGdiECZDOESBp6nOBOvCvx9LC4beou1a0LYMaqY1jJXMQ+b7Bb7GcRe075W5KLF0U2vy/CiQzaBM+6NN95wk8rKmkAJwETX++67T3/7299C23p4GeS0zuuvCPsVZrBM78HAOxG0Cg/5sXmdQERwHW5bJM140O5fkB+I6OjCtsoZU0/VWx5QixYt1KVLF1f45bVy/kcU0eAfSeumhHtXuvc4U/pX/+LZE7yK8XjV8Imj5F9cPyW9dopNovAlMF4rvo8P8yI+UN5csh4rkjGmWJAo0mNKoI0UL0xaw+Nh7oQZFJsYY0JhaA9uqqINnxzW4Z57juUfHIoY+fBFdO7cWfs7N9ebGM4VcZRrOlCjgdTnmnC/Fo+SAYYeMsP1rKYJJCIQG8goFz8XMyCqav4XKC44vPasjarH1w6qVkvkZMW7qjhs6dHlsRc+Zc+X6kJn/eOFQBUCORlKETxHIDESE3MUJ4y9xkCWjRKQaDDBadSoUW58WlGSjmAWOjc+X0wrKzP4nLl2qfZjtBUkpRAp7GfckiJaPN+eSG6eqo1ZoCW1l6jNtpFFNgE7kQiIO4IB1h/gsBs4cKBruWD/BQSi+EdxLbH2UTwRlKacIBDzP4JM7HiOqTUgrbKlXSyRIBp1j/XkBpPEigsqKYWdazn1DyjvvHd1qNYWzZq11Rl8jh492o3cLKqyozxbeAICBQUK0mcIlKAV0SPzcXhPREv/1lBHijC1Jl0gSZj2yB7tG7BN0VgGikcBU1MgTwjeaRkDA75n7Zu0ZJdmtGZh4Ewf8avMa+Gs6EiWm5IzvPbl8DyEkd2c+8R7btLTeksAKagch4hNT+O+NZuleA2lbWPFq6iqKRZPvVMacJ2NsafdwhW5Vti+T9WfIhcFPnzCOCv5/75lsWKCQj9kBsWC8jxbOVdpo+TcD4svIVAS23/jgToLI23a4Vj/tB0nAsLu9IfNjJVpbwxbQCFL+/U5/5Bev9pIzAKvb0dt9ah6poZcUVONGjUy4iThNVLk5rlR+IVN0XKTH4sZQ6EqSxaX4XtUGhVrsaMV2KVV4+OSwxwLfof+VHrhYmndB+FMD6ZNzs8kNk6PDwfDWBzRjzlUR4zV4r5I7FwFPXYoTr4DmX2OorO6qkqfRWrQqL6T/0CeECAGwfzRZhEtPd9k0kGSQwWPcWXJAJGDlI/NqChGO7BZeCXs32bsFdXTdLR/eOQDdh7FhKsSpUmN4kYr3iud9Te70CFRGA3K+qQflrFXkdoHtb7DG5o8pbH69evngq/Ckg8Ocq4L+gwxa3K9hsUgnvlbCBeqD/hnDPi+Sfc5MHkgCBTGpDH1IDCQDRJZKres/YqejFdk8DmPutv2io3F/O6KjEhUVU9aq/1DJ2n56t3a+cJWN/IOGWPYKMSKAA5kCDtaeAJHearLmBaT8Cb23dLehHppxB3WY87IXkgoHNMZx0tlgYCTfZaRkw262SEMYYr8ujhtbZkG3heeWfiavPW9oplQFhcQUn2vP6xtvWto+aqcY1MBXnzxRacY6tWrl/v+SrvW4hOX4j5UQKCwphIJlKFDh1YYoscjDYhakYCWiqKAZJW1lXI0fKzVNKVC9WhE0ek9VKXXHFVvfEQ9e/Z0az9s7CzVfHzt8NOh+lkWoz4hmDHITqZm8Cg5GD/NmiiMAOM+FHpJmPBBWzZOWvCE/Y4ziqSMySFMQNscmz4LUUFuQ1KZrEDKekXZki6QT5DAYtyPGXb+EwV3SLh/lp6X2YD58+frnXdiZjwp0KRJE1cExZ4iaM9PBC0vKM4hHmhNZBoPfqBhChfarjGxhUQJQyobjp5fyvcexXMt1D8xYgVf9rMZ95s3Kvdj7WIVcMFTUt9rwkkU14ozr72aNUs+eZYiGqO5iRuXvFB245fxjcPHsPWIUj5Ocf8ARnRDbGQq1WzaIGCN6J8KA4wV47xgtKi401IBs0XvXq+r7T5vXWfSZKrxZz1ikuWPfmPsFuwwUugzHpb2rpM2L2mvUX0/qx5jmrpqQmLQxWND6OBfgFqGw5G2kFRgARR1PF2Q9DIaCsAGD7oxO6unJxIEVKyPBU8ZKw+QTWIeh39AsgoE3w39yVsXhMssl4+Txn1eOumbZgYGQUOPLKMNt8yPqvp5C7QlukSbP1zkNkF6CwnA6PEGiRc+r4PkELJnzt9KqYCKjWzEaIzWkZG/jap22yOOVf7oo4+cQR6oWbOmS4pOOumktCRFlR18fFR0zviz9MbXYv4laSRSOFyaD4mqxU0b9MmaA9JhOXXHuHHjNGLECFcdrWjtCxzOOMmT7HJgx4+c5ZpIZlwWvAUqfh3PM+NdqnwEua5ix+eaY9c3Z4YbPVdx3naZwo0H/4wF5W9/P+bDk6Z1xlnMuTfwhro6HD3XTQSYPn26M52GeJ0wYYIjwvj+IIaLutaCfZbCAcUMqqKQ0YHUHOKGChQ+P9wwbnTvNRK+pmivYE3Fq5ogUCDmKto14FE6HD1sbbO0nhWGeh2kgdcb8UohIRH0q9PiS+COdJx1SHtc+ESdiA4tb6g+Nc7SsC81clLxZGuLHxFjEndO+I4pEtO592M6C0lD4u5bLdILvjsmBkJipGqpodrMaHPyCM4cEr+P7zMPB0A8Sfvu2AfN69AVUhh5OsDUlOQHyUbUs1bKoo2/wFL122JGgbPupZdecv6FqUD8/pvf/EZDhgxxY+A5H+PBvsXPf/GLX7h4n/OS9kQIl7vvvlv//e9/nVF2IpjYCdmC6u445Nlo+2Rgf4U8ROSA3xAFrzBCsvkQuyYWPZNPtBDfkd8f3GXXHHl8wfaZiCsgET8QO4TB2Qo0kMb8n7XMoxIsCwLllF9Kvb9csok8xz1Wcf+AJJGRYkh52o62D4s3nEy2zTg3TI/o/XZjwmLyHPpIP9PG5D8zHrLDEJNHSJe3r7eRZI5djpjvyMUvSv2+K034VnVV3dRWNRCeRJKPLj3lNqv4leUIJ2ToOGFzUPrYL/2gWj3kR5ZwcNHt32QEQypwQc/6Y/Lfs6YCbwLMXbnwOYD5u7pdjqj2Oau0d1fesZ59mGTGCJM4IgeOV32QYLAh0DNIC0+6PIJ4nCX/4wCPquG10zR/44fHNsqGDRu6TRUVVtjkCo+Sga8UVcT5T0rv/shaFkvrlg9IKtkDR98XUZ32/dVwTq4++OADd7ji2s764uAkiaTaUBHA2kfBgAcK7SCAdQ/hM2DAgCK3W/CZcuCiUPEw8FmQUNGu+v4t1rMfJlkt+gOaDJwKK22KVWpEVEU1HVmCRxIG1Cg/cMKfNWvWsfaeVq1apSQsAr8BfJOoLq183cYok3i4wCiuMkowhLlcndZ2nrPeUSTFFxYCAiWshYcA0pPB2Ye9G6yYVRgpwfS3U34l9fqSnd2JJApxFkQEY2EJ3A/vt+uHtod3rreiSQHkRXRwakfVvanw/Ydlx/QrJrFQVSUxSByBWVyw71HhHPFrqfUpnkApK0Bg0CIVRqLwmaPmoEWG/YgznQmFTNg7LlZjWskLpkYmB6GgQj4z/QEr4J3zeLhfSkBe121TZm/PI0NB4WLGjBkFfDvjgQ0FXoYXXHCBU5WEnX/4zjHZh8IHhMnMmTOdevmKK67Qgw8+6Iokzz//fAFFCgUwzGwhUfgdChdyiAO7j+jgPpLn8BgOMpFzm2uH6ZQUmxNBzrTgSdt3dySM5yb3Jh7geoEACQPxw+EUJArgo+DaRjxBnER+lczctrhwLTy3WBs/QyFKiyolORj5EPiQ1n5gHzaHBS8sETA8tLlwf9ik+I0LloveLBIXpGt8GUiIaO0hsDwmz4ua3G77EmOG8UshcWXNJIu5eE14PcAwf3Br8QxoigqCRjZnKq0+9isb8D32+LypMphCkU6TWTcKK44kpnI+6tdV1PKssZozt4kbh8hGyCZI0E8Vt3379q5nH1kdieShXRFH6hB0pbufmsdb/1FE2w63VvT8mlLN3a5tjRYQ/vUJR9mst8Z9pPOesGBr7t+lvRtLWJmMGCFM/zUVsBqNkJFXdYcik5QwB0aNwiHImGoSzDFjxhTZ26aswGHLxLPx48cf6+WFQCHxRjHj/SpKD5K6ZgNtnc1/Upr955jx9MHiJWm0CXT/rNT/u6YCiU/U+J4Yd4j8lzZAjKiD9p6XX37ZKdnwUwrrSYbU4cylYrtyfIoqLIhawslt9xpTEsx8yAgdklx80CI5+QoUnj8gUGjfgTz0ayo7QSwXZswfNiGt62eS/37wjVLnC8zglek7eOqRHKNwxoeJgkhYsYznJgakPaIwcAlQDEONgJx92u/Mj60416R7nNyY8uFrloBALHqUHep3sM+Y/TMRrJGz/25x4zs3mOI4LBcgT6la1/Y8irsoBV08F7XxxiSVycyDSRpT+aV4VE7s3LlTS5cmMAwxUNz4+c9/7hTuWAbgjZfsvl/5ylfc/a+//nr9/e9/P1ZMnTx5svvv6667zhUm8F9JfH78E8lfmNpDnEkh5dCBw9p5dIykdqHPd2wfzUmRN+cd36JZp43UjEEUvS1fo9g988HkcTPq/6IUKIM9+fQ/Sk1OsriCbpSSehDlVDe+AW8q9vjSKlACFPthYHz5ED7+rf030koOszCDSoiOVW+ZydOO5cf/jr/jcINg4VDksKQ/lf9ONJvlQ+PnbJh4ZaCGcSRKitfpxhZ9xzbAD35RSCBYTED60OPa7QpfZS1rMAYUjwUCMgL6sjB/YyQcjtDdLo0oklvXVdzZ4GCSYXPZhNiMli9f7pKArl27atCAIVrx1+Za9HSk7AzpqKZNb6uajUerzTdWaPTpp57wJLuySIRPud2q6gTuVKnY94ryPROAYVLFfsgkJIKs+D0CeSaKposvvtgdcsGUJTxv6GUtDkkWDcZnHzASmn2Wahp7pTOha2DjvBvGxuLyOtzDJmm1AMhEMZENvHdoYxs7duwxPw2P9K6zgdcZEbLkeWnFOPNlgJDgez3W/hRLJp2qJ1aYQO3BGqOCnqwlijVEvzWVLtp7aAskkCLA4jumWoViBQUU92UJUIGltXHGA7FJVMUlEGPqPJJdxhQO/VlUrS/aqfFvv+7IOQBpgvoEEsWvqewF+1FhKqvmg23Cw6oJZkqdCFTMrHMKdu//PF8hQkKMASHT7VAChJEoxHwE3UUhUeJjAXwBKI4xnQ1FKEUcyJRk3mxcl1yDvFbIQ/4eI1mvPil7BEpP1kf8XkWVmyInePUqI3eT7WV8b+QwGM1iIxAU61BI0UJG7OlafBPA99tyuDcL9igIlCVBy2oisKFACUpRg/yCwloY6tat6xTw69at07PPPntc2w7tOkxK7N27t1OpJJIoxHPkK+QLx/9Cyqmxs5gjM1Kj/RnSaQ+YqoNrgljGTWhMAlrqgrbfogC115CbTImIJQh7clHjcfd8VayY0+erUu+v2PWazhSq+NN58o7/N49e9ySbE2+SiReJ4APsfoXU/kwLGjlsYaZgk1G1UMFF8RKvEqBvlQOOW3xwmfLN1ZD6flOq11H68Jc2maU0/a4clq1G2OaMSWC6mCyP1KDiil/F29fFFE1H0msqOvRnUv/vxYKh2NVF2wzKAJJHvAWWLVvmPElIQvBKWT1jl4784XIdPWiGxmWGaEQH3+mhlmd1U/16BavGHumHa0OpIrU42TyPTv6Jrbs1k8yzxk0Ki5HJHAhM56L6CMnbdqwFXsjN2eeSJbdUH8477zzX2jN37lxH0pHUYgKK6gOVQKoEkyoBFVg8fjD6puKKfPLY2NuI7U/0pXKDzCFJYc9NVC0EgMhBIRMQKBzOyE1RNPhkN/0ICC3OO3zF6M+FuCBgZ1QgRse0LmBqCAmGzBwChaSwWv2iEfisNTyUIEsw1qS9B4URARnSYNp7mO5FULdnbUQTbzLSsNStifg7rbQKcO3JS7St21qbQpGb68iTgEDx+1n2AmWSi9bzkp/rGHpvnWvBMX5BYT36qI8ZLBBfGSVeRAVKjJfMD4PWs/1bi/eag+VIsQ4FIUMD8C5ir8UrI+jnZ4/Ff4/YlFYRzOm5PlEoWxxRvOf1KBn4nPHemn6/tN+su46Rc+yVkHMQKlSeEwHxhvoEtSnnd68vG/GLFyMDB5D7c16iyAubBgLJAsHnC6keiUD5kUhsBEDZfvXVV7uzjzMZggTCJBEU3FCRknckeqtwpqMQpsBLThKG0LOVM7jewbTOTljxmll8oBphD+98oRW/X7sq33coHlyPXF9FhXsbqHcHGNnJ0AKGeKx514qHFA6Pa/XBU6W++WwFxDbWIyjGyuJaLTYNUCon6piJFzJfAkY2pg9/Eat4RaRlL9ioy8E/tEOTL6BmI6tUEEQGyTNfQlFZfgJQ16PdU5r9iBmdcSgWRz1AQtWgo9QbJuvL1qPrD8nyQ9Afd9ZfpY/uMr+cVEZiRUKOJZN45+DKjnLp+OeMuIopqoBzzjnHKVCo5rIBHj6Qp0MTeylvR/m4CUcP5WrOn3LV9UIzX/Yov3VHkE5wzO2k71igRvBFQM8ewt4QeEIQUBfVYJr1xcGJvw3tPbT0cOiiRqGVBrUA05eoWsQfhqhMMD9ELkn1LZXnE+24VFC5D8E/XkBUSFHQnfQNq+Cyj0LgLFy40PmzUEEBvDYIFFRXfmJK2cONHq9rN85IApF0gr0MYg5yGCIlaO9BHfLKK69oaN/RWnZnN616MyftbZM7n+mrKhdukwYt0JChg1wLTzJjYo/sgRvLmqRuxXnLBAbW+kuXh7fNRGJtb+y1EHK09LQ9zcgXWm0Yo46BfLJYDqKlNH30Th1dX6re1xJyN4WPxzxo/5/YEiLFx4InFiRKqEZpvw0WG+aYfDfkE+3Ghv8d0xlf+ExsauPdNhX0oudsbVEY4cynQIFPT4E1BnlzrhVZPDwSQZEi2aQdfo4fHkAFmux+xIC33HJLgZ+jDv7Sl77kJtM+/fTToWQNMSOxI4pPimGQLvwdt1mLG2pJESemJQP7N0Ry3mGz5uAG8E+jTZlWOsxnuX7CvC7DxiYXBcTjtA016mU8AnEtRDmqQ84bCocosInH4QxqtUg+WeuEkSgwtlRfU/jlJJXk9PiCNPD7Vnlb+pI09Te2YTlETQYEg4zspvUo+2KQxnNg0ZNINZUKGR9OcdyqXW9VB1OQ0H+77CV7Lsxsg4M2vs+Kw5tFwo1Ry1Rw2aT58rN5DGdFBp85TOLIu20M9tS7pM1zSxAkxZKVrpdarzUeGKmfN+ICflow8EJZsmSJpv53jbZ/3LNsRuEmAfK4eY+bo3SySVgeZT9OEeIhnUZyHHQklSS3kBgcnFQe3n//facYQCXAIcjCRW2CQbebApXEyDsV2OPwCWDfZQ+EQGx/zlEtXDLPPTdVjcDw7Oyzz3Zr3isFsgdB5ev88893hDDtilTMdmzbrYl3b9XRN4n+0v6s0t7qir40Rq3qdtaQb3VQ1aq+dFsZkGrrIJ7CSBYjQ9QAqDnC/t55ikQtJkThx+QHqonEZBDBE28008GkZ3Eaty9eDyS5n8RYsUCcTlskatFgdDwFBqYtpVqDzlsxpmqf90+zHGBdMkkUdTwTOFlbTPhMRO3mlsukw5jSI/uASoTCRdjknJKCYhY5yE033aSvfvWrThH/29/+1p3hiaCdh5iS+DFRRbzzlIiWMF22hPkLufyY35sC5MNfHv84xKW0P0IwQmQUQMRUe6UNK+OLTg1C3od7+HIKXYuvRKlnZEJhhmHxYFNiig2yGsxk37vZ/C0SE2CqrEg0V75pbT18CTwPsp2hN0sH2sDixeTokZJJp2GXSZ6RTyM3RbqHaS1MFgQNG3KNJjb+FvmQG9XERunJk4qhDKhuARTyTHrjFj1rBBtuz0nnice+N5LfdmdIfb5irRp810X9TgNmt3vnXlr4cQ9t20t1PvyPWS9IkFHLuJHHiRd5UZ4z4W+oDtOjDfvKAe6RHWBdcaNlBvKCUbT0wFKdoM0HUgVFSO6WpnrrexGtfa/07WwEjltmS+Ovkdpdv0JL67117CDm4EV51bZtW0+gZCH4Tql+4fsUTO/Z9VFjHX1zkHSkrL7viLSvmjY92VlLT0HNyWFaRk/lUWFAy1lYqzcxGAQufnlzH0+hCo7JslH4EXiP/6ap6QAtHGMfsKk+kDAkvYngfKeV0SO7wTFFdRpl0wc/t7bWTTPsVlSwBhnPijqFanagOgpLNCliYRqMH48/Ij2SkSiYum/atCktj8eY9s9//vP6wQ9+4GIzJvLccccdrqibDKjoE0e7s65pkcSP7YANXiw2uC7Iz3msTx4+3noDAQJdA+Q9TPNLBKQHPkLpxIm+BotPotS1L6GoJApfFu66TN6hFQPDOdx7C4CWjTZ26K15S1r5mskpCfg5RCE06H9C2om0sqQI+quQ4CMD5AbcYc/NkyUVHqwLzIHoWaUndddqYz83TLUWMQgV1g2HHdIuvmMueiSekGLFIU8SsW1hRFtnh1dSkSZjUNt2lLm9I2XHrIxJL4F7PNfP2Y/FVE0pQOvZnEcKqlHWTw434PPIbHDQMY7uwgsvdIkt/iRBu8WL/x6vnCcu1o6ZdZIThSUA7ZJL/tBUeZe0ljqtUNNmTXXmmWe6aocnULIbQXtP3uqmevOOKjqyn37GsvzOI24/nHq3+Yk16l6GT+VRIYBHVGISiqcdimDO8Fl/kKrGPJsozrnQrLoNGKDABlnstqGoNPOP1gcf7H9LMJYdZsMDmvYPJ1GIEShmeGQ/ID6YpLP5E2nBUyWf5AiZklJlH7H4i2JWWt05PbIKFMQ6depUahKFOKxHjx668847XTHtk08+0U9/+lM3lCBoCQoDBV/afRLjOP4TEQJncJhfaVGAWTi+aQO+Lw260abm0BKHRxDtc10vkzbPNq+rRECGu3Mhi1BsEoUDz6kAni9CK0VE6vE5qc1I8zhhbFyyzY1NkFGNyIDeuEbaOC3fILHd6cZuzfqT1AKjscZKO5JNrfCouOA7gyRpyu0kqw4QeKFoCkiUwDE6HYBoowUMt/ZEQM6c87i9DqoZ3I9EAbMySL9Xroz1DUIA0bPXOEnLUhO7xjBmCmOAV79t5k0+x80+cODhj3LWWWe5f2m3OLj3qHZN6CJ9UjOtBEqA6Jb6ynnxLNW7erLO+kI/tW7jx2dXFuQdjmjZ3xrrSPgQgTLB9kXWjjbyt96QMduBArlKbelInCEshq2oQUlWR/8+/+fuvMsx3xNUBYv+YwU35z+1LxaQx+1/nPMbpkn9c6w9PMx8hTjR/c6jUoDC6Mi7rDiAQWw6vZ0AcSSePJghE6d5eCQD8VvPnj01ZcqUUj0ORMxjjz2mzp07695779Vf//pXbd4cIvFIAG27PH9YLEcht9cXza+kuCPcAXs3UytbDLWcnTZLVCc8LgXrvbE2H9rP40Feg1E3hHmlJlECN+zavwl3rI4HB2OHc01exwZHkhsG+vNJTPFHGfA9Y3nf/5kdoPhfINlkWgGTKMb8zpMdHskBGcfFXCaIWqUjUaKMhK3vNaa2mnKnNO0+Y2vpnWYt08pGT/fU39o47+fODV/DKLEueNpY3YVPhT8/1wFjHsvsPXpUCCko7Rb1ajfUe7/fqkOTaLUou4wzuqm+Dv/jbB09OyK19ZtrZQB7GK21kLIpEVNmxnuGJb1rTpyiM+kTmyEo+yVjBz1fl72AwGjcIzYVMQaUxAv+bQqUeFAModhAArx9oQXgBOtbF0qdmPYQMs2BM5D1lmwUN2SMT3YrD9hLGPpw5l+kiT+QlqRjylgMFOQo8DIdhIKuh0cq4KPI0IB///vfzuOuJKAV58Ybb1SfPn307W9/W0899ZQbAlAUQLrQIp4MjHAnt0YpH4qoWW5gc0FBOBEo68d9Xup1Vcz2orlN6EHdMv9JaVdMeR8P2ng6XZB9Z36JhvRSde/5JTP3TAWkmTBTeJucfHPy4IpDk3GOeD4gM+p+pbFbJIscgjizv/19kwGlw5TGw6MkIGBzbWwJ69iN0DvNSEXIj4Dd5d9FTxtby6i8T/5sxKDrtU0AUrhht9i1Mv7rBVncAASMmOt5EiV7QfUAM7Aay3vryPN50qGyLtlHdGBzxBGAKKmoFntkN6jS0h5BkJTs7OasxTuKyhF7G95TeOkcB/wIekqtR9pYb3ygaK1gglSYIWPQlrhmopEoHtkLpui0OsU8SwISjvMLM9jEEI5pCxTn8L+beIOUdzTmUzHJChFMFFvxhnR4dz7p0vUS68ffMi+c0OP3PlashERKS+nMv0pNTrJWg2BySEmBarjft6x1gRYxv6Y8ihLDoShu3bq1li9fXqLHoK16zJgxbpIeUxPPOOOMAvdhvPF777133JhjnvuKK65wxbhk4Ewfeou0eY60L87T5Bii0tpJdksGrqvpv5dyqlnxGnUgpGVYwYW2ymE/y868pUQkCgdU769YXxQsFdMeZv8lbtJOnFPvK58rfJrItgX5VYqJN5kjNuwWG9aOxdKqt80E57wnkzj+eniUB6Lh5AaKE6pujuBIGDlLaxHJBKNlndlskpG0BHxdLpE+vE3aMif5S4BYLM3YRo/MAOts1oM5yttffo3X+O3QpsnUCx8oZjcO7YlVoaLhhqAjbjfp7dHDlriyd6ESwBSeYkcQB3T7rI0GZWIeexs/I0BD5fLO9eF7GckxqtK+X7cR4R7ZCdq1un/OpsrFTxMLUwcEP3OeFHG/X/eB+Z/wOCgMFj9jP+e/Ww6zIH5rCInC6FmMPz0qaZt3fWkI/nRjbI1AziUjdZOB/AMSkEEU/Aux7OFRVDRu3Fhf/vKXnZ9JSab0QMDgk1e3bl09/vjjoffZsWOH+vfvr23b8qshvXv31rnnIndPfY3gE8napvWGPKUkiOZZPpJKH8N1M+gHNvY4G+PKkpEoMXMaNqkJ15q0JzDOjAfMVHEcsoMkkV6t+PnSsFx9v2HeKB4eJxJhPYSscwgUDm5UKfHVXYz0SED4XbJJAfXaS4N/aCTkgn+lls6jYkk6zcAjaxRPtFnQ81/eaxsz4x5XStWzrG/V43gwnhB/kgKI2PQyTBqXvWwjtan206o49kFp2K02xQKSr14HacQdFiS9831br5zV+EBRuR36M+nVq8K9CTZ9Yvsk5ItH9oJiGG0Qi55J3eaF8oS+ejfNLg4E95NulvasN3IXzxQeh/t99Btp1sMFz0MqoyilUSR4VF5Q3IL8aD7YCgTYBjAGec+GqA4fOaTo4RzpaOyWE3H3Z/oj6ndUUZ0vNDVebo3sTP48yt68/eqrr9YzzzzjxhGHgemLv/zlL50RLGqTeKBA+f73v19gRHE8mKoY/3c8zte//nW1b9++UG87zmqMuekCYR8t7dTHMLAXMwBkwHft+bIRJX5bfD9UoXDhnfFA2SZ2MGbDf+HH1XmceECKJAIF1boptlFw8GK6RFCIqW3vq6U6rS1BDTNSpHLb4wtS3XbSm9+0am8quAM9SzcjDwN76cL/pFYcoe4jAcWPB1llKoMw7kNVjb+B7EvVJ067GmOUCSJ94Ji9gLANI2tR1KEyZRIebRcEWGDpWqn5QJuGRhsOJArnMlPGJv3ERtUGxp/8bdvRlsDgixavQohPjim8eBIlu0HMRhUSJUCobDxu33mse3ggj4Jg8u3S7D9LjXra/rVtse1lYXFns/6M0U6fobxH5oIzDGKEKjiECKO1V8/apdfHv6aDm6qpyu4G6tN6hGrVq+68TmhNpLWMfYtz05+BHqUBZMaPfvQjXXfddaHTdA4cOKBx48aF/u369ev15JNPFuv5Ro0a5dQv+KkUBdXqSMNvj40r/kv6PIRAboxAYRpbNrbxBChVOsZGQ18VFaUFT4Z7PZQWSDYZkUx/rd/QPE4oIjFTsYRJAKz7WX+0KVRUZklA6ft3B3JPafcqG3kcL1MOULOZES0bP5aWv1r4S0AqHza5xyN7sHuNTSdLlpR0v8J8dlylNWKtjnMfk+b9wzwp4tFurDToJqlJH9s/eWy8eagMhxEvVHjXvCN1OEuKFNKG6ZG52L3aiN5ENOxmN8yx966P+0XUxhOzdgJZfG5Naf3H5n8SPzmFdcVjQxonS2QhCFHDeGQ32HNQAgz8vjT5VymI4WhqSTlkCeulsDVDnDjiV+HT7zwq9zqk+ET8VLPzHh1suFZH6h5RrXr1NPKaU1Wt2ol+hR7Zis9+9rNu0uLDDz9cZGPYkqB79+6655573GSg4oBpoQy/QFnKBF2m65QWtVuYh1C/b5vfYzYPgyl1TbtmQ2n0fVahJ4g/si89LwzpD+7BY+43ZtgTKB4nGqxBxhaHyZIxP371S5bculnonaza+95Pze+kca+QayNiCTHtPB/fe/woyGSo3cxagzyyFxgxJpIhgMoYKgHGOFLFh7hmTeK0ji8FMmRGyQfVBMw+z3rUJmGsfNMmQ2GATFsGbWezHwmp5DK1ZZqNFfXrLHvh1leIEgW1CWtgy1wzeaeFFrXJjpjzPh5ogVoAE+1lL1gRBbLEjWavbesRE/gVr5n3Shh4jMAk1CO7AZmGbBzvuwVPpX/0bADGKQ/5sY1Q9vGiRzLQ/hCNjVisUaOGa7vw8CgL0FJTu3Zt/fznP9fGjRv1v//9r0T+KIWhQ4cOuu+++5w/SmFtPGFAKQLRTdsuLby07Ca2Vhb1cVoOk07+qdTm1ML9ULMBpd89IiYVH32PVd4Z4wqTlTgGtjiALaavmmklXoHiUWEQkZr2tz6/RNkbm8Wu1bHJA7TYVrWKLOv35B9L25dYz3c8SHp7fcnGexc6ajTW+kOPOS09Hlk8AWqJtYglAlNtJlWwXjDsdpOiZO1jF/3PEhUMvnd/aqT2kB8aETLh29IizECjRuZxX/ysmM5ynNogBhJlqsKeRMliJDlTaSukzYdpYowjBBBtPRpL/a+V3r9FmvdPI2AgQQIihHGytNzilYb6bt1kM8lOmTD7c73SgL1k1D1G3kGupVu1zJnIntb/O76NxyM19u3bd4xEIcH18ChrNG3aVH/4wx8caff000+XeOxxIiBMOnXqpIceekhnn312qYULFICbDzJPUkbRk5e4YRbkO2E5fcRyHdqC2p0m9fiiqZiJPytL3p4WCpYPiwoU0h3UI7hhY+J0gEkkRSVTItZDRe/iwOtNho5hXWX5IjwqPliLTXpJ9Tseb8pIADf0ZqleR+n9n1mSG6j2GBmLKgXVAJtRPHgcxvAteCLfeyAV2Kx8lS27QdLpiI2QfRNZJBX+lW9IO+Om5jFSljGftGFA2kGiIM1EdUIyu+yV/MfbukBa9qI08AY7MINJK/HAw4JkB0mmR3YCCS/JZqIvCqNjWWeo5yBBWB+oRlgrYx+STrndzOLxQgtTSh3Bqf+gBWKYe1JUCeuzdoGXNy+uNODMYm866xHpvZ/ZmZfKx6k4qNnY2spP+qb3zfMonhKlVi3fG+1R9oDsaNKkiR588EF169ZN999/v7Zu3XpsHZYEKKhOO+003X333erXr1+RfVBSv06zC+j8GanD2ZbLrP3Apu2RoxzcZfEAhAvnd902pl7B/6xOG/OBrGz5SVp1bHywTfpKZ/zJ2hqWPCetmmB9rFThC1SlULE0kGq3si/Cja0baoFcZfsiPDIDJKetTzmeRGFTwR8Ao2VGMtLWxmjQRj2MXCEpxig0EW7kl8zIsyiVORITvC08shcktWEqFIA6hCkVkG/1O+evQVf97yHt32y/B+yjkNBr309oI4tKaz80EgXZZRiJwmsoiZTTI3NACyGkhxLOZFfFj0oLn7Z2r4AAYVIPew8kCoWSRBKFVo3XrzZCmZZHCJdBN0jrpxw/aS8AaxPDbY/KA2I6JtWd9n9m/jrjQTMhLnKhLSTexG+FiVHtz7AinIdHIshTOTtpZz1yMKo966OqkldTeTmHVad23RK1P3h4FBess/r16zujWQxg7733Xn344YduPHFxyBSm9eB/whSeq666yo1STvcadobMNaUGXexGZwjXEMQ3ylRiB87wKjV9rp72ZsDgw4cU4YAbcrPJzjGyg9Vi+ghBOr1TGCPCZFFBperpZZgeJT4gd9n6okKPAgpSAolZrZZSg07WcsZ/Yy5W0ouejS4ayVPry3ZqwXN1dXSXNfxBDs59VOp0nhk0weDC2LYcItVoIk36kbRrVcFKLEa0kC2wvIWB5KTnF32LRWUG0yim3mVr7JzHTBbPgYaRcb220vs/t6kpAKKF/TRx3QGneopKtZuX+1vwqCCAdCMJTVQDHIztnZtnJihIojaBjP22Tlvbv1DY4XeBuikg/9yksg+lOY9Kpz9kviphJArKVdaoR+UCZy+xH61hqI0xKqbYRpGtqC0+xJcYvDP5odcXPRnnURAUtsg1iK3WvG17FOcnXl8HjvZTteqddDRyUKsn1NOU942Ew46A6rrPQzzKEowhHjlypAYOHKi//vWvmjJlij755BM3jQeVVJj5LG1AderUUY8ePZxR7YUXXuh8UMqLAAwUKn6oRUFUKesPHqVJy5ONVHEVh4Bw47vHMbuSs1gexYcjbaPS3o3SinHSyvHWthAkkAHcEJ2oVahQSDF2s9OFthZJAoqz9iBQ6KXFZXv28rmqeuoIHX2jr3TUHgSi8KXLTFLMcyFd3jDd2nhWTygom4fFXfG6KQUwCS0MVO5QavkDPrvB9wvZFwYIk08nmQqABMSZgMYIuU2z7BoIjGJR8wFMPxMBwcclRMBIIp04VpTXUN23WmQ1arUwf5xEApdkQ5jEhlgFuLGfkfzpPGf+2VRQ/xyQ/7MAbvxsNHlizNr1hHDlBXsM3jljfm+qZZROaydJ62ce1t6dB6UD1ZUTzVVEEUWjEVf5RD1FLEnC2+FcqVZT76vjUTAupHAw/wlp0dPmL+bOt2PxFwuGeas2cxWbui1vW3GixcnmUdf1Mq+G9yh70H4DMTJgwAB3a9OmjdatW6e1a9dq165djkypWbOm81Pp3LmzhgwZoi5dujgSBvLEK6gqBsrNltp93/4790gDaI+Z/y9Tf2DmWlhvNYE8svINU6XZfzMFCAad9PkXJgGGPMFNe/ny5Zo8ebI2b96svLw8VRs6RdXmt9OhlfnjxCBSmMbj+gIDf4AkIx3xR/nk4aK93+oNze0aNY1HdgOlFO2NJLKJ01OouJ79mKkIcFBf8j8j5zqeb5XdC56SXrzYAsegdTKRvIsHZmFhY24Jg6FEAAEAAElEQVQJIL1fRXYDoq79WdLG6cevEfbJg9ulzhfZ9B1axALSl+SCNi+mN7GnQriQeHQ8x+4bPA6mcp0vMIXg5jkFnxviDtNa107koco+dpb2r4ZdpX7fkhbNWaPx49/U0YN5alS7rYZ2OUu1GlRzXlCcg5C7qDJ9/uCRCEyuIU9mPGCK5MTiQDJQeGCvYiIJMeLcx6ShPzNzbd8i5lEWIK/49NNPtXv37mMTfC666CLVrVvXkSfBBB+IFrxP0uF34lE28LO9PDIGtL6sfkuacoe0fmrxRyUS5FMxXfqCmSX1+ao0+Cbr0w4LyiBLMH/66KOPtGTJEh06ZPp2NrTG7Wuq848Padatx1dhSS4OpXHyAIf4gO9KHc/2gWNlAN8x7Y3Vahf0JenyGWsRm/Ww9OEvY47pjESeZetuxK+k7lfa9eE8qKLh1X5+FglUKiGtuCgUvEFjdgMiA6JkziPHK/i2zrNEBEXdGQ/bJB4I3y4XSV0vlhb/T1r3vt133mP2GKfeZQTfxmlmSotijvuveEP6dGLB50a90naU3888jlemVK0TleruVV7tXYrWzFPdTvvV9TPI30/0q/OoyOCc27VS+uBWaw2jeFVSQKbQjvj6l6X+10mDfhBr8fF7lUcaQW6xcuVKHT5syUL79u2dyTGECqSJH7udOfDflEdGgINx7t8teUQqXlpQYZ32e2nTTOn0P0gNuuYflLDEjCCbM2eOpk+frp07dx77OzY6ZrEjv6tVo7aq7pAm35lg3pkmMEq5xxeM6KES7FE50GygVK1+QRIFcgPOg5aegEAJKmmQi5AoDbuY4i/wPYGQSQQeQSAYkXwcItbuhmeFR3aDlhr8myBNAjINVd/k240YRq2H307wcwiVj36Tv/Y2zpTe+q506p023hhlCUQ1iQiP+dFdIQbFEanr5WaM7OGRiP3797sEIzhrfQXWozACZdtC6c1vW2t0onqzpNi/zfYvJt2NvsdUUJ5I8UgXyC9QtwdGsYwp5l+PzIMnUTwygkCZ+aD04e3pJStQsqx6S3r1KjPqbNQz6giU1atXu9Yd5HZBQEcwx0Y3bNgwtWzZMtaPGNGA66314qNfp3eiCaQJI0JH3WMJtUflQZ2WUism5/z3+J8z5cz9vnX4mE8SYTedhwk8H9iYYkbPoQ4Ixmsjn287xqTOEC+JoOqG34pvtch+8B0PvF5aM1HavSb/55h0T73bSBOIO8xjSVR2rz3ebJb9c9lLNpEMErpeO1tzTFzBSD6e6AvQuLfU9+s+IfFI7jsWAD8AT6J4pCJQ2GcmpJlACcBeN/+fptojDvM+YR7pbuUBtPK0a9fOe5xkKDyJ4lGhgWfDvMetRaEs1B4cvJgrvnVdVKc8uFML1n2shQsXHhfMMd998ODBbqxY9erVj9vsMFskEWFaABVc/ChKOrIxAKa0jAftd61NMvB7a+VLbmmJWP7q8WueQBEyhDa0jTNMRYUKhfaIAd+XDu2RVr6RT7gwvafLxVKvq8zgGMk8XhQoAUic3XjRBDTuIbUa4ddcZQDfcdN+JlnHyyneW4p1hZrJKZpSIWrKQG7rJ6e+K147KFYgWzw8CiNRAnm7h0cYKFqxb1EwSDeBEoA22Xn/sEliKIIhVDw8ijM5FF9E/j8DAMgXmPC5bNmyY1N42rZt64gUj8yE3xI8KizYeOhPnXKnJYhl90TWIvHKHcu0q/8st8kBCJPevXtr0KBBatiwYdKADiPZHp+Xmp4kTf8/admLJgctLpkCYUICe/JP7N9cm6DsUcnAMkMNwiQKyI4AjJiddp806EbpgqelbQtMUUILD6bDMx+KBZS0Xxyw+zLV6dTf5E92atJb2rdRmvyrgsop1E8nfdu38lQmkBQwKhYD2AVPFH3MbHGBx86A75mvj58w5hEUSNxY7D1227cjqp1Lqkg76rq9qMrhOjp6KOJ8wTyX4hFWXHPm6iHm6OkE5DJmtYxrbz3Sr0WPcNDKenCHtGOZFbwodBFrMVab37GPMa22VudDWlN/nyKqrpyaR9zEHU8WZy4iUeh/D48KCJK8ly6VVk0opyesv1f6xrM62nSTGzd2yimnOJYYSXFRNrlgpCfKFir/KAn2rrNDPnRKSsSSGIw+250h9ficTcwg4fB7auUGa2n5K9K4z1uiEa9SoR2HSSlMtOC/mURAWwXrLdFsuWF3qe/XpGaD7L83f2KGoFvmFST5WHsX/MePd6yMaw0PFHwFIICLOtWiqCB4xKh25G9tco9fW5V7rZGUYkK88nWb9MRehLlxNC9qt2qHVKXhQTVsW10t+tRQm9Hm3VOziRFwfv14bF8q/fcsadeK8nvOjudKF/7X1AQeHvFjtdm/GKlNC3YwvS4puZcTlaodVm7n9arac4PO/HYvdRlVR5EqjC0u5zfgUWp4EsWjQgLSgUrDW98rozaeMESiqjp4uYbcs139Tu7lerJLwhA7MuWQtH+rJa2oaWidwC+AABLDWHwqkLW3HGpGnrTwePLEIx5UMN672abxJB7IrKEqmA1HbE2lGvMNURdM28FfKExtULuFdOGzvpWnsoI9i5ac928xRQoy5HQAjx0Maof8yE+5qOxgP6PdcPZfbJQs52GqEewB2LsY+977y+YTRmuFX0eVF6wZBgxgcl2U9ZMuVK0rnf+E1OlCv/48DBS4Fj8rffw7M+qP9wwrHFEpN6qaDSPqelnEtdUyCMCvrcyCJ1E8KiQYv/riJdZmU57IqRbVuf9EFRJJu/yURJcElqQWubLvr/UoDHs3SK99WVr9dtnJllFCjbrb2jr8mqy8IBI4vNsMZaf/Xtq1quRJCgbGjXtKQ2+xFh5aHj0q77raMteS3hXjSm7AjgoFY2K8Kbp91isCKit2rZZeuFDaPDv5fVyhqqZ09HCMEE6R5aDmZC1xX9pgU6HHldJZj/q1V9kRjNXGBxH1SbxauESISE362ITFjuf7Vv5MgidRPCok8IL479nhzC7BFP4htVuaf8Oetab6SNbPD2FRp5WZvx7YbmPrDqWogmHGef5/YpV+D48TCHZnVExvfM0UTemuvKEOOPnm2Bjtaul9bI8MRNTW3NYF0id/khY/Jx3YUvQWH/ZaVHWYGTOFp157Fx/G/sejsoF1g8H1pJ+Yh1M6QEvYSd+Qht1qXlC+clt5wN5Ey+HLV4THe6iWOp0vdf6MqStRMe9cYcQwquD4QkRuDanjORbvsWdxX+4z5+8WU4YRLzWbSl/8yNRQHpV4rPYCU8mveSe9j12jsXnY9fmKj8cyBZ5E8ahwYEW+e5M0/YGCrutu9O8XpCE/tlGvECrIghf+W/rot9bbH4+67aQRt0udL7JDk8djcgnGmvQwhiUH9TtLl7wsNerhAzSPCkKkLJMm3miV3HQRKXgMDP+l+VXgHO/XusdxLYlHjMBb+oK09r2oNsw7qH1boorsDyaURdz92IPZi5sPlNqdLnW+QKrTxquaKjtQXy5+RnrnBvMMSCfYr7pdLp32QMwrxe9dlSc2/KEp5cIUJVTymVSH+oREl0S0cR8rmpH0spcF9z3lNpusSBsj5yvriJgPAhkvsu2Lwp8DU/dul5X9e/WouAoU1MHOxL8MsmcKW2N+b+2L7HMeFRs+zPGocEDuS0UgbGxdry9Jo39nZprIg7kvgbubKlJHmvgD830IWN2xD0rtz5AWPiWtelOq1Uzq/z1p1L2mSPn03YLPsXuVJQ8cqB4eJxokCIwxPusRaeaD0tx/mGFxiR+vivnwQC62GSPlcgr4JMQjYc0hKaYlp1F36dC10tuvf6zZM+YpN6+6OjcZoC7N+7tWMNQmJCA1Gtoe7BNaD5KNFa9KE29KP4ECUCHgRUCb2Jj7per10v8cHhUQedL6j8J/1XKYqd+2LZTeutaIEQje9mdKp//RFJfrp9rZ6e77DfPmwVB7/+aYwumb1oI4+Eb7eWKSHI09vydRKicObjcSD1VwWRAo4NAuG91NYQJDbX+eVmx4EsWjwgE1CV4QYVJKTAoJysZ9wQ5LgBHi6Q9L3a+wqTiMFwvc1DlAMbLDMNH1xkbssc/4k8k+nedKwmZIBXbbIqnTBeXwZj08igAO0trNpVNut3U98w/mk0IVraiHOQlHvY5Sn69KPb9o7XD+gPYoDK59sp50sPpWqdFO5UUiajXmkPqcfKJfmUdFhGsHm2+JwN71Zfc8nNMLnrIJZLQjeuVT9uMQo7BDYkPlSO1Ok6rVlz6+2yYkBlj6otT1UovnGnQyEgX/CYjfBf+2ghlA0UysyCj2xr2kanVC/Hui0s5lRqb4Ue2VCxC3n/xFWvZy2RsaQ+phnkwh17XE+jitwsIfOx4VDhxmYdUrqqL4msz/l5Ec8fdHpklvK+NfHYkSkXp83mSc9PYfmzZBheyNWPUhWQIakXavjv3Ob14eFQgkCkzQaXGytHGGtHxcVEsm7dD2OVUV3VtDuTk5iuYxkjv2BxGpVlOp9QgjFCFgarWI/cqvbY9iYNeuXe5fWnnq1q17ol+ORwUFBuof3SltnVcOz3XAWjs499kT/Z6W3XATDkO8UHJyrchG3LZtccHkl3gSZR1qE/ezQ5YI8zeQIUFSjFKZlvGDu6QjSSbeQaywxoOJdx6VA5hjU7xK5r2YbjAqec4j0og7fB5SkeFJFI8KBw6osBGbtOfQ4+qqW9GCBxuVqWYDbMOhyt6wq7R5jrR7rVUWYHSPHrJe1yXPpXgBUengjrS/LQ+P9LVaVLPx2M0HSaueGK8jKzeqRk599W06Ro1y2zuyhfYKDPDwp6CqhieQTzI8SoJDhw7pwIH80RWeRPFI1cazbFz5Pee+zdLH90nnPGb7nEf2ggQ2TAXAz9//uU1LgWiJB7Fg61NNgewMYyWtekvaNF0a+H3p8B5rH4dQwSOF+HPuo8nH1RJn4vfjUXnA9z37r2WrrCv4pKaUwqTdWwtUXHgSxSNjwCGI30mDrsdXDyBNkGnSrsBBCJjGwwQfehiH32qtPlXrWiUfY9kZD0jz/2kHpodHJgJC5ODh/dpzaIeitfbrUO4hdb0oV23anOhX5pFt2Ldvn44cMRfunJwc1anjs1WP8H7+OY/aqOxyQ1Ra+Zq0cZopUjyyF6hEkrXRsOYOJxkbS8vX9N9J25fYr1CsMFyAtm5MPFm3FBl4/JkPSSvHp3gNVX3rWGXD9sWmYC9K6zRriLWXalw2vnQUwgobv717jbTkeRukgdrKo+LBbwUeFQ5sLkglE2evIw+m1xovkw5nSWsZ+Rq1Np9gwkiw0bjKezWp7WlS0wHSjIfMaAw1CtWG0feY2gRzugKIWG+th0dFx86dO3XwoDGB1apVU4MGDU70S/LIcLCnBmpASGtUfps2HNbR7TWlQ0dUs2FN5aqaTebxyiaPOOBTtmZi0e5LYkvxY/U7CQlHjpxhcWiiikp0Z0FJ/eF91uaL4sAnuNmL6g2sWFYYGH1NBb//d837BGLk43vz1SWQK/iL4fXEuqE1tnYzqevlNl4W35Tp9x8/EjlAtQZ+/GxlAsVaBlC4Fv9C4KY+/cr2MzxNkoGiLq3V7xZivM36W/q8NOC7tlY9Kh78ceNR4QCBQW+q8yyJA9WCD2+zKSXnP2U9ivTHNukt7Vxhwf7+bXEViypSTkSa8O1YYBfzOMFI7Px/S/2vlZb8L+SgjEr12vk+RI+KDabT79ixQ4cPW0bRsGFDVa3qZ+J5FB8QIs40cYVNMVs/Rdo0S9q50giVaLSp8qJfUrWG+1Wl40F9OK+GWp9iPjuo/tgrPaFSucEaWvZS/nS8VKjbTjr3cTu7H+tlFdcAdVra+U5xJBGc8a9caVNVjn9y+xntGhRKPLITVWvZfoMyIBQRqc0oacSdUotBNkXl7euk1W9ZGw7IqSYN+oGNPp70Q2n2I0bKoXCZ/6R08YtmLrvi9RBfnxypUTdvKlvZWnnWvBtOqB2HiLVX9/2aTYFKhlrNpX7fNruB3CL46lA43rVKatK32C/doxzgSRSPCgcqB2w0bB6JgBF+4aJYn2BPq5Z+9LK0da503hOxYCxqgVz0iCUFx40jixqh4oKtjjaSE/PZeEC+MNbTw6OiY/v27cfaLBo1aqTcXK/59CgeSC7wBJjzN2n5qzb9Au+o4wFDUlV5G6tq90Zp7hSbikbC2+2zth/jO+WTi8oLzmKXPBQyuYK2iaE/k5oPDk9MnJdTB1OO7lxeUHGSzK/M3X+FETSe0MtSRExtFKp2itjkORQmKAHe+YG0+L827TEezk9smMWAtO0EqiYUB6yfla/bFEiGGCSSKKyrVsPL7u15VDygXipA2iZ4NWJqDWnX44umgkpETszDjjOy2+W2hhKLxEmf/4i0YbonUSoqPIniUeGAl0nTfnHqkRho12GDwiiW8YlI55wvSlTq8QWpam1p3Qf5ZnO0AzkyJSGo49BEwVI1Vn1NBCqUBl18IOZRcSq8rGHIPiqxrGlnbpeXp02L9yvnSA1Fqxz2JIpHsdcVoxQ/+bOZ5uEVVdRx2YBEhaQDmTyKPpR9fb5msmO/d1Y+7NtYBOPFHJuah5R9/yZLQBLBGc8UlY/vsXVVHBKHqX1tRhf7pXtkECBRaPdOHD7QYog0/JfS7k9Nfbx5dpL9LFZkqx7mr8JZe9RixDBj2TqtfTJbGfe1RLPieDQfKJ3+B8tbkvmW0IY25nexccXxfo5FQDAMw6NiwpMoHhUOBOBdLpI+efj4iigVpnMelTbOkt69Mb+KRcDV6TwjTjbNtJ/tWiHtWm3TSepR1YoZioGG3axdaNv8gr4roHFvqX6nsn6XHh6p4YiT3Taye9V4afNcq7Y6pYCrnuUop9HJqt62i/Lq7NKmNc21PmoEJNeET2Q9UhEoSOIn/kBa+aap9kr+YNYiOelmaf1H0uh7bSKUX3+VCwe2F15dbXaSGb3jRUZC0fmi8ESVNYWEvViIGKnnkd1o2t/OOFoOA+CD0+NKS2Sn3GEGsmEjiCF+OTs3zZB6fsn89WatsjiT/YoCWvuzrI1xZ+L6i0gdz5ZqNivzt+hRgbBvS+pWHkYR02IIOYLH0+l/LHgfBly8+S1TpLBGR/7GlE5FAsUOXoP3IKuQ8CSKR4VEk5NM7utaceIYYbxOul9uvde09pAsYgTW+TNWTaUSBahSzHtcOu1+adRvzeRpB1LfNtLwX5jx7Cd/Lbg50srT8/NSFRy2PTxOlEJgiyUatFhsWywd2RdWVYsob0tdd+OXyz6QVv8r4qTKfb9uASKBpD94PRLXF+07478hbZxePPVJKlC5RT6/d6N01l+9mq+ygTM3rCgRANXJqXdZy+2030uj7wu/X4PO0hHnwyN1usAKHpgv4tGz59PUr+FAzBPNIzvBfsJ6oIWQFougok8c2OoUO+9G3R3Wjmh467tGvjBooPVIafhtUrOBVnzDzLjLxbZvTfpxwVYyfg/x4k1lKxdSTdkJCJKNH9v/Z1x24L0TD5RNQYEXVcrBXVLd4ryGQ5arkJ94VCz4r8SjQoK+aPpbN86Uju7P36Cm3Cmd9qB0wVNWlcdoDIUKZojTf388KbLwKTMhw6H90tdMkgd5woE77XfSilcLPi99i+1O96ayHicGVMlWT7B1ToJb9BHcEVOu7DLVyvrJto6H3So162etbx4ewQQV5O6MhE032H8ht9/6nnT2o6Yq8ERK5QD7TzKZOq24A79vU1HGfcGmnyQDSSznP+bvJBxUeElcUZZ+9GtpyXPhiQpkYOLUHo/sA/sJ000W/MuItQC08RR2Xgbx4ZbZ0rjPSwO+bzEfUxz5W9rRxn9dWvpCArkcMdVUi6F+P6tsqAikGWO1vd9YxYQnUTwqJNgwMGBa+LQ5qwcH2vLXpN2XSZ0vkBp0tYOPUXTLXykoJXaky6/NWbvNSJNqBkazaz8oGHBhMkugh7Gth0d5g9adWQ9L0+4ruulYqsciEGSC1am/lrpcYgexR+UFlX18dd6/xdpuyu6JpNVvS5Nvl8Y+GC6r98jOZAOlqFPNJaDD2VKfa6QZ/3e8urQAItYKxtm85h3bw1C3YNw46EbptAdMlcLvwv7WjwGtHKA4NuTH0pvftsIBt5cvL95joAx48xtS7ZamkkJJBYnCYyWCNo3BN1aMhNqjfMHaOKEERkSq3tCTKBUVnkTxqNCjjumfxiH9mGFdnrR5lsnR3aYSM91MBnr9P51o1VEqCMEoz0TwWFQ3qDb4zcqjPMGaJPEg6Zz5x8Llo8UBXhWoDggMe13tiZTKDPbJuY9JK14rh+c6YkpATD57fsHvqZUB1euZKSzy9njgfXLqndZ+4Vpo82IjsWO/d2sjkn8uoyjF44IpKUFbBsQLxPIZD9t40ETTeYdobNy2R9aDNdP1MiODKTyUVIGEoon2svgR24mguDbsF9Zi7lUolQ+QbKyBE9UqSMzGOGSPigkf2nhUWLhxciOsJYFNLMxFvcgu1wHZkqT/n2AfZ3cqaR4e5QkCwKm/tWAwnQRKvOHjuz8yv4q8FAZpHtkNVHgzHiibNRYGFATTfyft3VA+z+dxYlGzqVSrScFkt9eXzMyd0cRMcEJBwK1RT2szhBTh504BGrU2WzzP4n0tOLtXTZD2bzXjd0iWRPBcjbqX/fv0qBigRWzYz01lWVaZTG5N6eSfSN0/6wmUygrUR80HnbjnxwflRD6/R2p4EsWjQoORYRjHEnThal0WoCcWA1rcsv1B6VGegNRY9Kw08w8FRzamEyQw798sbYgZoHlULlBxnf9P8w0oTzBRipYMpwD0yGqgQmmYSGJEbI+DSGs7Rur3rfwbBrIQHz2/aEQLsnnacRr3Cm+pdROkotbCG1Y8QYXizYwrD/ieWSeMju3xufS32tBCMfRmadAPfEtiZQaELXvXiVJTsk9y86iY8O08HhUeuTViJEodacpvpAOl9IuI3xzbjJLG/kFq1MMHXx7lDyYATL7NSI6yBsaMtAxhykyrnF/vlQeMf8eQM9moRvbYOi2l2q2kA1ulPevMV6eAcg/fibrWokH7Bvfjsd1Ulmh44rvoGanH580s1CN7QZLR6TxpwZP564x/P/mztXYlYtS9UpfPSC9/ztopmL6Hd9n5T5lq7u3rjx+93Wq4JbaYyIeRKI16SfU6lOEb9KiQwLyasbIkmrTDJraTFRs5sSmOt1oLbJjqyaMSIWJ5AiRteRcheG72yAJKfI8KA789eFR4kOxRZRhwnVW6GFdcqtGckagbV9f7KxFXaajZzCeUHidGHTDzIWlHwijFsgT+QEv+x9r3E6gqE/CQYlR2GEg+Trld6nie7bMkqLT+fHCrtHxcfkJMMtH1Uunkm2MV/xxTGTDtZ/KvzMMijKThubcv8pMtKgNov2U9bY9baxDEYSQxxu+AscXB6OLNc6Rdq2yE7cYZtv7wi2oxxNptUevN/UfBs5+2oK6XSFVrl+W786iIYE9hihO+JS2HmTH72veTTHAqBExuJGmlaNekr+1xfs+q3OD7Z6oYLf8QxOWJ2i3M+8dPV6y48CSKR8ZsZPQGdjzHNrR5j0kL/yNtX3p8tSo1oqrS6JCq9tyg0T9uqO5n1XMeKP6Q9Chv0N6AYfKyF80subxw5IA06892MEMkelQOrHmb+dkFf16rhVVxqfIv+LcZdqIyYSw8k1CovG2aYfdtd4Y09iFrp5jzN2nrAqlxT6n7ldKZf5Zeuypm+JkAEmgSYkgUj+wGQT9GwoxoL0kSu3+zNOkn0pjf223oz+x8p1Xo0F7p/Z+aQW0i8FfpdIE/yyt1oa2qEcFMclo9QZr3uJFyx4YSJCm6QQ6jYOLv+n7dCDvIOL+WPOLXSL/vSCteN6VmMqDI/HSSFQ2SgX2RyVAY1R5N1cIdkbpdZvmOX4sVF55E8cgoUBlgVDFVB3qpV78jLXle2jLHKlZsYi54i9p9kalTXajVTModuFTbm83Q/mbrtKluf/WqPkYRvzt5nCBQZd2VYipAcZFTzcQlRw+nVmltW2DJrptE5Zd/1gODTpKJMF8SqvetT5Wm/V766M6YmWeOBXij77PRtJAo7KUnfdPMHMd/w0bKo1jh51vmSWMfkE5iasq74WsvIGI8sj/Z6PM183mCJE4FVHj45SSOc2d88YuXSu3PlJr0svN82yKbxLJ1bsFWHvwq+n/Hpmh4VG5wntVqasRul4ulrfPZ+6JaMG2F1q5dr+iOmqqxu5Xq5jRX3TYRR741H2CqExRUvnXHIxlaDLa9jTHtyQjiXSsLH7WNAm/Sjwp/PgoUFDO8CqViw28ZHhl7WCIpr99Z6vNVad8mkxAjBT640za5qrWsMla/k9137qK9Gj9+pav8L126VAMGDFD9+vU9keJR7iBZhfxLTDgxMGt1SupWm30bpIVP58vhWd8kw87XJ9cCRzwFONDDwN/RetHx3PSb8XlUPLAfhlXPUPb1+IK0d6MpS45NQ8mTFj8nHd4n7V5tP8KLomlfa91hRHKQyPLvytekA7+Umvaz6WZh03/4O4/K41GBgmTCd2y0ejJQjeUWhl0rpDl/jdsHU5DCbU+Tel1lJvQeHoCQDnKNqSZN+0e1se1KLf/YXNUHDh2uU09tpkhOxIoIqJx9COhRCIiVhtxkSrhPKRaUIarWlYb/wuwL/Nqs2PAkikdGI2jzwfSJWyp07NhBDRs21Pbt291txYoV6t+/f3m9VA+PY9ixWNqztuDPaZlgpGKqg9N5BbxqZAhtGGf8yYiUnTHSpNvlUs8vSeOviXkHhYApPRiH1mycpjfkUWGBjwQtOInAPLFBR2ndZCOhmYrCz2j5ovK/8N/590Vxsuxl8+9JrMIxUYUK7hFUgKigQnBwVywR9gFh1oO1ggx94zRTm5SkrecYCvE9Q+o+8i5vvOiRHHnRPB04sP/Y3lOzdg3lVIkRKB4eRQXToJqZ6vLVq0z9XhaA/Bv6U6nzZzyBkgnwJIpHpUG9evXUtWtXffzxx4pGo5o7d6569+6tatV8Od6jfEFlHoVAIuY8YiqRxGSTw5TWioE3mIIFCTxmeqf+2nwt3rleWvmm3bfdWGnU3dKIO6VxV4Y/D89PldiTKNkP1CJhrTx4n9DqyPpgHXW92AgR1h6KPgwaUTRBjOBVMfHGgo+Br06/b5tSZc6jyaf/8POg/ccj+4EiCTUK+xTeZcnItdKgfkfzTXEGoD7Z8EiCvDxIFJPHoTquUaPGiX5JHplsMnuSeYC9dV3621Q5fwf/UBp4vVTFL9OMgCdRPCoNOEB79eql2bNnu0N18+bNWr16tTp37uxbejzKDSS0qFBQCCSC5JVbIlAJ4GGy7KVYdfewGeC1GCbNfVSa98/8BJZxoi2HWv9u497Sug8LPh5qg91rLRHxyG5guBjW6gABgoKk0/nmgTLjQRuD3bCrTUIb+6C0f6u0anzBv6VtrFF3S5SZ2IO8edafUrwGDLx9u0WlQo3GRnLg1cSehGdZWhCx1sXT/2Dtj/7o9igqiZKbm6vq1av7eM+jxGDptBwunfu49O4PzV8uTOlZvAc1FeiwW6XeX7bz0iMz4OtCHpUGHJyNGzdWhw4d3H8fPnxY8+fP15EjpdEbe3gUE1Hp4O6ij+hG3nnKbfbvB7/I90LBFI/keP2U4xUAECxb5lrVv83I5I97cHsp34dHRoD+am6JwHTbERsR6Y1rjERZ+rwpUAgO+T0TCRJRs4lVyy59zaZhfPJX6fWvSHvXJX8NhbVaemRnsgGRAhk34napVvPSt3NhbNz5Aumi52IEio9gPZIUKtwtjzHsUR04cNCdt45EqVY9VJnn4VGcvY0C1flP2eh1fKBKurdxzjJWmz2t79c8gZJp8EoUj0oFDlHUKMuXL9ehQ4ecLwr+KM2aNTvRL82jksAFd0Xl7SJS9yvMK+Wj30g7lub/Ku9QfmKRiGp1LcGo2z75Q5eFxN6j4qFanXASg/GKkG/b5ptHTkDqkXhgHosZLRMrIO+caooK3DBp5G+kZgOkT9+Tpv1OWj+5kEocEui+Zfb2PCp4slG1pjTgeqn1KCPoVr9lyqdiPU4VqWkfmwDV/bPWPubFBB7HIWqT6XausCEDGKzvXC7t3VpVhw8OU53aO1W13lGtr9pA1Qaa4g6Sz68jjxKTxA2lIT80EmT2349qwas7dWBlXUX3Vy3kj41UbnmyKYYxx3Yxm1+LGQdPonhUOjVK+/bt1aRJE61bt04HDx7UvJlLlNOmqTbPirhxnUykYDIFVX6CtUbdcHiXGnazUclVYIr9ZudRQkBu4EVRFFD1H3STBYULnjhevcIIUSahdDhLWvRMvly+ZtNYlZb1Wz/5Y3szxsqz3lqPkBY/e/x42D3rjfyAIEkcG8vP3ajsODDp4py/S1VrS5NuthaNQzuL9vwtTk7Tm/HIPESspYyE4Zx/mHIOr51VE/BMierQvqPKQ0l3NEeRaGxcCsFpLalGA1Pc9bjSPKGo+Hr1iUdiUQJfp3UfSPP/ZcbrTKZzRQKWUpQ0p5e774GI9GFUmtHIJp90OtfGIdfvEF6M8PAoDKwb2q17/3ib5jV/QVpVX/X3dlKdVf2099OqrtjFGnX5RAOpcR87jzlP+TumiHpkLjyJ4lHpULVqVfXu1Ueb1+xSlSXdtPj5Tpo9Vzq6L2bAmCD1DHr5qeZi2tntCktSqdB65tijJMDQFT+KwiZXdL3MxnMzaWfvhuN/h3pg2StS5wul0+6XVr5hBzqTMVzSGpUO7w1/XNYt4789KgfajJaq1JYO00YWA8Tc3vVSg662t8VPi0KqTJVtzSdGsqAEGH6rVKORNO4L0uq3i96Ohu9Ok97pf08emQcSBqqunJ8HtkubZkX19qtTtHntduUcqKk29XqreZOWrkpLYkvxgvUTJLj+vPU4TtF51PyYPv6dKZwCdWb+ncL/GyUUCroNH0mzH5H6XmMqJ4pkfo15FBcMqli5coX2Rbcr2nabarQ+pMs/N0A6ajEYcR57H8RwMFYb+LWW+fAkikelO3gJ3g6900vV/tpFh9bX0uFDlLaS72aB38TuNdK8x6Ul/5Naj5SG/NgYZUgWvxl6FBWsFRIDnNhTydpRoWAyRrK75t1wc9hJP7FDGnPQnl+whHf7EmnGA7HpGJuTPHYze3yPygHWG4nr8pfzf8aI6/lPWE83k5ym/iZmNtxBOuWXVvFnWhRo3ENqPljattgCwY7nFnwO1uGnkxISl4jUaoRUr6PfIz0MQRIBkdxmTFQ5ny5TtNkGem110rlt1b1by2NePX7NeCSL41DBTf8/adbDNgWqRI+TZ3HdlDttuh1T7VoN92onj+IBX8U1a9Y4MgXgu5hbJUeRqn7KTrbDkygelQZIhjfPlN67JaJPJ1bX0UMlc3Ai+VjxqrRhmjTwOqn/d02m5wM+j6ICSSetNqlIlHanm/fE9PulfQkqlAB7PpXeucG8KSBFMJ3du9FkyqzHzXPC/w4pKT24HpUDtG5ByK15O06dFLUqLIqk3l+R2o62sdfsZZjdTb0rn7xDrcJ6pSXj/H+HP8eOZdKTJx9fDcaPhSov7RweHokg6di/f/+xVtvadWv6tgqPQoGJNS2FtCiWejIKseERm2L36hel0b+Tulzk23s8ir6H7d69W5s2bTqmdG/Xrt2Jflke5QRPonhUGgKFUZ1vfc+Mx4oqRU+F/ZukKXdI2xZKY0hivRTUo4io1dRMOt1aDAGV2G6ftYQ00csiQP1OUqcLzGNgw1Rp+yL7OcEfJo6QfRs/DnnsKlLrU83bwqNygH2pwznW1oNpbLD/MaHpvZulleNtTdRpaWQIprEkFYEKb/Msm+CTqkLLeos3TOa+tKNB2Hl4hAFPsqNHbZHl5OSoZs2aJ/oleVRgUOjft0maeJP5gKUjjst/cGnXKmlCbCJZl4vDR8N7eCQCAgUiBTRq1EgNGzb0Y7QrCTyJ4pH1IAFd9aY0/ptWuU8njh6SFj1tpp5n/tW7vXsUERGp2+XSkufNHDYRGCjSQgFBRztPGFAGjPiVtHyc9MbX8ityLQZL7c+QVrwh7QkZO4uMHh8VL1muXIA0G3aLESLx64I9bOXrRjKzLh1hl5CcQPYlI/ySoX5na3nEO8rDIwwHDhw4RqKQdHgSxSMpolZUmHy7GROnlUCJAy2wE2+UareMtfb4eM4jRuDZ/4n7YWxtMO0zaOVp3ry5atf2FarKAk+ieGQ12Nc2fyK9fV36CZR4KSgGn7V+IY2+11f4PQoHgRm+Oi2GSGvfK/h7xi/WbWcePGEkC4BcweATQmTkXdKqt6R67a3FDL+UGffHRtMe98RS10vMrNaj8q25FkPNK+fdn0hHEkyHw9ROJQUGtKfeKTXq7pMQj4JgrR3aI+3axOiKnGMyeG4eHsnWzIJ/SwuezFfIlRXwSUGhd9Fzphr1qLwgvsd0fcdyyyH4/4f2opyz6Z21Wh/W+jV7FHEmTlLHjh2dqs6jcsCTKB5ZjQNbpfd+Ju1cXrbPwzi9+f80FUDvr/rEwaNw1GoiDfietHFaQbIDJQqV/7XvJ5/gg7fFuz80Y1DaJhjVCFCv0Ga2cXrBv6nXTur7dZsM5FH5gDwd/xMMZDEfDsZip9t/ZditJof3aicPChmcj6ifNs+W1k6Stsw3P6iDBxrp6L5LlJNzQNFmeZq6vorajbbpUExK8fuUR4Ddn0of32O+TWWOqLQ+Nrnn5B/7dVgpB1BstZbWhU9bIXb3amtZLYDcKspteaZym2xSzcFrVWNHGxfP4Svm84DsRyQaaJA8PLKwcjHjQWnSjwofJZsuNOopXfKKTcPwG6hHYaAa+9Z3S1ddwwMF1QoGoRArKFTCkmO8UEbcYUGhT24rLzjxCfI+vttMi9OZlFCZo8UMos5PJajcCMiTLXOkOY9auxjVXPtlwn0VNWU8xdyciBr3NL+n3lebas5PwKvcwNOONp6P7jred6mswbn6uYkWz3lUjj0L5S++YTMfktZ+YHtYEf/a7V81GqL2jWjQD6SGPbyvTrbDkyge2YmotHOV9PwF0tZ55fe0JKdUYYf93FcvPApHNGZm9/Ll4cqRtCFH6n6FdMYfrdXCo3IjSHAX/seSk1Ir9SJSkz7WwoOBbW61NL1Qj8zd11ZbSyEKzQPbi+9hAelbu7nU7ztSv295v7HKDNbSc+dI2xaU7/PmxAoPQ37i115l2LPcuOtfmfqESYclRo5Ut7U09BabiudVKdkLT6J4ZCVY1Z/82bxQyrp/NhFULT7/oSkDPDyKslaRDr/5DWnL3PQ/PlXctqdJZ//N2nkCMzQPDyq8JCbTfm9j25l8UaxkNyLVaWUmyYx6d6oBv74qNThvV78jvfdTadPM0p+/KO3ajpFG3S017edVdJURi56Vxn0+fC1hXA2By61KbWnXSmnHUlNkBj5PtIYxzS7V2sGYfev8gq21TDTDGwVDdo/sjcHYq965Xlo3OX05A/6IqDKH/cIUKv5szD54EsUjK3HkoPS/c6Q1E0N+GZPccbDiwL5vo/Xbhsnaq9WXqoUYxXLR0B95OKRHEtb5rL9IPa/ym6ZH0cAuTP/t29+36SnpMvmkktbhbGnsQ1K9Dn49eoSDCT0bPrapF0x7YkKFG1l8NFwhUL2u7Z34nnS91JJbr7zzQN20+H/m1ZRWI/eImW2f+WdLaj2RUrnOxvHXSHMfS94+2OPz1j4IEUL8RUyHfwp/w89oCxv7oJRbPfnzoEJ4/kLzFItHtXrS596VmvVP/3vzqCDDJ2ZLr3/FYq90I6earb/R90nV6voYLNvgwx6PrARVCKoRiSD4aneGSTSb9LYqPX4pmN29f6u0aUb+fUkKTv211OuLcePNYiDJnXq3NO3egs9BTyXkDUafXtbuURRwsLY6RbrgKavgksgGI4tLY/CJDB4pcs0m/vD2SA72KdZfy2HWjrh5prRk0g4tnLlSuXlVVTO3vpo2bKnaTXKdT0CzAVLTk6RqdUy67NeWB2ckU+pQf0LCpffB7Uwf/w3pnH9IrUb4NVdZQFvF5jnhsdxJ35T6fsMM2FEeY/7JvsSZx8S6HcukVW/a30/7nRHAiajdzApeGLk7JV4CKK7REu5JlOxt4Xnzm2VDoADGctPSSAw2/BfeKyzb4EkUj6zcGJFl7gsJ5FqcLJ37D+nwPmnqPdKOxVZF7XW1dPbfzUMlqKBBsDTuZf3ca95JeI68ghWLeGyZJx3cYWoXD4+igKSgQVfpnMdMvvzJw9KmEqhSkDeTEA+6QWp3uv23h0dR1h97Xs1GUtuxUe1vs1Ez6kyQohE1btdBY867QDVq5XqTT4/QM3fDVGnSj8uAQIkDxrTv3CBd+F+pfoeyex6PioO9G6SDOwv+vEotK3DRvjPh2/lFMxR1tOSc9YjFdZAoFMfiC2TxhYbT7rdEetJPbGJUAURSx3oemQuM+PEEK1M/ulirGEa1TfpKPa7052c2wZMoHtkHTO1WFqzkk0zSt8/B+ea3pGXjoIktYaUKcdoDUq+rpKm/tcdAiUKgtvxV6e3vFe8lYNTI5BVPongUBxyuyIf7fFXqcJa08g0z/9y2yJKTY2s6OIRjCil6wamoNRso9fyS1GakGcj6w9qjpDhw4IDyME1hfVXPUdWaOb5lxyMUh3ZKH/4iXP2ZVuBdMEv66Nd2XletVcbP53HCAYFyZG/Bn+M5R0shJMlx6w6Psal2VhK/4akTNmEFJUvPL1o74js/sDG2oYipFTyyj/jFB2zR0+Xjm4iiCuV6q2G+tTqb4EMij6wDG+Le9eH9s836Wf/j2g+NQAnuv/ptk3JiYDfrYQsKazaVqteXtpegCkFF42iCQZmHR1FBgFenjdTnGqnXl03KjunsjuV5WrhgkQ4dPKTcnKpq1bi9mnaq7Qw9qXLUb+/bKzzSR6IEqFatmnJyvBGFR0HAs81/QlozqXyej/N64VNSl89IHc/3e122AzIkrLUVhTBGoMfGZsehYRcjT1CxJBtR26iHNPgmG2O75LnUis+DaRwD71FxyLnp95sapbwAAczedfJPy+85PcoWnkTxyEqGOdFhHWAqhlEs4/IwUowHBy1/g3kdff6QKEEVg7agjudK9drbhkurDpthQMIkew2HPYniUQoEyQF+FUweaNxbOnokqll/m6Rdu3apatWqOvPSy9S2XW27r08mPNIIT6J4FAUHtkqz/2JeYOUFzuFZf5Jaj7RCh0f2wrUPVglfdwv+XfDnjXpKw35uMdii/yR5zBxp0A9MrTntvsLH2aYypPXIPLA2UPlumV3eTywteFLq/RWpTksfs2UDPInikX2gtz9kZSMJ3bfBxrwyXSJ+sg6y0NrNrRoBcQIgTZy57G/MVRuihZFlBHALnpA+ust8T5IhN/Y4Hh5p86zIkfKiRxVVnrvlVIn4SRUeaVcWHN4b1f5d+Uxz9erVFfElf4+QZIR21+1LCr8v+1SNxlbAoEiR+s42UhbT92RnLObttGC0GVWil+6RISDmKoqvF2Ral0ulIT+UarcycgSD9jBgjN3xPGnxczYVLyUiUq2mJXrpHhUU7EFL/1e4CoV1xzrZtUraOC35fVhPDbtZjLZtsZnUJntslFNr3pZ6fNFzKNkAT6J4ZB3YyKo3CG+xoX920A/N1f2Tv5r6BPJkyI/NiyLewAxihSCOVopZfzRpaN02VsEYeIN5nuCfEiYXxYE71xt6eqQZeXl5ip9K79UBHqXFkQM2EnTTTJtyQUJ8cEdE23b1V+6hjlKNg/r048aa+YlNRanf0RIWT955HNknLX+5aJPEWg6VRv9OWvGaNOWO1PdtPtA8Tz59V3r/luTPvfQFU6N4fi97AYFBESsZKHQxVezkm6XWp8amOF0jLX+loOI4uD++YRTLFvwrebtPPFAoe2QPDjIs4t3C70eL9Dl/txb/MBIFi4CRv5G6XWHrCnU6hdglL0jv/zR82lMwvZO/8dM7Mx+eRPHISvln/XYxqVzcaGIIkRl/MNZ48A+l7p+zVh2SAqb14MCOUV1wqFLFIKEgkNu/xX7GRorJ56XjpD5fsdFlsNSJqNPam955pB/xBArw6gCPkoBlxD63/iNp/r+kla9Je9bHfAHcEmNdNVVETdx/rp8hbXjBgj4Slm6XSd0+Gxud7cmUSgtHvhVhNCgFiVPvsrWDt1MqYMbOfZkwFna2xuPT96zi60Zte2Ql8KajeLVlTnibTb9vm8cEo2Q/+o007x/hyWuAeh0t9kPFBHFclHiSZNoje8D3nqyFi+8b9VPzQdKwW820PwyQJkxA7PM1yxXmPCIdPSz1/ILdUNC9d3M4Sffp+7ZePYmS+fAkikd2jortItVoWHBkHe08474odbvcxh2jGKFiQTA2+l7p0O78qhqHdtjBzSQfxuh1ucSYaK0KNy2rXq+M3qBHpUVeXtQlwAGB4pUoHsUF62fbfGna76ySf4B2ieO5uThE8gdBHTXlHuPekcDPfkQa8iObbuHHaFdOQLwxCS8VSDZQejbrX/jjkcCg8mx5ctGen+IGz49nlEd2IidXane6tOL1hH0qYoqS4b+UNnwkTbrZCLrCJq1AANdqIn34bNFMRVGhUGjzyB5sXZBcPXfSN4yUI3+ATEkGjP/xNtk6X5p4Y/4+iM8KvjyMMp7+f9KeTwv+LSQfexeTQj0yG55E8chKNO4j1Wp+PIlCxZSxeGDu36U5jxrhQvW1cS+pQWdLKmjpQeoJEULSwLjiROd2V8k9lGR0Xq7Jkf0G6VEasMZYizuXGWnHbduSKjq05Xzl5BxSXo709qv11fIkq/A2Pcl8fCAGPTyS9oK/IH14mynvkpMnhTzOQavkTrhW+nSSGTnWbZvuV+tR0bFjWeqpJkwK64pq6XKTxEOmJEVE6nyhjZ3lvqhFCwPnO0SOJ1GyG5AoiUUxTGGZrsMaHP8taffqwh+HpLjlcJvsg8K4UESk1iNMSeWRJYhKe9aaMj0ZwTL3Udu7GnU3xWUYiPHrtJIWP2vDKuIJEjxPBt0ktTvNlJ6J4LnZtxh17JHZ8CSKR1YCSTDjiuMTBUiNkb81aeirV9lG6qr6Ean9mdZ3u+otI0b4+3P+YX/74qXHH9B1W9sGSqtPotIF8DidLvQyd4+SgaRk/1Zp2YvSwqelDVOlQ8dGLOYoqvbHOtXWzZHWvWW/YV1jstj7avOuQB3gu308Ahw5KM35q/TBL1IbYhcHrEsUKQSRYx80BaBfc5UHJAycc8mIFJKQU243tSfJBsqlZGjQSRpxhyUgGLeTIBcG2nALNan1KDMQP6H8IGYK1gDrgSKUm6qTpr2gQZeo2pxxWEufrSpF7UEpHEDcQubSUhGG3WusYBbEgLQGNetnExZTtfzEky6oXXzbRfZP7wwAuRYQbJ0vNuVSGJoPsbW/cXrBSZ0bZ5oCj/MwDFwXqN49Mh+eRPHIWiCnYyZ7kDAg3dy7Uep+hdTry9KsP1hlFvftQTeaP8DK12MVrh22kfa7Vhr6U2nGgybLq9/J/psJP5N+Yo93PKJqfsoRNe5dxW3W3rPCo6gI1E3LXpam3mPBIf+drL0i/w/zA0ZGPqI06HSBGe2hTvFknge92iQT7/2s8HGexUZsXOSEb0vnPG5+UH7bqxxINdYYc/dT77T1hldFlRQeYZi6n3KHJeKT7yi8JSNAkMB7lA+CVlKSUFqdN86QdiyV9q63+IqzBuKBPaBBV6nFIKlRr3wSorj7Ah5gmKmvWr9c24bPkiaPktY0c+cgymFnKnuy1GJI+N+vn2IeKcF6YoIKqhLO2KKQbx3PTf7YHhmK2JTD0sJN84xK+zcX/B2tOqx1PMMSvRkdyA1yS/8aPE48PInikZVgA8MYqsO50qL/2KbFQTrzIan5AEsw6WfMO2gHPmax7/4wP8EgeZ16t/U99vi81OViCxwIENiAp98vzX2sIAMdabJLmweN14zZ7dSnTx/Vrp2iqdLDIwYOY0wanTHe4/HKk+I+kAWzi54xUpB+8R6f854VlX1tYRz7wS1lQKAcexKbODDpx9IZf7LpPR7Zj9wkrYOckSd9y9QCb3xN2v2pJbDJ7tvnq1K7sdKb3zTPMYoURQGJCKoHj7IHBBffzbKXpIX/sf/PlJNkbRE51awFh7ZojDYhJIinikqkQKBs27ZNU6dO1aJFi3TwwEHljK2inGfOk/ZXd20SGHpGCpk8Fk/I4eX0WC973Snb0GLKTlrK/NmZXWD9uVb7MHKjGOAxXMt1SKzmvHYiZnzslHohpLD3TMwOeBLFI2sB4THkJunTd2w8MaAtZ9wXpA7nmOM6E3Q2zzEFSqJBHknt+K/b2LwWg61aRv/t5tnmT1FAJZCbp8ioWdpde7kmTVqpZcuWadiwYWrbtq2qVKniVSkeoeAgZl2+c720bJwUPZKOB7X1/M73TUGF0spPi6qca4tKMR4o+ACU6XMx2vF/VrkdcF1s5KNH1o+fDUtG254m9b9Wmv1XadWE1MkKrYcDrzfFAOOPi5PYVK1p53K6VIBI7GkRCtpTMDWFKCJhYgJQpVT1UW3fKi14Upr9F2nb4qIphfg8iaG4rftAanKSNOC75jHB55ksHII8OXDggBYuXKiPP/5Y27fHNi4q+/3Xq+qiHdozpbkjQrgVB5DIO4tAJPOdc2YS9/mwLftQv72UWzV8BHZRQaGLpRHW6lWFn0VtPwm7ViAYIek8Mh8+zPHIajTtZ07b7/1UOrLPfkYvLKOJi7pRrnjVboWhfp/9yjvlUx0+GnES1DVr1mjjxo3q3bu3Tj75ZNWvX98TKR4FFSibzKATIq+w6lhxwSGOuoVlN4iqWvX0Pr5HBUeemXQWZQxtOoDh7IwHpPZnSU16l89zepw40FKBAWO8IrNafenUX5spNuokTNtBfUwUI2YISgEDyTvnKz4orBtawlAtgDoYeUasJYj7OgPZtQWfn8cqqelnMOZ7zzp77vWTrUjCWGVeO8kPyTRm9BhAsp7bnWHtI/ieubaALD/OOY+2zrM2QAiuorZZJQK1yqYZds6tetvavDBBj//8gtYdYqYPP/xQK1ascP8NcnNz1a5dO51yyimKDmqqN75ihv9lAdRNfb8u9f1GTK3gkXVgT4HIKA2JAjmooGUnAQy1cN52Ia0+gGlP7JMemQ9PonhkNaiGMrIMg1mm8RT0mEgPareSzri3lhoOuUDTpk/TnDlzdPDgQR06dEizZs1yAQFESq9evVStWrW0kylBr3Iisj3Iy3RA7NFmsWp8+gmUY8+xX/oIQ+X2Us/P+17cygL2BAwUMfQsjWy5uMBkFv8VRsZXysp9JQIERv120s44FWf1ukaukCRc/GL+z53RaI5N4Gl/hjTnEWna722ELIqWzzwfd1/MSauY4TvTUWjdmPiDgq0jGIU6cqYEZqgbppn6xU3k2x6eUCHLP7DViAQKKRCSmOX2/prU/XJ7/mw9YzmP1r4vTfiOtBWD/jScT5BltFfvXGptf03d2GvbnPbu3auZM2e62/79+c6fDRo00ODBg117NLGTWkV0+sPShG8Z4ZU+RBWpQmtZRKfcZsojj+xE49625xRFlZQMnK3sUaxh9pB4oLpiP2GPCUPb0d6sOFvgSRSPrAfVpBG/sk0N1/9k8+FLirrtpNP+j57uiHJy62v06NHq0qWLpkyZ4tQoR48e1Y4dO/T2229r6dKlrsWnVatWrrpSapfxfdaqhNyWYM85fkesUlazsbHkVNL4DLI12MvkIJXecsxgk/WVpwvImCffZkaz3DyyHySKi/9rpo9JQX94bQvo2DuKYtKJR0De0RSEdNSmsdDO4ZQKHlkLKq5NBxxPouzfJr31XfMDiAfn0Ig7zZcCko3CBqbvtDEm+k7UaCyNvEvaNFP65E/S9qW25o5DJKpWp9AaEinWmcn1gFoKYsa1+UaL4Te1x6Zx0AK84F/S0J+Z6irbFH6cTXxPr38tpviIpndfwq9r/Dels/8uNehxRCtXrnTx0oYNG46pTyBMunXrpqFDh6pRo0bKyYkxskxTPEM67wlp4g+ljdNKrpA5hkhUkRbb1OtLORr10waq0dAHS9kM2snw6IEULSm4PlDHdTjT2tyCcxbfn07nWRs1azMRtB/S7uiLWdkBT6J4ZD0Cl2wqo7WaSLP+VArjzvjHzZEa95FG32eHekBS4H+C9LR58+aaO3eupk2bpp07d+rIkSNavny51q9fr379+mnAgAGqW7dusVUpkEFMYsFhfvVbNmoZT43EsW0kR0hmqfThAeOM3VrJVVs8oVIBvCqWm0FxqnF76QRS9em/l878q6+CVAZAiix5PrnCCRVBn2ukNqfa5BTaKzBqZKJZ0PqYCPbRMb+X1rwrzX00+XNjOrn2Q5tm5vea7AXV+k7nSyvG5Ss5WDsYWyeCaS3Db7fzCgIjAERfIhhdS5sP6yj+vvGI1N2v3V3masuWjmrYsKErSqQ6S7kOaCmZeJOtzdJ4T0EgMvnltS+bnwvjmFP5fGQa+I7e/n76CZR4kGC++b3DanjDJC1cOdupdgHfIbETxabOnTu7eCoReNXgpXPh09K031khgpavkrzWSM3Dig6Yp6MjP9buQQ0UqXUReqp0vEWPCgoUJF0usSKWWzclAG2Ai56WBnzf2tM++YsVL/nvRj2lD3+ZPxk0UQWDui5b9orKDk+ieFQa0F99yq9svvvH91hFqaQVDJKO7p+Vhv1cqt+54IZIIFCjRg0NGjRIHTt2dA7zCxYs0OHDh51UlaoLxrNUWbp3715oAHjMHX+5bdaYvNFvmUrBgBR563y7kRwxhajXVdJJ3yyeS75H2WDxM/bdlCeWv2rBf+uR/vvPdlC9T+YbwPV/7uNSy6HmlwIJiyy5w9nWr/3hLwqSL5DGjIPvfqUFkKmAomXtu7H2MR9lZDUYp04iu21BOT4pyoF+S7V473ta9dQU9ezZUyeddJKaNm3qztHEsxTS+tP3pDe/YQRBukCSNPW3dj1QpKHKnNH7KmqbA9LkX8V8lMq4DXDDlBxteTZXh046bP4SNWu6AhNxE5MNU8VE/AoVMEWsbpdbexjG7BTICo3rIpZIN+kjtfj8Bs3Ru8rLO6BVq3Zq/vz57jUcU754ZB1YO21GWhy0LK7lsADyLMYOK0RApH5MYbal1P1zUo8v2M9RuqNMwVQ78e+YJNbvm94PJZsQieLm5OFRieAkveusegETvWVuTMZe2JXAId9YajNK6vdtOSkxZEphQROXGCoUJKsfffSR1q1b534Gqlat6qot+KW0aNEiadDAxAAqxARsVOdKSv4gIaSne9itNra5SpIRlR5lC6r+T49OH4lCuxZjZVEfJFMROESk/t+RTnvAT0/Jdsz6o/TOjQXbbvjeaZXod6009S6TNLNmareWzv+3TQ149nRrtwCQKoyLJ+DsfoVUq5kFj+/dnPr5MRT90nS/x2Q78o5G9dFD+zT5p9UUPZB83jDKJ1RMn06yFp1UQPHEHsUUvBn3F/x9Tp2D0nef0aGG+WxerVq11LVrV5cAo2QIkmCOWh7n9a+UHdGDso+RzhjqOtPZDAVJHyoiRk271uCyf0blNNqjnGufV6uTamr48OFq2bJlsVudXWvzfmn3Kmnlm7bGIIZpc3ZtikdskhNGxPjYQJ6gzG0+WKpa/4jenDDe+dgFHiyXXXaZmjQJcQz1yBqwZpga9dJlZu4fBtoMWS8Qc2GqEnef2lLz/jbEgnVGcRa/lKMHEu4YsSIFbWisw4wmWz2OwZMoHpUWBAx71puslDGM9DgyBpSkg/5rNjkSDjZSEol2p0ttx1hyUBTypMDzRaPas2ePO6wxm929Oz9Koa2nf//+LgAkGAzIFK5OvE6m3CHNeUw6nKbABiZ84HXSkB9nlww5E8B3yihYgnp67ANQVRv569QjO1mztORQVQ0SEwi9dmMteIdsW/uBJSnJVAj1OkpfnGLJsEf2rrF3bpBmPlSQHEY5d/kb0u5PpZcuPV7O3OPzUrfLpI/vM8USQJ485EdWRcP7gfVZFBKF9XXVrNikFY+sBH5fn376qd59631t+U8XRd8eLB0t22Z/FFHdrzqiFt+Zp7kLZ2nz5s3udQRAwdCpUyd3nqJMObi5il75XMSZpJYliBNG3W3kJO0mmYiDu6T/nWfJZbkBb5uLduj8v9VQ3cY10mK6f/SwxU0kvpArxHN49NB+ht8OZ2XwNMRlW7du1fPPP69t22wzRNV0xhlnuCKXR/aCFkRU6cTXpZnUUxTQSn/hs1Z89cge+FqkR6UFwRgbW52LpM4XmRqFsWWoBI4cMNUGviIkAbQCBePuSnrGExzUqVPHVVto8UGVgtEsASCEyvvvv+88U2jx4fdUYw7uiLge7oVpNh89tNMSoQM7rCpdrbYf51deoBd/3eTjCRTAWmt5iqmdQg1Aa5kCad7j9iOqGec8ZgqBDVOljTPM/2bA90yq+vJnzTsnEQSXuMZjfuaRnYBsS+YR0KibqU1oC2SUK8kfex1ra+HTpniLB54VEMwAEpn9oihgv2Iv9SRK9oHEEw+LTz75xLWm0qIaGb5VuaubS4val91hErHJF6f+Mlf1OpykXid10+LFi91Ely1btrizlCkvFCqWLFmirp26K3fCSK2fwgFXtiBZn3qP1HK4KbcyrTAB8YrH2uZU49ApLOXmTzgqDG4iUyRmCpysXBuVtk5qoN2LIqqbpgQzt6oZGXMr9DVGImrcuLGbADRhwgRnbLtw4UJHxGFsm+5Jih4VBzlVo+rx9f2aM2Wddr/ZQdFDZZMSoxI+9TdSi5PL5OE9TiA8ieJRqRF/PiLJxdCOW9k9nz0hrTvnnXeeI1EIQqmmEZiuXbtWr7zyinr06KFB/YZq9q8bauF/ImUyvYXeTfqIq9ez9h4vuy8fHDkobZ5Z8OfbF0tPDA4fC0sL1jn/sGkVq9+xn9FaQVLLpIkpd9rj8h0Ou0UadKPU+yv288TgFfIGg0XkzD4+zE5EU0zPaTbACA6mB0C4YQzKnsdoYsYhY5YXL+Wn9ZFb4KVSVJ8EEq0CkmaPMke8tpjE3lXjD5iqkmDetZvEXfclUVQybW7SpEmOqAhUINUa5qnbdTu04hftS2zWWBgwSh97v1SvA74n5qGBagBfMV4LpE7QLnvgwAEt+XiT9ESVMp9+FoBpHdPulc75Z+ZN7CEeWPaSeakVQCRmUH+W1KCLKT1QOjpj+8UF7w4xy9QiPJdQutJms2aiEf0F94+IGzG94rUTV6UnLiPmoohFTAZBOHnyZLVu3bpQbxaPzAR7BOTvpKkTtGPEMuUeGKnI+/0VPZjetBjl5vDb/p+9swCP7Cy/+Jlssll3d3fXrm+7dS+0FClSKH9apEiheIFSWkoFaIECxbUCdeq2Xem6u7u7bzbJ/J/ffTObyeROMrHdTfKe55lnJZOZO3O/+93vPd95z1HgmeIt1FUPfkodjrMAbspE+GGIx4167ty5Wrp0qY4dOxbcwNlJ2zI5Tcf+dqFys2pU6MJp3iPmGI6U39cKFQ+K232rwgtfFpOJoPAZ9nVb3E75tqmIWNTSg0uLxapn8oteAg5QDtCCQbwsO3KJMlVUChTMKK88padqAiKOsZGszYYd4oG3mRIPn4g9y6RmfaSJPzeD2clfLzsBwlTi4+vMIdb6icfXlqnStql2nXMeg/bUNDsfdZpLrUaatxeEGobjqbafQJhQaEKgoPyIoUWLFho3bpw6tO2k1fWkKd/JJ97KBRGbzyb9WmoztuB9Kmbi3q9fv0A9sHHjxkCZsmvnbqXNHKis/WdwEEalDa9J2983grsygVZmFJJhgGjFz4b54thuU1PSSorSkXO95tn859IuM+Fnln4SeEkctDHGPYkWwESlWwwbX5dGfb9wNPaZAmMIlTAkHOswNrZIVmRcl9SjxXFuI0ayvvXWW1q5cqVy03KVNmmaWrZspr3PdVT2sXJYCEfsehn9I6n3TbYWc1Q9OInicJxFsABs2LChJkyYEJjisfuxefNm5R7O1OFn+yh6pOId4lEmzL7fdo08krTiwS5eGFkSBgqffp+2guetL8b5nETNNA9CpGlv8/WBhAlit/vajge7osl2YNkpdhKl6gKSBPIt2c4YhQotPa/erMArgrHDtY/aiQSv9S/bo0zHkG4tZ46KJ08wRlz9X2v7JFUlVE2QB5QDRPwG5HlvS9dBtdaoS/Kd0ljRQVE5b9684O+A+FlUIGPGjAkMObmf9bnJiDoKZgwWS2uCHgPHRJzt+J9JrZIo9QDvjWqAjQnIlGWTd2jawy2kaCQ50VjT5tBkqq3S+opAFLQZdfYIgdIA0ivMRwv12egfGjky+RumKOG+ErT23SuN+ZHdf4LW0TRp4Ods1x3j/rk/l07ssfamCx+TRnxb2vS2tU0Xev8d0sGN1m54NhCLVh48eHCwDqOtB2UTxv/t2rVzNUoVQWwue/vtt4O2Lc4zJtS9+3fXmFtaav1oSxs7tDE8lScVoAimNZtrg/a+yuqR5CgeTqI4HOcA2OngRn3ttddq8cIlmv2LHB1djXb+zNy4dy+WlvxZGnO3e6NUNFD/pGrn3aSPNPSrZvS37qWCP1v2d6nrVRbbTUG8b6VJrjGa3b3Ifp5sEYA6pbQLBMe5D9b7yO5RowTJY3EIiLWoFXpbp+SPAwqoRb+VLvu7FT3I68sScVqrkaWsOCp2Lln/qjTzXmvRS7ltBU+LbGnPYiM6IF+GfEXq/5nCRuMUHbt27dLkyZMDlQdFB8AAffTo0UE7DWRKrMiEwOt0qdS4pzTnQWn5P/OUciUcS5AcJGMM+D9rO+PvqdSxHEdmZqbS1nRUVpgaJiK1GGTpdBjG8x1SvBN1undpwXmxVmNTaIZFkmYflZb+LSS1I5pHFOy2ebmygOjnsHtCyyFGzEOI0P4bG2PMFyiZ+n/aSK6VT0gNOki9P2ZqKEi047vtuetetvVFt2vsOw8jUbKOWKLO2SJRAMU0JMr69esDRQrF9vTp03X11VcHrWOBF0y2qWu431Joo/5iYwTPMjxYMO6GkCQxjzHs3Mu5hZMnT+rdd98NlN/MbcwXEK8XXHBBMG8MvC2iDhdaTDHpnYzhlOZV/IIypOb9pX6fkXp/tApEnjuKhZMoDsc5AibzjIyaaps2SLPf4259Bt88ajtHLKJZ5DgqDqg/Urmx8jziiInQm/NwYSPaA2ttN3nig9KoHxgxgmQUbxSez8+TvnZG8h1dR9VAq+G2i59IosQWhbR6JBZNBzfYzzA3ZnyURUVAy4j3gFcMKOYo5NgxXfCo/b3UoL1vozT120aqjbtfatzNfnTq1CktX75c06ZN06FDh07fp9q0aROoJ2lF5d+Ju/QBiddZmviQ1OcTVpAQO3twfewDJDmWiI07PKDw1KBAh4yhOClZMRLRxjdC3idixPMFj5hKCvUE10fPG6VBt5rSAk+g2HUBETnye0amJF4rXEdrXwyPPuVzMv9WJhIFYoPvOPErixFNO2YWLCb5PohBT8trEQNNetnz37/byIX8J0uzfirNf0Q6mSRhkJh1jKjPJhjHkIO09eBNR8G9adOmoODu2WqoNr0dCXxjMHKH9OF7QcXE9ci4RYEQfB8tpU4XSZ0vN58XTOO9mD77gBSDDF6yZEkBAmXSpEkBgRLMY/j/9JDG/VQa9AXbvMKH7sBqmy/i/cIgjNkowKupWV8jZlGgodry9VX1gC9xHI5zCfRUv5qmo1vDf4wpIIsUHix2DqyxxVpR/gVM5uwUsUghwz4ZaP9Y9z9p0Oer3w0gliTCQpJdyeBmaTVDkMzETZJdNhZHtZvYzbO0YJHFDkVRknvQqIf1la97Udo+M+GHEannDdLYn0iHt0qL/2ALWpQotP9gLsuYWPR4eCHMZ/ICt2qjSW+T4rP4iwftHowfIrUDoiSuOMS7gHVkQLDklK2VhxY0/nRUjPfJ5DtNQUIhVx7AfHb1s9YaNOnXUWV2OqIZM94PCg7IFICPV9++fYMik6S5IlscImYwSptoy8FG1OxaaO1jexbZfBsU5REjdZlbWwyR2o6VmvUzAqK0cxSFTpjvFGQIhDOkzBu3mW8Mx0DC2fk/l877XsF2E5QFSPHxCKJwjkdgzlyE7wsm4O0nqNKA+1GYQjKWzpVoIEurUusRtq5gvgAo2PheUGl0vMgemFazRsE3hbknkdSNAd8exuDZBmO6U6dOQXG9YMGCILFl/l+PaOmUqPYtiyQ9fubLrLyfcX3uWyYt/YuRyUNul9qfb2oVx9kBhNiUKVMCv0HUdPEKFPxw4ucy/somFj5MePmghju609qgmVvY0GJuQrVH2ywqOTYefE1V/eCn3OE4h8ANmt2tsJ06ih5i0rpemTdZR6RTh61tg52feIY8HnXbSFf8U9o8WXrl40W8d5YtIPvdbDsnVR0sGEmv2L3Qdh+3zZD2LskjN9iQyLunBgvLqN0sWdyzyO/xIalpH1tIlnSHiYIBsuPo9iKeFJH6fsLeE0lpIknGzh/ye4rg1z9rRoZgw6u2m3zNs3bzD4qiROl0XgENmeOouoCo63a1qZLi55Mds418hYQjjYcxD5HCjlr/W6Rje2weKAsatLfrxFH+YJ7HYHr5P5IXpKVG1EiOVz6bpRqffU1b960LdmwBnidjx44NPFBoP03VIyIoSDKlRt3t0eN6+3+Kb+bfgGypUzAdrqy79sytp44V/v/mA4xcXPArI6Bi1wVEAeapJJ7h53KaRGlrJBXfCfeJVEFBDaldmRDcDyLhmys8EucWCkvac7hvcs8BgfImKnW/zggUzi9zC4oMyP1p35cW/9HUT6EeNedIRcL4HjZkhLYsPKLDL/TT4YVdFT1Z8kGJQgzDXEgo2jtGfiePvHZVylkhUCDFYgQKHoQQKCiPiprLInlkcMNO9kgkGv1cVm+cI1OWw+EA7NYd2lD4/yE1JjxgixG8DNY8b8U4/ccDbrWF9Yx7rV83BnbbkCWS7IKTfirYPsNkqlWZRImRIiQR0A6D90PQKhN/c4wWvlmyINo6zRaNKDxYQA76ovXXl0S5Q680CSjI25OBHRBen+QUFmCJyGxsknsW9omLdXYBdy2SOl9ii91EEqVmXanFQL/5V3VQkHS/PhqQrMd2RQrMMbPukyY8JF39tJG2XPMdzpeaDbCf4ZVRekTV7oJcNe6RVq5mjMl8hKrTOMZ7YeFvzIuj3AmUOOyem6EaDTpL529UWkauOnfurPHjx6t58+alPqeJv8Y9pqLuM9wP4++FMTCENr5mCTrx8z3HFqRZRfNbVlAbNuxoRrGoc2KtRoFyK4VWW4y/KxNQWBb7uSJS6/Ok4XdKnS+VdsyRJt9hO/QA0j+jvrVivXen+cygMIFQvei3lr5DWxCeXYkgEjqZGfaZBPNMbnZE215opBMPX6OcnchOyzbJsL5ALYpXDAlTJOtVp3nrbBMoU6dODRK7YgQKZsEXX3xxsQRKGPy8OeLhJIrDcQ4BCXKwO5eAViNspwxlAYuWmOoEIuDKJ21XaPGfpCNb7P87XyaNuUeq1zrPkC/F9hMWQ7xG3ZaqsuAzzn9UWvT7YtQgRewy8hpL/qKg737oHda7z+IxlRssxW3rkVEtrs25Dv8FzPxoIUJhlOiFElMNMQaQkybu3nGukaKy+M89GU7AMJ4cVRuMxWYDc9R40h4d+08z6VTeQMkzlaVlbejXrOChZYF0jLe+YCkvyVp5IOQ2vF5Y2p+PqCIdd2pH7+lav2FgIIsvSzxooBY7ZsfK8dECEmuz43pDPcNcRctAoiFqVQPfBQbTKIvKM00mFDlpyp02QPV67FP/G2tr2PChhSTv5zIgUMJMUvn+SJKhbQRChLkQA2TaeUibgVyJtegEJAppdWl2f+U5qFQOrpVW/dc2MlCCJkOFn6NyBv4vRZEoxLWifiSulc82637zuilwD2V4RKVlfzOPtRiRxffK/Za4VzxCwkgUrudzwUOGY174mDT9hxGd3F9+JRLjkQ2Yl2+SLvmTeVZVksup0iIrKytIWoonULp161ZqAsXhSISTKA7HOQSKhTAShV5jimX8MeLbdihqWBi2Oc96z1fnkSgn9pvSgd+hyOh6TerHwE4171dhuzx5BABya/7OThVFHDuByLqDCNYUzVdL+t64/+MlQH92mXdyeb0t0tTvWP/zmJ9YUVfUcWdnZ2vfvn1an7lY0Q69pJVtCz8pIrUdb98PyqCwYuD4PluI4iKPYmXFk1ZsIptHUUAUKATbiRDTQ36HYsBRdUELxuHDhzVz5kxt779aWj5JWkDshQ1OdtuJKmWM4EURkCg7bQwVBdrGnr2yiCfUPqXohTO0M3u1Xnppc+CfMXz4cDVo0KBEC9YgMWWDqWQowA6tt+I2cW5E1QVR3KCT1DHvWiAdg13tqpYyRpvhrJ/lJ55UNKLH05X+2kT1vDVNtWrRvqNKgyAZJYS7Y86PzfvMgZf9zcYOxTu+HUFbbJ5Jb1oeiYLijzY3FH/cG/FtYQ7FD2TKd5MTKdzLKhNoM2HDJSw5B+XkBb+Qmg82dcm8X5rnS+K96eR+MzhHyVZACRSVduDHFs3zXUrwYgKoUPi+zyZYi6x82sYBn6X830Dau0x681bpyidMKeyoWAJl7ty5ysmxXQGizy+88MIgCt0JFEd5wEkUh+McAqoDbuRhRqAgrCgOdtVqmEcHu8iA4jtmRor8ttNlqR9DmZIeQhDEAubYTnLg2THNFqQH1tlChZ9x/LFFFA7/7FahpmH3i5+V9X4XECibpTdvs0jQ8kw+orAjvhHiatJjeckmCRGh7IIQE7po0SKtXLlSx48fV/roI9K6lvkKgTzgDdBunL0e8aNhYKE/4yfm0TLxYTOgJRGibgup/QW2Wz/zvvxd+xgym1hkZ1mMcR1lQywmM0iKjSskgjjMGmUb74w1HkTRvvfee9qxY4dFL148VenbWil3V8NCZAXEYrkgIjUZu1cHe21RNmqtEyeCBSzHQhQuPehFeWnE2uzwa5n/K/NroY2iqChuvIIY9zy2TLYY1u4flAZ/yeaRqhIxynez6U0zQj1ziOjwugyt/JfU/F5VKmAgS7trUaDYx2g9GGM5Nl5Qb772GekoapSIKQ65X0z9bl78cdTusxc+JvXHi2qmRTgXAr5TIfz4uQzuWxBDgSdbHCBZSVki5vidr5iKLZmR/Z5lNn+xEZKImvXse4EEDLum2QgK+70zBc4t5A/nukCyULm/kRns4g9z0e8sPrsqzFHlcl/MNZIzUB9uteQr/o0hL+MQ4pPNtuLSujDCZgNh9uzZAYESMwtGgVK/fn0nUBzlBidRHI5zCLFCIhFBwkbEvCwwKovfcWvW34oFFo4FXyzhz5QPosSHnfR1+DzsvCz7q6klWLCGpUlw80Sqz4MUBIxeZ/xY6nGD1PeTZggY3PdKee+DTKANKrEXvrzAzv6a50yJMv4BWzBSzHID37p1a+AIv3bt2qCwjCHaebPSO+1RzuqWBT4YiypUAkFaEL34ScD3hCpg4K2mHGo0yUg2joOdQvqvCyKqjpOkdmOJJC3/78CRHFwHFB6kk+xeYCoiyAJ2fSnm8IYgSQfDYTx2Wgy2XcqSECqMN8bXnDlzAvkyRB1Iq5GmToMbqnFGVIsfDG/xKg/QgnbJz1voeP2rNX369CAalGPas2ePXnnllWD8jxw5Us2aNQtdxDIvYJZKFCoqlJKmA8XmkMWPG1mLFxRmllUhEYNCguI1mXl4ogIC9eGR7eHFbqyNBZIBkoDxlxRRayElXSRVX61zAXVa2TxaFCjQpv/ArjHijjFt7/Mxu9/gC0Qh9+INNg7jk9QgU2hlwU8Ij7IwEoXh3aSPKhVoh8MMdv1rBVuRUHhBrkz/kZnYh3nNxCvVTu6Tun9AWvlUvmoKY05Me/nO8VFJBArKkqhlKwKBr9w9pjaqcEStHQxFEyqn6ozYvRFCE0NzyHDIOP4v3gcrLe86ZYON1rt2EyylEsTfTiBQZs2aFZAoMQVKhw4ddMkllziB4ih3OInicJxDoJjiZhEzt4uBZJ2D62z3a+9yK8Jg41msdLjAnlPUjm1JUB67Qdz8WDAt+aM0/zd5ZrklIC8oqEgEgAxY84w04HP24CZa0nsgRcK8X0hrXyi/7ygMnLOlf4XUiqrXp7O0ffs2LVy4MNiJjydP2I3HoHHAgAE6WaexZhBHHFfYHtshvfPlFN4wj6B692umVKJ4QhWDkqhwP35UaR33qPGNe5Rbo6ui0QxfTJwBcB3gGcBuNj4BKLCS7cTG4se5rlFgtRxuXjuYMhbn94HSCdXJtGnTtGHDhuDfoHbt2ho2bJgGDRqk9CtriwAUrqkwn52yALXbBY9IzfogcWqvq666SkuXLg0IHdqKWNjybwhF2nv69OmjzMzM02MQE04KGOaLVIiCosB3SyE05Vu2s4wPA1G1lRm0DXIPKA6Q6ph3Qj7/76MFY3khC9qOMZVO0972b8gBPG4wveT+Evrem601lESyyjJl0A7aenheok7svhMxspmxsPkd++zBfTbbrtElf5J6fdi8KmLeHolKvhho3+R32R3ne0wk/Ei6gVQsDzCeMX7mfsr8zvuyRuBcZzYwsoh/lxWc285XZ2v2I1EdXpMn40kzYoU5qdOlUsth4b+79M+2QQFxj1fYoM+b7wcEE2RUt2ulTpfY/TExKhpwXphDzub42vSWtP5/FbPJEgbu+XizdbnSWhKrI7gGIU4WPGZeRZDgRX3/kL5sPpBYiOlz12stTZINB64B7jOoT+IJlJgCpaQtpQ5HKnASxeE4h0C/cI3aUm5CIYHk/r1vmqyWPm78AVjYZOapFogyjTnklwlRM2ks00tErYh57xsmDQ5TnpTkeA5twuTNpNMTHjQDvNR356Vd86w9oMgd13ICi9xZP8/S+rqTtX7vkuCmHkN6erpatWqlgQMHBr25FLhZnSPaPTPPzLOUBA+L6mDxURTqnVD0inc1c8MmHX2nn8aMGeN9wRUJDFFPGHky5wFp++yid3ATVQdBStdGaf3LUpfLpRHfskSHRBNhlB4xgoL+bwgLwHllrJGowi5cWprFR533XVMpzPxpXstCGUEh1+kiafzPrB3CEAnGFuQN740qZf369YEf0IEDB/T2228H/6bFp2XLljq5P03vfcsKsZKqT4pboJOiRQHKvBkUvJV0uAepacWRSxGp61XSoC9INesXjAwGeGZd/i9T5vB6jC8UfsO+ZgXsSzeG+61QBG+fZYR9pJgWmXMJGCYv/nPB667/Z2zn/4mx+e2uMcTuD8H3HDWVAHHMqC/wHYsHqjEUPdyXw8YsarKymKTS0kurK8cIyUVKG55F/B/zCiQR5Antm9wP258vtZ9gnib8rMTvl5urgwcPataK2YqOq6/IxhGKnqoRcElsXNBa2qxv8t8n8Qhwr5/5E+nYbmnIl6R2v7P/zz5qhMHcXxTeIGIdg2KMz3K2gP8Yxrd8t2cSGHSvfkYadFvJEv4qO1jr7F8tzbzXPn9JSX02ifj9OQ/aJhuqwx4fzdai5fOC+yD3GtC+ffuAQGncOFGm7XCUD5xEcTjOIWBih/Q10ayOmw6EBDvZ7OiQ3EKrB/G3TXrbbgYS+LIivUGOMltlKxq1lVhJi2xIC1qP6Cvn2MpL+UFhufYl2zFkh4v+7FQOjQIAn4QzGTeJj8CJl+ro1NDsoLBBedKuXbtAeUK0Xs2aNU9/ryyEKUD5XFsryO+gRq1c1bpmoQ6136Bodm6gjqHFAoO1Fi1s5epkSvmBa4CeejxpSK+IbwUoKRi/JIHgIzTqB1K/T9kuN6cLAgVSYsqUKVq1atXpnbeMjAz1799f5513nurVq1fg3DK3DPy8mWOygGX3tVQkJ54P7a1QQh0Xpl6DuIEkufLKK7Vs2TLNmDEjKNQ4zjVr1mj79u0aNnikjjw9SEv/lFExKjGSiJ6wz33BL02tVdmGOt8Liooww/F4QGIxRnh+Yhw0Y4Y4dpQLr/9fnrF2thEqeCr1/rhF1kIYhAEiGpIhiAGuJEBR0rRXXFsj5qZzbOe650dMzRn7ThkXvfO8ojBbBoyT3h8zld+eRfkkFikyA26x50JyJiKSkaPWlx9RzUb1FY3SOhkpmXJtm7Tsn6ZcY9c9IHfCdue3miIGhRKR1yhset0o9f1UHqFJJHMxbx0jYbk+aYHYv3+/MnrUUUafDspa2DYYS7Q0Ffc6RG/HwPc07+fS8r9LTXqamgWyIOY9U/DLkjpMMh+js2UEzXfO9cUYLw60HdEKx/yeaEwfkCDFfd8J8djMvav/Yy1kgWdMNQAEIUqwd75qrXFlUv6w3lwrvXuHtGryIW0fOlfZOUagtG3bVpdddpkaNcozFHQ4KgBOojgc5xAadTUp//EEL4xaTW1nC2k1OzosTDCl5Mbd5xN5u4UJO2ulQcbATXrmlXfVoUvboOCnyE4sxIpLv3nz81b0lbssNtcWwW/cKl32F0vhKOqwYosjdvLOlETXjjNNOdP6qd7wlWrVtVHQSsENPb59IQb+CSF20e/te6MfuDyPleJxwP9F1OeOnpo2e1vgS8Gu45YtW/Tcc89p3Lhx6tmzZ5liaB0Fx9yRzbZAhPQscwJU8KJGsqFEo8VtxDelSGZ2cC4hUPbuzWcImzZtetrEFeVT2HWL7Jm+8iv+KW1800yRd8wuXkodGNRmWCsIps8Ua8xXieqYAr8SiQSkIQQiu4LIrFesWBEUbkePHNW8f+/Vqb9HKrTNjnkDP5Hm/Y1IqGymykFr45aizw3KkzF5ceiQRkTyxgNDRpQEnGdaFmLEGQXv6ufsXJJSE2tjScT+NYUVBOcyGPaoRVDP0PIYG1/4RfX9hClS+E6C+RbJ/yX2oLWAnW0AmbL+FVP31Pir/Z2xhA8Kz0U9yPVTEFFFe2zQklpvSlN6BOO+SZMmKd0/Tx23XfnZ99tmScpzR17iHfde4q/xImGco+6o3ST5r0Fmbtu2LSA38S+K7d7n1Dihth9Zp73b2gbKpNKQrHzfkCZFeXqBhp2ksfeYovasIS86HPVMceh5oym3XvmUeVvFgFrn8n/Y2q0o0ErJfJvYxokqrCilT1UiULgGaVcOosTLaa3D+nfzfxopY+sk1bzsLTVv3+A0geIbRI6KhJMoDsc5BHY6kO8X6OXO6xm++PfS/F+bhDGW6kEvKEkuqBgotMoCdtBOtFqnEwd3ae/8XYEZKjJIip8ePXoEhpC0oMRaAxLBrh7mc+wyVBhpETXzuqnfky5+3IqHosANOzFtiJ2kIIq4iN9j94+CFS+K4mKL2YXjufGL3ujuBup14jqNu7KeamYWra8OTAh7SZf8UZr+fStqiouaTQV1mkvD78RLJqKMeo10WbPLgn7hefPm6eTJk4GK4Y033ggil4cOHapatWqVXHmUa1JoijcW2yySeAkk5el1bXetqCK7qhEoFB1v326qqfJsTYklMnHtRyM5yhozTYuWzQ/OI4AwgTihTSuVoo0fs8NOuwJeB7QLUGAzj/B3iuvT5zJTqtvGzCXbjDFz61hMaapgzoDgueiii4JWtkCVsu2UTr0yXNmHKn6AcD3NecjaHgIj7kq0rmZeOZnEmwNE0s0vCr+KNz8ntRpZ+Dmoobh3oHLgej39u2lS4675RW+yeTsYD+VBCJ5BQBZCDmFovn+l/R8Kgtc/K513Fwbb1orGdcrnw5CYdpNY4c+cRhoN7aREZ+MnwzXBdcjz2MzAp6QA6p5QdMwcHcs5ECg7UFyhCiPqO9lmBPMGBtOkreEZkiwyORXwWSjISZjhPolZbmBQnZAWR9sfisQFCxbo2LH8AdGwYcOgDa9X9z5a08CI27IcT3Hmv+PuMxPts3k9ck4DFWgRaxbIY5IDB35Oqt8xvGUqSI0hWj0BfDZUQoEKLmSq477JuarqJEqQMPaWebixXir3189OU9a0HmpUt44m/rJ2yuSlw1EWVJPlrcNRSRCRulxhRn/xuzi0yHATwmhy52zbveBmPu4nVuSz813WwjujaZbqDN+lrKy0QK3AztTu3buDB4stSBRMurp16xbI9GM73TxYhLMDtuqpijVvPd3a9Ly0bJw04Nbkpnosgje8UXhxhJwb2XtRZnzIj5+/znbcWegVJWMnXvX5a01WevoYs9N0aFYTiRo3ZGGVCO71OM2jSGk7zgq+IKEku3Q+FSz4R37XCDYjMSIBAUaRzXmcPHmyDh06FBje4llB/PL5559f7M5NLD0Kn4ldc6WNb5lcHoUU6il6ynk/duYotFnAcyzELgfRz+UQV32uguuPtI91/yt/AuX0exy3sZGx55hOdjACBf+RUaNGBcUarTwlXThCdFGA8xh4W15RCSmWF53OzyFS4lnH0p5D5gyUT62at9XrdxzWlk1nrledhfv8R6QLHjWFVmUB11xRKhCMh4fdIS34tSkjwkgUvDQW/z7/3/ho0I7VapgZzaJiZNwmP4gz4ylV3mjQQRpxpxGbsbY65vbXPmVpQ6g7Y4R5WJw2pNN7d0pzH7TvC7UIcx1EShga9T2ptOEntGe/3UMhqJlrUV9BVLMZEd/OGRAoO+z4VqGAKa/21yxTtZDQRIxus37BuwX39HXr1gW+ETt37jz9fOYNrktaACk+AWod5rT3f5z885YWqIDG3Wsk7tlq44mBe+zuQkl2Bua/AZ+1a6rNKEtPCyM08aP776Xhn4UW7SufsnG36j/hmzC7F9tYqKr3Rj4bxtVcSyimKgyYir/VTuv/KrX5USSUtHI4yhM+xByOcwjcRFGdsGNKzG8Msd2lsT+Rrn3BbtpIYGPqj8Ky4gSkoAzpc30tjbj5Km3eskmrV68OWj6OHDHHLxaEFNoQKsSnstCi3adjx46BgWXW3oxgl/zU0TOzCqBYx+uk8xUmCQ4DxmOJbVGAnXYIn7CddBZ3nS+Rju6wZAZeY+XT4S0ASKWRdiMDTlS7AHru8WJJNe2Ic086Ex4TkDf0uaOkoR+f3bLiWi0odmnT6PNx88jhfRMXZbTt9OrVKzh/b731VnCO2ZnkfONXMXHixIAos+OJFFZa7DEZ+9K/SHuW2iI7jDTj+0NGv/ENS73A1JN+fbwXmuZFf1alBSPfDd4l7CRXdNtDztE0pb18nup8area96wZtGS1bt06qUIsFcSfCxaetSqojfx08bivnva9Xk/R7DM3CBinxPVCGmDSW1nAPEUccRhoWaEY3TlPWvjb1AlsWn9oU2HnHNPPVU+bGqLIY0iBDD7XwLzd66NmjEvyU+za5E/IEB7FItfmMx5FAbXBRfc1VNOR1wdGz2w84DECICxee+21QN1JOlXnzp2DufjkgUiwAcK4LC8CJR4YCL/xf9Llf4/qVMM9gToGQifWugO4f0OeoBCLbwFEbcG1Avk97a4Uv6viEDFiHR8wFLfnQmsdprnJ1DZBotVYU4qi4OK5yY45jDhnjTb6h7axMPU7RmYW/kW79lB6lcYUuDIAtQ2x4aSlVSwiwfeISXCH881vpyqtMxznHiJRVtAOh+OcAjuDz11TcGHLQpabOUkK+GhQoNPTTbFalNQaGSneBRT6yXYBWNhc/R+paT+76eBZgOSXIpsCG/IE5ULidEELCC0/9VeO1IYHuys368xZzLOYYYEy8jshhEjU2mKI+WRHvfAvF/4vWoMm/tyUE//7sLRrQfLnQnaM/6m1Qrz8sXA/Go7vozNst7c0gDhhd3TfUjMPpN0CwobPQ7FEsYsxJLutqFc4bs4zZrWpGApyflGhsOCPLapRNbCgppefHdP4RRDeMpiR7pwv5ZbSjBS1DX4N7HLi81MVFjhBGhVqpOuMODsjiETV9gP7dMUfaqtew9qVSrbM9wUByq5koWjYDJvjuL4K/V6OxbsnTXKIGIlI2xEEJK0bhZBmEcAo0SrLV8a1hw8UxGU8UNNMeMCI3Bc+KO2ab/9/3vct1vmpiRZNHAaSehp2sxYCTD0hj6d9V1r4u/BikFaGj883Y9rKhkDtsUt66/PSmucrRiXGXIZxMZ4ZFMxsOtAuuWTJkoA4iW1GAOZViOohA4Zr4x/aaO7DaSGR9OUINmau2KWjl7yoA0fzY9xoLyIpjkdRvmcQTpB0s+415WFp49FpH4S4G/FtM5w9V9JoUIj8a3SS+SLv3HJOIRFJByTy+b+XhEc1JwJlHxtfk7+R54WShCjje7ni3+HzXlUA8dcvfih5ZHi5IyJ1uli68smz7LfjqPJwJYrDcQ6CPuEht5uUNkYCUDhDmPAoCVALFLUDwOKA94pPvEHei1qBR79+/YIdtc2bNweEyo4dO4I2EBaK/Llj607tmXxC0azyq0qCBJK0PAl5koUHi+G1L1hBjjS7wM9kqolQAiX2hHhEpD43We87pmcFiuGQ5/b8kO1wUghCbiQ7vqD3t5QkCu1G9Vrbg7hNzj9SYmTpSJBjMZcUUyUtCFkwN2jQQBdccEHQ3oO8m974o0eP6t133w1k6LSIsLjOOhwJWkgWPGo7caVGnqR32vfMyG/8AyaPPlcW02UBO/lIss8YohHte7epDsyX6k1UpQLz0cbXw4tZWsAwaGRcJG7vsAB/7urk1xuy+Yt+a0onFu2hRVGumYcO/WrlWVwzF0Kac53EK01QK/b6mCnDKFDbTbD/jynzmg+y56MYw7sDUjf4zvGVmplP/G541Qo4Cj5SYcJaNyC2Kqu3UeBJ0VI6/5f2HXDPKFUiVeiL270HQp17Quw7QhXGvXPs2LFBCw/qTe6dx48fV1ZWVpCmtWXOSUV/e33FbzxEpV1vNVakfWOp455AbQKJM3LkSLVp06ZYBRufCWLysr/bdcUuP+3EjKnilE/8Lh5kbUebb0/7iZWrlQ7E5hHWScUlZCVuTA39iqX+0OZcEUqjygBaQ2lPLzaivTwRtQ1GHl2uqjyEuaPyoZLeFh2Oqo1ASvtFM5ilTaCiPBZY5BCv1+eTyYvZ2IKQB74L7LBt3LhR69evD4iVrF2Ziq5qX0CywWv1/HDxbvWQFbR8xIgKigF2RtkpZbGFcmbdy2a8FrZgQy0CqYRJXTzbkZsbzbtpp7ZAZUd26B22c0tvelGLQxZHw79hRQhJGEmfGynfnRe+U9osyrPVgl3RIUOGBETK22+/HSiOSG1g0U8M8rjhF2rpfc214l+Rcis82NnEf+HwVjMHPtvGguWxyF72rxRa5mKfMRXtZ5KUlHigRMM0E7PXyiQDJ5Uh3j8oHnjp4PFEocvudzwYf4dJqQkBBtCoS4JWsWLG0qEN1h7ZfIAqVYsnisJ4FQDtIzXrSr1vknp/NO75eau6CT+Tso5K/7vRFGyktaDE2Px24TkU/yV8M9IzpTBRBGq6ytjOk0jQ4TkFQYfJbjA3l0WHnSa1HCyNv98IrbD7Zyzm+5JLLgk2I+bMmWMJadlSzuR+yjlwZvpZosfTpTfOU7MvHdewCf3Up0+fpOldyYBSk3szraLctzEJhSDYt9oSyWh3ibWkQvo17mnjtuOFUpPettY4F+d55o7ybiuK5K2rULFgTlwcgcC1XZniw0sCYrpRMJXoWsszp2eOi5lel3T9Qcv38n9LHS+W0muV9KgdjtTgJIrDcY4ig/aSh43JZ7ewvA1buWl3v04ac0/xMtLTfdI1agQpGzGFCi0+Ux/Zp7V7C1b2LEoG3Sa1HlX06y76rS3GIIlY5F74mNR+ghVLJw6YJJPFP3LY5f9IUpC/e1zRbrsCVQxKCtpUeOxa2UJRDVekmKqKxd2wb1hrzNyHpZwidptYKLOLTaH31heLkTZHK4fKgoU+3jZXX3213nvvvSBRgnafLZu26sU3Vujkv5sp92T5rn4Zy7QfvPE5+vVtwX0uLrCLA2oJolBR2CQDngKoBCjwUVbhs0MaBEk+iSBmFgNDrgV8jyAS8MRJdu2ve0ka/YPCSqxzvbUiGRnCd0WxvjQWKZsKIlK/z1hLG4RqzHMnGWL+FpUppafVCPtu4ucb2uuevbrwc3veYKk0JJhBwnOdoTKkjY75aOuUgu2fzP08SHcLM49FsUMLaWWYy4pNpGoojbrLSI/ZD9hOdYnJ4YgZ0qI8GXy7/b2o7yZmvt6uXTu1aNEiiBOe9pct2reoa1k/UokOOrq+lTpv+6D698tUWo1Iqb9DClKUKRBrKDN48B3G5iju/VzDbIKURiV5plGrsbWphfmnlRb120q9P2bz/CbSCotCxDzDqiqJsunNkBSrIoCXG3MVUeS1W9g6ipYr4qFL2o7H5hvx8BhpOxwVASdRHI5zWYbcRrrwNxY1yA0kaXtKCcHihh3MsfcUH+EbfmyRQMXQtGkzpa9sVkiqCrnBDgztJslUNigQ1r5oN0UWXkO/ZkZgREdiksjuTYtB0iV/svQJWgDiE4sCRKVl7+zS/CbPBD4u8Z4tkZMnVSNjsHSq6NUJqSSY3KEq2Taj6M+NIWW3q+1cJPMbiEfY5z8XwflEjXLppZcGMciYImataqbjzw+WyplAKSAzn2cE2WV/NRVCZQNjFxIlGZnWqLs06VemrGLnm+HJ59w2XXrztjjyJWJjH8PFJj1s7HONsviklYrrIcz3iPYq2lu6hhTT5yogj5LNY+xgM3egFEkVeAEN/bIZGKN8K45E4XssziT0XENgYn2FtPCx/B1digMeiYgpbPBsiM1RKH9I0aJdkTmX4iL7pCn/+t9irVCL/1Qw/jgGfJbwmjnXi+FUEIvs7jjJ7j+QKMv+bkTT0Z1x4zL+s/J9R6zYxsi365XmfYK6Ipnhb/h7R5SZmakuHbtp4fwu2ns4ufyBYyRljeMpt9ajnDRteLaWTtwh1Wle9peDOIoRcJUZqEYgVFFMlMvrpdnaCnXsin8Xv2aDPGleiQjdkvq6bX43dRUK4x5Pp4G3Wgs66yzug7TkTPq1PScwYU7x9Q5ttk0LJ1EcFQUnURyOcxjcWOt3yIsp7G8RnUizSy1DjpjHxvA7zUskvW7Zbt6BxB7fj0RErW867P27f8B2VjEpZTc11iLD/7O4n/mTfPnr5nesnxYzN3bnC5EoFGWbM5V7KrsggcKCtUWWcutmK/dARpE3bXZt2UXDuLGoSGEWO30+YX9SsBVl5hs8P912KSsTiEEeN3acMva11oyHGim6v16FZlDyvdNnD0lAi1Rl242j4E9mMIhEm5YKdvFJrgriv6MWsY2aaciXjUDCVBLZ8vm/sOJmyndsB5NI1fO+J42+W9q3Iq/tLUSyTPJIZer7LqrFjYKd74PP3vUau+ZpFUAOvntB4eQjChUiyPl+FvzKvquUjqGcI1vPRKHX68PS6v+Ez4HFAW+meb+wMYbya+cc6cRBqVFnqdlAm6sh6xINTiG3aUtINWGs0iBixFS364yAhGSirYlxBIFHigrRs7RLMcYYl7Q7Qerj8RG8RCmvt/2rI9ozv0bSlhnOM5HwkK2QqKirUGYFxGI0/94CkdNySHjhSvGJ71TYOKCoJVq4sswXFQ28xyBiScIrU3tXHlAFcm4gyLdMKf75bCpBUlZF0Op6qASRxvg4sS5lHfjKJ/LJ7mX/kK56WhrxLbsPpuyvErXWM9IOHY6KgJMoDsc5DhY7LGJH3Gn9xSyG8QkJbiSp3vSRMjeynUgKOHZry8MoMJDylkAdw2IU9Qs78UHaRN7xs0DF/G/2/da+FF9kz/qpLfCT7chlpNVS4+ZtlV4rEpAAJMzwSD/YVPOeTNfRIqSk7FDEyBsW0EWBXXIWR7RYpGIiWr9j5VNXQD7lZEW080/dFQ12uSt+pQ0ZhUcBxQzjoDKBop6drjC0G2eGwMv/Kc26P79AnfeoKVMw6wxk5HusNQAiccaPjQwI2p3mmcLluhfN84KEpkIpHrnmG1SZ4jGjRaU3dTVp+xVP5M0tx+3f+BVBRCHpjs0D7NQzJ0K4kE6DR0zKx1DpMgmjwZhpfclRrf1H3cBYOBmYn7ie8J6JB/45/B+kMSlZSOUpUmbcbckhYRHHqDUgGqokIja7QVCRFsP1x3UHkR6MjzwFCkQShXbw9zJOh7wuXlphBt2oFlGd0mLLWEYZwRzBvAih+NrN1p4VT/7TJpiYlMacxO+GkSiYku+Yae8R886p9shL9YLALg1BGaY+ZDwt+l3RseExtBpuc1hVBPc2xlyqgMxi8wHSMF4tyLjHywkz/2YDwsd2KPJagRyOioJPow5HJQCLt0iGFV8XPW5SRxJB2LFGsohEvlBcaLoVINyg6QHv8UGpSR/raS6vXSjeM1WvFooe2nKI03zj/+L6ZPOMEyma2LXo9REji9iF5gbI56TNJlkvbN2aDXTZBdeoXssagWcLhnkg+0REa1pLR4vYCWHXjz75Vf8p/mZPoVunmaVhnEphJ6TFQPMxqExgkU96yZb3IuWyK5cq8GNAcUSEZGVKASFpJmzcIOnGz4frjHaBePKDxSHR23xOiink8O3G2+tguBt/PXGd71tp3kKQjIc3F34vvIPwsqgsJEpRMbkQvRT6eBPhicLnoj1l7L0WZX5gnakxQLdrba6Y/oO4OPJUj6GStNnFQKvismXLtL3fIqX1Ha/cJR2TEpx4EPBIBOQAyj4IbMw0IQZo32EMh83hPAd1GC0s1UG1ENxjIUsq0OuVe9i+ZeEbAt2vNUIf/zP8bI5us3tm/89KI78lDf+m9Ppn7D4ZS2yiXQRFZ8E3KZoMICac10D14rDzjgKixVBpQ6o+TEUATzfO85apxft3pNWMqvsHo8qom1Zu92+Id+4r/AmhxphmzRd4vzQ6s95GzC8l2WRjrcp3Vki9kjemucehBkuZRMkzYHc4KgqVaLnqcDhAzXpmPkmbAOaTyEYpPFh0xVQcKFfwB4CIaNTFdrkqYiHMTS3VFow255mMGO+RWLxmfEwkaQf050MUBS1LeakQqD+m/9BMaMMW+zVqpql+o7rKTHBgZ+HQ+XJrdwgjBPiO+B7ZLSnO/I2Cgt0qFieB03wK3wsqBHZVKhNY8LBjXV6pQizY2DVlcRcUDsmImai0/jXzykGNUFmKNtppwj4TptBN+5uBKq0B7CQz1vgu8F+AIIjttDG28FdArp+4c8n1TKsB7WzJip7A1LGC0rsqArWb5/s9JGLy1+3PvUvyr/WAOIpYG0rfjxuJyQ79qO+bpwUEaDAHZeSnbASeEpl5RqkJ54fnWprXuQ9aFDHJnjFjhhYvXqzsaLZqXDRZads+oOi+0lXBjJfifDYo0ineu1aiNrHKAMYjmx6JYEzieYOibNoPpL1L838GuQzBwsYC9/EYAcIGCWq1/StLdgwH1+adfydRTiOjtjTwc0YylsV3jvs+BtfcF3bNTeH5XXZqZ+sVOnhokBo0aFBs3HQhREkitDUMLXkQcHgf0UZz8pCdZ8YWRD2qWJS3Hc63qGlaSCs6cYs5nONLFaxhmcMT26D5P0zX+RMPvxIdQxEt2g5HWeEkisNRScHiFqUDj9Py9NifeQvfil4A46mSSr88Nz8W5Swu5j1SMAUikrczzIMe79duMekmN2DknRjLshPH7kNMzpxIKlGIhoEiYMFvwmW13KhJhWEhSqFbFFhwkHBBfy0LluLA8zEgrGwFCORVmPcGcdW0YhUFFv4xo2AIMczy2FklUpRzye4SSotkcdUHVhvhBYlSWRAsQkPOMQQe8nAWsefdZT3ZEFOM04G3STtnWzIRfgcsclngHliVR8rEgaLq5EEb48kIORbulSU5heuB7wVyl2KuAKKWRFQItEDMsO+BIoDP2+UKk81DRJ3/y/x5D68lzsfoH5n/AwksiclJFJ/1WpXPtcm8G7SA5BYkazgfwTkpZQsI5AmPrVu36p133tH27dtP/6xhr1Pq9LUjWn5f/RJJ5VMFczXtHpBUkF2O8gNzY1hSCUUuCq19ywubBaN6pCBuXMciqAEqFJRstBIyf/B3Xhs/lOII1eP7CnsLOUytyz171X/DiXG+M1SxeDOFKQJj5OPKJ6XVz4STZfmIKq1elqIXTtfC1au1ee9qjRgxQr1791ZGRkZK0dPMOcx/y/5mG1N7luad+yIUpKyhSDlk04p7c/9PS0375c1XFbTJll4ChSTKakhCfMPwk+HzcVxsInS4IN8YuiRgQ8PhqCg4ieJwVAGcvgGe4aIdSTj9v8QUF3Xzpl2H9JF1L1qxnGxRsPB30sbX8ovsze9Zvz6xlPTnh5Eo7EqHFZF8J7Qvkaaz6I/mH1Hg93ob2bF0od24iwJkC6TLmmeLiTXO67Hv88nKEzsbDxaIJxJJoojFDdKeUhSW/yuvBSPHFqOk0mDCiMKC3T0KXxZHM34iLfh14fHCOUdZQIvVmR7HpQWx2BAmiQh2/+pJDTvZ53rxBlOgkDQw6PNmKjvmx9KrN9s1xP+fOlHYrJhFcbAzWsTiEfKxsrTyABRyEHKJJApSbvwB2ElN7GMPWhBrmPKOYob2LwoCrvv4XctYUgjXNQj7zihA2dUsCzAdxUOJdiuKF4garhtIMEhlrn3IQ8zAaUfiM5eE6KJ9Z9GiRYEChdh2QGHVrVs3jRs3To3rN1PDjDwT7nJSjcXGLS1S4+/Pk/5XkuuwsgDSrZCvkYwgpMUPJPql4BPFWIIw4XkATxvGE6qCoV+xn9PWRxoTCU5F+XsFc0yl8wSqeKDuwch7x1zp0PrCP2cuJlK+KLA2WPx4au9Xd+xWHW6/MTgX+/bt05tvvqmVK1dq9OjRatOmTZGqFNYrEDX4xdHuWZzJfeLngKjDewtVH0k4PEqq8EgFtBCVRI0b+Dk9amP6+tftnsl8SqQ2xBWt4MVteBXy2epcmiN3OFKDkygOh6NMaDvW1B7JwO4MqTYUiuzyJKoQWFgiP6VYpHgq8PNcK1RAQEpECi8Aef9kYOdu8O3S+lcL7x5RhD0xzor84nxdUA7wXF6juJ2+Zn3MYT4wI6xk2DEnRH4btRSZmfcVfj6fEWUF8mDOLQojCsaR37Xz/fYXrVWKRR4kGMQKPguQJbHzGv8+xPVyLiqLsoKiE3UEqodC/gp5n2H6XRbPHQMGqbT2QCric0LfOLvNNeLaUfLfwAgSvr8w8i4gEdrbNVZZgHqNnUWI1/hrCWLjwt9aYtfrt+TL6vmMEHAsyNlF5XcYa5hrJ2LsT0yW//aXzXsi3qQ6AIXnBaVMm4lafz3pJhgfokqDzAkKmMR5Ke/fkDqkqxGpS+wpEelhpNvpt4hGtX//fk2fPj0oqLKzTTKAYfbQoUM1ZMiQ4O9gyO1GIL3/Q2n/mrIXxhRRAyH4bq98Xk6VBYzl0KKSe+C+gv+FuqTVSGnigzaOUHDGCDNadPk591XG4cY3zRgXkhpihWSTZD5BzMtcB47CczaqjHE/sfkDn7mKeSPbVJr0s1bacmKs5s6dq4MHDwbX+vr167Vr1y71799fAwcOVMOGDQuoUlgrcVyQp2wupZxSEwLus9y33r9b2va+EaeobcvNLw81Xb1jqtGEm1oSyWYCUG4SLoAiq8eHbIMONUrg+xORxvUrYTx9VGrau0wfw+EoEk6iOByOMoEFATu/FBTJdspRg3BjpEguBGT8S6zIoFBKBK0OLB6Cm2e0sDSf3euigFJlxLel9+4sWIjyeqnekDE1S8W5n91bCAQKp8qipoiB7zhImgkhlOJ79E8jYm0q9ICTmrL2hbxFSx/bfYdYI5kmJh1fv92KT3b7SJUoRKKwC7vPHuw4VQYEhc5wI4XiEZAeR631i5adePB/FD6Qf4xfdhIpzinsKbDj7SogUGJRp2FqKZQWXH+VSTHAsZI4ApkU32aHyozWum5XSYd/YNJ5vkNIj5Hflg5vtPEDIFjCvAti/0eEcWzXPh7pTY4ru+9aHT3aKUjwSkk2n6cegDyhPYjd/kI7v9Hwf3P8zHs8MAHtfoOZa1MEJxKFOTk52rhxo9577z3t3GlfDMfXrFkzjR8/Xl26dClwvIwVVFsQM3yXxNoGrSLRkqupMC7GuJQCvBCR5yjX+aJYgipi7W6DbpP6fdqKXcyTV/wrn+w/edjmnLm/lNa/bMQiRMvgL0mjfiid933plU+GE6+BF0Yli5I/U2BTgOL92B4jJxOJrTIjIjXrC1kcUct+tdVKw9S5c2fNmjUrIE2zsrIC5RkKtHXr1gUtPt27dw9afPhl1iBsTHCtl0R9UhR4HcYQa6GLfiu1HFa2FkQ+w969e7VmzZpgPjvSo5P0/riU5qXA2yrN5n7mSzYHYsqtCx61+Z2NnlTBPRU1oMNRUXASxeFwlAnshlLo0M4RdqNEgcAuJ7vHKE7CsHWayTSRkrNAiO0CQcDgx0GxhXFaItjRpzWgqJs+C1d26PBbWPzH8lt8hL3PkK9YjHRlBGRHKqlD8ed93H1Ghiz8bf65h0BiR4nzldh7f2iDFWkBWRaiKqJNguK3spAofBYMk9ldJuUkhqyjplriO0qMEg12o2vnf1Z+j0hS1DyYrsaTkUjMUbrEiJZE8HOMmCsbGubFhcfinAG77O98zXZEaXdCEcH44PuCdJryreJjyAPCI5lSLC1XaSOXavbGyVr/n6aBsqNHjx6qWbNmUjKF1+McQVJwrHhTlFbxwflD6o955bh78+aJgEixwmPevHlBMXXihDFByPl79uwZtO80atQo9Bj5biAtL/qdzXHL/mGqJwyNE31a8n/J5ktMSvGBgIjpfIl5B1QmMq4yAvKiQeei5xNS9EZ+z4g22iNn/cxMSuPVkvN/aeMx3jgZwg51AqlVELTcO8NIlMBXyL1ukoJrivYWfKimfqeEyodiwHm54BEjPmPXc9OmTXXxxRcHrXqQJzH/IxQpr7zySkCujBo1So1rtdI7X2F9FKmQNQytM2/cKl3xT2tfTmUugDQBqGh27NihDRs2BGqa3bt3B6QwP09rmq202iOkY8UPOhSKw++0TRlap2MECsQf393uxaYcThWsPatqfLTj3ICTKA6Ho0xgQdb3U6ZECJOXEt/KDgOmYcmA9Hjxnyyd5Yp/204EC0QMDikSKWJI7CjwvrVtpy6Vnlt27MfcLZ06bjt65b0I4TugrYVe3op2vK8oBMaEKRoO8hmHftX8JVD4xO/YQZaxa59o5svvtL/AFBWB4iWswItWHMlVEWChyS4+3ifxihOKl61TLFED1QWRvbHWlYZdjPxD3cN3RPFDuxlJUqRw8P88N/A8uMBk+nMeCt8V7XRp5SGc4sEO46Av2A4oBFIMtOC88nHbPYQcYCxAINHmFxg6F0NgzLrPPCEKRWSiuGh1TNnD5ysnmh0oPV5//fUg8WbYsGHq1KlTIUPHQP22zVrZ8B8oLtEmFXBeSVN58zYjOgZ+Lqp9h/Zo6tSpWrt2bVB4AFQyI0eODGT9tWoVXXwEZos1bZ6l/YMxBbGJRB8FDOoUCDt22QOfni42p6JgQvGAgsHJkzOENKnFQDsPiQQH9xAM1Ad9UTq4Xnr54xZVHXZPDebpkLmac835D9rGrOurAJhTUAkW1VJW3RFcTxlSn48b4TTt+3YthXnZpAq8wSArR3zTNhnirzfmnPT09EBxghfKwoULAz+kQ4cOBfPB6tWrA4Ki2YqLtPWZbhV3f4zaOoyEtEv/UrRHCuTIsWPHtGfPnoA04cHxxgjgeNRvH1XagEM6PKP4QcccT7gAKY2olmk3Qok59A5r93zn9uL962Jg84J7api62eEoLziJ4nA4ygQWBG3GSF2utHjc+HYQCA7SOCgwMS1NBhYo9L1ieMYiEsIDYJY37S5p6Z8SfEtoJbnEdu9TKQCCJKPm0oQHbfeetoCSqC6KAjd5jnn415PH0FYGoKRJ1aCURXrvj0lrnjMX/XjQSsEjHrWamqEqpBgL0niPkEKRyJVsgR9LOti7PK7Ij1orE/9P+wZFC14ftfO+B3xjUF3EorwxLMTLg5/hkbJtuhW6KDJQtCz7a2ECIViY31i5/FDir8fG3cwf592vF7wWKQQhoHiUFCzCw9KzKCiHf7WGjg/uphUrlwexweyebt68OSBUOnToEChTKGJQpsR8mjhHECjlHZOJLB+vnP2H9mhjq/9p99789p1WrVoF7TscU0kjT/GAatDBHvjIBNHiWTa/Btd3ZuXxG6qKCEjXkXZPPJhAoqBMGvJVS0eDmE5MlYqBuWP03RZ9PvfnBdvaUBfVbmFqtjDyhZ/TfuljoHhwvaB+uOopU9kSNc05SbWIj83RmKLSZoUHVlHkFdd+vXr1AmNZWvdmzpwZtPRgMn10S5qOP9tAKgORkxJypQ2vSYt+b4oQvoPTP8rN1fHjxwMTXIgd5k7adji+eDBn4dvUvHnzgBhq17adNmQ30bR5xRNRkL9L/iT1vVm69jkb48xlhADgh7X62dSVgPwe90cf646KRCQa02M5HA5HKRHN8zV5/rqQ6NJI3qMY89b4hV4Qp5trO3JhEvp67aRrnil5/27M34ACF9KG1y/OVLaonQ6Mz0b/0BbA/Lsy7+jy3bx2c77vRFKkWe80svH/XBSemHT6qRnmf8IOHAtS/CTe/Wphn5AYcNK/aW7l2j3iFrppwUG9dH26TqwjHiZ/ELD4w+yUZCOk+ow1inwUEywW46O+W58nnf8L2ykO2jxIbVhhO4Ob3i58DfD9o9qqrLvKQYF/QpryHWn+I6W/DotFmrW6XPDLaEDqIjWndWb58uVBG00MKFFY9A8fPlxNGrTQlG9GtOh3FSOdP/2ezU4ocvMLOtZ0vWrUqKG+fftqzJgxql+/fkp+LY7KB9RIb9xWMMUFZcpVT5v66pnLpANJCBTUUCgErnnWFEUvfFDaMcuuHQpeFKHMIcv+Lr3z5cLqKVRzN7xpPjiOks1VEJ8bXjWSAVVtjACPRZwHRvIR+xMFC61yEJltx9jGQMnWKdGA5IWsmDF9pvY/0Ud6e4QUPTNzAuurD/wvqqb9ogFJghIGQgePE+ZPCJV4MFdlZmYGJDQEEAQwbUqxOezYroieuyqJJ14I4d3jeiOdUMpxv6StDTK7uGTEGCCLue+yCRFPBDkc5Q0nURwOR7mAhQQ7Nm99oXxjNxPBgnPiQ1K/MiTg4JvArhLS/1X/MWl9yl4H7KJ3N0+HAf9X8vjScxmz7pemfrfoBCJUKNe9aD42pEAkKzLpRcbQl+8Jvxt8U+jZT+aLA1A03Ti5fJONEu9w5Zk+gNyaYnz69Pd1al4HZf31QkVPphcar5CCGByzIKTF4ujO8O8YxU6rYbZ4pBd/55y8xXq0sPrl6v8a8VLZa22+E4gi/DyKS74qMSJSjxussKSvnu8qdt7wHiAZI7bbG0OdOnXU/vgYbfzxIGUfqegLO6r0HjtV59a3NWxi74BESWwrclQtMB9tfc8IkJjPER4U179mxuTsvoddBxCu/A7FPK0hFz5m1w7FJWo1yNdu15mak7jkRKKawvKCX1lynA+vshG/pH7h78UmDG2W2SelmnVtXmau508IWwr4snzX0dyoFj95TG/fWlM5h86gG3BE6vWlfap73aJgfjxw4EAhxUms7bB169YBadK1a9dASRM2f7E2XPuS9OqnUowozmtR5PtjrRaQgSWoVCFgUBChEnY4KhLO0TkcjnIBRAI7CPSxzrgn9V2DkoCFCe0R9CuXpdDmdyFCxt0vDficRUTSUhFbFNGOgo8A4GbO4pYbMr+DAgB1BX4glTHGuCggNec8JitmUVNg/otMGfIpGYESRDj+xhaUK5+Q5j8aEl8dAnbtymOBz6KLYoMiBUIvFnWLWSCO/ezmIqkvrX9NLIoWI8BYqkJa5yOqd0EHHX+9t6I5+R+C6wC1TlGKnRggmNjpLG6nDu8EyJaqUAxxLsY/YOcFdQ4eMeUBvidazmgNDOLREzwI2rVrp5YtWway9Dlz5mjr1q1BoXDs4Cmte7GWokfOxJcbUc7aFmqz/BoN+GJt1cioImysIym4ZvGuwStpyV/yzZPxiSrK3+t0K0TU5l7aSvAVomWQYhMD5LUvSrN/ZkRtImgrIfmqKswZZwt8d8wrPLh/FJcMWFbkZEW04b91lVOKTamAxMmw+0+JyemotPLlo8puPUfRSE6BuRPihFYdSBO8pFDNFUf8Mr5J8sMsnI2asGS1xPfnHl4aHyo2K8bea2sUh6Oi4UoUh8NRrmBxR7ztzJ9IJ1LZdUgRFFkQKDwwQyxvYNaHIoWdPHb4srnRR/IXTCgrqpLqJAyoH56caOaXYcDz5aY5trh5+sLw2Gdie6/4l9Ssn4I0gVRNOZE8X/0fM4MrzUKf84epIt4jyK4xJMXJP4i6jd3lIpZagUldkx72Xpi/csypyH5jKgZMQKdNmxZIm2No2LChRva5QKt/1E1b3k2rkPYUvqMBn7VUpIq4Bs4mMH3G+4UikPNW6u8vzVKRMD7GoBDirDicPHkyOKdzZs/Rntm1Ff3DNdLJM2c2w/j74CtS80Fe5FYX7FkqPX9tQWPl0hTK3JMg+lG3BebTISt6NgEu/6fU5fIyHbLjDIOx8c/zilZvFkDEWoz7f1Zq2ts2CY5sk1Y+ZV47xZIX8S/V8IhyP/1fRVvvCjxOUJyQIIRnE7HrtB+WFNyLSVkjRKAsRr3JgIKTpLLSriEcjpLClSgOh6NcgUcDvai40E/7XhFJLKkiYl4ZY35sShd2VyoCFNGkrPCorqCdpPOlphoJO2ekptRpZelJYQQKaDfO0mfe/7G0+r8mx0XBEo/C8atRNex1So3783dOcGoroNgWAOonVAzL/mbJLEl9LIisPWAmxzwwqkNR1PcT1h4WUyyELcAgUI4ePRrE0JKgEPPTwEiPXbmxY8eqebPm6vRYRG9+PtzHpKzXVd9PS2PuSS2RqrKBz0d7HCovlEuMMYrCVMkUyE0IMnbmB98uNesbixAuHvTz9+7dWy3qdNbzvz6lAydTnGRCYrpL8xyuJVohm/azZBBH1QdFLve0Nz5X+vbXWDx6UeB+CaHYcVLp3sNx9oAJe0mUeXjEXf53u4/RCsp9kE0CDN1n/MRSDlP1eIoeqqvGu4doyMdy1KlLRzVo0OC00XVp2w3ZCGMDgM0AWqnLI/EsBlSvtKsRk+wEiuNMwZUoDoejQkDxQ3/3vF9aMc1OWYmKyogpQDBnG/o1W3QmFuOO8gV3g51zpWcul47niyxOY9g3pHH3Sq/fktyAll59FAAYDYfF8oKlfzHzw9NIi6rOZ95TgwlbNWzYUHXu3DlISSkO+ASQEITqiXSc0pqAUmhg6nje90xmn5hShJHepk2bNGXKlMBLI3bbpAccI1KiaNmtA/wIj4LpP7BWpkDRVA4tL8O+bvL9qh5JG0uUwaCac7vxLfs7O6qJ5xfik4IBk02MHCFQaLkLUmgiJX/fdS9KL92Y2jnDPJLWts2TzeMizDcJzxpSIjDHptjFBDQsPSgmQ7/xPZvzHNUDFJHzf21zRUW0vzKv4YFC22pmg/J/fUfFgfkIrygSmFJZN3GuMRonsRBfOtSYrMGYVy75s1S3pfTkhOSm7mFoMzZXN7wRUXqt8r3hQAwt/Zs0+wHp0PqyvRbpdBBFY+8xv7aqrBR2nHtwJYrD4agQcDOjkMAEtu8nTVKKyzotI0GbT27ITm1EqtXI1A6dLjFT0haDKm8CSWUDhScLERQ/i35XUAVAYUoxv3uxtHNeEa+RZq00LOpoUwhDwVaUqDL77NSx9kt0aMsR7dy5IzCqGzZsWOD2n6zfGmnwnIekBb9OTtakCopzPEvYFUZmz84tRQdkCbGO8+fPDxJdjh07FjyfHbn27dsH6hOOMT6KlkOlcL7gl+bxMvcXpuwpDcHD90QhjkFvu/HVQ6UQ+A5kGqlFuhHx4bTYMW/wCNqz8lrL6rW2MUb7DqleZSKXohbXnQqBglkw6Q+Y1k75dgKJgpntBy2GFpUTKgPIXxJRiK5+9w6L8kwEiSx4WTiJUn3AnDroNptnZ94nnSzjPBYP7pn9bpHG/EjKrF9+r+s4cwQbCstUN54YS/je0AqJNw4qJbBjjhEq3NNoYy0JiXJoXZrdt8p5/cV9DdUhx8v9ER+wgFwuwSYb6wuIbK4fyGxUtFV5c8FxbsJJFIfDUaHg5o7MlAfF4J5FdiMPTFz3WtESFOhN87wqekstBtpNEfiN8cyC3X0WXFsmF1xwsaib8k17FAWIiJKAgrTdjXu0sX52MBYw98SfAuVHLHIWIzuICsgUdugoTNm9xXunPCNoIWOQPZ88ENWoH0S179gOvffee8GxxNQntWrV0pAhQzR06NBAfZJM2pxR31qEkBevfNJitfevtb70ooz+WBxSANES1ecTUperjDCojtcBnxkyKzOPUEn2nPIAhSyJU8UeU7oVAF2uDP85C/txP7VC4b07jThht7TXjaYkooWDVKvEFg7GxPYZRpY5qg/w3MJws2FHS0Zjjji9wVAaRKTazc18Gv+k9IKp645KAtR4WYdLNn/hKwVZy1rqcB6JQutn3TZmlJ+yt0oeSB06dczuP+UNTPlbDJEu/ZO0fZapDiF/IMw51uC+HkeqQESjEI2RRV2vlbpdU3QLrsNR0XASxeFwVDhiN7jaTUxuiu8BRUPwIJ0gYgoGChS/GZ5d8P1TCFLsvfYZ8xCpKMTk5mO+1lu79zcOUlLWrFmj7OzsgExZtmyZ1q9frz59+mjgwIGBoR0GsrPuj2jBY+VLoMQQPWXqloM5O7St1/M6fDi/2sVcb/z48YEKpThjvdg4Ro0w9A4rvHfMtvYPWo8wwUVVgcEeC10UWBjjYS7a4QIzCKTAcnmyoaLnBdopgp3fYgC5RWvV1qlGkCUCBR3KmPe+aX3/MTUXaiRUXsx/nFvGQjyYC1FwxeZDR/UBxWH3D9q1j29F4AdUClN25pFOF0sjvmkJaRXlH+Y4A4iaWXqqgJxnvhn9I4t0p2UWUoU5quuVNqb2rSjFMVSAAWwMwbovQ2oz2oiRkd+xY0QVSis4bZDRbNtUQHEIkc64Jq4+UCfz+z5XOs4inERxOBxnHMHNk9nHZ6Bz9vyw0z7qB6b4KK3xYXGKFwzvzvteRBm1a6htnbaB4mTLli2aPXt28CdkCu00tNJArvTr20/1Ng7Wwt/WrdDFHeTM+n82lD7WRGpzSJm1MtW3b1+NHDkyiHQsqbEeu24kZLCg7XChJVhRtLPbSJHNzzHbI0mmtLHLjrKBOOzixhQJXbTx4G2y4t/Wix8PilZaGFn8o0CJb4c7dVjau9RIFNrikimhKJyqQ9uWoyAgSyGvKYD7fdqK4C1TzTMiMBdNNCjO+zcEDCbuFJf9brb2Pww8vbis3AiUFyW4FzDXoHZs3l/qe7PdazB1R0VyYLU07xf5rZApH0PamTExj5EptRobSc0DQCjzuYINNh/PjnMQXsI4HA6HoxBYnA/M69efdV9yQ8xSvXammYBOeKBgLzNmspjK4jNCSw/kyc6dO4NY4YMHD2r6u3NU66nOOrW/ovN9I8rdV1fpb4xT8y9ma/SkoUG8Y3p62W+ZfFYk1zwc5w7Yyc3NLXrMokBBLYRxY5jfD2oSDJfxfkJVkti21rinEZLJriVINQg8J1GqJwI/oNpS65FSq+GmjMKgG5+m/atztWXjVp08maUaNSNq3aqNmnWrFUTJk0SFuXIqMe2OyoGgxblJyZ4//OtS9+ulbdOkVf8xJQppebS9QM698ikz6U4V+EydTT+6gFzxMAHHOQyfch0Oh8ORlEghrhqj1KnfMYltqpGzocjzuBj8JWnYHeFmoKg88B2hhadLly5BSw/Grvv27lON1d10alUSt9oKQM6aVuqy5UPq3iVdNdJ9K6wqA9+SpLudmMXeYIbLXAcUtR3CSJRcade8cH+cwV80FQp9/4kEy+ljSDdVksPB7jvKp3rtrEXs1KlcPf77F3XkyBHVzKypITd8SG3atvGWhioK5gI84hgHqdxzId4GfM7mHxLGjpGuF5WW/VXav1I67/vmkTPlW6kfQ9NeTsw5HEXBu60dDofDEQoW5xR1tN1c+6L5lwStCKVYtLNT1mGSdOVTFiWcWUyaCmQKxq2DBw/Whz70IY3odb5qvD9MyjpzVWY0O00r/5ahw1u8SqnqYDwm85AgKWjUXdK6/0krnkjd+JMCiPaKy/4qjfiWtO19adr3krfHIb13HwtHPGJ+YczD0Qi9f5jmRIMdem9zqLrgvDJ3pKoEoZ0LdSMquGO78lu/MIRHlUILKc8pyb271ci8tmuHwxEKvzwcDofDUSRYsGOGef4jUt9PScv+IW16Szqyzbw9Cv+CLeLY3a/b0gw1SZrpcH5yP4hkIJWnQYMGqr9+mHLWxV487ucZUsNO4f3j9IQfXGcLyRh4Hjt8/A4JKgc3WM94siSEo7ukdS9Kg2/3gqUqI7OxpVqQDlHg/xuZDwqRoRgOM96CBKXa+WotWnUYY/FGx6gIiGfu8zEbh3MeNuPHo9uL8MTo6kbCDofDAOlB2yBJhsWBFJ1o3j036XOOpB4jzHyIN0l5zUdBuF3U/H1I4cMwlm187sfch1G8+P3VUdngJIrD4XA4UgK7Yq1H2Q7Zke0mHd6zWDqwRjq60xZIFJX4nEBSNO0rtRgkNellRExp10gsuta9FAlVADTsLF37vJkrJi4QSbj4zyXSvmX5BfGwvKQcCt/TJrKvWJrK4U2FX5/F3obXLa4Y41dH1URamvlQkAwRD9KVOl9qhOHYe/L/nxhZBnSvj1iyxJK/SKv/az/jdSY8JLUcIq1+1kwdd84ruoBhx5cYeIfD4Yjdr4jyZf4ojvxg3so6Yv4nmF4f2mBtQKhTet5oxuWb30v9vRt2z1HL4dyxY4/SESd4TR3eIm2ZIu2cZX/H4Bb/J9YEEChstDTrb/HuePzwf04mOyoDnERxOBwOR8oIdosiUv22Ur02UpfLbac96NvOW+gFMvMaeY9y2F06tNEeYajTwnbN1r9sMbLxQD1wImbiGcn3Ytn0thmAQvrgN0CqBcf52i3hyhp6ylEQ1Oxe9s/iOEeRJrUdLy35sxnExsB4oI0n0RuAMR+M92jBsY+HxaRfmwHtm5+3aFGk9MUBFUyLweX8mRyVG4yt2F+Dv0RO/0eQXBJXWPsuftUDc06vG6UV/5KO7Sz6uTvnSosfNw8zNhXW/89SwlCzcI/DbJbXSQWRjBydGjtVi1bWVr9+/YK22pIk0sXIk41vSsv/YX9mHZVyTxb9WVGlNB8g9fqo+U/FzLt9bDvOVTiJ4nA4HI5SIVjcEMVYwTYlpFQc3RH+s9hCa/6j0pYidtowx6UVCVPQ12/Jf73Nk22XrvdHTEGw+Z3Cv3tok3R8t0WQ+oKuaoLT2uY8qVEXklDy/x8p/SufKLwZy67pNc+ZR8rcn1s7D+Rh74+a8uqdL1sBkaoRM4UORIrDESPpDm82Ynj3AmnvijRFdoxTzaxTqpFeQ7NmN1DLHtYqyXhj3Ho8etUC9xpIEJLsFv62aDUKisoZ99jc1fcTUrcPWMoXsemLfmdzVHFEjL1pVOkDN2pfkwWaPDlLy5cv14gRIwKTd9LziiNTiGhHdTf7fmv5TTVWmd/jgW8Uv7/4j9Kwr9lnR5nicJyLcBLF4XA4HOcs2NWCwEjmvUIbD7tetFsUBQoNitSVTxQkZLKPSpvfkvrcZOkpYSQKC9TiXt9RyRGx1rCOl0j71+QXLJAgKJYSEahL2HHNyh+bLPY7Xmi/026i1Gxg+Fst+q20b0VBU9ueHzIyz1F9EdvB371YWvpnactU82sKPJ2CmJYBwfPg5bCHWpdnOgtBTCtY309KbcdKNRs42VtVgJpzyO3S1ikWd10UmIeW/NGMZFFo0lpLnDqPeHVdUcioH1W9K1ZoT8ZJRXOj2rFjh15++WV17tw5IFNatWql9PSQ0jEqZZ+Ulv3NyBwIwNKC8Q5x+OZt0rbplixUt7WPace5BydRHA6Hw3FOA1lyGFhUsQOLqR7tE50vs8Uj3iab3rVI5piPSpB8km4+KWGLNgrfpv2SH8OJA+X0YRznLBgf/W8xb5NkBrAxUJTg1cPu6enfr2ntPBjPIkdPhnUvFSRR2k2Q2p/vRUJ1BvMP89WcB6WVT+Xt4McrD8JUCLT0YJ693h7EZ5OANvJbFnnrSU+VH8wJjXtKY+6WXv20dDKF+1DWQXuUFJC4Q2+PqPcXxmjhktpavHixjh8/ruzsbK1evVqbNm1Snz59NHToUDVu3DhQpQTKFAiUE9LMn0pzHwonnUsDXmfR76WDG6VJj0oNu/gc6Ti34CSKw+FwOM5pJGuJiCWaYGJ71dNmAosMvlZj6dhu6f0fScv/aUoSdsZQDyB/DzZ1816Tv0OeYJpbVDtFqjt5jsoN4owH3ia9f3degkQSIDv/U3czc4yBAufJ8bZ7XBTiiTx2WIff6a0Y1Rmomda+YPHXtGOk2gJW6HVOmDfUjlnm/TToNpQFXnhWdnD+Ol8hjfmxNPU7yZPkygIIN1oRh98ZUUa9hho/frx69uyp2bNna82aNQGRcvLkSS1YsEBr167V4MGDA7+UunXrKudERLPuNwIQH7LyBNfCxtektz4vXfIn82ErtUO9w1HOcBLF4XA4HAUWLcd2WfRv0Iu/zAgI1B70WJO8Q2tMi4FSo+4W5QoBUZFARRKKNKlWEyNM6Pne9KaUc8qiGUf9QBr/MzOkpUUHiTzFBWoVvFEwl+WzsmPb/zNW+BZVvGQ2qKhP5ziXwBin+Nw+Q9rwavIxATHHdVIAtJ7tKcF7ZUrDvm5j0Avd6gl28Im+nvHjcJVciZHX/jj9h6ZOIZ6bOdLHV+UGrTmo5Di/tMsUmnvKAOLa+3xCGnOPpdYxVmrUqKHWrVvrsssu08aNGzVr1ixt375dOTk5OnTokKZMmaKVK1dq6NBhypnVXXMeylD2sYoZZAGR8pY09XvSBY8UsR5wOM4wnERxOByOag568SkKD66Vlv7dyAhIB3Y2g12fRCl5XkIPvfhtRtsOFi0JQTRhBayjIG6QGgfHE3/c2dK7X7Wd3PgI2VhKz4W/MZ8ASBR2yKZ+3/7v/J9bRDOtGLT/oCqg0Did5JP4cdNMMeCoHmC8TXjQvHN2zS8+XrS0RRGpUAM+Wzj5x1E9wLyFYei075dfC8Tp1z4hLfmTlJslTfy5+e44KjfYrEAlhw8YY4YNjjIpJCMW1Y6B66DP5xMop38ciQRmst26dVO7du0Ck9m5c+dq//79ys3NDfxSXn/uPaX/tYVOHWmmigSfk+hmCGfmzOLUfg7HmUAkGo0PSXM4HA5HdQJ3APwf5v/K4l3ZxSzRwixiizs8HUZ800iV8iwKuUXtWSw9e5V5naSq5W3QUbpprhEqT4zL/0x1Wkldr5Ia9zBTWcgX5NFX/0da/AfpvW8Wfq26baQb3pCa9PYd3eoCrgtiQ9/4rLRrYfkSKShQen/MiJrMRj6mqiPYXccA9PXPSllJPJ/Ki6wbdqd03ncrXjHoOHNj59AGU18u/1eegqmE8xObEiSCjbhTajUitXs29+LDhw9r/vz5WrRokY4dPab0OUOk5y6Qcs4Mq4H69YMvW1Kew3G24fsfDofDUU2BEoOI36nflnbOL9oDIikwlTue14s/25IEBn9Rqtmw7MUhu13sem04vEnZbZpKm9oX+DmtRBAb7Mgd2VK4x5v3x6ARAiW9jpnQsuBc/HhBhQ27eywqScMIQ5Meng5QLeNFh0iX/9OItQ2vmVqrrMhsLA35kjT0DpOl+5iqnti33DxQKpJAiald5v9SajVU6nZtxb6X48wgSGTqLI1/QOp7s7Ts79Lmd23DIPAkSVSP5v0booTfazXcYpBbjyrZHIQypX79+ho7dmzglzL1+SXaNGWoomeIQAGYLy/9mzT6h/Y9OBxnE06iOBwORzUEi+uVT0qT75SO7SyfnXZULPRrH1wnjfupSYVLWiRCnBw7dizov16xYoW2bdumgwcPKn1AN2l2Oykn/wWbD5Iu/5u09K/Su1/PJ4GQ+na5woiT7e/b/2FId9nfrT2D3d+YMoU2nm7XmH8AipdEsFAjNtTl8NUPnHtIukv/LM37TbbmPX5SWdtrSzlppVKftBgsjfyO1OliN5Kt7j4ocx42E9kzAZR2sx+wVoi6rdyYsyqA+yrKIojeFoOkI9uMROEeRvLX4a0WeZyeKdVuKTXuLjUfYEk/RLnj/VS6940EfinNG7dSo8UttHH3GR5MudKa56ylh00Uh+NswkkUh8PhqGYgwQYC5Z2vSCf2le9rExfMzhiFwqRfGUlRFGIdpbj/79q1K4hSXLdunfbu3RsQKqdft+UOZbTZr5zN+RE6O2ZK+9dYa8ShTZZwgVqg40Vm2AkxgtwZsMhEhdLlKqnnh0w5g1pm6FeltuOl6XeZCW0i6BPvfr3velXnYqVW06ia3bhWkYzpypgPmddfudsbKudk0QUEYwaFE8ULxo09bzC/FVefVPM2sTk2V51JoBJc87w04HPOoVQ1MM/UbyfVa2tttaEbInknvbzmHu6l614i5q7453JMHTiuIu6hbOQs+1tetHcxgCzCJJ44eZ9LHWcTTqI4HA5HNVvEb5ksvXdn+RMo8W1C9Puj/iCWEff/gscQPf0n7Trr168PnP4hUU6dOnX657Gdr2bNmqnz0K7av6am1j+Rv0gkoeCd203WPPYeadT37fOxy79niTTlmyb/Bcickc9f8Kh08R+kE3utwGVHbtHvpEW/DzfQ7X6dtfM4qi9IpMAD4GjaLkWG7lGv606oa84F2ja1hg6sMQNa4o0h8BjrKLDqtZaa9Zc6Xmw7xYFs3ok4R9Ri10uS4lQeYGwu/qPU+yapZr0z+96OM4PThMIZIBZ2L5KObU/tuW3HSCO+VbQZLAqa1c+mRqKwUbNlitTtOldVOc4unERxOByOagS8Q/B4OEoLTwUv2hf+Xmo5VOr54fwFHuqSI0eOnG7X2bp1a/DvROKkQYMGatOmjXr16hVELdatW1e7GqZp5zvSsR3570OyzgvXW593017mz7J3hbR3iRW38eC5z19n7TnIm1HLEGW7Y46ZzCYCEmjQF41scVRPMC63bNkSjFOQUTNdA0f3UIcOaer9EfO0QDaffdJaxPDigUiBNKmotCpH5QXE74bXw38Gydaoq9RsgFS3pc3RtEbuXV44mSwRtGnQJsZr718Z/hwIv+0zpY6Tyv45HNUbKEFQtKaCxX+S1r9amPBgbmTMQrCsfDqvrThFcC9nvnVi2nE24SSKw+FwVBPknJIW/EbaFRcHXJE4ldeL32ZcVJnNs7R79+5AcbJx48ZC7ToQJ5mZmWrevLl69+6tDh06qHHjxkpLy18l4SmBKef7Py5YVECqrHvRHkUiagk/K/JafIoCapZBX7Cecy+Eqy9oM1u8eLFOnjwZ/Lt9+/YBucd4ZWe1VmN7OBzFAZ4YEiNMhQL5RsvXyG9ZWwa77fwfRO+yf0gz7k6uHCThiaQnfKD+99HkJAq7/FunSB0mekSso2zpQPj5pJrid3SbPRKB31SPD0nr/ifNf7Rkxt0kCmYd8bnXcXbhJIrD4XBUkwU8i2uk5CyCzhSQ6b77u/U60ntGoD6hKI0HJAnESZcuXdS1a1e1bNkyMK6jSE0EC3+Sf5AS4+lSYaCN54PSoNtcLlzdsWfPHq1Zsyb4e0ZGhgYMGKD0dF86OUqBqLR7oRm9JgLT1wk/M6IDpSDR63WaG5E76FZrP5x5X+EENYiWwV+SOl2U4vsvMmIGlZTDURpAdqTSdlMUUOuNukvKbCBNu6vkKVVsCOHL4iSK42zCVwIOh8NRTbDyKelwQhRwRQN/lLUv5yir1jYp3bauIEgaNmyodu3anW7XqVWrVgHVSRjgVTLqS+PvtxaKdS+Xv6IGeXDny6TxP7UdXkf19kJZuHChsrKygn+3atVKnTp1CiX4HI5UEtEObig8ZzHn9PqIlF5Xeu0zZjobI7pp57nmWanPx01FCJkSjw6TpEGfN9Kl9XnFHwMtPU6iOMoC2njKFPcekXpcL3W6RJp9v3SgFClVXB/Ftbg5HBUNJ1EcDoejGgDjy3UvhZMOxF4mIwzo4U+UkddpZTGJJAIgTWehf3hzcoVLdFU71TzeROktj6pFixbq06eP2rZtGxApECclKUp5aoOO0gW/kmp8w1p4kL6XB9JqSt2vNaNanP8d1dsLBRUKSVGAcYoKBTWKw1FaQvlESCsPhEbjHtKRrdK26QXn0QPrLXkMzyd8duJJFPxTRv9Q2vyOeUSkQqLQBlGmAthR7ZFWwxRQpQXqkaFfM9P3pX8rnTKWdYDHxDvONpxEcTgcjmrQyrNnkXRoc+Gf1agpjfup1OOGwgQLi/7pP5Tm/SLvP9KkLpdJo34oNeuXZ6SZLh3eKs19WFr8h3BCI3osU+12Xqixt9VUy1bNTytOyrKjD5FyyePS3J/bDu3xvWVTpdRuZq1CQ74i1WzgPihV9TpgfCId37fSdkAhCTEjZkFep4XUqLvUpJeNgeXLluvwYeu9oM3MVSiOMiFqKpCw1gQ8IWhXTGyTqN3U5iZaF07FmV8zXkd+z5J2aIcgASUV8P5nsp3TUfUAgUIbTmnR/QNm7P7mbUbqlQasW7yVx3G24SSKw+FwVAOQ8JAoBQeRdNsFPbheWk97TBxYbO+an//vJj2lCx6xv0/+uhWi7IYOuV0ad6/tpK55LuTNcyOKrG+vJnUiqlFOhobUsjUbSud9T+pwoZEpG16zgjhlMiVivdm07wz9qtR6hH0fXidXPfIExRTjAzUW5poUq5CEPILxEjFCkPNPYdpqdLa2dzmkSLM0KS0aqKdIiHI4So1I+A4+bQm08CSC+Y15ifl54WNS1sH89p8+n7Rkk3e/ai06qZIoRLr7/OYoC4IUqW5G+qVqLhtDrSZS309JB9ZJm98t/THUaWnKLIfjbMJJFIfD4ajiYKGDdDaMXGBHB1XHsr9LU75V9OuQ/lC/g+0gLf6jvd6mtyzx5prnpW7XmdN+Ybl4RIc2mo9Jee4eUQxEMiyyuMUgadcCM5zdOt3aiwLSKPEzR2x3t357qc0YqeeHLPXH42irJnlCK9qqp6VFvzPSLyDZkiCHguCkxV2vezZdqneR0rv2UuaENerYtJci7jLsKGMbRGbDFJ5XU2pznjTi2za3rX9Fmv0z81QBLYZK531XWvGEtOb5kh1DZhMjCh2OsqDVyNKRKB0ukFoOleb/yjZdSos2ozze2HH24VOpw+FwVHGgKDm2My9pJlrY3ySjnrR/VfGvQ1oECycIkdOvE7U2IVIjUHUkW9jw/mFS9nJTpdSX2o2zHdlju213FhPdI1vynf9p0cDHpV57U9AEn8cXYlUSLO53L5GmfVfa+GZpfHMi0pHaii7soazl3fTuskjgP4HvBMWww1FSoEJp0KHo5zToJA35shnN5mZJM++VFj+eH4tMy9nYeyzWnTbGWGtPTOGC0oR/Q2SHte0w76W7l4SjjGjeX6rb0jYrUgVrB1p5mJshtkvbVgbJyL2e9mKH42zCSRSHw+Go6ohKp46F/6hhJ2tjQCXS66Mm04V02DlX2v5+XrtDHvi/7GOWFEGbD2a1JEr0u9nICJ6frFjNPlnyXavSgONgcUexESBEiRL84aKCKqs+Ua4lN73z5fA0lJIiNystUFztWyFNfFjqfp1dMw5HSUAR2aS3VKNWSLJImtR+vDThQfOLWPkf86Lau7Rgsdl8kO3mE5VMRGw8OQL63yK1v8CIF0xqE9G0t5HdDkdZgJqz8+Wm8EsVGNg37WuKwP0rS//eXB+tRpT+9x2O8oIvAxwOh6OqAyf7muE/ImWHxf3EX9gOJmQKknN2Mlc8KU37vnRyvz137YvSnIfNgBVJ7v7V1hbDombJX6Qlf05+CEHReQaJi9MkiZMl1Q6rnjGviLLIxQshaqomWtkogFEKOJHiKOmchJKJlsZEQ0129i963P7+2i0214ZFuEJwx8gRCPD4ohZAHkO6oC5MRHode39v53GUFahBen3YFCWJ6X3J0LCLrTdW/Fs6VURbZZGISF2vtnWHb4Q4zjZ8KnU4HI6qjkhehHHIjjyLbjwgNr0tLcxLuanfVhr+TWnAZ81XZMY9Rqqwg4kxHMUjHiIsiAIvkRqm/iBFAnVKGCBmkhE5Dkd5gOJxy3vSe3eWM4ESB66Hd78u1WIn9jJfyDtKBgpJDKzjvUxoxRn8JalOM+mlD0sbXk+unkIN+Py1hf+fdLVJv5bmPCSt+m/BJJ8Y6rWR2p/vY9ZRToTgKKnTpUaKpKL2Q4mCmm/zZGtVK+310/eT3obrODfgJIrD4XBUceDhEL9rGQ/MCbdOlXbMkrIszTWQ2h7ZJl33ktTnJmnR4+ZpMuwOI1aW/UOafb/1Q0PC4LaPCSLtOq/enP868cALAMLF4agoMGYnf0M6RAtPBeL4Lmnqd6TG3SwS2YtSR6qAgO59k7ThDWuNjJEbGGWePCS1HS+1Gl7492irxAMFJQpxx4nIOpL/Z0w5WAARI1rwgXI4ygNsirAmQBmVypyLaoVHaZFeSxr6ZWs5djjOBTiJ4nA4HFUdEalxTyMxEnco9yfpT2ZRtHuR1OVKU5FgHIspHEk8s+4z49bgeRul+Y9K7cZLHSZJ9drablMiGnY1Y1eHoyKAF8+8X0q7F5yZ9+PaoLVt0q+8PcKROuDbmCfbjJY2vWn/h7qPyFbUgsO/Ef57tPYs/2e+SXYYAu+UJIoAVIO9P+btjY7yA+QxqXgkRb19e9HJZ2V+rzSp6zVS35vd2Ntx7sBv/Q6Hw1ENFjvNB1gSz0GijvNAe07LYbYw37Mkwfg1zdp02C2lQEVyntnYyJPElh2UJ5ArOOazWxQa2TnKPFccjorA3mXS0r8WNEKuUESl1c9IvT8qtR3nahRHiohItRpJI+6Uds6xuXTvculfo4oeQxgmJ/qoxGPdi9Jf+4c/h7l70OelJr18nDrKn9zAkP7geiOVw3x8yv4mUruJ0vj7Xc3qOLfgJIrD4XBUA9BOg6lgPInCzucFj9gCnT57iJAYMIttOdjSIVjo8xxUJw06mtokFrkZvE5DqUFna6c4cbDwe1M0uH+Eo6LADjzGxsd3n9n35RpY+jdLiggjDx2OUESsbWfw7dLsn1nheWB12V4SIjusjZL36nKFtVz6Dr6jIpBRBw+1qPYf26nVf6ur6D5cjcvnZs9GTvuJpvir38HXEI5zC27N43A4HNUBaeZvQiRxDJjIIilv1kca8yNL3KndwlpzJjxgMvNFv7fnZR2U1j4v1W4ujbvPdt/5O5GFI78ntR4prXlOOrotZBF/lbnpOxwVgcNbFEQQpwJ2MlFf1W1T9PNQaTXoVEwcbFRa95L5BTkcpfGT6PHBim0Hg+AbnzeXOxwVgWjUCJQtPZ5T9KMvKdJxpxQpY6583hzMmuXSv1o7shMojnMNrkRxOByOagAWILTUdJxk8ZkUgDjkz7rfyJBu19mDFB7abjAonP5DaeVTec/NNu8TevdpYfjAS1JOlu0UYZa4+r/2WrT+xIPn9/+MScodjvIGCil8UA7EKaySImK7//hOTL9Lmv+r5E8lRnPMPdLrn7HEn2TA5HPLFKlPx1IdvqMaz8c160sTHrI2ylX/Kf9WNNJTLnpMatTVC1BHxeHYsWN65513dOjQQantQTW89S01f/96bX4ts9B6ICVEpGb9pCFfthhlorl9/DrORTiJ4nA4HNUEmQ2sgMRNP9aOw59vfUFa/AepxRCpZj1p/xpr49m30gxlY0AuTnzs8n9IrUaauuTYLmn3Qmn7DOlUXkJEDBAsfT9haRO+CHJUCKLSznkp9OJHpI4XSUNul2o1Np+eZAoB0h8Gf9Fiu4sj/yAdt001YtFjNx0lAXMi6Wbn/9KUUUv+KJ0MaYcsKSDBaZ9EMeg7+I6KRHZ2tmbOnKnNmzcH/86slalxFw1S18/VDKKMFz8u7VogHduRZ3wcBsZn1NqLmXshTjCxp3XY51THuQwnURwOh6O6IGLkx9A7pBl357vpk9hDzDGP4sDO0o7Z9igObcfYzj9kisNREWBhjilycajXThr7E1NPoV4phIgRLLSpkThBmkliklX4AUgHN9pzURY4HKUhUsbday2RM+81k2TIuRK/Vg2LSx78Jan/LUYWOhwV2cazfPlyLVy4MPh7JBLRoEGD1LNnT6WnR9T9WqnLZTY/75xrD8Y23mmsPSCoaTNr0tPmXNosaSmGTHHiz1EZ4CSKw+FwVCPQesMu+/5V0rK/F1SalCcwpp3wsC3qfUHkqChAiBxcV7wPyqjv2Z9zH5YmPlz4OYxR2h7qNLdFPkaxqUZyn9zvJIqj7OqRHtdbwhlRxsv/ZcVnbnY0LrY4ZCKN2G49qkB+v/+npUY9fAffUbGANNm5c6emTp2qrKys4P86duyo4cOHKz09vcC4RuHKo9+nrXWtgCIlktcSXCPv775WcFQiOInicDgc1QgsUigmkXqzoFnx79LtehaFJr2lCx+zdB9fFDkqFFHz7ykKPW+Uul4jvX17kgSTPEXLlO/Y7ihEI6oVZOWpIPuEKVwcjrIA4qNua2nIV6U+n5B2LZTWvxLVsmlbdPJortJzM5V+tIlqptdURt1IoDRp3EPqcKHUdrT5T9GO5nBUNI4ePaq3335bhw4dCv7dqFEjTZw4UXXq1Cn03NgaIDBQ9qrTUYXgw9nhcDiqGVjU4Pcw8SHbeV9ML/6Bsr8uxWebMZbsgzTXCRTHmUBRu+6Mw+F3SiufkNY+J7U/P/lzY54+jOMSGSL6DqqjHMGuPC0+nS6SWo49qS3/mawd23Yqmpup88+7Tm1atQ2SS2iFwMPKVSeOM6lAwQdlxowZ2rJlS/B/mZmZGj9+vFq0aBG09Dgc1QVOojgcDkc1Ra2m0tj7zCdl5k/MTLZUCRFpucpsjJQ8omFfjahOKy8qHWcIeSknYWBMoijBPDlIjqogtQitP8mMah2OsuDkyZPBI1c5SquZpea909W0lU+ujrOHZcuWFfBBGTx4cOCD4gSKo7rBSRSHw+GopmDNUyND6vFBM4EldWfFk9LuRdbqk9+LnwRpuarRcY+i/Vapzydqa/SVg5WeGXECxXHGwFhr3M1MC+PHK6TGsK9JTftIr3xCOro98RfLl4xEEeBwlDfwm4BEAbVq1VJGhmfFO8qGUGPtBITdwyFNtm/frunTpwdqFNClS5fAB8UJFEd1hJMoDofDUc2BHBwD2GFfl3rfZJHF61+1wvTEPvN8wDclLc2M4jLqS427SpGB67Uy+w2dqnVIa/Y10sicXsqI1D3bH8dRzcZus/6Snij4/xArg75g6ipMjknbAc36GoHSergZHW6fac8p/QFIjbpI6YWtAByOMsNJFEe5+kcdlQ5vlg6tl/atlo5uM1NsWshoD2vYRWrUXWrQwVp+45P1jhw5onfeeee0D0rjxo2DNp4wHxSHozrASRSHw+FwFCBTeHS+VMo+KR3bLWUdkHJO2c8xpa3dXMpsIB0+0lyb/yUdPBjVwYMHtWbNGg0YMMB3pRxnDhGLxWRcxkcSQ/bha9Kkl7X0xIBxLOhypdRhkjTtrrKRKCi52k3w9jVH+YOd/xMnTujUKXP+rl27tpMojhIDVSn38bUvSWuek/Yslo5syUvJwU8nXpkSlTLqWexw27FSr48YSR1NzwoUKFu3bj3tgzJhwgQ1b978rH0uh+Nsw0kUh8PhcBRGxLweGrSXxCME9evXV7du3TR37lzl5uZqxYoVQW80O6YOx5kA5EXzgbZ7untB/v/vWy49Panw81uPki56TFrwG2npX6SjO8v2/nVbSW1Gle01HNUcpBhHpdwcK24ZuwfXSycPSTt21lXG7oFKb3VEdZo1Ve6xDOXWNELbiTtHUWBMYZZNXPaCX0v7VoQk8cXHDeeB30GFumu+tOTPUvfrpHrXrtLiNYsDYi8tLU1Dhw5V9+7dfcPEUa3hJIrD4XA4So1evXoFRnPHjx8P3Pp3796t9u2TsC4ORwWAaNeuV1obWmxXlRa0vcsKP5cIWZ5ydEf4z0uENKnrtVLtZmV8HUe1BWqA/aukDa9Lq5+V9q+QTh2Xso/FTL5bSektlFMzR5tqSf++t4baT5S6XSu1GydlNjrbn8BxrhIoe5ZI075nYyvnRCleI1fKOiQt+7uU9m4r1bimvXJab1TXrl01bNgwJ1Ac1R5OojgcDoejVGAR1bJlS7Vp00Zr164NzOaWLl2qtm3bBrtVDseZAGv5Xh+Vlv5NOrzpzL0vKpS+n8hvEXI4SlKgMlYX/l5a9bR0cF1ee0UB5BWpp2ool8dR6eBee+7Kp6RWw6XBX5Q6XSyl13VlisOAomnLZOntL+e1KqZgJFsUGJc5G5sq7e9XqMFlSzXy+q5Ba5nDUd3hq1yHw+FwlBrp6enq27fv6V2pDRs2aP/+/YHs1+E4U8A8duDnio8aRtL+1ueljW8WXYSseMKKEForwoDhYp+PSy0Ge/HqSB1Mi/hLrX1RevZqi94+sCaMQCkatFxsfkd6+ePS27cbIeNTroNxBIHy6qelvUvKTqDkI6Lc/XV1/MkRWvZg06DVzMebo7ojEvWVrsPhcDjKgMOHD+upp57Snj17AjLl/Innq3+PYTqwOqJDGy3hJydLSkuXajYwn5VGPaQ6eNLlFaBeiDrKiuN7pZdulDa9VcFvFDHTxSv+JdVr62PXkRoC35Msad4vjTxhXiwPQOhB5l34mJks+3isxi08i20OhCyuKNSoJQ27Qxr9Q7unOxzVFU6iOBwOh6NMwFR22rRpmj1jniLbWqrxjmHKXdBVWQciOnnQUlKCndaIVKOmESk165shaM8bpLbjLBEIs0SHo7RgNYMvCrvzwS5sBYG45Mv/YSa1XrA6UkX2cSNP5j4sZR0u/9cnReWi30mtz/NxWR1xYr/0yiekdf8rTwVKOIhDvvhx8+bx+7ajusJJFIfD4XCUCRQES549pPcfPaislS2Uezgz5d9lJ4vFf99PWZwiJp1eADhKC1Y0m9+W3vy8GXaWKyJS/fa249/5ElMAOBypAJPYhb+TpnzLWnEqCi2HSFc8ITXq5vNodQKbFCSOTf5G6UxkS4MWQ6TrXjA1nsNRHeEkisPhcDhKBe4emBy+/yNp5dPRvMVb6VbukXSp/Xhp7E+klsOlNC9QHaWNi5W0fYb01hek3YukaE45vG5EatZPuuARqd143311lGye3PKe9NKHpWM7Kva9GJfdPyBd/Ecps0HFvpfj3AFpY09OqADiuJixNu4+adg3nLBzVE/4MsDhcDgcpdr52v6+9OIN0vJ/sfvFKqr0K6lotrTpHemFG6SVT5r5osNRYkRsQd96pHT1M1LfT0o165XtJdPrSL0/Kl31tBMojpKDlsYZ91Q8gRKbl9e+IK16yo0/qws45yufVuA/dsbf9ynp6LYz+74Ox7kCtwRyOBwOR8kTAKZIr98iHVhbjv3XUenIFuntL5l/gMfHOkoLiI4GHU05Qt/++w8d1e5lucrdW1fKTY0FyWxohp2DviB1usR8fByOks6Va56Ttk09c++JifeCx2zM1mvnKoGqjqxD0roXzXusJPNj/Q6mrqvTQjq8Rdq/Wjq0oWT38z1LpJ1zpbp4mvk4c1QzOInicDgcjpKZdy6W3rytnAmUOJBaMeXbUu2mUterfeffUTqwqM+oK3W6PEer06dp15StSt/QUbVWD9CptU0VzYkEXhW0++Bvgj8Pf9ZvJ3WYZEVo2zFSRj0vEBylw6lj0qLfS9lnyKciBgyW178q9b/lzL6v4+y08mx7P/XnM8f1+KA06gdmkg35kpYpHd4sTf2OkX6ptkBC2G18Q+pyVakP3+GotHASxeFwOBwpEygn99tCK4hQrEC5+PHd0nvfkpr0lhr38CLWUXocOXJEmzZv0KmG+5U7eK8u+mZbtanXNPDzOb7HCtwamWZq3LCTGSXyb4oNH3eOssyXO2ZJe5cW8aQ0i3pH9UREN/NrkGQW9tQMG6MknB3bbWq9ZHMwRfCKJ6ydjec7qi52LcgbCymizXnSxF9Ym9kbnzPfKBR3RBZPeEDaNd+8zlJCVNo63YyTa7hq1FHN4CSKw+FwOFICi/slf5Y2vlnxEYoAefHMe6ULf2OKAoejNNi+fbsOHDgQ/L1ho4Zq07GlGjWKqFHXs31kjiqNXGnDG1JWWBpPxArXEd+UWg030g5FAMagsx+UNr+TrwbAdLvTxdLQr+YRymlWNG96U5r/6+Qkzf6V0r6VUvP+FfopHWcZwflP8X6cXlsa+HlT3b1zu6lIwJ7FRuZhEttqRAlIlDzl6LGdpuBzOKoTXCTtcDgcjpSA3JcYxdysM/SGudLq/9pOl8NRGuTm5mrlypWKBRG2a9dODRp4bImj4gF5smehzWOJaNJLuvzvUufLpD1LpaV/lrZOtdjYS/8ktZ+Y/1xSyy75k/lXbJsuLf+HtVL2+ZR08eNSvTbh74/KirYeN5it2ji0Kbl6KRG0yHa8yNLL4u+r/P7iP0jPXSNtfa9k709LD8pRh6O6wZUoDofD4SgWuTnS8n8akXLGPQV+J7Uba7toDkeqgDjZv39/oEQBkUhEPXv2VFqa7x85Kh6njpqaLgz9bpYadZOm3yXN/YWpUFCY9L5JmvSoNPRr0tZppjBAOYBS5ZWPS+tftoKXf4//mTTwVvPuQSGYCF4TRQHPpzXNUTVxKkzplARN+1n0NSQKf3a+VGrUxXxVMIvfMTN1QiYGFFMlaSdyOKoKnERxOBwOR7E4sd+iM3PPdPRw1KKU962Smg9wjwpHybBlyxYdPnw4+HuzZs3UokWLs31IjmoCvHaOGH9XALQmtj7PCtfFf8pPVaF4xQz24Hqp5RApo46ZGjfpad4qm9/NL3D5nQ2vSYM+bwk8ga48pPg9vDWvLchJlCqLkhBkGMnSSoa3DhHwTXvZmGKD4sg26f0fScv/LUWzS/D+xMr7+HJUQziJ4nA4HI5icXCttHtB+M+6XCEN+3r4z9jxImkHjPup1Hpk0e+z5V1pxn0FW4YoRHgdSBSHIwbaFLIOW08+f8bGTFpN22XNaJirVatWBS09oFOnTqpTp87ZPWhHtQGFaE5IKg9Gr3ifYOjJuI1HIJJKs8KWB+afM+4xz4n43X6KVuZDCJIjW8MJFIplVAolVRY4KhdqNU79uZmNbOxgOLzuf9K7X7MxCGk3+kdmLHtgTQnTftKN7HM4qhucRHE4HA5HsdiW58AfBozo2oy2Vp/EBXutJvl/r9/WdlXDkF5XqlnPev0LqU2i0pbJ0oD/K+OHcFR6ML6yDlmCxNr/SXsW2Q5qfJGZXkeq21Kq3SaqPa1bKr39ftVoeFLdunXzVh7HmQPzWCRc1fd6SPQwZp9EuqMWWP+KjWcUJyufzH8OxEnDLjbn9v2UtOkdK4aTHoIr96o8GnU3YiSVWGIIPMbE3mXSO1+TTuyx/9+7xOZNWsl63FAyEiW9llS3VemP3+GorHASxeFwOBzF7vgTo5hsR5PefhICnr6ocH92/MLutVts1yoRSIknPWKmivN+mS9vjweLPo9RrOaqk0PSqv+Y/8POuWZoGLYDjxdFYHS4pIZUY6widYar1tAdOtSijbI+YO0UXlw6KhppNWxuO5WgNglDZmPzN0HRd3S7NOu+8Hlw5HeNaIFwYZxvftvUKqGISjXrm9dKWXHanDZa0Kg2EkcW+TV1dtBiUOrPRXXCfXzH7HwCJYZNb9ic2qCzkS3B/JoCGnQsmRrG4agqcBLF4XA4HEUC8oKd/jDUqCXVa2sKEhb1YQv/GIKfJfycHbQBn5XaTZTe+YpFLSZb/J3cL9VxS4tqBxb9JJe8f7cpolI3MYxIORFFD9fW0Xc7661Z0qonpZHfkdqMKp/i0uFIBggUYl/3LU/+HOZPjGGHf11qPsjG+fQfGGkdhhk/lpb+VWraR+r9Mem8uyzddu7D4UqE+u2NcCkLUM4Qlbw/73F4m5HlvC5KQ5QzKAwb97K/O9F9ZkHsNQlNqZi+c5+GBAv1MEkzIiz7mBnJp4oOk8IVVw5HVYeTKA6Hw+EoEnhNYJIYBhbR7EJhfEh8Ir3RLMROHpCO7Sq+H59dtOHfkDa8asa1SY8h25J6HNULECZL/iLN+ql0eFMZX+uYpZugmhr1Q6nXR6T0zPI6UoejIJgLG3dPTqJAPqMs6fkh6ehO6b1vSCuflk7szX8ORB/qPfxVmEv3LLEH4xjC5cqnpP6fkRY/bnNuIomDSrA0BS7z7aENpvxa+5J0YJV0DOVCNO/1YmqUvL/jQ4QiocVgqe8nrL0z8N/w4rrCwcYCRAbk2unzkgSQc8d3WTtY3TbS0W15P0iTOl5o5Aoqv1Rag0CtpgXjuB2O6gQnURwOh8NRakCcsFgmbeLaC6WGnWwhxgJ8yZ+s9YI2jDDUbGBFBJhHzGcSouY0ilkgOqoWIM0YFzN+YgRIeeHQRumdL5shLekm9PQ7HOWNjLpRNRuUq3UvpSmaW5BNqNtauvQvZrQNSTjnwXCSsMVQaciXpRX/ltYneJ9ABvI7zfpZ+0UiajeXmg8sGZGBSuH4HmnxH6RFj9s8XmjejRb+O0T7gdX2WPOc1OECadgdUvsJRgI5mVJxIO66x/VGeBUXdwxpwngb+hWLyGZ+ZbOj3Thp+J3WNrvyidTfG0Uf48/Pr6M6wkkUh8PhcBQJFujJCs2YEgVCZM2z0rK/Wn9/jw9aGg+7ZNPuCt/ZajtW6niRLeRYvBUFpON4WTiqAaISgToLfyvNvLd8CZQYIPbe/6HFyGJY7K09lRPxXh20nRAbjAcJ7QgoMSB5Mb1EKRFDRRZ80bwDOnXqlDZs2KCtbdZKjcdIexvEHYARI23GSFO/LS38XXICmXm3+3X2mTa+UTC1rGZDm3dJ5yncRhlVnf57ldm2rqJRm7wjxXxwlC4750iTvyltm5rcSLw4cIzEL+O7Meg2I1M4Vi+0KwZ8r+3Pl9qOse+9KOSekmY/YL+DB0+3q+28p2WYSuW9r4fHcoeB64vX8GQeR3WFkygOh8PhKBLsJLJzGgZ2LdnZIj1n7Yt5i/yIJUpc9ZTU92ZpzfPW7pPoBUDxyu8v+n3xC3YMEt28rnqAOnTDK9Lsn5nPTkUBnx1ULqRbsHPuRV7lGiOMDcjXze9Im9421QTtX8wlQWGYbgQwpC6Kj04XSy2HmX9ERZBmECiHDx8OyJPFixdr165dyjpxSjWHNVb09ZFS1AZYvdZS50stNp6UqSa9Ql4rxz4byo7dC6Vu10ob37TPikKrTnNp8O1So67Sgl8XbnWMNDym3T1f13/+e0oDBgxQ165dVb9+/aRECt8ZiUD4Uh1cXw6qP0itvVaw48Mx/n7zZ3FUDCA0aIuFCEnmXxZD1kHzl0K5QrQxBBdjbfcCIyFTAddP9+uldiiNfN50VFNEojHa3OFwOByOJCA1592vFe9xchoR6bzvSaO+L732WVOoxIMe7iuflJb/M+91i+nBxkTxsr/7gq06gJ3QZ66Qds8/M++HJP3aF0214Di3wYoVZdLWKabg2DLF2rJSKfrZbW/azzxIen/UTF/LSqawhM7NzdXBgwe1fPlyrVy5Unv27DmtSAG1D7RX7uPXKmdvneDftD5e96Lt4AckYcixk7jzz+Gmrul2nXTBI6aaotjNOmJ+Ko262Od/7TNx3hZ2VIqMWqLsq15TNC0nIE6aN2+uvn37qnfv3qpbt26BqG/mXgiU1z+behFdUnT/oMXn1mnlc3hFASLs/R/nauZ9UUVPhTnHlh+a9JauedYMhR2O6gpXojgcDoejWGAUiNdJIokSJD9ETCZcAFHbEaNISa9ZWNnS5xP2s7XPp2ZiR3qPo3oUAot+J+1dcubeEyPFpX+RhnzFYmkd5ybgJVA1zPyJtOqpkhtNM0dBzO1ZZB4j531X6naNtfqUtLCPkSeoTZYsWRKQJ0ePFpRNZWZmqkuXLurTo7+WrMzU2ufy5sVd0pyHi07Nob0nZuaNko/kFYhkjGohU1ANLPiVtPKpwoayNWpL3T5xUutPZerYsWPBsXKcu3fv1qJFi9S/f/+ATEGZAnbOi+jt2yuOQAk+w7PW+gkZ5B5E5Y+AtEuLqtYlS1RzfrZOvjJQyq6YyQwibOKDNhYdjuoMJ1EcDofDUSyIrmw1TNr2ftx/pkmTfi016y+9erPFX8YA4YJ0nl3TgxsLvhZtOR3Ol/avkrbPLP692XVtPbz8Povj3AXF4rK/h5ByFYicLDNAJq2HVg/HOdri9Zr0HuaXS0ugiAt7rRwjUl6/xQi0Ed+0lp9UiZSTJ09q8+bNAXmyceNGnThR0NCkYcOGQfsMZEXTpk2Vnp6uBt+PBO9JC8/BddKs+0pwwHleJRAntEFCPjNmAx+UaGGCut+npLEfHawjWZ0C0gSC59ChQ0GhvXfvXr333ntauHBhcHydW/XSlG830sENFSsP4XxBXJEK0+9mJyvLGxB68+fP17TZ03RyRK4yc3KU/cZg6VT5lnl1WkrnPyx1vMR9pBwOJ1EcDofDUSwoMrpcJe2YE1fgRqX9q6U+HzcDQWJoj++1hT5xifTx75on7UggSiBXeL0gTSAFzwsMaBv3cBl4lUdUWv9qXiJICmCc4QVA6wOFZhhQGbDjT8FZlOJp30rz1ehzU+kO3VFxwCSWxJfJd1iyUnl64tCmiDJkwgMW15psjoGAOHLkyGm/k507dyorK9/ltUaNGmrSpEmg8OjRo4caN25coGWm2QBp3E+kNz+f135USpVWbhHpKxS17cfTRhlRrQY1VEvNNHHixIAsWbp0qVasWBGQKRTc+/fv15QpU7Rw2yEdf/8CKZqhigbJMbPvNwIdLxdH+SA7O9sIlGnTAoJPGVLDa1apaYu+Wv90ern4SjG2uAeT6IOfj5NgDoeTKA6Hw+FIcRFFgUn0JTupAaLSsn/YohiTWHr9+RmRx61GSsd3SdN/lFfknn4hqe0oK363TSv+fWs0yFK3T2apRi2ieZxFqcrAFHTdi6mpDCBQUBBAsL38MSuE48EY7HmjRayS6nR4q5kdb5sernKJZlvbBH4ZYXGxjrMDxsKmN2XtJgV8P8oHkGv4MnHOJz5cMAEs1rKDWSx+J5AQtMTE+51kZGSoZcuWgXkrrTt16pjvSaKBK0Vn9w/YXDjl26UnUoqan1uPkib9pqAJOEROs2bNNGHChOAYly1bFihoAmVKVg0de6+TosfOXCmAaS3RyWPvKbqdyVE8GIc5OTmaN2+epk6dGiRCAcbjZZddqCY31dbyUdKsnxkxnUrbbBjw7ulyhTTqLvNC8c0Mh8PgxrIOh8PhSAkswuY9akVAfCRn7eamRmk/UardTDq+W9q3Slr8uClV4iXnmDvS29+0rzT/EWvfSIZIeq4yrpyt2pct1agxI9WzZ89gx7e4qM4iP0PeLS+2AI1J8WvXrh0UHLHXLst7OEoH2rueudISSYpDrw9LF/1eqpEp/bFbwXFEu9j5j0g9brDCG68HdlHBlG9Ji/8YbuZJWxqGnw06luOHcpQJKIRe+KC18FQkIHXH/kQa/EXmnWgwP6A2iZEnqFDil8s1a9ZU586d1a9fP3Xo0CEgU1KZM1CTrHjCxuERSKFo+RAonS+Xzv+51LBr0WqaWIIQapqlLx7QkUculk6cWdaQNKzrX7MWUUfpECP4Zs2apffff78AgXL55ZcHRsKMR1Rch/KIK/xzDm9KsRUuYnMrJHT/z5r6JL2OEygORzycRHE4HA5HyiAt4tVPSWtfSPhBxHZx0zOl7JOWoFEW3wJQo+c2ZX/sGeXUPBoULd27d9d5550X+AyUlORA8owUH4+A6dOnB54AW7duPb345PXbtm2rwYMHa9SoUcGuLcURfgaOigcrEWKyn7ncFClFAQLuyifyiZFEEmXgbdLEh6TVz0rT77Ld/0bdpEv/Yrvfz11tKSeJIOqT4o44XMfZB+PgjVvNI6c8yIbiUKdlVJc/c1LZLbcGJAO+JxizxsCcg99Jp06dghYZFB7MGyUFRMr2WdKMu6XN7+Z5m5T6mKX+t0hDbjcyO9Vp8fi+XD1/4yltfZPjT/5LvCZJakTUn1YghqjCWgyWmg8wMornEbVLwZ6M9Lnsb0amO0oH7luzZ8/WzJkzT7eVtWnTRpdccslpAiUeOadsjmSOXf+ydGCNtd5iSoy/DueE+3etZlLdFqYq7Xq1zbWZDZ08cTjC4KtDh8PhcKQM2iTG3GPy4N2L4n4QtZ53HuWBum2knncc04pTNXTosIKFIn3927dv18iRI9WrV6+Udn9RmiBf/8Mf/qB3331Xa9asCXbwwvYPkLq/8cYbgdoFX4NJkybpM5/5jPr06VOqYslRMqAYKY5AqdlAGvNjI0a2z7DFfmJB1/dT0okD0vt3W5oLoGBY+Jg07n5TTIWRKFmHpON7jNDxouHsgnOw8U1L7zoTBAqgJezVh1fq6Oi3TpOrgPmgUaNGwTzAvID3SbzfSUkBkdd2tHTFv6VVT9u4RLGXqncFBS+Kvw4XWhslr4XCryQ4uj1Ne+dlFvu8zpdZNPHrnwsnUbgfEGWPEjHwHsqSata36+udr4a3bEKuE6nc66N+nZUG3AtRoECgsDkQI1AuvfTSgNgLuyfWyLBIbB4k4zHWabdlnszNI1HS69q4qtvKCBU/Nw5H0XASxeFwOBwpg4VVs37We//ap60Fo7yBwSPS+j4f76o+e+oHhnlr164NyI99+/YFRAepGKNHjw4KmrBFIyTJli1b9POf/1x//etfAyPFVIWXtPnEZPxPPPFEQKTcfvvtat26tbf5VLDKqShQpGFgjFLklY9L/W6R4uwfAjTpITVobwQfu62nEZW2zTAz0Y4XSQseCynOo4XjYh1nBxTjC3+b4KdU0YhKR+a0VE7vmlK9UwF5wjXft2/fQAWXzO+ktICAoFWi+/XSpjekjW9IW6bGtVxE4sZo1NopUHy0Gy91udKUH5AnpTmcfSuS+7LwmqgP2o6XRn67aIIGEmfQF6V1L5lJL62cnS6RRv9IGv1D6fnrwon1PUvs3NZqVPJjr67g/gVpAnnCg/sUaNeunS677LLAzDiVsYk/T73W9nA4HKWHkygOh8PhKBFYp7UZJV3+D+ntL5k0vbx2ixt0ksbfL3W7jsVeRC1atNAVV1wRqElYONLPz0IS1QiqFFpv8EqJV6WwuCTG8xvf+IYWLFhwerFZmkXrnj179NBDDwWv98ADDwTvV5ZdaEdyFBdr3G6CNOQrVqxtec9IlLDxk8FO+IrC7WRHNlubWb12NobDODVk746zD+J8URoVR0KgeqAYR0VU5HMbmoqp6OdGpK3NVWtzD3W49lhAnrRv316ZmZkVQp7GXrJ2E6nHh6Su10rZR6Vju6WDG4zk4JpAFYA6gLGdWd8UAygHynJIkCgFSJo4kFSEKTPfLcRNvP9VPBp0lvp+UtqzWHrri9KxHfnmsc0HSa1HSA27WJx0IjgHR7c7iVIScN+jFXXOnDmn72m0nF588cUpEygOh6P84CSKw+FwOEoMFvFEFV/+T2nmvdLq/5Zt1xgTO4pkWjVaDZUieRGKLAwpYvAqwbOEReT69euDBSXqkpgqJeaVwuLyueee01e/+tXA86Q8bL94L8z7Pv7xj+uRRx4JZNPulVL+iE9GSQRmr6PvlnbMlhb9znwlwkChTNLKib2Ff4YKhaKUgpro47DisGa9MnwAR7kg1u4ROp9EzN9m6FekNqOljDrSqWNmQDvvF+bdEU+eYV465Mum3mB80S5G6wxE3Lb3QxJLsmuo9uxxuuC+dNVvlX7GClPeBj8pHrWaSE16Vuz7Hd6SnPjme6HNA7QbZ4R5GFoMMmJn2vcLpmPh8YJxLgbPh5L4otC65Kqv1EF0MfeguXPnnm7h6dixY0CgoMZ0OBxnHr4KdDgcDkepF/7sNF74G6nbtZa2gyqFYjVVZQo7nbRgDPicxctmNg7fYUX9QfIAqhS8UegJP3jwYOBdwL+3bdsWqETwPPnSl76kHTvytkXLEZA3n/vc5/TnP/9ZF154YfVRpETtdEJABORF3rmF6KLXXmXcFY83yYScS1SQQLCN+LZUp7n05q1Ft/3Q8sOxhHmrcOynObWQ8UnbAq1kjrMLCuwdc8IjWRt0kK74p7UU7pxnapV6bS09BOUDpteolEC9NtJlf5daDjWj0+0zTdGBUSotYa99xlpoEnFye23tXyo1qMLtDqeYo5OAKPAAEWnkd5KQKBH7XiFMUKK0HGxx43y/kFQbXjNiK9l9gN/LTqJwqQpgnmG+5DPiOYLCjX9D8DJHMael1yIJqui5k00APFBoaYVAoaUVYg8CBRNZvHocDsfZgZMoDofD4Sg1WACyIKRHv8MF0tZpltxDcbNnmZRL8kQkr32CX8i14huDu1Yjzbiw08UmzaeALvq9IoHBa0yVws4cpAnqE1QpKFD++Mc/VgiBEgNkzVe+8hU9/fTTgdFkVZVQB2RD1AiLXfPMYwRjyWN7TMFBIUCaDUVt096mSmrYqWyESt2WtguPuetpRKTu10k9b5Dm/sIIulgEcaBciUj129nxHttp6gX8NDJCFCUULfgBxNokwkgcjBWr6CmtNMBDI1mkMQamtIrM+7k0414jXOAy+3zKEpmG3WFELmO010ekViPMuHX6D6SsIzbHEI+NWeqwr5vqItGzI+ugEQCQLVV1LKDEKgv4HrkOuY6YwzGJ5dqCFMiobUQKxrKb3kzy++k2h1TFOROD7E1v2z0w5s0UqKowrE6zjQKURs0HmpoKo2tUO7H7ZP7rRYNNgilTpmj+/PkBgQKI1kaB0qBBg7P3YR0Oh5MoDofD4Sg7WPxR1EKIsChk0UixSopPLHWFXTiKVNQrtZsacUJhW0TCZsj72JPxSsFMDwNYVCl79+7V22+/HcSSVjRWrlypH/zgB4Fhbd26RfSgVFJQGLFzv/Ipae2LRk7gYZAssho1EeQHSTl9bpLaT7BzWxJwWut3sPaAeBKFooOimdcb/CVp4Ofyf0YxQiF29TPSkW3Syx+zMccud/32hX1PIElI7zm0OdwPBRIIQsZxdkF7Dn4ZiSA+l5a/E3ukeY8Y2QEQrJDiE5gOn5dPoPFcnkPrTqx1hNNOxOvez0uthlv7VyKJwjg/ssWUMBT7VRGoupJ5oqQKrkmUWz0/LM26z+YK0OUKM5XF2+r5awvGj8dAG1ZVap1jzOxZanHcq/9TdNIYxB/ja9Nb0qLf21xF3DPkHvfGGJFCCw8EysKFC08TKN26dQtUkBAoVZXAdzgqC6ro7cHhcDgcZwuQJSzSeVRUb3/MK2XgwIFq1aqVHnvsMc2YMeP0YrMiwXu8+uqrgfLlIx/5SJVp66EQwNBy/qMWvQoxkUqRhVnrkWNWPGx41WT9w78utR1nYyFV1GmGoiVXO+fCfkROH9O6/xX0XIih141SiyHS4sfN4wHyBeUMxpz46lB0x8fGNutvZpmb3yn8uSBr2BUuKflTFQChFM2WTh03AiogD9JMrUCxW9oEmNIi8K4J8bzhfHKON79b2E+DY44l2nCoeIswFjZPtnjrQs/N+4zJPhevn4w0rApo1LXsrxFrnVv2N2n+r/Lbr1D+NO1jUeO0TYWSKHWlOi3K9v65OXaeSAQ6sE46tDFvXESNSEMlx+es3cLMe0saA50SotLxfdKSP1maFKa6JSGmIPD2LTel1MonpKF3SD2ul7LTjgUEyqJFi0638HTt2lUXXXSRK1AcjnMETqI4HA6Ho9KCxSWy51deeUUnTpy5JvujR4/q17/+deDRUun70unfz5U2vCJN+bbtqJZ2h5qiACIF/4mhX5WG3G67/cX1/VMokIR0uPdqReoPUfSQxclyHFun2CMRkCKoVBb9wWJhY6CFgNSQHjdIy/9pxR1eDcSxoqiJeWbEfwFpdU+py3VB+V0yaVQlJk4gTA6ste+DNByIKIpQfBwgvzhv+Io062uqDgpjiIzEtoPyRjLTYFRG//to7APk/z9kSPvzrWAO2nOOmgoAf5TE53LskHtNeku75idP6gnavcopcexcBJ4ykCDFJWIVBb47vmfaVuL9azh/kFfEN6MuC1O84GNTGhIl5jUC8bDqP/Y+qOaYdxLbQSHBaDVl/ELsdv+AtXfxf+UxfjkWWhzf+5a05tlwD5+UXyvHYp/xfNo+K0dZE9/X0g0Lg7mRexwR27TwxGK2HQ7H2YeTKA6Hw+Go1CCxZ968eeXyWkQlk/JDUb9v377TSQhhoE+d3cIrr7yyUkur2dFd8kdp2l3hio/S4OR+acY95gcw/mdWMCV+RRQIPHbt2hXsuK5YsUInjmUpY3gtRd8eIkWLUfgkMYld9LjU8WJ7X3wHUCRgPNpisDT7Z2aEWQA1cpV7wXS9v3GPhrcaFsSGcj7L9ZzmmfMWh4oeRhR+FJzrXzUFwbbp5hWC+WUysIMPeUIaS++bzES6Ir1jYl434R+gcCsZrRCj8Dw5LM36aVwbReJza5t59egfGYE08748E+wQECOMv09VBeQGKkEK99IAgmL/KilydbjCIzCcjuZ7gRRAJBqQXmkZqQ+gWPsdcckLfi2teiZPLRRHXISRGHjj7Jwr7ZwvLfmL1OH8vGSnMebNVdoxzPFA5Lz+2TwSqZxUS4zLxb9PU61V7VVj4jJlZxxTz549NWnSpIBAqcz3GYejqsFJFIfD4XBUWmAq++STTwZ/JqJ9+/Z68MEHg3afRJDs84UvfOG0h0qtWrX0gQ98QJ/97GfVpk2boLjHrPapp57SP/7xD+3evbvQa9CzTksPkceQL5URpEYs/bM05Tu201+eoDBf8W/bmcbIM2aeGLxvTk7wnUKerFq1KlD2BIhIaaOWKjKvn3L2Y5iTHJhXbptmhUc8IElQIZx3lxX87LgTeTz9hya5Tyy2Iu13KnvQIq3bcFzbd24Ldn2HDh2qZs2alblVK9g5zzIfloNrpd2LjViCrGJHnfaiWEGLOgBPmIoiKDgPKDUgGrZMLtjqVOTvnZKyTpliBYURhNuwb0hdLjePmfI+VkgUvheIuGSg1YhWEcxh8WDau8xaIsIUSxT5+J9gOotZLMX/+3ebJ0VRJscYpVZV8Pnajjfzb8y+SwwUYtOkwbdLnS+3ljva+mLEFv/HfMJ5SURa42NqcN5RZWc3UY0aNVIiBiD+IP3mPGwthyU+5lzzx1nznBGHqGSIvi7ttUbbzhufK18CJYZoTkTHJ3dTnbSo2nx6gyZNGqN69aqQgYzDUUXgJIrD4XA4Ki22b98eRByHoV27dpowYUKgJjldpOcBP5X0dLsFsoj/2Mc+pocfflgHDhwIlC38OXbsWN1///3BTuAdd9xR6DUgWjD9ow2ldevyyUNlQQ6xgUcFBXjgTZFeMb4UvD4RrxVBoMQX4Pir1GstjflxVJGauQE5tWDBgkB5Ev+dUlBhGDyo/xDt2FtDS35ftEQeM0sehT5XrhVKL1xnpAStKUd3SsdCQpvS0qNqdMlWHWiQExAFx48fD4idDRs2BH47/fv3DwqYku4AxxQfHMeyf1ixReF1uuBKaDHh37TP0KKEaoZkGTwdysvHAcUFJpazHwz/HlIFhBVEyms3S/1vsQhczEXLpz3CvpRoxik16BLR7rnhHx4TY9QE/f/PCKo5D0qL/ygdxcMnAZkN8wyJP2/fMSazi2n/KsJ/GrNrxk1xaWGVGYyrPh+Tlv8jeUtTAYTIqBjbG1+XenzQCK8VTxhRh1cRBNvyf0s75yT8UiRX0WGL9dbSOdpwsosGDRoUXPNFkSl4HL3/I/MdSWbWWhJAYAaKtCXSBb+Q6ncs2fhF2Tb1O/b5K8w3JztNx9/uofr9uqrWNV6qORznIvzKdDgcDkelxfr167Vz587Qn7Vs2TL485ZbbgmSe+IRi48EDRs2DGKLaSu58cYbgyKan0OM/Otf/wr+77e//W1Q+CcCIgBFRWlJlMDUM0c6stWiWYl2xd8DGTwFCTv9JBmRHNN8sNRyqFSrYdmihGPvS1FPMYBKoyIBkbLwt1HV7nVAhzvP07Jly3TsWN62NR8lLS04V0RXowKB4OryvYgOLDfPg9J6U1BwIblPiojU4SLp4h8O1q6jTYKUpy1btgStXIcOHdLUqVODYx02bJh69+4dxGsHv1bEFx+cT0icadLsB0ztgMdIkZ8h72cUs/wehMvC35mHC2lEtZuXre2A10V5gflnUW07JSVleD3G0KRfS3Vbl+UYzRMHcm3t2rXasH6D9rfsKkWGnjYYjk9kuuh3pnQgkYeWsX0rwotZSCmODTXS+lfsO+D6Kq7wzWxibWBVHbS3db1KWv6v5OOT74rrNyzNinH1zleslQplx8Bb89sDSamZcXdhz5VI46PKHb5Ix44f1eLFiwMVWo8ePQLCkjk0vo2O94Scee9OU6GUJ2HBca170V7/0r9aXHMq45djgnha9d+KNx5GkbL49+nqMEHqenXVjdt2OCornERxOBwOR6UExdeOHTsC1UgiWIi3bds2aLmhZScrK3n12KtXr6CFh8jieG8VCup3331XY8aMCW0JAigpUMMMGDCghMcunTpsJMGKf0lbpliPf7IWC3bFKQpJPOp6rdTzeiuCSqtUoACY/0iIP0gFgc8147cHdPK6xcpNP1mAPOG7o5CqXbv26QKKonziL0zxgHFkuZt8RqwdZOJDEdVvla560S7BGFi9erXmzp0bEGqML6Kz33zzzSBKe/jw4erUqdNpBVMYKCzxbJj3aOkVHwGptkWa8WNp42vSuPulNueZh0NpiKTyJlDiC1FibTk1Fz1m5r0lOrbs7OD73bp1a/C9851jDg2hktk7V5HJ/RU9mlngdyjUO19h6hMeRako+n1a6vYBS5tCsZSY6JMMjbvbo6oDgnbo16QtU6XDG0OeELWxDGmQmHAUA+TvO1+256GiQkGHMTUta4mqEcZvx2uO62TPBtq281DQ0sf8DJkCeUZ8L3MBcy3KFFRP03+UZw5dAYQFr7l1qh3/xY8XT1YyZ+9fKc3/tX3OM4Hso9KcB6Q2o8qeZuRwOMoXTqI4HA6Ho9ICAiXMD4UCvUuXLoGigBYNFugU6YcPHw6UK/xfDBAxd911VxCRHI+6desGRTOqCUxmkyHML6Uo5GRJO2abNwUkCmRKKgt+ikAeFI8QL12uNJ8HUklK0npAMQB5svKpMxnjGtGpJe1UY0RbRbpsDPxG2H1GecL3nKju4J/NB0iX/FF664vWQlKW9IsCr13D0mYueERq0iv2fpFgfNC+wzmnsOOBdw7ja9OmTcG4YUxBpsRaEOJxdIc05bvSin8W9mkpLUmxdbol0kx8SOp6TZ5hZ6q/n20mu/jAlDeBEgPnhB39me2M7CESOelzo9GgaOYa5Ptcs2ZN0Ap35MiRQs9Nb3tQ0VbHlLU2n0QhqhZVyaEN9p74WfAo8B65puRCsUIaCy0+KFZoAeJR8MnSoU0Fx1UkXep5YwXF4Z5j4BrDLJg4ctQeYa0ykFTFtfvwe7sX2qMoQLJM+m5zZba5JlAQYszN3AuZxhyLApAxQZTvwAGDtPf1llry5xplShAqDowXVErzfmGGw5GizntUWvGkdGC1zih2zJHWvWyJY65GcTjOHTiJ4nA4HI5KCYqyZLHGkCidO3cOknZ+85vfBMUxUcQUbSgL8DpBSg7wvyCuGFAY81wSWi644AJdddVV+ve//x0oEZIhnpApDrTpLPiNNPeh5Lu7xSJqO8B4XGD2Of6n1t6QatoEu6gYvh4L74IyEGMLR5CbGtESPDevlSUpTqar5vujNfzG7uo/3JQnwe8mOeiASBkkXfEvadr3TUIfM68sLTLqSb0/Io36YXgLCsdSv359jR49OlDHoEqhZYvinwfjgPHCjjntRzyXsYZvw+Svmw9EqYw6kyFqpMCbt9k56X5daoQZRBnFFwqMsn5nxb4X6U5/kVqPMi+X+O+Ua5QiGTJz3bp1QfG8bdu24LtM/N5R+ECuUUR37NBJGw7V16y788dUg/ZSw45SRgPp2hfCW0wgJP89WqrT2shFiJOr/xv+XJJb/j1GOro9//+a9pY6X6bqg4jU91OmHKlIhQXtMhCBDbvQrlM7aI+DkESBQpskaj7ISsgUyMvVizar5j9u0KnDicxX+QOShpj0jhdJ7SYmn0MhsFc+kTrxjBcTap9kRDrjLxVAyC77q81bxDM7HI5zA06iOBwOh6NSgsKLVJ0wxJQokCg879FHHw3IjokTJ+qjH/2o+vTpow996EOn03lioLCnrQeFBB4YtPQ8//zzhUxl40H0ZHGgiMN7ZOp3pSV/LuwVUCrkxWy+dos06q48T4IUiBSInKANIzecDKFNqMMFRjJQOJCCg4Hk8T2Fn9+om6Wj8CeFwa750qY3k0XHRpSzqo2aHW4j+JNUzFp5Cuk1Fz4mdbrEjEF3ziu5KgVz3lYjpKFflbpcUXSqTOy4KOgvvPDCYKzMnDlTGzduDAo9xhH+KZBwpPj07NZHs++tpRVPRMqXQIkD3/27X7OWmTajiz/HqAdm318MUVaOwEQXs84O50dVp5VOR4SjOKFQpkiGOImZx8YAcUJLV8eOHQPyhO+c/+Mc1Pt4RGv/kx/De/KQkVRFqUQoTLNP2vGsfNJSfJKBazBefcHYx9sDQ9/qsuPP56xZz+YPvHswby1v5Ue9dqb6Yp6Ifa+xuZtri/MOwQaZQmsXYyd7VUtlr8T86czg+G5p7s+l1udZFHYiGLa0XB7emvprDvmKJQAVfjEzm57yrdRfixapvctNOeRwOM4NRKKJdzSHw+FwOCoBuH3997//1Yc//OFCLT0QIN/97neDnc3HH3/8dDsOapSf/OQnQZTxPffco7vvvrvA76FEGT9+fOCnMnLkSF1//fXBa99888164403Qo/jtdde08UXX1xsosO7d5i/QEXI09lxR44+4HPFt3xQDPzn4sI7obQy9P+MGUUSM0thAflAywTkyJufj/NQiViKzIQHpXpt855b09JQkMdjOJmsgB/xTWnsvSVPP4H0IVljw+u2I0x8KkVNsHueWPRGrdiu305q2k/q/VEjhoJI05K+b14bCmTAnDlzgrae2NKJ8dJ03xAd+e0EZe2v4EzciNR2jCkrivNHQP7/4vXlk2aSKvi+h91zSHUvXKvVq1cFbW5cf4nLTIjKJk2aBGov2uy4JuP9cGLg1zAUZdxVtJqGMdHxEunyvxVuEaougFyd+7ApUsrDbBpSCoNeFChtxxUdGc0YgZyEqJw7Zal2/WK4oss7hFzYFQc8p655DiIwvDVu2l3haWDJcPk/pE6Xmll04rje8Jq08LHUX4u5lSShAbdWH4LP4TjX4UoUh8PhcFRKUHRhQti4ceOgTSceGMn+4Ac/CPVQ+dOf/qSbbrpJ55133uk0CJQrkCU83nnnneC5tPFgNPurX/0qIF3CSBTaOZKZzsZAIUu0bEURKICI4uk/NNVG1yuLJgrwF6EoSESTnhZZyy7+218ywgRJep9PSIO/aOTHK58wMoPdejwwata3526bYdGwPJdEGVJbpn0vXO2ydZopSUpKZvB8lBh9Pi71vMFaEPatlPavsshalArBznoDO77GPaXGPUwlw+cobfERv2uOYoLEHvwc8EvJPZmmAy91Vm5FEyggaueONi7ORTJFBhHZS/9cNIECYQbZRpJKSn4pESPUgnGTZOuNsT3/+e06kf22ojWyC3x/kJpcp6gO8Jxp3rx58H9FqZH4UY/rzT8Ib5eK8nUBtP6Mu9fimqsrIEBHfs+UaDN+Ytd/ab19IHV73CAN+3qeZ1Mx1x7jAEUfJt/H3+2qnau5nkp3wXJdtBuHGkra8m7qv4d6bs2zUtuxhYloCGdI29QPQmrYVdo6RXrhg2X3nmLso0QJEtuqgV+Pw1EZ4CSKw+FwOCot8D2hHSCRRIEUoS0APwbk4fFghxyyhChdlAT4nuB/8stf/jIwNoyB5xCNTCsPhR+vmfhaFIWYI9I2xHHwevGFIbuO61+1hJCKNEgE7B5P+bb5OkAchBUuHE8QCRtCorQbb+k/7Pyvfia/WCbdhRaYViOlGrUtMaLjxUa6TP1ewfQMnkvqTe+PWetNmBrl0Ebp1DEr2koDPheSe4wqUZkEXiwJhX3w2SPlu2vLecUEF2NZ2r3mzpmrFU9FlbWqvc4UAv+Gx817hOK0EKJmfLljbvICs8Mka42q18bUB7RrrXraDFuTAZNfyKvFf7DxkwyYB6dNqqOcuoeC6w+yhLY6yCfIxliyUSqtXCC9jimsGEdrngsn/8oKlFQXPGqfsbrv8lOgY2BMyxjX9dK/SfuWGTFXXEIWyhM8h/CUQdHGfMJ4K9F3Go1ow4s1pdKeZxRyl1ls8Z5F0pMTSvLeptKDkK7bsuCPaFU8uC71l8okSa2ZeVaVl94fohhSy0kUh+PcgJMoDofD4ai0aN26dWAEu3Tp0gL//8EPflA/+tGP9OCDDwbKk3gMGjQoIFAwCYVkgYi59dZbAxPReBIFxDwa8E5JJFAgVWhJoJcfdQLPxYy0ffv2wd8z0jN0ZJul8KSSwFMe2L/coosn/tzUBmFF+Mn9SX45YmlBqB3iCyZUIxQCFEOxeoiCk91ZpOrxu6x8Tnaw2c1uNUxa97/Cb0NBQqFSWhIllCwp+0ul+H72TqgqhveeqA1TafU5AyqUOBB/jN/HiG8XLlA5bXiI8JxEUNCe911p8O12Pom1xRsGwguCBPPaxNaD2O/1+5TU92Y7n0WRKDpaW433DFK3SafUvXu3oG0HxQnXSmnA50PVMOnXRpzxuRk/5YKIEVH4dXS4sOTKqKoKvnPaxRgnvW+SdsyyFj2UGCRQ4c+DygllEqQJ6jDa5iBeIDAadrFzVRpC6uhOI/VKC5RntAoyt5TmfOIxxWes1SwnuDecOnUqeBzZnaNjBxvQ9JPS60BQQgAe2Sy1Gm7fD3PvwQ3S3qWlU6bgT1VeCWUOh6PscBLF4XA4HJUWFGcYxOKNwmI3Bkw/KXRvu+22oCWHhBX67vFguOOOO4JUnyeffDJ47nvvvRcoWT73uc8FbRqrV68OCJN27drp61//ekC4vPzyy4Xem//v16/fac8MTBFJHkGWjjIFlUr2tD7aObfeGfs+KI5RkdBW03JY4UKGnfxkEn0SIPAaQZ0AAYMvCjuq3a6z3XrakVCQAF6Xgj1slzVIpahprUWhx5h7Zr06Kgq7ZqXrcEHO7TQoLlHqUFAGsbubbSc7zJy3ACJWdGEgCWHAuUgWy4r/Te3E9pNci5oNUz21GSUN+oIdB0lC+1bZrjZtF6g9zvue9PJHzb8HNOxsx9/1KktwSakozYmo8dYRGj0sTRl1y4faYqzVbi6d/0upSW+LoyUNqThlRFEg5QSlBC08EH5BupSjAPAwQZmGCo30L8YiBGzWEWsvYTxAwkFYQHTxnZZVyROo1AonXqcE2vgwyOW4Sku05ZyK6q1XJyt70eaARIm1eOYcS1NWnUslpaY6Y76ESMLse8S3zNOEa43vcMUT0uwHSu47w2dyF0uH49yBkygOh8PhqNQgjnbIkCFBgkoMS5Ys0S9+8Qt961vf0jPPPBOoRVgMDxw4UC1atAgijklYAZAsf/zjH3X77bfr2WefDSI2WUDjgYHSBLLl6aefLvS+I0aMCAxlIU5o6YHEgVCh/YfHpo2bVfPfzRTNSU6iBLHEaUV7TeQ/2XZ/A2VIETuZR7ZbAR4Uh3l3eY4rSL3IyVE0cHikIi5Y8Zw6ag/QcqgV1g07WW//xjctfSV2jKgd0jPNP4Cd6tgOKYk+LYbY56KoCf0YfI5KLkkP2rReMU+RsF1odsMhnyicOFecY7w9MKdE4p/sXKOMuPhxqWlfafO74SQKwB/hwForXgtGChs5kgjeH5NLPGze/5G9dgy06PAzzmW99kaicP7OjyWqUCxnWHpL8Yjo+I4aQToQJFx5gc+Y2Uga/g0z16UI3TI5WQpUcjDuiNsd9Hmp7yelzMbewpOyIqiRPSoStG2V9JwCxmi/T5sXyjtflS78bek/576dB3VE2wr+f3a6atZLnfmt09LGGvPD9B8ZcYliB+ITnxhAbHtJWjxRtrhayuE4d+AkisPhcDgqNSBFUJFAlKAIARAmP//5zwMy5cYbbww8TVCfvPvuuwFR8uabb55WrkAu/PjHPw7Ik2uvvTYgTiAdeL177703ULmQMhKPevXqBSqXcePGBe+1d+/eIKaTdAniXHnt9ENNFd3WPPSYM+pbug1qkYw6JiGHjKAwTNxFpaClwCXhggKdHUwKclJqEhN2AqBGeS6qYd/MVXbNI0EyEYa6HOOePXu15xA5mT2K/E7xQKDlhp1mImvZte9/izTjHlv4k/4y8DZp6FdsV3rnXCty+33G/FhQRCRTvEDs8NzKDKT1SP8LkSERafidUs8PS+teklY9JZ04YMlAFO6kGb1wnXQ4pN0G0oGd9GZ5Pi9FgTGwb0U0aJnKyc093XZw8vgpHdpRr1DbATvh9dsawZZIsqAKOrrdxiEkC4D4IYlk0e+MuOPcMwZSAQRKaoRLyRC0k6XbddByuF0rK5+SNr9jSp/guBNJrTSjCtPrSq1HmKKi1402pilInUA5t0AKU2m8o4gmHvY1i2jeiP93KRUbgdLjRK0C/4fPVWbNWkprEFWq3TSkmZHGhrEtxtcxbJsuXfeStdDhOXM67SwFkBrFtehwOM4N+OXocDgcjkoNfCrwQHnxxRf1/PPPn/YuIaHnf//7n956662gxQayAzIkvu0nBsgXFCcvvPBC8NxY5CaPsPe74oordPXVVwftRDwwzcRYFkUMpMWa1Wu07I+ZOrqv8HY8CSB4PHS5TDp13AriYIGcIa18Wpp8R76kneKXxJxBt5laBWID9cHgL5nRJlHCFPSJOLwzW8/8/VUdrrU5+B54xKJm63RsLtXoHrReJMPeJdIbxCVnGily0e+sFYTCddPbVrS+9UVp7D3Sed+3ogXyB3KAth92hTnWMPBZy8MP5Wzi+F57hMn4u14t7Zgpvf7ZfP8Z1Cd85n43S416hJAoEWuZgaggdSjUNDbh+Utmr9HsnGnKOpUVjPlAbZQdVe6pS7A8LvB0CK33vmXn88jWgi9Vt421D0GwECEdIGoFH6BwwzMkVRIFAiVMoVNegPjIqC11vsSUMrT2UKjSxsSfXE9cK7RUkdKEqoeoXQhIyDsnTs5h5HkclQSMX5LCICSIZy5rEk7nhgPV9dpOQSIXj4yMDEVP1dD0ebUVIvIKBb4nPBLBdY+qD0KV8VgSEoX2Oq5fh8NxbsBJFIfD4XBUehA1fPfddwfeJxjGxgMFCo9UkIw4iQctQXfddVewwA6Lwm3Tpo1aNG6tXT+N6mhugv46Yiae3a6VVv83z99hlxUCY34s9bnJiIoV/7Knt58gDbld2jlHmvIdk7tj+oh/BQktKFIW/LrwMeaejGjP4jRl9SqojYfwqdX1oE7ViBbYtUftglksx8cuasy3hAfvvezv0sSHrFUHEoUim/jOF2+wKGG8EyhmaTGhQEAhAxkQhtbDK78HBS0HMe+QeNRrbSQCrT7xBr6QGBRQEGVhbS7BTvodloaCCSUtVEUCY9gdp7R3fMMUQQAAOzhJREFU586CRWduRLXrhIzfqKlNChmrdjMirHF3ac7PzWy2rCDqOugYq2iQ0lRLatDeHh0nnYH3dFQoIL64RlKNVsZ3hBav+u2lF6+Xju8uWxsZc3jLlq3Us2fB2Hrmw1YDJJKXizV3jZjqK5p3zcWTOvydzxa0ZJaAaOQzQXSekevK4XCkBO+uczgcDkelB4vfvn376qGHHlLbtm0r7H1I3uE9evfuXWRMa252RPuXF77F0i5BggXpKVO/ayQIZorb35dm3muGrBiLxnrf2WnH3JUCFzIDBQitMzPutUU6/hChu5Onaih3kyULQTCRQDRs2DBddNFFmnD5CNVqVPDY+CioTa55JlwlgjdHUADktRo17SP1vNGUMpApmNnyJyoAjon2pN0hu6xBa9L4yt/bn3syvNAjweT566RFv7d/8zlRcqBQ6XiRgrSmQ+sL/g6k2PifWioJyUqpxvjmHs5UWo0aQbsBD851Rs101WyUk1Kxilro2udsPC76g3nelEfyDV44kBsOR0mBb0gyL6UwQEZDPM++35RIQYtWHNEQzDMlULYwp6L4SATzIy2VqbQh4l1yyV+kD75amAxl/sfgmfmRuSBV1Gpsv+dwOM4duBLF4XA4HFUCkBoYvf7qV7/Sl770JW3ZEmI8UQaQtvPoo49qwoQJRRIoADVCmCkoBTVECMashZJaYjuWcf38QQ98nqFsPCBbKBB4n2Ty9ba1+ur8D3dTgyZ1ThfaKFFyTkSCVgjUJaffOtuKELw88GrBayL2uhQ13a6xVI6Y/LzZAOni30vv3y3NeTjv2CNSxwvN02PR41J2nkltPChQ2pynSo/AEDhkVxhj3v0r7e/sqNN21ayv1GqEqXVQE+2NiwiGABv2DalBp7yd9OLSe+LQtEErTbzm6iAlihjh4JGRqXl7MjWniN+jGBv1AysKOedTvy+t/1/qu//FAVKoZiVv13KcHWBkXbOeFDJ1hJIVXF8x4gN1XmxuxN+HdDDGOS1etEni3VTsa9bK83RKRMSMurmWA2PoIoAKDzXhyG+bSgZyEtIcs1l8pCDJF/6uZFHOtNNh2u1wOM4dOInicDgcjioDiIKrrrpKzZo105133qnZs2cHSTtlATv8Y8aMCRJ9hg8fHhARxYE2mLA4SkxGMRxEyo3vCYUAUm12YId82VpENryRT2Cs+5/U+yaLyTx1WDq0Warb0gxI8ULBFyXciDGimtF6alS/njJrF/wJZrGoSNa8IGUdLJzSMv5BqXEPa+uhGOa5GKOu+Le07X177s7Z1p4y+HbzPoFcoR1oxLelQ5ukpX8OIXcgWS622NzK7kvBeaPYKyqmFJKFdqwmvaRazay4g2BCWRTzi4GcwmRyxo+lnfNKcAARqXGruurevUehdJ6WyP7TQxQtaVLvj0pj7jYCjqQgzmmhNp8ygNel0KTNwuEoKfB7gqCNN2NNBsY9rZCoNIjhPv3/pEnVNO+lnh8y0mP1s6mRKMxhkIBhgPDEYHn7rCSG3nlgbl/4mHnx8P4QyxDqzBdEda95XprzYOoGunwnzBGVvQXS4ahqcBLF4XA4HFWOSIH0eOqppwJVyp///Gft2bPntOFsqoAsIfnn//7v/3TrrbcG5rHFKVBOH0NGEhV5bkHiAv+Q7tdZ7CpS75n3SZveyv/51mnW9jPhIenaF8x3hEU1RQIqELw3koEkk7DD5f8gRdqNtZSdmPJlx1zpzdusyB7yFXsPDEIhb+Y9Yv4tseLhwDrpnS9LY+6xY6NwoF9/9yLpvW+G77KyE9v/M1UjYYLCDYNgWrGSge/qlU/ZecVzhF1pzHkxcJ3zgHnJQIbRBoUHTtCKEHvkKVmCmNSw+Gt237uEn1uImnrtpEMbCv6MJJ9x9xlp8tYXpB1zSp9ikgy0d3WYVPlJMsdZQsRadNY8W7xnyKlj0hv/VzguHRXKhyZLRzZLz3/A1HqQ2sW+dZqZJ3Nth/48YulOtCvGz9FhQFHGsUGgtBkj1c6bK5gfN7yWbxxe/EHlv6dfUw7HuYUqsJRxOBwOh6MgIDvatWune+65Rx/+8IcDIoWkng0bNpxOMklGnPC73bp105VXXqmbb75ZPXv2DIiZVAmUmFIBxYdCWnrigSIBdQkLeBJE+n/WFCDEF1PgomIY8H9GyrCjenCd+Wu0P1/q+0lThARGryFA8ZBYYMTAsRHFSyGNWS2IZlssL0U9vfyYI6Kc4T3p4S9Q1EQtSpTfb9LTlDQQPLSHhBUIECcD/09q1l9VAuxyYwC7K0E9gqqIc49KiJ1mzGV5HN5kZrQfeNlIs7kPW7HYqLt0cIM07qf5r9FmtKlGzvuuqX3m/8p+Px4QXPjShAEVUcshBUkUxgHjhQITkgvVSzCcE4Z0WZNNGAsth5btNRzVGxAGzHth6TYFEI1Lk0q4Bpmr8PdJTKIqCnhB9bihaPNWCGxUgczRydLH4qO+8YpCeRJcZ7klv75Il2KedmWXw3HuwUkUh8PhcFRZ0IpDms4DDzygb3zjG1qwYIEmT54cpPgQRUz0L8BXokmTJoFh7MSJE4Pfad68efD7JSFP4kkDlAIxgiIZlv3NWipYJHe5Urrgl9LoH5qJLDuoo75v5Mrkr5uHCTuqyMoxKb30T9Lou83AlVSKAoiYGiFZJCYfiWJ96Fel6XcVNBRFeg45kEgQhAGCYPuMYp4UMdJn0JeMDKoKgMRg1xrS6XRhFDGzVlJ2Xv20tDmB3MILB9+RmM8N54xiDEKGR3yhBiBJ+DvERyLwbWjcLXx3mu+Y40BlFFMOUSC2HmVKEZRG2Un8T9653cxxS/udkDzlfiiO0oLxTNISyWPTf1CyBJuyvbHU9RqpOSRvpOjj47qH2Jj+w6LbemIo7WfAxBalXzKy1OFwnF04ieJwOByOKg1IEEw3UabwuOKKKwKflMOHDxcgUUixKaniJBkoVlEDkLqTuDBuPsiUBSg8aNXgATmy9gVTC0BuZDaW0k+aFBx/ALxPYgqP7GMmJ6fVh9YJCvBEEgWDxBYDi5aAB+qQWy2KeOlfU+/RLynwGSAeGUl7VUKnS6QZ95jqJEBUOr7XPBU6XSRtfS/OlyRihpKk4mAqjOpnxRN2zhOBKqXvp6Q3brW2qDCDYl6LWNdkYNxwDGtfsuPCTwGjX8ZdjKQJQ5hyCc0WSqR9K804Nxka97Li12NYHWUBY5V5kJYekshKCsjn/300tRaeeMXH4C/avJ0KWUgbJuk6i35XfobM8cDMG6KmZzHKGIfDcfbgJIrD4XA4qhUgSTIyMgLlSUUBgqL1eWbWGr/IbtRVuv41acmfzZcifpcSEoOFP7ubwf9H8hJ4jhUmODBJpD0k+DwhPrcU6ygPUlmsj7vflCgrnyzngiDPn4Po5Kb9ql5PP0qjLldIy/+V7y1CixNKDgwoIVTwrKGoo0WBtA7OZSwVib/zSESs+KNdIMy4llasPp8o2lsG5Qkmv6iEaLNCEfXE+BQ+VEiXG4TPzHukmT9J7qHCeMPzxRNEHOWBem0sWeeVT5rarSRg7qQlMVWg1sPUu8WQ1OcoxvuYH5vqa8FvSkbYFAeIdlqGhn4lNVLH4XCcHRQfMeBwOBwOh6NECGTf48y/JB4UtHhVEC8LoRIP2naI0dyzzNQHkCQoEZr0NjVHPFAh8NwDq8N78zucn1pBy3FipEgbEWaymKCW125yx0nSFf801URVI1AALVh42NRqlP9/KIIm32kmkmN/In1kuvTxudJFv7UV15RvSVtSKfCiRRhNXmbfaXFtBxjJDv9WnJ9CNIVHKY+JNp7uH0jhczkcKQBimKSw4V83j6GKeyMjJGl/K9GvRWyuHP0jafzP8ubass5xEfOiuvC31mYJgVIV502Ho6ogEk3mrudwOBwOh6PUwCvj3a9Zsk2sAIVcGPZ1S2XBhBUVw5EtFofJYp6WlzdvlVb91xbVyLkvfMzSHhb/0VpB2KVF7g6xglcKapd4w8KM+tJlf5G6XZf6IpyVAAqY9S9Ls35mXh2lau+JGMHT72aTvBPpWZULARQ8k++QFjxWUFWEHw0GqxitAlqm+E4PkuZTjLkkxBvJP/tXFlYGUaxd/R9TGaXyvdJ+M/U70sLfFvS9KTekWbzshb+xcelwlBuilsBDCtn8R8tX7RFLL+t5vTTxFxYbX1rQsse1PfsBaeObJVfOgDqtpC6X272hSQ+PM3Y4KgOcRHE4HA6HowLA3XX3Aum5awumq9BCQ9TtgM+adJuedwpcYoPnPGheADEvjWCh/yHbmWzUxXYnITdIpSByGC+TxOICr46r/iPVrFe6YyYCd/V/pcV/Mt+WoG2omJUCLSYQJt2vNTIIlUx16OXn++LcvnijtGNmxb4XihJaHCi0Uv1uOT48W6Z930i4VIwwUwVjk3amCx4xTwmHoyKAF9TcX0pzH5JOlIKgCAPqFtRTY+8paOpcWnCdMQ8HceVPSpvekE4czPOxioYrbWgJ4r0hIUkFQl2G34rD4agccBLF4XA4HI4KAmTIzPuk93+UkNKQZgakDTubfwVRnLT5xHxOEgHxQnQtSgRUKQfW5pnJJtzBUTBc9ZSl4ZRWARKsCqJS1hFp61QzSN29SNq/xrw1KMQxICWFBWKH9IjWIy0xiONjF7Uqq0/Cvq9t06WXPy4dWl9BbxIx0m3Cg1Z8leT7DQq8o+bdMOv+4qNZUwGJQX1vNlKHQrA6nW/HGQYquWxpw6uW2IOCr7RR3JAX9fNig1HzQUyW59jlWmOeh+TePkvas8ha+04eMlVZeqYZOzPvYzBOyx0m4hyXX0MOR+WCkygOh8PhcFQg8EF59ZPShtdKv/hPBRAb533PTAnLa0czKApyrQjHIBXFDP/Hgh81BAoUCurq3r+fmyNtelN68/Om3ilPYCDb+yZL7SlL2wHnDlIMImXL5NKZCEOQoTJCDdP1StvRr87n3XHmwDwE2YwpN8lWtLulPJ8SndxB6nq1tRkSEV6UMXN5IWiTZM7MtmOFLGGe5uHXjcNRueEkisPhcDgcFQjusvuWSy/fJO1akIKBZylbK/rcJE182IxiHWceFEnb3pfevcOiWSmcygrOJUk/w79ZfhHRKJnwbliS57HDrnmRxSjmw02kxt2tVYtCtB6KI48mcJwFoPRA3bFlqrU+7lkqnTpsag9aHZlv0zKiUoPDyql7UDW67tTIy3up+0X1Ar+RsBhvh8PhKCmcRHE4HA6Ho4LBnZbC+vXPWmtMceaiJQFFAb4pE3/urRXnhEfKZvO2IcqYlKXSkGac06b/396dwMdd1/kff00m932n932X3qUHlVJuigiCgIKu4C7KouAtuoAK/931WEXXRXFRVsT1YFFBjnIVEGgLtKWl90HvK0mPNPedzPwfn/mmVzJJZ5JJmk7ez8djHrTJzO83yfys833P5/v5jIfZ98Co69yn5pHedmALTtsaUbzSNcYs/QCqCqGx1o8vpRxfv8N4cssYNr4fky8ZRMFMD3Ep7vG6xuRMOr5y8Z+YeBa4dqtdIBib0syyfX/lcN3uQNh39dVXM3bsOF23IhIxClFERER6qiJlk6tU2LM4Mlt7bFE7+Z9h9r+4agEtEs68YyX81mh27SOul0OgIebp3m153HaZ3Akw7iZX9ZHSr/srPuw6tN49drNP+X0+H0uXLWH1mpUQ42PW7FnMv2A+MTEqPZGzgy1tnn/+eTZt2hT4+6xZs1iwYAEe/QMpIhHSAzsCRURExN6/Z0+Ahb9zI2c3/AYq9nayUiEW8qa4BokjrnJ9SaT3vM7WQHLg+VAw0/VI2fsa7P27m+Rj2w6sx4yFFtbY0hrFWgCWPwWGLXSjkVPye27MqYU01kPnWB8dv99DZn4KxPoCi9Gy8rJAsKIQRc4mAwYMOB6iHD58mKamJuLitJdHRCJDIYqIiEgPLrBtKs+ce9142I2/c/v6rSQ9lPGzcWlussM5t8KY6yF1kKpPeit7XSzcyj3H3abeCXUlridJY42r+vAmQEI6JOW7qqLe8Frap/VpaWl4vd7AwrO8vDwQooicTQoKCgLXcHNzc+Aarq6uJjMz80w/LRGJEgpRREREelhgysl0N+by3K+7yS6Fy12jz/IdrpdGoEFirOtzkj0W8qfCgHkw6Hw38jiw/aMXLLolNDbNyAI0u/V2FqLExsYGQpSKiorAQlTkbGFBYGpqKunp6ZSWllJZWUlVVRUZGRna0iMiEaEQRURE5Ayw9/IWpqQNhgm3wribW8YI15/oUWFbLCxIsYoF2/ph99caQLqbLT4tRDGNjY2BBWhSUtKZfloiIUtOTg6EJhaieHxeijZW490PzY3u39X4NNdzKCFTk6ZEJHwKUURERM4wC0YsKLGbyJlkTXEbDyeRVDIMb3kMcdXZrLw/kUSvW3CmD4Gsse6/tgiNiVewJ71IS2Pnyp1xpO2cQcrLU2nal8V7vgxW1Z0IUWITXZCSOQaGXQaD5rutksd6A4mIdETTeURERET6MHsn2FgFB5bC1iehaDmUbfMHqqGwT+kDLVE8x//siYWsUdDvXBj7CRg4z20xU5giZ5IFJCXrXePunS9C1QF/S+Pu01yYHlcRaH2qJt/uJmTZNa7rWUTaoxBFREREpA+yd4AWlOx/A957EPYvgaba8CZGeRNh8AUw/csw9BJtOZMzcx3Xl8L7P4c1D0PNoc5NPbNtPckFMO1OmPp5iM/QtSwiwSlEEREREelj7N2fLTZX/QTWPwp1R7t2vMQcmPhpOPcbkNI/Us9S5PTX8dGt8NbXYfdi8DV0/Zi2rXLY5XDBjyBztIIUEWlLIYqIiIhIH2Lv/Cp2w9+/BLteAl9jZI5rn+QPXwgLHoSsMaffRSHS1ev48Dp45TY4tBr8kZzE7YH+c+DS/4bcSQpSRORUClFERERE+gh711e5D178NBxYEuGFZ4tBF8AVv4H04Vp8Svcp3QYvfAqKV3Zu+04oBp4PCx+H9GG6lkXkBA31EhEREekjbNuOVaBYE9nuCFCM9VZ5/cuuT4U+qpNu6YFSAUvvheL3ui9AMYXL3Hkaq7vvHCJy9lGIIiIiItIHWBNZa765cxH4m7vzRLD7JdesNhI9KkRO4YdNv4Mdzx2bHNWNp/LB9mdg8x/A153/mxGRs4pCFBEREZE+8Ol98QpY90jkeqB0xM5ho2aLVnT/uaRvqdgLq38GzXU9c76mGteAuWp/z5xPRHo/hSgiIiIiUc5Cjfd/AdVFPXfOulK32G2s6blzSnSzapDN/wsVe3r2vGXbYcsT2p4mIo5CFBEREZEod3gt7Hmlh0/qh31vtExO0eJTIqCuBLY9HX41lTcekvIgPqNzqx/b1rPjGag9HP5jRST6xJ7pJyAiIiIi3fvpvS0ArTIkGFtYZo2CjOFucVq2Cyr3QH152/t6EyFjmJtWYvctt/vudf1W2lv0Wg+WAfMi+zNJ33Rkg7uFKjYZxlwP426C5ALXo6eqEDY9Drtfgaba0I9l45RLNrkwRpN6RPo2hSgiIiIiUayh3E3MCdZMNnsczP8hDLkYPN4Tk052L4Zl98GR9Sfum5gNc++H8Z+EuGT3Nftk/v2H3FYh6x0RzK4XYe53IDapO3466UussinUpsgxcTD9LpjzHag94iqiLPizscXDLoMl98CaX4R+PJvQY+OUbYS3iPRtClFEREREolhdGRxaE7wCxQKUYVe4fg9WMWLNOkdcBRM+BfEp8MzHXAgTEwuzvgVTbnfbgrb8n9siMfFWt0i1c6z/dfDz1xx2PSVyJ3X7jypRXlFl1SChjuZOGwTTvghHt8BLt0LpNhcS5k+HK38PM74EW/7oApZQHVzV6acvIlFEIYqIiIhIFLPtNvYpemv5U92n6h/8Bf7+xRPbd/a+5qpOhl0Ogy+AHc9C9ngYf7NbRC6+48SkkqJ34bpFMOVzbkEa7DwNlW7bj0IU6YqGivB6klhYkpQDK//j1Iqq4uXuOh55lbvOwwlRjgUxaDuPSJ+mxrIiIiIiUazyQPBFX/ZYV02y64VT+59YELL/TYhLcX1S7LH9Z7ueEtufgSo7XouSzVD4DmSMhIIZwc9v23yqD3bDDyZ9SlOdu4Wqch8s/4HrfXKy+HQXrth1Hiz064hNmuqJEeEi0rupEkVEREQkijVWuUaYrQfkWIPOpd92IcjJPDGQNtgtFuuOuk/e+89xC07rK3HKgfyuT8Toj0HmKNj/VtvzW8+J5np9gi9dE+hdEsaUp+IV7tq0x8TEQ+YId12PvNoFfuseCa8KJcDnmih7E8J99iISTRSiiIiIiESxGGsYG0Th2+7WOkAZcolrHlu248T3Uwe4UCXYotN6nljPlMSsdp6Axx1XpCusasqus7C0hC4Wnlz7PCTnuYk91iNo85+guSG8w9n5FaCIiEIUERERkSiWkBlaM86U/jDxFph2l/u0/Z37XZBybAuENfa0/iat2dcsJIlNaak08bddeNrWIFWhSFfEpbrrsDNqD8HS+1wYaH1+Bl8Il/8PPP8JKLM+JyFK7qdAUETUE0VEREQkqqUP6zjA8Ca67ThX/xXm3OcmoCy6Cbb++cR9bCuPbQkKjEFu/fg4F9I0VgbfbhGX5ialiHSFjchOHxrGA2JOBB4W9G19Alb9BJ67Edb+EvImuylUIfNAnjVHVhgo0ucpRBERERGJYin93C2YpFyY/wO44jGIS4bXvwjP3dCyjeekQKTmIHhiXVVLsGME+qeUBT9HQqrrlyLSFRbiDfxQ8CCvzX29MP4mmH1v2+oV68+z4zn358B1GUYoYr2BREQUooiIiIhEMQs++p3b9uvWG2Lev8I5/wSb/wDPXAcbHnONaFs7utWFLDbRp7WcidBQBUc3Bz9/5mhIHRiBH0T6vAHzOui906oJ7eCLXGWVXZ+tJWSB3w/VRaE3q7UqGKteERFRiCIiIiISxawfydBL2jbEHDgPxn0CtvwJ3vwGlO9sf0G573VoqoVhl7X0N2lh4Yj1l6jY7bYBtWbbKUZfG1r1gMjppA10jY9DqR7Z+6rbZjb18+46jYlzzWmt+mTqHdBcB3tfD/3cA+ZC5khXESMifZsay4qIiIhEMVv0jfwIvPdjKN917Isw/Eo3+rWpzjWUDebAEheOHNkI+9+EUR91f9/6f5BcALO/BRnD4M2vQ1NN28dnDG9Z9IpEgFVPTfgH2LUoeJPjk+1cBDtfgDE3uAoqu24tzLMwxKb1rH0k9BDF+rFM+qz734uIiMfvt2I2EREREYlWNlln+b/BO/8G/iZXi3z9yzDkYjeJp70KlDe/Bu//3P059xy49BHIm+YCExudbIvS9Y/C2/dDQ0WrB3tg1jdh3r+1P2ZZJFxWEfXirfDBk6e/b2I2TPk8DF4AKQWuH0rlAffYbU+5Y51WjOuvYtf+yVVYItJ3KUQRERERiXL2bq9yHzx7HRxc5b5mvSJOtyis2OOayp48BnnIRe6xNrGn8B0oejd4FUruJPjoM64aRSSS1/Kh1S3jibef/v62pcwmRMWnuQCxviL49doe28JzzdOQc4628oiIoxBFREREpA+wd3zbn4GX/xHqS7u/me1lv4bR150YMysSyWt542/h7186/baerrBKlov+C8bdpOtYRE7QPwciIiIifYB9ij5iodti07rJbKT7Vsz8Koy8RgtP6b5r2YKN2feAN7F7zhGbArO+BWM/rgoUETmVGsuKiIiI9BEWnkz9gvv0ftVPw9vWEGqAYtNQZnwVYvQuU7pRbCJMuws8sbDi+1B3NHLHTsxx45En367rWETa0nYeERERkT6msQbW/ALee/DUnied5vGTkNPMrK95mXqnh/jUCBxTJATNDbDjWXjnASjZ5MYad5Y1SrZJPnPvd5OovHGRfKYiEi0UooiIiIj0Mfbuzxabe1+FZd+BQ2vA19CpI0FCE95pO8j9yH4+9uXzSUxK0PYH6THHVjLWZPb9h2DLH6GuNLwwxbadWf+T8Z+C6XdB+nBt4RGR9ilEEREREemjbKFZWwKbfw+bfg+H17YsPv2hfWqfNbGBqvNepXbwFmLifVx66aVMnjwZj1ag0sNsRWNB4NGt7nre+3c4st5VqgQu6JOv6cDl6Qlsb8ub7CZOjf8kZI0Bb/wZ+xFE5CyhEEVERESkj7PgpLoYile4rRGH1roJPnVl0FznFp22uEzIgsRMyJsCI6+G/JnNLFv3Mus3rA8cJy8vj2uvvZasrKwz/SNJX2VhioWDh6BiHxxZB8Wba9m2eQfNdTEkpyYwfMwQ8sbHBQKUtMGQlAcx3jP9xEXkbKEQRURERESOs3eG9eVQXQi1h13/FAtRYpMgKRdSB7gRxlZsYm8jDx06xNNPP015eXng8XPmzOH8888nJkajeaR3KC4u5vHHHz8e9N14442kpqpxj4h0jvpNi4iIiMhxFo5YtYndTn9fD/n5+YEtPEuWLAl8bf369YwZM4Z+/fppW4/0CvrMWEQiSR8RiIiIiEinWVAyadKkQGhiqqurWbFiBU1NTWf6qYm0CVHselW4JyJdoRBFRERERLrEtkace+65xMW5mbA7duxg586dqgAQEZGooxBFRERERLrEPtkfPXo0w4YNC/y9sbGR5cuXU1NjDVVEzoCWMd42naepFjw+LzR78TR78TV48DWdGI8sIhIONZYVERERkS6zt5SFhYU89dRTgfDEgpUFCxYEKlRaZsrSXA+Ve6GqEOpt8k8jxCVBYo6bkpLSz41ONtpxIeE6tqpprIKDq9ytZDOU7KmhpH5vYGpPQnwC/XMGkz06NjBlqv9sSB/qHqdrTkRCoRBFRERERCLC5/PxxhtvsHLlysDfbdTxlRdeS/OeXHYu8rD/LTfxp6kGmupcpYAnFuKS3S19GAxfCEMuhpwJ4E3QwlZCY9dSxR7Y/gxs/iOU74C6UleR0h6bOJXSHwYvgIm3Qr+Z7msiIh1RiCIiIiIiEXP06NFANUrJ4VJij+aT+d7lVK8soLE6xDTEAykFMPQymHYngWoBb3x3P2s5W9lKpqESPngSVv8XlGx0gUpYbCJVNoy+DmZ+FbLGgEdND0SkHQpRRERERCRi/D4/7760iXcfrsC3ZDJUJLd8J8ySEg8k5cLUO2D6lyAhS1Upcipbxdj2sCX3wra/QnNdFw/ocQHK/B/AiA9DjOuTLCJyCoUoIiIiIhIRVgGw8wV48+s+SrdbE5Suf5xvC9lhl8OCByFztIIUaeGHIxvg1c9D4dudqD7pQGIWzPsenPMZiE2I3HFFJDooRBERERGRLvM1w7a/wJvfgMp9kT22ba3oPxcufxSyxipIESjZAq/c5gKUjvqedFZ8OlzwYxekxMRG/vgicvbSbj8RERER6RKrAtj7GrzxtcgHKMeOb4vlxbdD1QGNpu3L7LWvLYG37obCZd0ToJiGClh2H+x5VdebiJxKIYqIiIiIdJotMMt22hYeF3B034ngwDJ45wForO7G80ivZoHa2odh98vdf66aQ7D0HqguUpAiIicoRBERERGRTrNmnit/CEc2dv+5/M2w+Q+w89nuP5f0TofXwppfgq+hZ853ZD28/3PwNfbM+USk91OIIiIiIiKd4m+pDvngL9YUpWfO2VQLq34G1Qd75nzSezTVwbpfQ00Pvva+Jtj4Wyjf2XPnFJHeTSGKiIiIiHR6a8W6X0F9Wc9XI+xaFNmJLNL7Wb+d7U917nW35sQeb+fOa6HN1ie1pUdEHPWaFhEREZFOKdnU0tzzNGLiIS4FGso7XgB7EyExE+rLXcVJe5rrYcsTMO6TGkHbl1gflNoj4T0mKRcGXwi5k8AbB+W74OAqOLg69Ka0ds3uWQxTvwBJOZ166iISRRSiiIiIiEjYbGG5/w3XfLNDHjcmdvzN8OItULG77V3Sh8PMr7gxxvGp0FAJO56DdY+0f/xD70PZdsidGJEfR3q55kbY+3p4VShZo+Hih6H/HGisgqYaSMxxk3dW/hjW/jL0XidHt7gAJjFbI7ZF+jpt5xERERGRsDU3QPFK1zOiPZ5YyBwBk/4JsseDN77tfdKGwFV/gom3Qn0p7HvD9b6Y9U246CGISw1+7IYqKFrefSNupXepPRQ8gGtPbBLMvgcGfsj1NPnbNfCXK+CVz0JjDZz3Xeg/O4zzH3GhnYiIKlFEREREpFMhyuH1wb/nTYCxN0LeVBh6qasWqS1pe7+YWJj6ecifBkvvhTUPu606cckw97vue2Oud4vgNuevg9Itrk+FKgOin10/NYdDv39yPoy4yjU+XvbtE317yrZBYhZc/At3bR1YGuIBPVC6rVNPXUSijCpRRERERCRstg2iYk/7vU2sX8mIK13PEvvkP5iEbBi8AI5uhi1/dNstbIyxbefZ8Fu37WLEhyE2Ofjjqw640EWin23HsZ46oUob7K6b4uVtGx/b2GKT0j+8ZrOn3bomIn2CKlFEREREJGwWXvgagn+vsRIWf9ZVmth2nAt/BrnntL2fVZzYQtYWtRacnMwClLpS97iEdBewtDlPdcfbiSR62OtsfVFCVbIZnrrS9TFpPaWn4Fz3Z/uehXahCnYNikjfoxBFRERERMLW0eLTmn/aOFoTl9b+4tO2BNkknoRMtwWIk4IUm+aTkOUWvTbdp73zSN9gFSMWyoWaedSVwP43T/2aPX7Y5TDzq1BdDFufCO85WJ8VERFt5xERERGRsNmC0hrHdoU1kj38PuRPhUHzT7wztWOP/xQk57lwJcbbwXMIYzuGnL2soskqkjorfSh86HtwxeOuj84bX2m/p09H45JFRFSJIiIiIiJhi4mDlH7h9alorakWVv3UjTa2STyjPwaV+yF/CuRMhJqDrtqkvS07yf2CT/yR6JOUA0n5UFUYfvgy7uMw/SuQ2h92vQQrfwSH19keoTAO5IfMUeE+axGJRqpEEREREZFOhSg5E7p+nENr4fmPw97XoGAGjL0BfM3w9v1wdCvUHnXbftqc38Ynj2y/SkWii03bsWax4UjMgQU/gQsedGHfS5+BV26Dw2vCDFDsWFmQNTq8x4hIdFIlioiIiIiELTYR+s2E7X9zn9J3lo0ntqoAW+BaMGPhSFMdpA+B2fdA8QrXZDZYhUH/WW70rEQ/uzYGXQC7FoXWC8e2gdn1M/5mWPdrWP49qA1jRHJrWWMhY7jGaYuIKlFEREREpDM8MOh8t82isywIOe8BWPCg63fRXOdG2fqbXJ8U235xYKnb9tOaLWhzgkz8kehk4YWNzLYmxKGwKqVzboWdi+Dt73YtQLFrfchF6okiIo4qUURERESkU4vagpmud0nrKSih8jW6LRrWC2Xv67DrBfD5IHsMzPgqlO9pqXRpfe5YH6M/7iMu2fbydL40wCoa6o5C1QE4sgGObnE9WSy0scqH5ALIGgN5k1xljP3dKmXkzMgYBiOv8bHxcQ/4On7dBy+A+HRIGwIX/mfw+xSvhLX/ffpKKgtPxn7CTYoSEdH/DYiIiIhIp3gTYcrtUPQuNNeH/3h7jC1ih1wMFz8Mu19245AHX+gqXP7+5WCNRP14B5dQNGA1RcWTKCgowOsNvTGKTWax8cwVe2HLn9w5D70PjdXHD39CyzrdghPr/2LbSSZ8EnInu+0i2trRM/z2ogEVtaXUz9mE99lpNJekdPiY1EFufLYFcnZrz7pH3DXRHpv+NP6THR9DRPoWj//Yv0oiIiIiImGwd5H1ZbDoZhdGBPtE38KGibe6EbOr/xNqDrW6Q4zrrTLlDte40+5fvgM2PAZ7FgfpfxHXTOwtL1I3aiNJSUlMnjyZ6dOnk5aWFvi2p71kw8IT3MSftY/A+kehuthtHQqVHdqqG0Zf56a95LZsJ+qLYUpgBeGHhkoo3+Wqeax3jVUXxaZAUh5kDIXUgSfGUHfm92RLlebmZjZv3sy7775L6dEyktfNpuEv8/A3eDsM+LxxHR/bpj4F2yp2suzxcN0L7vrti6+ziLSlEEVEREREOs8PB5bBszdATXHnD2OL3rgUN22nocpVpLThgcwFxVQtfIo6X6X7ksdDTk4OM2fOZOzYsSQmJgZ/ms2w/y1Y+m3XrNYW+51l2zpsUT3rWzD+UxCXTJ9hK4fqIld9ZFutbEtMfYXrZWOVRfZ9q9yx34m9nrYNaviVMPQyyJ3oQrJQ+Xw+jhw5EghPtm/fTmOje9ESPWmk/O0Gyt+xJiXdl2zYdJ/LHoFR12orj4icoBBFRERERLrEPtFf/2t461vBJ+lEhAUoI+Hy3zdQnbmL5cuXc/DgwcBC29iWnqFDhzJ79mwGDBgQ+PuxqhQbkbz1SVh6D1Tui9xTssa40+6CWXdDfEZ0VyrYiqH2CHzwJGx8HA6vDT56OigPpBS4IGXanZA31QUt7RYN+f00NDSwZcuWQIBSVlZ2/Hvp6emB1zi/aSKvfy6ew2u755cemwTz/hWmffH0FS0i0rcoRBERERGRLrMF9fLvw3s/Oqm/SKR4IG0QXP4/rn8KHj+1tbWsXbuW1atXU1VVdfyuCQkJTJw4kRkzZpCVlYW/2RPoffL6l6C+NMLPy3YjxcO0L8C8f3djn6MuSLFtUH4oWg7L7oN9b7qqnk7xQHKeC57sZlujWv++jlWfLFu2jB07dgS28hgLxUaNGsW8efPIzXUVKFYFs/hzbkR2V8Zst2YVNHO+7Z5jX6oyEpHQKEQRERERkS6zd5Q2onj1Q7Di+65XSqTYBKALf+rGzB7rr3HygnvlypVs3br1+HYPq0DJyMjg3HPPJbVwAq99NoGa4u5LN2zRPfc7bqJQtE3vsSqjTb+HZd92fU8iEVbYlp7hC2HBT9yo6pOrTzZs2BCoMqqsdNu1TGZmJnPmzGHcuHHEx8cfrzCyfjk2Uemtu2H3K13bonVM+jCY+10Y9wkXiomItKYQRUREREQixvpi7FwEb98PJZu6ULVgWyqSYeilMO//uSau7fWlsPBkz549gcV3UVHR8eqFeH8aiU/cQM3a7u2dYZLz4co/uEqZaKlGsddy3a/ca2mjoCPK435Xl/wCMkb5KC4u5u233w68jk1NrtuvBSZWfTJ37lyys7OJiYlpd5vRxsdcw+CK3UGaEYfAqmLsWpv1TSiYoR4oItI+hSgiIiIiElH27rJyL6x5GDb/wU3EsYqGcJrMZo912ynG3uh6j5wumLC3tHV1dYFKhlWrVlFRXknCuuk0//kC/I09Ux5io5k/8iQkWWZzlrMgwl67178Y2aqi1oYubKL/19ayZsu7x7dlWaWJhSYWnliz4JP723Q0KejoVje2essTUF0Ijdac2N/xVqyEdBh0Pkz8jAt1rBdKtIRgItI9FKKIiIiISMTZO0yrQinbCR/8Gfa+BgdXuck7tkgNVAvYu9CYlkWrH1L6w4Dz3DSXkVdBYrarWAh1UWtva+1WXl7OsufXs/3uKTQXZ9BTPLFwxW/cxJ6uLMSPvzu3EdLlbsuKVVhYxYX1nomJg4QMNyHIwqak/BOVE5EIAOz8B9+D52505+1OnoRmYq9dSu2U5YFeN7GxsYwfPz4QoNg2nsB9QvyhAtecz/W+sYlRdivbBlWFLgiy7T5xaZCSD2lDoGC6C76sYbEFKgpPRCQUClFEREREpFsFFrblUHMYSrdC2Q6oPQxNdW4bRUo/FwZkjICkHLeNpysL2uZGP0vv87Pqxx78vp5dGdui/OqnINGt/8Nm78xt68yBJbDtaShc1mqEsIVPHhekxKe5fiz5U2D09TBkAaQO6vpWFAu6Ft0MO5+PbMPW9niyqvB89ikyxzUHwpMRI0YQFxcXcnjSHgvxGmuhqbalX4rfBV3Wk8UaxtrvUMGJiIRLIYqIiIiIRJXqg/DkRXB0U8f3s60b+dPc2ONgo48tzLGAJxhromsVDq0lZMI1f4NB88NfoFtYYv1k1v4SDq50IVOorKGtNeCdeAuMu9n1aOlMQGAhjW2JWXx7N0xZao/HT8Z5R/jw72LpNzyzy+GJiEh3irL+4SIiIiLS15VugbIPTn+/gpnw0WfgnQdg9c/aft+2FF36q+CVHUXvwl8Xtm2ca9tGCt92IUqo7CNN23ay7Luw/WlXcRIu6zlzeC28+Q3Y/gx86N+h/xyIOWmaUahVKOse7cEAxfg9VC7PpWoteEYoQBGR3k0hioiIiIhElcJ3TuorEoT1v8idCHPudU1r2xvckznafW/rk9BkTUpPYr1e2juHbcXhbuuQG1rlh/XueO0LULKxc5NlTjleM+x/C567Aeb/EMZ+HLzxoT/+yHooXtG5c9vvNT7FVdSEO5XJ1+QJNLIdcRV44zp3fhGRnqAQRURERESihgUbHY1WnvoFmHgrpA6A5IKOQwvr0VK+E976JtSVhP4crOeLNYC17UIdPlcf7H0dFn8Oyq2Ba6Q22fuhushN1rF+IDZ5JpRgwn53O54NbxvRycbfBJNug5f/CUpDqARqzSppKnZB1pjOnV9EpCdoArqIiIiIRA0LL2o7CDwaKly1xe6X3Zac9lg/lNT+bjqN9T8JhwUXdaWnDywOvQ+v3Qnlu7qngattLVpyjwtGQqlwaaiEQ2usLCS881iz1vThMPlzkD3BNW7tjMr9bkyxOjaKSG+mShQRERERiRo2hcWClPbYlpHNf3R/tqqJfrOC389GCFtzVtvaYmOXs8a6cMB6rex704Ux7T6H5rbbf4JN4FnyL52r2AiHVdAsvQdyJkD2uI6bzdpzqtof+rFtus3YGyF/Ogy5GPImQV1Z55+rhVVl21sCJbVGEZFeSiGKiIiIiEQNawLb0YjfkysyOqrOsNHLSXkw+mMw5gZoqofYBBekFL4Lb90Nh9e08xw8rjqj3efQDOsfdWFMT4wQLt0Oy78Hlz7iRvu2x4KhqqLQj2u9VqznSuYo9zu3Mcxd4oHyPS5kUoYiIr2VtvOIiIiISNSwhX1cStePY8eITYSaw663yNNXwVNXwYbHYOB5cOFPITGnvSfhwxdXi88XPKWp2OPGGPs6qJiJKJ+b+nNgacehjVXxdFRB01pjDbz6eXjqw/Dsda5Bblc1lHf9GCIi3UmVKCIiIiISNTxeSBvcsh2kC1Ue1uD0pc+47SUnb7mx6pOkXBj1URh6KWx9ou1jG1NKWPT6K+Tm5ZCXl8eAAQPIzs7G6/US4/Gy6X+h6kDP1lpY4LHuVzDwQ8GrUfx+Pz6fH7/fnleIz81/YvuP9ZCJxFjkrk4nEhHpbgpRRERERCRq2FaaghkQ47WxuZ0/jjWG3fVC268318Pul2DcTW4bSzDxkw9w4MB+DhTuJyYmJhCepKam0q9fP/JShrD95fH4msLrvmrbZRKy3H+td0m4I4Qt8ChcZqGQn9xJ7ktNTU1UVFQEbuXl5RR9UIMn+xwoTudMsW1UIiK9mUIUEREREYkq/We7yoiOmr+eTvZ4SEiHw+vctJ1TxLgQI1gPEI/XD/2O4InxtFR3+AK30tLSwG3r0YN4148L/Yl4YPACmPFl1xg20DdkB6x5GHa9GF5QVH0Q1i8uxlOyhSNHjgSCk4aGhuM3T1M8cfkDzlyI4oe0QR03vxUROdMUooiIiIhIVEkdAIPmw87nO3+MKbfDhE/Di7e0HKdla5A1lh1xpRsHbKOSW8sYAfNvnEpT1kCKioooLCykrKwsEFI0NjaSUJtLY23oVShDLoIrHnPntUa0FugMvgAue9SNR/7gL2FsW/LD5jcOUdW4IuiOHb+3gebEyjM2Hsem/WRZdY9CFBHpxRSiiIiIiEhUiUuFMdfD3teCVJGEaNdLMOEWOO+7ruHqwVVu7PHEW2D4Qtj9Skuj1pN5YOjFHkbOysXjzWHcuHGBKpSqqioOHTpEcXEx2x4qoDTEvh/xGXDu3RCbAq/cBjuedflGv9nwkf+DOffCnsVQH8ZY4bpNOXjOdymFx+MJbDdKT08nMzOTrKwsjm7JYd/Knpka1FrqQDdKWhmKiPRmClFEREREJKrYdpBR18Da/4aidzt3jL2vwzsPwMyvwVVPuDDGmtZaU1brlbLkHtcf5WTWcHbSZyEm8A7bRQHWDyUjIyMQVIwYPpKiB6A0xOdgVRlWUWOBjvVhOdYHpXg57H4Zxt0M/c51QUqo4psyGDl6GjkD0snJyQk0vE1MTCQ2NjbwXPc2xVD4J0+bn60n2HalQJ8ZpSgi0ospRBERERGRqHOsiuPlf2y/UmPHc1CyyU3iac3GD6/5uQtTBsyF9MFQVQxHN7uqlNbHtOBk8m2Qe07wc1nVh7/Rgy+McKL/XBfcHFhyakWNTbCxKpiJt0L/OeGFKIneVOZNvYSM4cGTCgtlrPHswffoUfZzjv24GystItKbKUQRERERkag0/EoXNLz/UPBpNjXF7tYea9p6ZJ27dcjjRgdP/5Lr6xEpgf4gfqjY3fZ7FXvdeVMKwjum5zRlHolZcM5n4MgGaK6jx+RPg2GX99z5REQ6K6bTjxQRERER6cVbemITYNY3YfgVbjRwd8keCxc8CEn5HU+W8ca7BrGhspHGFqLYuOXW6svdueIzXRVHqCzksclF7bHf07hPQL+ZhMX6xux8AdY9ArVHwnusNxGm3QUp/cJ7nIjImaAQRURERESiVnIBXPQQDLkEPJGuwfa4Ph4X/9xVUpxuNK8FFDbCN1S2bcf6uwYdY2zf8LttR3a/UMWnQWL26cObOfe53104IcrGx2DpPVBdFPrjLACyJsCjr9NoYxE5OyhEEREREZGoZQvz9GFw+W9g/M0QmxSh43pdr5QrfguDLwwxAPC0hC0hvgOvPeR6rCakB+/54rcqlaPhTdLJmXCs8W0HT9PjRivP/LqrEuk2HiiYAfMegLiUbjyPiEgEKUQRERERkahmoUDqALj4FzD/P1yoEs4WmFMP5vqGTLsTPvJn6DcrvK1CgWaxIVbEVO535wtMrGklc4SrQCnbEfq57VgW+IS67WfqHTDjy+FtQQrnuVigY69J+nBVoYjI2UMhioiIiIhEvUD/kFSYcgdc/zLM+Apkjg5jnK4HkvNh3E1wzTMw/4eQ0j/8xX/G8ND7jexf4pq7Djjv1IoQqyQZdIGb2LPvjdDPbUGSTd8JlVWH2LaeOd+G+CDVMJ1loVP/2bDwf6FgugIUETm7aDqPiIiIiPQZMV7IGgPnfw+m/DMULoNdL7lRxw0VLphobmhpwJoIsSmQMQyGXuK2uFjwEtdBY9bTScqBEVdB8UrXR6Qj5Tth31vu3KOuhp2LXBXLqGtgxJWw97Xgk3s6qkLJHBleaGFBysyvucct/3f3ewqnB0trCZmuB4qFM2lDFKCIyNnH4/fbbkoRERERkb7n2DvhhnKoLob6MmhqAG8sxKW6iTFJuScqViKx6LfxxE9eBOUhbMWx0ckLf+eeS/EKt7XG+ohY89YX/gEOrQ69oey1i2DQ+Z3/PdnzXfcr2PxHqDnYTsPbYDwujMmf7rYHDV/ofg4FKCJyNlKIIiIiIiLSg3zNsPZhePNut13ntM1Xp8Pkz0HuOa4K5NAaF2Yc2RBaU1nbPjPpNrjwP7vWWNdWDf5mKN8F2/4Ke16Fg6tcBU+gSYBNE/K39Jux/7b0c7EgaOTVrhLGwhSFJyJyNlOIIiIiIiLSw+rL4cVbYMczod3fKjcsgLB37o3VbrRxqHInwzVPQcaIyAUYFubUV0DdEdfctmwn1JW46hSrerH+K1mjIXUgJGSDN17hiYhEB4UoIiIiIiI9zN6Bl2yEFz8Nh97vvvOkDoLLHoFhCxViiIhEgqbziIiIiIj0MAs0cia6Eb/ZY7vnHEl5cP73YdgVoQ8hEhGRjqkSRURERETkDAn0OHkfXrsLipa7viKRYNN05v8IRn7EjUQWEZHIUIgiIiIiInIG2btxG1W84gew9c9QX9r5Y1nj2CEXw3kPQP5U11RWREQiRyGKiIiIiEgv0FwPe1+D1f/lqlKs+Wwo03eMjUDOHg9T74DR10J8hnqgiIh0B4UoIiIiIiK9hL0zt7HHB96GnYugcJkbZdzccGooYveLiXEjhAfMdY1jh17iJuOo+kREpPsoRBERERER6YWaG6Gh0m3vKd8NNQehqcaNO07KhfThkJTjghP7mipPRES6n0IUEREREREREZEQqNhPRERERERERCQEClFEREREREREREKgEEVEREREREREJAQKUUREREREREREQqAQRUREREREREQkBApRRERERERERERCoBBFRERERERERCQEClFEREREREREREKgEEVEREREREREJAQKUUREREREREREQqAQRURERERERESE0/v/xtYvbURaF1wAAAAASUVORK5CYII=\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"qubit_color = []\\n\",\n \"for i in range(backend_num_qubits):\\n\",\n \" if i in bad_readout_qubits:\\n\",\n \" qubit_color.append(\\\"#000000\\\") #black\\n\",\n \" else:\\n\",\n \" qubit_color.append(\\\"#8c00ff\\\") #purple\\n\",\n \"line_color = []\\n\",\n \"for e in backend_target.build_coupling_map().get_edges():\\n\",\n \" if e in bad_czgate_edges:\\n\",\n \" line_color.append(\\\"#ffffff\\\") #white\\n\",\n \" else:\\n\",\n \" line_color.append(\\\"#888888\\\") #gray\\n\",\n \"plot_gate_map(backend, qubit_color=qubit_color, line_color=line_color, qubit_size=50, font_size=25, figsize=(14,14))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"c43f5d1f-c7cc-4584-81e9-43db1a56e2dd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# select the initial layout\\n\",\n \"# ---- TODO : Task 4 ---\\n\",\n \"spin_a_layout = [2,3,4,16,23,24,25,35,44,45,46,55,65,66,67,74,86,87,88,94,107,108,109,113,128,127] ### add your qubits list ###\\n\",\n \"spin_b_layout = [0,15,19,20,21,34,40,41,42,54,61,62,63,73,82,83,84,93,103,104,105,112,124,123,122,121] ### add your qubits list ###\\n\",\n \"# --- End of TODO ---\\n\",\n \"initial_layout = spin_a_layout + spin_b_layout\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c929a180-c518-4cee-abd5-67b239f381ea\",\n \"metadata\": {},\n \"source\": [\n \"Check your layout here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"b3d191ac-68b0-4c1d-846c-30730856b876\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"qubit_color = []\\n\",\n \"for i in range(backend_num_qubits):\\n\",\n \" if i in bad_readout_qubits:\\n\",\n \" qubit_color.append(\\\"#000000\\\") #black\\n\",\n \" elif i in initial_layout:\\n\",\n \" qubit_color.append(\\\"#ff00dd\\\") #pink\\n\",\n \" else:\\n\",\n \" qubit_color.append(\\\"#8c00ff\\\") #purple\\n\",\n \"line_color = []\\n\",\n \"for e in backend_target.build_coupling_map().get_edges():\\n\",\n \" if e in bad_czgate_edges:\\n\",\n \" line_color.append(\\\"#ffffff\\\") #white\\n\",\n \" else:\\n\",\n \" line_color.append(\\\"#888888\\\") #gray\\n\",\n \"plot_gate_map(backend, qubit_color=qubit_color, line_color=line_color, qubit_size=50, font_size=25, figsize=(14,14))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"cc2bf757-2fa1-47b9-9993-36cf163d99f0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations 🎉! Your answer is correct and has been submitted.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex4(initial_layout) # Expected result type: lists\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"531344cb-0b6f-45fe-83ee-9b99801f2dd5\",\n \"metadata\": {},\n \"source\": [\n \"## 5.3 Add more interaction to LUCJ ansatz\\n\",\n \"\\n\",\n \"The LUCJ ansatz used in section 4 has the form\\n\",\n \"\\n\",\n \"$$\\n\",\n \" \\\\lvert \\\\Psi \\\\rangle = \\\\prod_{\\\\mu=1}^L e^{\\\\hat{K}_\\\\mu} e^{i \\\\hat{J}_\\\\mu} e^{-\\\\hat{K}_\\\\mu} | \\\\Phi_0 \\\\rangle\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where $\\\\lvert \\\\Phi_0 \\\\rangle$ is a reference state, often taken to be the Hartree-Fock state, and the$\\\\hat{K}_\\\\mu$ and $\\\\hat{J}_\\\\mu$ have the form\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{K}_\\\\mu = \\\\sum_{pq, \\\\sigma} K_{pq}^\\\\mu \\\\, \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{q \\\\sigma}\\n\",\n \"\\\\;,\\\\;\\n\",\n \"\\\\hat{J}_\\\\mu = \\\\sum_{pq, \\\\sigma\\\\tau} J_{pq,\\\\sigma\\\\tau}^\\\\mu \\\\, \\\\hat{n}_{p \\\\sigma} \\\\hat{n}_{q \\\\tau}\\n\",\n \"\\\\;,\\n\",\n \"$$\\n\",\n \"\\n\",\n \"where we have defined the number operator\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\hat{n}_{p \\\\sigma} = \\\\hat{a}^\\\\dagger_{p \\\\sigma} \\\\hat{a}^{\\\\phantom{\\\\dagger}}_{p \\\\sigma}.\\n\",\n \"$$\\n\",\n \"\\n\",\n \"The IBM hardware has a heavy-hex lattice qubit topology, and it yields the following index constraints on the $\\\\mathbf{J}$ matrices:\\n\",\n \"\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha} &: \\\\{(p, p+1) \\\\; , \\\\; p = 0, \\\\ldots, N-2\\\\} \\\\\\\\\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\beta} &: \\\\{(p, p) \\\\;, \\\\; p = 0, 4, 8, \\\\ldots,\\\\; p \\\\leq N-1\\\\}\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\\n\",\n \"Here, $\\\\mathbf{J}^{\\\\alpha\\\\alpha}= J_{p q,\\\\alpha\\\\alpha}^1$ and $\\\\mathbf{J}^{\\\\alpha\\\\beta}= J_{p q,\\\\alpha\\\\beta}^1$.\\n\",\n \"\\n\",\n \"In this case, $\\\\mathbf{J}^{\\\\alpha\\\\alpha}$ only interacts with adjacent spins on the $\\\\alpha$ or $\\\\beta$ orbit. This time, to model close to nature, let's change $\\\\mathbf{J}^{\\\\alpha\\\\alpha}$ so that the interaction also works with the next adjacent spin.\\n\",\n \"\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\alpha} &: \\\\{(p, p+1, p+2) \\\\; , \\\\; p = 0, \\\\ldots, N-3\\\\} \\\\\\\\\\n\",\n \"\\\\mathbf{J}^{\\\\alpha\\\\beta} &: \\\\{(p, p) \\\\;, \\\\; p = 0, 4, 8, \\\\ldots,\\\\; p \\\\leq N-1\\\\}\\n\",\n \"\\\\end{align*}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c0292b43-2e65-43bf-855b-2fda443b8c82\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"Exercise 5: Add more interaction to LUCJ ansatz \\n\",\n \"\\n\",\n \"In the LUCJ ansatz, modify the code of `alpha_alpha_indices` so that the $\\\\mathbf{J}$ matrix interacts not only with adjacent spins on the $\\\\alpha$ or $\\\\beta$ orbit, but also with spins two steps ahead. Submit the list of `alpha_alpha_indices`.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"fad7278a-9178-4ac0-9c7f-01648d46cd9f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"E(CCSD) = -109.2177884185545 E_corr = -0.2879500329450033\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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0WbHSNtRrX7WmOa/O0xnGiBmtENSQAUITUA8WhrdcDzc35VAAA0HrUhcVBXdi64xMbNYaxhY7rNGVOjPqOny2eNrvJcwMDAADFR11eeGry4pQ7hy93DDd33HZN5rHuvm6/WDj1g+rHC9+dGV0G9I6SstKoXFVRa/0hB42MgbtsldXjufmpZ4wemz0/+9V3YqMv7hYfvPhWdOrTI9bda0R8OGHa/182ITb43MisNi8tL4/1D9gxOvbs1kx/CQAAgJZjv0RxSGnfxBbfGhVTHnihzuXlXTvFQfddktXtU+57Pl694h9RWVGR1d6bHbN/Nld97rqeGx2ye3Ydz469u8fyeQvrfL/ug2vuF1i5aGksX7gkugxYKxZOmVlr/f47DItdfnFc9NxwULxx04Mx5f7abe216eDov/2m8cyZ12aPe286OFYtXxH73PTD6DaoT8wZNyVeuOCPsWz2/NX4CwEAtB71QHFob/VAY68x2tD1ZVry+qONHcfb0DwDdY0zbvB1AABQJNSFxaG91YWre5yo96brxdLZ82PP//1e9Nx4nVg4dWa88OM/Zsdz6lvWnONHPzL0iL3jhQtvyr/sq/vEW3/5d6PHvQIAFCv1QHFIpR7Y7cqTY9AuW2b3Hzryosb136d+kB07WpN+t5oAoG76AsUhlb5AY8eRf2STw/eOqf9+KdsnmDPn9UnRd/iG0XOjQTH/nemx8aF7RElpaXau+bK5dY8jz/Ullrw/t8b3/sL3ZmXPf/L6bPWNB2/sWPG21heQA8UhtRxojTkKGnpNSXlZjfkHUp17QAakkwG5sZhL28ZXU7XOZdnwzOSYl6V1ycHioC9UkxxoXXKgOMiBmuRA65EBxUEG1CQDWpccKA5yoCY50HpkQHGQATXJgNYlB4qDHKhJDrQeGVAcZEDE8vmLq+/nrqmZHdzN4/1nx9X9Jqsxv6ZxFXKgWMiBmvQFWpccKDwZUJMMaF0yoDjIgZrkQOuSA4UnA2qSAa1LBhQHOVA/51O1LDmQTgY4pwpIXe47b9WqthOEZWVl+ogAAAAAAAAAAAAAAECLK2/5j4CmW7xkZXTfOf9k6sVq4bNHRbeuHQrdjHZl+CmHRI8NBsboL1/Q4LpDj9g7njvnhmhNY666I7b/0dfioHsujuULFsesl8bHoN22ypaVlJXFOrtvHfccdHYsnjEntjv7qzHykm/HY9/+ZY2TjDf8/K7ZOm1F7sT0mzc+stDNSN7XJvw5OnTtXOhmFNUFbd/555OxYtHSePXXf4919hoRW59yyBpfqKah915nzxEx84U3807a3dgJy9siOVAcUs2Bhrat/jsMi069usW7D73YpGVrarcrT45Bu2yZ3X/oyIvyrrPl8Z+LmS+8EbNffSfaMhlQHFLNgLroC7QuOVAcUsiBp075TfZz4y/tGTucc2Q8XMd3bDEbd/292W2tLYbEHledEtMefyWWzV3Y4LJiJgOKQ3vLgG7r9osl78+NylUV1c8tfG9W9vyCSTMa7NvPf2d6dFqrR/b8B/95M9bbb4dswpvu660d058cU+eyOWMmRjFqydqlOciB4tCecmBNM6A+9eVDc2ZAc+0XaMz7FJoMKA7tKQPW9FhhbnseMmqnuH3kSVmWbPaNA2LPa78X933+3OjQo2ts/q1Rcfeos+LDt9+LwftuH3vfcHrcscd3o2LFymZp3zanfSkm3/tc9v7dB/dvdNty/rbDCbHovVnZ8cKRvzgudjjv6/HsD6+LYicHikNKOdAY9g+2HhlQHFLNgMaMIcp9x9+17+lR2qE8dvrZN2PY1/eN166+s9XaOPmeZ7Nbrl+w9w1nxLsPvxjzJ0zLxgbNe2tqPPjlC6OsS8fY549nxdCv7hNv3/JItDVyoDiklAN1fac2VPM/fsKvYu/rT4+Vi5dFx17d4tFjL222OqA5tNY+i+YmA4pDShmwuttNc/bHm7r/sq79gO3lWIYcKA4p5kBzjR/o0L1L7HPTWTHm6jtj9isTmrmV7Z8MSCMDcpPb7X5vi719i3tyVEQXZ0oCAAAAAJAA42cBAIDWYuxYcWhv40fX5JqSjXl9S40bb2i9+q4rkTs3bvm8hVmNPWiPrWPV0uXx8mW3xfSnxrSpa04AAGlQBxSv9lYbNKS+vn8xnU+er4ZpL9ejBwAgberD4pBKLbg61+1pjmvztMZxooZqRDUkAFCM1APFIZV6oLGcTwUAAK1HXVgc1IUtpzH1XL51Wuq4TlPmxKjv+FlJSUmD75M7f6mkJOKDl8bHixfdHMtmz2/W3wUAAFhz6vLCU5MXn/IunbK5oe856OxWncf6zZsejFeuuD07Xrz2p4Zlc1XffeCZsWjqrHjhgj/Gp847Kg5+6NJYOuvDmPHM2Ojct1f2ujFX3RHb/+hrcdA9F8fyBYtj1kvjY9BuWzVbuwAAAFqK/RLFIZV9E8NPOSS7TufoL1+Qd/nimXPjtm2Pi6Wz50fH3t1jr2u+F1ue8Lms9p8xemy89r93xT5/+mF2jHzKfc9lr8k35ntN5I7L3/np06JT356x93U/iAE7bx7vPzuuennXQX1inz+cGc+c+btYPH1O9lxJWVmss/vW2X6MxTPmxHZnfzVGXvLteOzbv2zWtgEANDf1QHFo7/VAXWN4G7q+TEtef7Sx43gbmmegrnHGjZmfAAAAioG6sDi097qwsceJSspLs3GX93z2hzHvrakx7Kj9Yq/fnRZ3H3Bmvcua29o7bR4duneJ9x55qdayboP7xYAdN4vHT/h1o8e9AgAUK/VAcUilHnjqlN9kPzf+0p6xwzlHxsOrOXdza/S71QRAKvQFikMqfYHG7B/8uKFH7B3PnXND9eMFE2fEM2f8LnbPXUutvCymPvxiLJu3MCpX/t+4jzVV33jwxowVb4t9ATlQHFLKgdaao6Ch1wzadau88w+kNveADEgnA5auitj93mhTnhwV0aU8kmNeltYlB4uDvlBNcqB1yYHiIAdqkgOtRwYUBxlQkwxoXXKgOMiBmuRA65EBxUEG1CQDWpccKA5yoCY50HpkQHGQAVE9t+SgXbbM7j+0GuOrV2d+TeMq5ECxkAM16Qu0LjlQeDKgJhnQumRAcZADNcmB1iUHCk8G1CQDWpcMKA5yoH7Op2pZciCdDHBOFZC6VatWxe233x5txaGHHhrl5UIQAAAAAAAAAAAAAABoWc5eAIrSliccHENG7RQPfvmCWLVkeb3rDhy5ZZR16hjTHntljT934dRZ0X1w/+rH5d06R8ceXWPJ+3Nrrbtq6fJ4/tw/VD8eftIXYt6b72b3F703K6aPHptN+Jvzzt+fiH3/cm6N12/wuZHZ+h++NXWN2w2p67PVhjHuuqqz6ftuvXHMeW1ig68Z9a+fRc+NBuVddte+p8fiabPrfe/1D/hUTL7v+TWasBxovMZsW0O/8ukY/7fHo3JVRZOWrUlG5Dx1ym+ynxt/ac/Y4Zwj4+FPXFi797D1Yshnd4r7vnheoz4baDp9AWi/Jvzt8Rj58+Oi01rdY9nchS3+eQvfmxU9NxhY/Ti3jyBX46+Jua9PzvYPDNxly5h8z3ONXgbk98m+/YoFi+Oxb18W25/91Wxf3gf/eSvmvvludqHJ+pY1d13QXJpau0BqmrKNtFYGNNd+gYbeB1LTmGOFQz67c8x9Y0r1cbzxf300dr7oW1HaoTzW2WPr7ALVH779XrZs6kMvxm6//p/oNrhfLJg4o87PzfX/uwxYK0rKSquzpvu6/fLWBbljlN3W7Rebf/OAKCkriw49usRhz18d/zrwrHrbVrFiZfX75S4M/caND8Qulx7fDH81SJf9g9C+NWUMUU7uuzb33bvLZSfEa1ffGa1t4dQPYtZLb8d6n9k+xk6YFpsds3+MPv3aqKyoiJWLlsbku5+NgbtuFW/f8kirtw3akvq+U+ur+XN9+RHfPSwePfbSeP/ZcdF3xMaxzx/Pijs//f1YNmdBUewPXNN9FpCixm43Te2PN7TdN0V9+wFb8lgGpGRNxg/ktq99bzknm5ju9WvvbrE2AgAAAAAAQGsxfhYAAIAUrynZ3GPGm+tckYauK1FSXhrd11s75r09NV686Oas9t7v1nPjjj2/F0tnfZit45oTAADQuL5/sZ1P/skaxvXoAQAAolWu29MWjhM1VCOqIQEAoGmcTwUAAMCaakw9l2+dNTmu05xzYqzJ8bNc23PzZ5aUl8V2Z34ldr/iJOcvAQAA0CZs8LmRMe/Nd+PDt6au8TzWC9+bFevsuXX149y1QJa8Py/vNU6WfDCv+v7MF97MjiP3G7FxLJo6KzuX8anv/rZ6eW5uvVwbc1YtXR7Pn/uH6mXDT/pC9TIAAAAgYssTDo4ho3aKB798QaxasjzvOhXLV8bS2fOz+8vnLYy3//rv2OiLu1fX/m/+8YHsltN/u6HZ8fAVC5fU+7kLp86K7oP7Vz/OHXfv2KNrLHl/br2vWzZ7frz3yH9jg4NGZtc7yukyYK3Y/7bz45XL/x6T736met1cO6aPHhuLZ8zJHr/z9ydi37+c28i/DAAApDmGt77xscVy/dH65hno3LdnneOMGzM/AQAAQEoac5woN05z9msTY97/Hzc64W+Px84Xfys7N7K+ZfWdZ9mU8aMf2TSbg+KxqKyovc7Qwz8dU+5/ITuO1dRxrwAAQFVfPnc+Vqe1useyuQtr9d97bjCw+nFuzFduXNaa9LvVBADQdvYPfmTgyC2jrFPHmPbYKzWen3zPs9ktp0v/3jH8xC/E/EnT632vXF8iN/47Nw7lo+//7uv2q9XHaGg8eGPGiusLQHHNUVDfa/LNP2DuAaBYmJcFkAOAHIC0yQBADkDaZAAgByA9T53ym+znxl/aM3Y458gmzS25JvNrGlcBxUlfANImAwA5AGmTAcAnOZ8KAAAAAAAAAAAAAAAAAKD5lbfAewKskS2OPyg2/OKu8eCXL4zl8xc3uP7Qr346xt/2aFRWVE3S2xj73XZ+/Peim2PWy+NrPD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\"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Get CCSD t2 amplitudes for initializing the ansatz\\n\",\n \"ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\\n\",\n \"t1 = ccsd.t1\\n\",\n \"t2 = ccsd.t2\\n\",\n \"\\n\",\n \"n_reps = 1\\n\",\n \"# ---- TODO : Task 5 ---\\n\",\n \"alpha_alpha_indices_1 = [(p, p + 1) for p in range(num_orbitals - 1)]\\n\",\n \"alpha_alpha_indices_2 = [(p, p + 2) for p in range(num_orbitals - 2)]\\n\",\n \"alpha_alpha_indices = alpha_alpha_indices_1 + alpha_alpha_indices_2 ## input your code here ###\\n\",\n \"# --- End of TODO ---\\n\",\n \"alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\\n\",\n \"\\n\",\n \"ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\\n\",\n \" t2=t2,\\n\",\n \" t1=t1,\\n\",\n \" n_reps=n_reps,\\n\",\n \" interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\\n\",\n \")\\n\",\n \"\\n\",\n \"nelec = (num_elec_a, num_elec_b)\\n\",\n \"\\n\",\n \"# create an empty quantum circuit\\n\",\n \"qubits = QuantumRegister(2 * num_orbitals, name=\\\"q\\\")\\n\",\n \"circuit = QuantumCircuit(qubits)\\n\",\n \"\\n\",\n \"# prepare Hartree-Fock state as the reference state and append it to the quantum circuit\\n\",\n \"circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\\n\",\n \"\\n\",\n \"# apply the UCJ operator to the reference state\\n\",\n \"circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\\n\",\n \"circuit.measure_all()\\n\",\n \"circuit.decompose().decompose().draw(\\\"mpl\\\", fold=-1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"f01ad27a-5a0a-46f8-afaf-da18af7d1956\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Submitting your answer. Please wait...\\n\",\n \"Congratulations 🎉! Your answer is correct and has been submitted.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"\\n\",\n \"grade_lab3_ex5(alpha_alpha_indices) # Expected result type: list[tuple[int, int]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5db71e0e-4c4c-460d-99d2-8b13acfa9bfd\",\n \"metadata\": {},\n \"source\": [\n \"Congratulations! You finished this lab. The next session is a bonus session and does not count towards your score.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"109cb3e9-987f-4990-acb1-eac3c706627f\",\n \"metadata\": {},\n \"source\": [\n \"# Bonus: Real hardware execution with error mitigation \\n\",\n \"\\n\",\n \"Finally, let's run the modified ansatz circuit on a real device and run the configuration recovery loop to find the energy. In doing so, we will use error mitigation to reduce the effect of errors as much as possible.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2581d200-3327-418e-afb8-b71413c05d56\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"backend = service.backend('ibm_torino')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1a7c1c3d-19fa-430e-898d-4bdcec737922\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pass_manager = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend, initial_layout=initial_layout\\n\",\n \")\\n\",\n \"\\n\",\n \"# We will use the circuit generated by this pass manager for hardware execution\\n\",\n \"pass_manager.pre_init = ffsim.qiskit.PRE_INIT\\n\",\n \"isa_circuit = pass_manager.run(circuit)\\n\",\n \"print(f\\\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e713e002-0187-4c4f-9b7f-17843bd49eb3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opts = SamplerOptions()\\n\",\n \"opts.dynamical_decoupling.enable = True\\n\",\n \"opts.twirling.enable_measure = True\\n\",\n \"\\n\",\n \"sampler = Sampler(mode=backend, options=opts)\\n\",\n \"job = sampler.run([isa_circuit], shots=100_000)\\n\",\n \"print(\\\"job id:\\\", job.job_id())\\n\",\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d3b6559f-2bce-4d05-8ec5-fd818c4535e9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"\\n\",\n \"# Get job id from cell above\\n\",\n \"job = service.job('INPUT-YOUR-JOB-ID')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"21f262fb-e8fd-414a-98d0-7e497dfb79c0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"job.status()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"59f86a08-c703-4362-8957-e30aeb94e3c5\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"Warning: 20 minutes needed\\n\",\n \"\\n\",\n \"When running the code below it will take up to 20 minutes (depending on your computer) to execute and will block this notebook for this time. \\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f329e74f-b905-46df-af8c-e0044e6606d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"primitive_result = job.result()\\n\",\n \"pub_result = primitive_result[0]\\n\",\n \"bit_array = pub_result.data.meas\\n\",\n \"\\n\",\n \"# SQD options\\n\",\n \"energy_tol = 1e-3 \\n\",\n \"occupancies_tol = 1e-3 \\n\",\n \"max_iterations = 3\\n\",\n \"\\n\",\n \"# Eigenstate solver options\\n\",\n \"num_batches = 3\\n\",\n \"samples_per_batch = 200\\n\",\n \"symmetrize_spin = True \\n\",\n \"carryover_threshold = 1e-4 \\n\",\n \"max_cycle = 200\\n\",\n \"\\n\",\n \"# Pass options to the built-in eigensolver. If you just want to use the defaults,\\n\",\n \"# you can omit this step, in which case you would not specify the sci_solver argument\\n\",\n \"# in the call to diagonalize_fermionic_hamiltonian below.\\n\",\n \"sci_solver = partial(solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle) ###NEW###\\n\",\n \"\\n\",\n \"# List to capture intermediate results\\n\",\n \"result_history = [] \\n\",\n \"\\n\",\n \"def callback(results: list[SCIResult]): \\n\",\n \" result_history.append(results)\\n\",\n \" iteration = len(result_history)\\n\",\n \" print(f\\\"Iteration {iteration}\\\")\\n\",\n \" for i, result in enumerate(results):\\n\",\n \" print(f\\\"\\\\tSubsample {i}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tEnergy: {result.energy + nuclear_repulsion_energy}\\\")\\n\",\n \" print(f\\\"\\\\t\\\\tSubspace dimension: {np.prod(result.sci_state.amplitudes.shape)}\\\")\\n\",\n \"\\n\",\n \"result = diagonalize_fermionic_hamiltonian(\\n\",\n \" hcore,\\n\",\n \" eri,\\n\",\n \" bit_array,\\n\",\n \" samples_per_batch=samples_per_batch,\\n\",\n \" norb=num_orbitals,\\n\",\n \" nelec=nelec,\\n\",\n \" num_batches=num_batches,\\n\",\n \" energy_tol=energy_tol,\\n\",\n \" occupancies_tol=occupancies_tol,\\n\",\n \" max_iterations=max_iterations,\\n\",\n \" sci_solver=sci_solver,\\n\",\n \" symmetrize_spin=symmetrize_spin,\\n\",\n \" carryover_threshold=carryover_threshold,\\n\",\n \" callback=callback,\\n\",\n \" seed=rng,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a7424751-f167-4006-9d48-f738791dab09\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"exact_energy=-109.2177884189209 # CCSD energy\\n\",\n \"plot_energy_and_occupancy(result_history, exact_energy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c9894969-4c9b-45c0-91a8-097f17d7462b\",\n \"metadata\": {},\n \"source\": [\n \"# Reference \\n\",\n \"\\n\",\n \"\\\\[1] M. Motta et al., “Bridging physical intuition and hardware efficiency for correlated electronic states: the local unitary cluster Jastrow ansatz for electronic structure” (2023). [Chem. Sci., 2023, 14, 11213](https://pubs.rsc.org/en/content/articlehtml/2023/sc/d3sc02516k)\\n\",\n \"\\n\",\n \"\\\\[2] J. Robledo-Moreno et al., \\\"Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer\\\" (2024). [arXiv:quant-ph/2405.05068](https://arxiv.org/abs/2405.05068).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fbfca50d-c810-4f56-a0fc-ab3b6fc64228\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Yuri Kobayashi, Kifumi Numata\\n\",\n \"\\n\",\n \"**Advised by:** Yukio Kawashima, Toshinari Itoko\\n\",\n \"\\n\",\n \"**Reviewed by:** Kevin Sung, Jennifer Glick\\n\",\n \"\\n\",\n \"**Version:** 1.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"38eb8499-55ca-4d5c-8ca9-2441b03b52b1\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit packages versions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8588a958-8208-4f81-88e1-cae2e27d04ee\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qiskit_ibm_runtime\\n\",\n \"\\n\",\n \"print(f'Qiskit: {qiskit.__version__}')\\n\",\n \"print(f'Qiskit IBM Runtime: {qiskit_ibm_runtime.__version__}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.3\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
},
{
"path": "solutions/lab-4/lab4-solution.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9c80575e\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# Lab 4: Quantum Error Correction: From Core Concepts to the Road to Fault-Tolerant Quantum Computing\\n\",\n \"\\n\",\n \"Welcome to the forth coding challenge of the Qiskit Global Summer School. This lab explores error-correcting codes, beginning with foundational classical error-correcting codes and key concepts. It then transitions to Quantum Error Correction (QEC) — crucial for fault-tolerant quantum computing — and covers the stabilizer formalism along with key examples. Subsequently, the notebook introduces advanced QEC architectures, including the QLDPC, Toric, and gross codes, with exercises. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"690bf7d6\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"- Chapter 1: Classical error-correcting code revisit\\n\",\n \" - 1.1 Classical [n, k, d] Codes\\n\",\n \" - 1.2 Hamming Distance\\n\",\n \" - 1.3 The [3, 1, 3] Repetition Code\\n\",\n \" - Practice: Lookup table-based Decoder of [3,1,3] Code\\n\",\n \"- Chapter 2: Quantum Error Correcting Codes [[n, k, d]]\\n\",\n \" - 2.1 Stabilizer Formalism\\n\",\n \" - 2.2 3-qubit bit-flip Code Practice\\n\",\n \" - Exercise 1: Lookup Table-based Decoder of 3-bit code\\n\",\n \" - 2.3 CSS Codes (Calderbank-Shor-Steane)\\n\",\n \" - 2.4 [[7,1,3]] Steane Code Practice\\n\",\n \" - Exercise 2: Lookup table of [[7,1,3]] Steane Code\\n\",\n \" - Exercise 3: Detect Error with a Lookup Table\\n\",\n \"- Chapter 3: Exploring Advanced Quantum Error Correction Codes & Their Efficiency\\n\",\n \" - 3.1 Foundational Concepts and Key QLDPC Architectures for Comparison\\n\",\n \" - 3.2 Qubit Layout and Conventions for Exercises\\n\",\n \" - 3.3 Toric Code Exercise\\n\",\n \" - Exercise 4: Find the parity check matrices of the toric code\\n\",\n \" - 3.4 Gross Code Exercise\\n\",\n \" - Exercise 5: Find the parity check matrices of the gross code\\n\",\n \" - 3.5 Counting the Number of Logical Qubits\\n\",\n \" - Exercise 6: Count the number of logicals for the Toric and Gross codes\\n\",\n \" - 3.6 Concluding remarks: The power of the connectivity\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fa09aff1\",\n \"metadata\": {},\n \"source\": [\n \"## Requirements\\n\",\n \"\\n\",\n \"Before starting this tutorial, please make sure that you have the following installed:\\n\",\n \"\\n\",\n \"* Qiskit SDK v2.0 or later with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime v0.40 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0f6a30f8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Needed for grading now after summer school before other code is run.\\n\",\n \"%set_env QC_GRADING_ENDPOINT=https://qac-grading.quantum.ibm.com\\n\",\n \"%set_env QC_GRADE_ONLY=true\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"558e90cb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"48794705\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1d9769d1\",\n \"metadata\": {},\n \"source\": [\n \"You should have Qiskit version `>=2.0.0` and Grader `>=0.22.12`. If you see a lower version, you need to reinstall the grader and restart the kernel.\\n\",\n \"Also make sure you have set up everything according to lab 0 and test it with the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a60cc22d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check that the account has been saved properly\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9984274d\",\n \"metadata\": {},\n \"source\": [\n \"# Imports\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"776ad97c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import common packages first\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"# Import qiskit classes\\n\",\n \"from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"\\n\",\n \"# Import utils and cosystems\\n\",\n \"from lab4_util import hamming_distance, minimum_distance, bring_states, matrixRank\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"from qiskit_ibm_runtime import SamplerV2 as Sampler\\n\",\n \"\\n\",\n \"# Import grader\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab4_ex1, \\n\",\n \" grade_lab4_ex2, \\n\",\n \" grade_lab4_ex3,\\n\",\n \" grade_lab4_ex4,\\n\",\n \" grade_lab4_ex5,\\n\",\n \" grade_lab4_ex6\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2ac33837\",\n \"metadata\": {},\n \"source\": [\n \"# Chapter 1: Classical error-correcting code revisit\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b51a2f0f\",\n \"metadata\": {},\n \"source\": [\n \"## 1.1 Classical [n, k, d] Codes\\n\",\n \"\\n\",\n \"In coding theory, a classical binary linear block code is often denoted as an $[n, k, d]$ code for messages composed of binary (bit) strings, that is, a series of 0s and 1s. These parameters represent:\\n\",\n \"\\n\",\n \"* **$n$**: The **codeword length**. Each codeword in the code is a binary string of length $n$.\\n\",\n \"* **$k$**: The **message length**. It represents the number of information bits encoded into a codeword. There are $2^k$ distinct messages and thus $2^k$ unique codewords in the code.\\n\",\n \"* **$d$**: The **minimum Hamming distance** of the code. This is the smallest Hamming distance between any pair of *distinct* codewords in the code. The minimum distance $d$ determines the code's error detection and correction capabilities.\\n\",\n \"Only a subset of the $2^n$ possible binary strings represent valid codewords that each carry $k$ bits of information.\\n\",\n \"\\n\",\n \"## 1.2 Hamming Distance\\n\",\n \"\\n\",\n \"The **Hamming distance** between two strings (or vectors) of equal length is the number of positions at which the corresponding symbols are different. For binary strings (composed of 0s and 1s), it's simply the count of positions where one string has a '0' and the other has a '1'.\\n\",\n \"\\n\",\n \"Mathematically, for two binary vectors $x = (x_1, x_2, ..., x_n)$ and $y = (y_1, y_2, ..., y_n)$, the Hamming distance $d_H(x, y)$ is:\\n\",\n \"$$ d_H(x, y) = \\\\sum_{i=1}^{n} (x_i \\\\oplus y_i) $$\\n\",\n \"where $\\\\oplus$ denotes the XOR operation (addition modulo 2), which is equivalent to counting the number of '1's in the result of the bitwise XOR operation between $x$ and $y$. This count is also known as the **Hamming weight** of the XOR result.\\n\",\n \"\\n\",\n \"**Important**\\n\",\n \"\\n\",\n \"The Hamming distance is crucial for understanding error correction codes:\\n\",\n \"1. **Error Detection**: A code with minimum distance $d$ can detect any error pattern affecting up to $d-1$ bits. If fewer than $d$ bits are flipped during the transmission of a codeword, the resulting string cannot be another valid codeword, so the error is detected.\\n\",\n \"2. **Error Correction**: A code with minimum distance $d$ can correct any error pattern affecting up to $t$ bits in a codeword, where $t = \\\\lfloor \\\\frac{d-1}{2} \\\\rfloor$. This is because if at most $t$ errors occur, the received word is still closer (in Hamming distance) to the original codeword than to any other valid codeword.\\n\",\n \"\\n\",\n \"Here's a Python function to calculate the Hamming distance between two binary strings:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ca1e251c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Example usage:\\n\",\n \"str1 = \\\"10110\\\"\\n\",\n \"str2 = \\\"11100\\\"\\n\",\n \"dist = hamming_distance(str1, str2)\\n\",\n \"print(f\\\"Hamming distance between '{str1}' and '{str2}' is: {dist}\\\") # Output: 2\\n\",\n \"\\n\",\n \"vec1 = [1, 0, 0, 1]\\n\",\n \"vec2 = [0, 0, 1, 1]\\n\",\n \"dist_vec = hamming_distance(vec1, vec2)\\n\",\n \"print(f\\\"Hamming distance between {vec1} and {vec2} is: {dist_vec}\\\") # Output: 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3ee79d97\",\n \"metadata\": {},\n \"source\": [\n \"### Minimum Distance of a Code ($d$)\\n\",\n \"\\n\",\n \"The minimum Hamming distance $d$ of a code $C$ is the smallest Hamming distance found between any pair of distinct codewords in $C \\\\subset \\\\{0, 1\\\\}^n$.\\n\",\n \"$$ d = \\\\min { d_H(c_1, c_2) \\\\mid c_1, c_2 \\\\in C, c_1 \\\\neq c_2 }. $$\\n\",\n \"For linear codes, where $c_1 \\\\in C$ and $c_2 \\\\in C$ implies $(c_1 +c_2) \\\\in C$, the minimum distance is also equal to the minimum Hamming weight of all non-zero codewords. The Hamming weight of a codeword is its distance from the all-zero codeword.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4fd8d205\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- Example: A Simple [4, 3, 2] Parity Check Code ---\\n\",\n \"# This code takes 3 message bits (k=3) and adds an even parity bit\\n\",\n \"# to make the total codeword length n=4.\\n\",\n \"# Messages: 000, 001, 010, 011, 100, 101, 110, 111\\n\",\n \"# Codewords (adding even parity bit):\\n\",\n \"parity_code_4_3 = [\\n\",\n \" \\\"0000\\\", # 000 + 0 (even parity)\\n\",\n \" \\\"0011\\\", # 001 + 1\\n\",\n \" \\\"0101\\\", # 010 + 1\\n\",\n \" \\\"0110\\\", # 011 + 0\\n\",\n \" \\\"1001\\\", # 100 + 1\\n\",\n \" \\\"1010\\\", # 101 + 0\\n\",\n \" \\\"1100\\\", # 110 + 0\\n\",\n \" \\\"1111\\\" # 111 + 1\\n\",\n \"]\\n\",\n \"\\n\",\n \"# Calculate the minimum distance d\\n\",\n \"d_parity = minimum_distance(parity_code_4_3)\\n\",\n \"print(f\\\"Codewords: {parity_code_4_3}\\\")\\n\",\n \"print(f\\\"Calculated minimum distance d = {d_parity}\\\") # Output: 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0c11207c\",\n \"metadata\": {},\n \"source\": [\n \"This is therefore a [4, 3, 2] code.\\n\",\n \"\\n\",\n \"For our example code parity_code_4_3, we found $d=2$.\\n\",\n \"\\n\",\n \"- Error Detection: $t_{detect} = d - 1 = 2 - 1 = 1$. This code can detect any single-bit error.\\n\",\n \"- Error Correction: $t_{correct} = \\\\lfloor \\\\frac{d-1}{2} \\\\rfloor = \\\\lfloor \\\\frac{2-1}{2} \\\\rfloor = \\\\lfloor 0.5 \\\\rfloor = 0$.\\n\",\n \"\\n\",\n \"In particular, because any single error will result in a string that is not in the codespace, we know an error occurred and, as such, is detected!\\n\",\n \"\\n\",\n \"While this code can identify errors, it lacks the capability to correct them in all cases. Consider an error where the codeword `0000` is altered to `0001`. The error is detectable as `0001` is not a valid codeword. However, correction is not possible because `0001` has an identical Hamming distance to four different codewords (`0000`, `0011`, `0101`, and `1001`).\\n\",\n \"\\n\",\n \"Hence, in a [4, 3, 2] code, a single bit-flip error can yield a binary string that is not a codeword - but one cannot reliably correct such an error, as there is no unique closest codeword for correction - thus, it is impossible to know which of the original codewords was sent.\\n\",\n \"\\n\",\n \"In general, the reason a code can detect up to $d-1$ errors is that if any such set of bit-flips were to occur and corrupt the sent message, then one could identify that the transformed message is no longer a codeword and, as such, detect that some set of errors occurred. While correcting such errors is more involved, any error of weight at most $\\\\lfloor \\\\frac{d-1}{2} \\\\rfloor$ can be corrected, as one can consider the corrupted codeword and look at all possible codewords within Hamming distance $\\\\lfloor \\\\frac{d-1}{2} \\\\rfloor$ - there will only be one such possibility. Doing this task efficiently, also known as decoding, is in general a non-trivial task that is an active area of research, depending on the code.\\n\",\n \"\\n\",\n \"To investigate this further, let's recalculate for a different code, e.g., the [3, 1, 3] repetition code $C = { 000, 111 }$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e79adbe9\",\n \"metadata\": {},\n \"source\": [\n \"## 1.3 The [3, 1, 3] Repetition Code\\n\",\n \"\\n\",\n \"The simplest error-correcting code is the repetition code.\\n\",\n \"To send a single bit (k=1), we repeat it `n` times. For the `[3, 1, 3]` code, we repeat the bit 3 times.\\n\",\n \"- Message `0` is encoded as `000`.\\n\",\n \"- Message `1` is encoded as `111`.\\n\",\n \"\\n\",\n \"Here, n=3 and k=1.\\n\",\n \"The codewords are {000, 111}.\\n\",\n \"The Hamming distance between 000 and 111 is 3. So, $d=3$.\\n\",\n \"This code can detect up to $d-1 = 2$ errors.\\n\",\n \"It can correct up to $\\\\lfloor((d-1)/2)\\\\rfloor = \\\\lfloor((3-1)/2)\\\\rfloor$ = 1 error.\\n\",\n \"\\n\",\n \"**Encoding:**\\n\",\n \"- If the message bit is `m`, codeword `c = mmm`.\\n\",\n \"\\n\",\n \"**Error & Decoding (Majority Vote):**\\n\",\n \"Suppose a codeword `c` is transmitted, and `c'` is received.\\n\",\n \"If at most one bit is flipped, the original message can be recovered by majority voting.\\n\",\n \"- Receive `000` -> Decode `0`\\n\",\n \"- Receive `001` -> Decode `0` (1 error)\\n\",\n \"- Receive `010` -> Decode `0` (1 error)\\n\",\n \"- Receive `100` -> Decode `0` (1 error)\\n\",\n \"- Receive `111` -> Decode `1`\\n\",\n \"- Receive `110` -> Decode `1` (1 error)\\n\",\n \"- Receive `101` -> Decode `1` (1 error)\\n\",\n \"- Receive `011` -> Decode `1` (1 error)\\n\",\n \"\\n\",\n \"If two bits are flipped (e.g., `000` -> `011`), majority voting decodes to the wrong message (`1` in this case). The code can detect this as a 2-bit error but cannot correct it.\\n\",\n \"\\n\",\n \"Let's first check the minimum distance of this code with both error detection and error correction capabilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3d9af05b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- Example: [3, 1, 3] Repetition Code ---\\n\",\n \"repetition_code_3_1 = [\\\"000\\\", \\\"111\\\"]\\n\",\n \"d_repetition = minimum_distance(repetition_code_3_1)\\n\",\n \"print(f\\\"Calculated minimum distance d = {d_repetition}\\\") # Output: 3\\n\",\n \"\\n\",\n \"# Capabilities for d=3:\\n\",\n \"t_detect = d_repetition - 1\\n\",\n \"t_correct = int((d_repetition - 1) / 2) // 1\\n\",\n \"print(f\\\"Error Detection Capability (t_detect = d-1): {t_detect}\\\") # Output: 2\\n\",\n \"print(f\\\"Error Correction Capability (t_correct = floor((d-1)/2)): {t_correct}\\\") # Output: 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f32e68b\",\n \"metadata\": {},\n \"source\": [\n \"### Practice: Lookup table-based Decoder ofthe [3, 1, 3] Code\\n\",\n \"\\n\",\n \"Since this code can correct errors, we can now create a lookup table-based decoder in the form of a dictionary to perform error correction. A lookup table-based decoder maps every possible received 3-bit string to the most-likely transmitted 1-bit message, with regard to the corrected message. Here we will use a helper function `hamming_distance` to see how the Hamming distance affects correcting errors. First let's do a simple test with one single case.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9dc8d7e7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"test_str = \\\"010\\\"\\n\",\n \"\\n\",\n \"print(\\\"Hamming distance between 010 and 000 is\\\", hamming_distance(test_str, \\\"000\\\"))\\n\",\n \"print(\\\"Hamming distance between 010 and 111 is\\\", hamming_distance(test_str, \\\"111\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"96a83fff\",\n \"metadata\": {},\n \"source\": [\n \"\\\\\\\"000\\\\\\\" is a logical `0` and \\\\\\\"111\\\\\\\" is a logical `1`, so this `test_str` will be corrected as `0`. Try to complete the dictionary below to complete the whole lookup table-based decoder of the [3, 1, 3] classical error correcting code, by mapping the final corrected string to the received string.\\n\",\n \"\\n\",\n \"Before checking the solution table below, it's helpful to understand its columns: 'Received text' is the 3-bit string that was observed after potential errors; 'Hamming distance with \\\"000\\\"' and 'Hamming distance with \\\"111\\\"' shows how many bits differ from the two valid logical codewords (000 and 111, respectively); and 'Corrected str' is the single bit (0 or 1) that the decoder determines was the original message.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5bad9fd5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hardcode_decoder_3_1_3 ={\\n\",\n \" '000': '0',\\n\",\n \" '001': '',\\n\",\n \" '010': '',\\n\",\n \" '011': '',\\n\",\n \" '100': '',\\n\",\n \" '101': '',\\n\",\n \" '110': '',\\n\",\n \" '111': '1'}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3f8018cb\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \" Check your decoder with this! \\n\",\n \"\\n\",\n \"| Received text | Hamming distance with \\\"000\\\" | Hamming distance with \\\"111\\\" | Corrected str |\\n\",\n \"|---|---|---|---|\\n\",\n \"|000|0|3|0|\\n\",\n \"|001|1|2|0|\\n\",\n \"|010|1|2|0|\\n\",\n \"|011|2|1|1|\\n\",\n \"|100|1|2|0|\\n\",\n \"|101|2|1|1|\\n\",\n \"|110|2|1|1|\\n\",\n \"|111|3|0|1|\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fea7ef37\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"# Chapter 2. Quantum Error Correcting Codes [[n, k, d]]\\n\",\n \"\\n\",\n \"Quantum information is much more fragile than classical information. Qubits are susceptible to various types of errors, not just bit-flips ($X$ errors) but also phase-flips ($Z$ errors) and combinations of both ($Y$ errors). Quantum error correction aims to protect quantum states from these errors.\\n\",\n \"\\n\",\n \"A quantum error correcting code is often denoted by `[[n, k, d]]`, where:\\n\",\n \"\\n\",\n \"* **`n`**: The number of **data qubits** used to construct the code.\\n\",\n \"* **`k`**: The number of **logical qubits** encoded by the code. This means the code protects a $2^k$-dimensional logical state space (codespace).\\n\",\n \"* **`d`**: The **minimum distance** of the code. This is the minimum number of qubits an operator needs to act on to perform a non-identity operation on a logical qubit. In the case of stabilizer codes, it is the minimum number of qubits a Pauli operator must act on in order to apply a logical Pauli operator. \\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Note on resource overhead:\\n\",\n \" \\n\",\n \"It's important to note that the `n` in the `[[n, k, d]]` notation typically refers to the data qubits involved in the code block. This count usually *omits* any ancilla or syndrome qubits required for tasks like error detection and correction. Therefore, the total resource overhead is not simply `n - k` data qubits, but rather `(n - k)` (the number of redundant data qubits) *plus* the number of ancilla qubits needed for syndrome measurements. In many stabilizer codes, the number of independent stabilizers (and thus often the number of ancilla qubits) is `n - k`, but this can vary, and in some schemes, the ancilla count could be significant, potentially as large as `n`.\\n\",\n \"
\\n\",\n \"\\n\",\n \"Similar to classical codes, the minimum distance $d$ determines the code's error correction capability. An `[[n, k, d]]` code can detect up to `d-1` errors and can potentially correct up to `floor((d-1)/2)` arbitrary single-qubit errors.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9895c32e\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"## 2.1 Stabilizer Formalism\\n\",\n \"\\n\",\n \"\\n\",\n \"The stabilizer formalism is a powerful framework for constructing and understanding many QEC codes.\\n\",\n \"\\n\",\n \"- The **Pauli group** $P_n$ on $n$ qubits consists of all $n$-fold tensor products of Pauli matrices $\\\\{I, X, Y, Z\\\\}$, multiplied by overall factors $\\\\{\\\\pm 1, \\\\pm i\\\\}$.\\n\",\n \"- A **stabilizer code** is defined by its **stabilizer group S**, which is an abelian subgroup of $P_n$ such that $-I \\\\notin S$.\\n\",\n \"- The **codespace C(S)** is the subspace of $(\\\\mathbb{C}^2)^{\\\\otimes n}$ simultaneously stabilized by all elements of $S$. That is, for any state $|\\\\psi\\\\rangle \\\\in C(S)$ and any $g \\\\in S$, $g|\\\\psi\\\\rangle = |\\\\psi\\\\rangle$. So, states in the codespace are +1 eigenstates of all stabilizer operators.\\n\",\n \"- Typically, $S$ is specified by a set of $m = n-k$ independent and commuting generators $S = \\\\langle g_1, g_2, ..., g_m \\\\rangle$.\\n\",\n \"\\n\",\n \"A comprehensive resource is the IBM Quantum Learning course by John Watrous: [Foundations of Quantum Error Correction](https://quantum.cloud.ibm.com/learning/courses/foundations-of-quantum-error-correction). If you wish to delve deeper into the mathematical details and further examples, we recommend exploring this resource. Click `Stabilizer Formalism Summary` to see the summary, or you can skip this.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" Stabilizer Formalism Summary\\n\",\n \"\\n\",\n \" Many powerful Quantum Error Correcting Codes (QECCs), including the 3-qubit repetition code discussed below, as well as the famous Steane (`[[7, 1, 3]]`) and Shor (`[[9, 1, 3]]`) codes, belong to the family of **stabilizer codes**. The stabilizer formalism provides a powerful framework for defining quantum codes and designing error detection and correction procedures.\\n\",\n \"\\n\",\n \"**1. Stabilizing States and Subspaces:**\\n\",\n \"\\n\",\n \"* An operator $S$ is said to **stabilize** a quantum state $|\\\\psi\\\\rangle$ if $|\\\\psi\\\\rangle$ is an eigenstate of $S$ with eigenvalue +1. That is, $S|\\\\psi\\\\rangle = |\\\\psi\\\\rangle$.\\n\",\n \"* An operator $S$ stabilizes a subspace (the **codespace**, denoted $\\\\mathcal{C}$) if it stabilizes *every* state $|\\\\psi\\\\rangle$ within that subspace: $S|\\\\psi\\\\rangle = |\\\\psi\\\\rangle$ for all $|\\\\psi\\\\rangle \\\\in \\\\mathcal{C}$.\\n\",\n \"\\n\",\n \"**2. The Stabilizer Group ($\\\\mathcal{S}$):**\\n\",\n \"\\n\",\n \"* For a given stabilizer code, the set of all operators that stabilize the codespace $\\\\mathcal{C}$ forms a mathematical group called the **stabilizer group**, denoted $\\\\mathcal{S}$.\\n\",\n \"* **Key Properties:**\\n\",\n \" * All operators $S_i, S_j$ in the group $\\\\mathcal{S}$ must **commute** with each other: $S_i S_j = S_j S_i$.\\n\",\n \" * Stabilizer operators are typically constructed from tensor products of **Pauli operators** ($I, X, Y, Z$) acting on $n$ qubits. For example, $X \\\\otimes Z \\\\otimes I \\\\otimes X$ could be a stabilizer element for $n=4$.\\n\",\n \" * The identity operator $I^{\\\\otimes n}$ is always an element of $\\\\mathcal{S}$. By convention, $-I^{\\\\otimes n}$ is usually excluded.\\n\",\n \"\\n\",\n \"**3. Generators ($\\\\{g_i\\\\}$):**\\n\",\n \"\\n\",\n \"* Instead of listing all elements of the potentially large group $\\\\mathcal{S}$, we define it using a smaller set of **generators**, $g_1, g_2, ..., g_{n-k}$.\\n\",\n \"* Any element $S \\\\in \\\\mathcal{S}$ can be created by taking products of these generators (e.g., $g_1 g_3$, $g_2 g_5 g_1$, etc.).\\n\",\n \"* **Key Properties of Generators:**\\n\",\n \" * They must **commute** with each other: $[g_i, g_j] = 0$ for all $i, j$.\\n\",\n \" * They must be **independent**: no generator can be formed by multiplying the others in the set.\\n\",\n \" * There are $n-k$ independent generators for an `[[n, k, d]]` code.\\n\",\n \"\\n\",\n \"**4. Defining the Codespace ($\\\\mathcal{C}$):**\\n\",\n \"\\n\",\n \"* The codespace $\\\\mathcal{C}$ of a stabilizer code is the **simultaneous +1 eigenspace** of all its generators (and thus all elements in $\\\\mathcal{S}$).\\n\",\n \"* If a state $|\\\\psi\\\\rangle$ is in the codespace, it must satisfy $g_i |\\\\psi\\\\rangle = |\\\\psi\\\\rangle$ for all generators $i=1, ..., n-k$.\\n\",\n \"* Starting with the full $2^n$-dimensional Hilbert space of $n$ physical qubits, each independent generator effectively halves the dimension of the subspace it stabilizes. Therefore, $n-k$ independent generators define a $2^{n-(n-k)} = 2^k$-dimensional codespace, which is exactly the space needed to encode $k$ logical qubits.\\n\",\n \"\\n\",\n \"**5. Error Detection using Stabilizers:**\\n\",\n \"\\n\",\n \"* The primary function of stabilizers in QEC is **error detection**. This works by measuring the eigenvalues of the stabilizer generators.\\n\",\n \"* **No Error:** If the system is in a valid codespace state $|\\\\psi\\\\rangle \\\\in \\\\mathcal{C}$ and no error occurs, measuring any generator $g_i$ will yield the outcome +1 with certainty (because $g_i|\\\\psi\\\\rangle=|\\\\psi\\\\rangle$).\\n\",\n \"* **Error Occurs:** Suppose an error $E$ (a Pauli operator product) occurs, transforming the state to $E|\\\\psi\\\\rangle$. Now, measure a generator $g_i$.\\n\",\n \" * If $g_i$ **commutes** with the error $E$ (i.e., $g_i E = E g_i$):\\n\",\n \" - $g_i (E |\\\\psi\\\\rangle) = E g_i |\\\\psi\\\\rangle = E |\\\\psi\\\\rangle$. The measurement outcome is still +1. The error $E$ is *not detected* by $g_i$.\\n\",\n \" * If $g_i$ **anti-commutes** with the error $E$ (i.e., $g_i E = -E g_i$):\\n\",\n \" - $g_i (E |\\\\psi\\\\rangle) = -E g_i |\\\\psi\\\\rangle = -E |\\\\psi\\\\rangle$. The measurement outcome is -1. The error $E$ *is detected* by $g_i$.\\n\",\n \"* **Error Syndrome:** The set of measurement outcomes (+1 or -1, often mapped to 0 or 1, respectively) for all the generators $\\\\{g_1, ..., g_{n-k}\\\\}$ constitutes the **error syndrome**.\\n\",\n \" * A syndrome of all +1s (or all 0s) indicates either no error occurred, or an error occurred that commutes with all stabilizers (which might be a logical operator or an undetectable error related to the code's distance $d$).\\n\",\n \" * A non-trivial syndrome (containing at least one -1 or 1) indicates that a detectable error has occurred. The specific pattern of the syndrome provides information about the likely error $E$ that occurred, which can then be used for correction.\\n\",\n \"\\n\",\n \"**Connection to `[[n, k, d]]`:**\\n\",\n \"\\n\",\n \"* `n` is the number of physical qubits the stabilizers act on.\\n\",\n \"* `k` is the number of encoded logical qubits, determined by $k = n - (\\\\text{number of independent generators})$.\\n\",\n \"* `d` is the minimum weight of a Pauli error $E$ that commutes with all stabilizers ($[E, g_i]=0$ for all $i$) but is *not* itself a product of stabilizers (i.e., $E \\\\notin \\\\mathcal{S}$). Such an operator is a **logical operator** (acting non-trivially on the encoded information) or an undetectable error. The distance $d$ quantifies the code's ability to distinguish between the effects of low-weight errors.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Stabilizer formalism provides the foundation for understanding how codes like the `[[7, 1, 3]]` example (below) are constructed and how they detect errors of any type.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88f289c0\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2 3-qubit bit-flip Code Practice\\n\",\n \"\\n\",\n \"(Note: Throughout this explanation, we use Qiskit's little-endian convention, where the rightmost character in a Pauli string corresponds to qubit 0, e.g., `IZZ` acts as $I_2 \\\\otimes Z_1 \\\\otimes Z_0$.)\\n\",\n \"\\n\",\n \"This code is the quantum analog of the classical [3, 1, 3] repetition code. It can correct a **single bit-flip (X) error** on any of the three physical qubits - however, it will not be able to correct for $Z$ phase errors.\\n\",\n \"- n=3 physical qubits, k=1 logical qubit. The goal of the code is to preserve the state of the logical qubit, despite errors on the physical qubits.\\n\",\n \"- Logical states (unnormalized):\\n\",\n \" - $|0_L\\\\rangle \\\\equiv |000\\\\rangle$\\n\",\n \" - $|1_L\\\\rangle \\\\equiv |111\\\\rangle$\\n\",\n \" - An arbitrary logical state is $\\\\alpha|0_L\\\\rangle + \\\\beta|1_L\\\\rangle = \\\\alpha|000\\\\rangle + \\\\beta|111\\\\rangle$.\\n\",\n \"\\n\",\n \"**Stabilizer Generators:**\\n\",\n \"This code is stabilized by:\\n\",\n \"- $S_0 = Z_2 Z_1 I_0$ (or simply $Z_1Z_2$)\\n\",\n \"- $S_1 = I_2 Z_1 Z_0$ (or simply $Z_0Z_1$)\\n\",\n \"These are Z-type stabilizers, which are used to detect X-errors (bit-flips).\\n\",\n \"\\n\",\n \"**Logical Operators:**\\n\",\n \"- Logical Z: $\\\\bar{Z} = Z_0$ (or $Z_1$ or $Z_2$, as $Z_0|0_L\\\\rangle = |0_L\\\\rangle, Z_0|1_L\\\\rangle = -|1_L\\\\rangle$). A common choice is $Z_2 Z_1 Z_0$.\\n\",\n \"- Logical X: $\\\\bar{X} = X_2 X_1 X_0$. $(\\\\bar{X}|0_L\\\\rangle = |1_L\\\\rangle, \\\\bar{X}|1_L\\\\rangle = |0_L\\\\rangle)$.\\n\",\n \"\\n\",\n \"**Error Detection (Syndrome Extraction):**\\n\",\n \"Let $s_0, s_1$ be the eigenvalues (-1 if flipped, +1 if not) for $S_0, S_1$ respectively. The syndrome is $(s_0, s_1)$.\\n\",\n \"- No error: $(+1, +1)$\\n\",\n \"- $X_0$ error (bit-flip on qubit 0): $S_0$ commutes, $S_1$ anti-commutes ($Z_0Z_1X_0 = -X_0Z_0Z_1$). Syndrome: $(s_1,s_0) = (-1, +1)$. Correction: Apply $X_0$.\\n\",\n \"- $X_1$ error (bit-flip on qubit 1): $S_0$ anti-commutes, $S_1$ anti-commutes. Syndrome: $(-1, -1)$. Correction: Apply $X_1$.\\n\",\n \"- $X_2$ error (bit-flip on qubit 2): $S_0$ anti-commutes, $S_1$ commutes. Syndrome: $(+1, -1)$. Correction: Apply $X_2$.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \"**Vulnerability to Z-errors**\\n\",\n \" \\n\",\n \"Keep in mind that this code is primarily designed to protect against bit-flip (X-type) errors. It's crucial to note this code does undergo any Z-type (phase-flip) errors. A single Z-type error on any of the physical qubits would commute with the stabilizers that detect X-errors (e.g., $Z_i Z_j$) and would manifest as an uncorrectable logical Z error on the encoded qubit, altering its phase information without being detected.\\n\",\n \"
\\n\",\n \"Exercise 1: Lookup Table-based Decoder of 3-bit code\\n\",\n \"\\n\",\n \"As we introduced, the quantum bit-flip code, defined by two stabilizer generators $S_0=ZZI$ ($Z_2 \\\\otimes Z_1 \\\\otimes I_0$) and $S_1=IZZ$ ($I_2 \\\\otimes Z_1 \\\\otimes Z_0$) and be measured into two classical bit. So the syndrome outcome will be a 2-bit classical bit string. This bit strings serve as the input of a hardcord decoder, which is a lookup table that maps the syndrome to the specific correction operation needed.\\n\",\n \"\\n\",\n \"\\n\",\n \"Your task is to complete the dictionary that maps these measured syndrome bits to the corresponding error code (e.g., 'X0' for an X error on qubit 0, 'X1' for an X error on qubit 1, 'X2' for an X error on qubit 2, or 'I' for no error) for a single (Weight-1) bit-flip error.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"655e2db2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Solution\\n\",\n \"hardcode_decoder_bit_flip_syndrome_map = {\\n\",\n \" #{(s1, s0): \\\"Error Code\\\"}\\n\",\n \" '00' : 'I',\\n\",\n \" '01' : 'X2', \\n\",\n \" '10' : 'X0', \\n\",\n \" '11' : 'X1' \\n\",\n \"}\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"87281376\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab4_ex1(hardcode_decoder_bit_flip_syndrome_map )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"53623200\",\n \"metadata\": {},\n \"source\": [\n \"## 2.3 CSS Codes (Calderbank-Shor-Steane)\\n\",\n \"\\n\",\n \"CSS codes, named after Calderbank, Shor, and Steane, are a powerful family of quantum codes constructed from classical linear codes. \\n\",\n \"Here we provide a high-level introduction, and for a more detailed explanation of CSS codes, refer to the IBM Quantum Learning course material on [Quantum Code Constructions (CSS Codes)](https://quantum.cloud.ibm.com/learning/courses/foundations-of-quantum-error-correction/quantum-code-constructions/css-codes).\\n\",\n \"\\n\",\n \"**Construction:**\\n\",\n \"A CSS code is defined using two classical binary linear codes, $C_1 = [n, k_1, d_1]$ and $C_2 = [n, k_2, d_2]$, satisfying the condition $C_2^\\\\perp \\\\subseteq C_1$. Here, $C_2^\\\\perp$ is the dual code of $C_2$.\\n\",\n \"\\n\",\n \"* **Stabilizers:** The stabilizer group $S$ for the CSS code $[[n, k, d]]$ (where $k = k_1 + k_2 - n$) is generated by:\\n\",\n \" * **X-type stabilizers:** Derived from the parity check matrix $H_1$ of $C_1$. For each row $h \\\\in H_1$, create a stabilizer $\\\\bigotimes_{i: h_i=1} X_i$. These stabilizers consist only of Pauli X and I operators.\\n\",\n \" * **Z-type stabilizers:** Derived from the parity check matrix $H_2^\\\\perp$ of $C_2^\\\\perp$. For each row $h' \\\\in H_2^\\\\perp$, create a stabilizer $\\\\bigotimes_{i: h'_i=1} Z_i$. These stabilizers consist only of Pauli Z and I operators.\\n\",\n \" *(Note: Conventions might swap the roles of $C_1$ and $C_2^\\\\perp$ for X and Z stabilizers).*\\n\",\n \"\\n\",\n \"**Key Property and Decoding:**\\n\",\n \"The crucial property of CSS codes is the separation of stabilizer types:\\n\",\n \"* **X-stabilizers** commute with X errors but can anti-commute with Z errors on the qubits they act upon. They are used to detect **Z-type errors** (and the Z component of Y errors).\\n\",\n \"* **Z-stabilizers** commute with Z errors but can anti-commute with X errors on the qubits they act upon. They are used to detect **X-type errors** (and the X component of Y errors).\\n\",\n \"\\n\",\n \"This separation simplifies decoding:\\n\",\n \"1. Measure all stabilizers to get the full syndrome.\\n\",\n \"2. Extract the syndrome bits corresponding to the **Z-stabilizers**. Use these bits and a classical decoding algorithm associated with the code $C_1$ (whose checks define the X stabilizers) to identify and correct likely **X or Y errors**.\\n\",\n \"3. Extract the syndrome bits corresponding to the **X-stabilizers**. Use these bits and a classical decoding algorithm associated with the code $C_2^\\\\perp$ (whose checks define the Z stabilizers) to identify and correct likely **Z or Y errors**.\\n\",\n \"\\n\",\n \"This powerful CSS construction, which distinctly handles X and Z errors using classical code components, is exemplified by the renowned [[7, 1, 3]] Steane code. In the next section, we'll explore the specific stabilizers and practical aspects of this important code.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9da222c6\",\n \"metadata\": {},\n \"source\": [\n \"## 2.4 [[7,1,3]] Steane Code Practice\\n\",\n \"\\n\",\n \"The **[[7, 1, 3]] Steane code** is a quintessential CSS code. It encodes $k=1$ logical qubit into $n=7$ physical qubits and can correct any arbitrary single-qubit error (since $d=3$). It is constructed using the classical [7, 4, 3] Hamming code for both $C_1$ and $C_2$ (i.e., $H_1 = H_2 = H_{\\\\text{Hamming}}$).\\n\",\n \"\\n\",\n \"### Parity Check Matrix Primer\\n\",\n \"\\n\",\n \"A **parity check matrix ($H$)** for a classical linear code is a fundamental tool. If $c$ is a codeword, then the product $Hc^T = \\\\mathbf{0}$ (the zero vector) must hold. Each row of $H$ defines a parity check, meaning it specifies a subset of codeword bits that must sum to 0 (modulo 2). If $Hc^T \\\\neq \\\\mathbf{0}$, the result is called the **syndrome**, which indicates an error has occurred and can be used to identify it. For CSS codes, the rows of classical parity check matrices are used to define the X-type and Z-type stabilizer generators of the quantum code.\\n\",\n \"\\n\",\n \"### Stabilizer Generators from the Parity Check Matrix\\n\",\n \"\\n\",\n \"\\n\",\n \"The parity check matrix $H$ whose rows define stabilizer structures (for qubits $q_0, ..., q_6$) of the Steane code is:\\n\",\n \"$$ H = \\\\begin{pmatrix}\\n\",\n \"1 & 1 & 1 & 1 & 0 & 0 & 0 \\\\\\\\ % Corresponds to X0 X1 X2 X3 (and Z0 Z1 Z2 Z3)\\n\",\n \"1 & 1 & 0 & 0 & 1 & 1 & 0 \\\\\\\\ % Corresponds to X0 X1 X4 X5 (and Z0 Z1 Z4 Z5)\\n\",\n \"1 & 0 & 1 & 0 & 1 & 0 & 1 % Corresponds to X0 X2 X4 X6 (and Z0 Z2 X4 X6)\\n\",\n \"\\\\end{pmatrix} $$\\n\",\n \"Each row $(h_0, h_1, h_2, h_3, h_4, h_5, h_6)$ of this matrix $H$ corresponds to a stabilizer generator. For an X-stabilizer, an $X_i$ operator is present if $h_i=1$. For a Z-stabilizer, a $Z_i$ operator is present if $h_i=1$.\\n\",\n \"\\n\",\n \"So, the stabilizer generators are:\\n\",\n \"\\n\",\n \"* **X-type stabilizers** (these detect Z-errors), derived from the rows of $H$:\\n\",\n \" * $S_{X0} = X_0 X_1 X_2 X_3 = IIIXXXX$\\n\",\n \" * $S_{X1} = X_0 X_1 X_4 X_5 = IXXIIXX$\\n\",\n \" * $S_{X2} = X_0 X_2 X_4 X_6 = XIXIXIX$\\n\",\n \"\\n\",\n \"* **Z-type stabilizers** (these detect X-errors), also derived from the rows of $H$:\\n\",\n \" * $S_{Z0} = Z_0 Z_1 Z_2 Z_3 = IIIZZZZ$\\n\",\n \" * $S_{Z1} = Z_0 Z_1 Z_4 Z_5 = IZZIIZZ$\\n\",\n \" * $S_{Z2} = Z_0 Z_2 Z_4 Z_6 = ZIZIZIZ$\\n\",\n \"\\n\",\n \"\\n\",\n \"These $m = n-k = 7-1=6$ operators generate the stabilizer group $S$. The X-stabilizers commute with all Z-stabilizers. This is because for any row $h_a$ from $H$ defining an X-stabilizer and any row $h_b$ from $H$ defining a Z-stabilizer, the number of positions where both rows have a '1' (i.e., shared qubits) is even. This ensures that $S_{Xa}$ and $S_{Zb}$ commute.\\n\",\n \"\\n\",\n \"**Error Detection and Correction:**\\n\",\n \"A single Z-error on qubit $j$, $Z_j$, will anti-commute with a specific subset of $\\\\{S_{X0}, S_{X1}, S_{X2}\\\\}$. The 3-bit syndrome derived from measuring these X-stabilizers will uniquely identify $j$. For example, a $Z_0$ error anti-commutes with $S_{X0}, S_{X1}, S_{X2}$, giving an X-syndrome (eigenvalue flips) of $(1,1,1)$ (if 1 means flip). A $Z_3$ error anti-commutes only with $S_{X0}$, giving syndrome $(1,0,0)$. Each column of $H$ represents the syndrome pattern for an error on the corresponding qubit.\\n\",\n \"Similarly, a single X-error on qubit $j$, $X_j$, will anti-commute with a specific subset of $\\\\{S_{Z0}, S_{Z1}, S_{Z2}\\\\}$, and the 3-bit syndrome from these Z-stabilizers will identify $j$.\\n\",\n \"A $Y_j$ error will be caught by both sets of syndromes.\\n\",\n \"\\n\",\n \"**Logical Operators:**\\n\",\n \"A common choice for logical operators, which must commute with all stabilizers but are not themselves stabilizers, is:\\n\",\n \"* $\\\\bar{X} = X_0 X_1 X_2 X_3 X_4 X_5 X_6$ (product of $X$ on all 7 qubits)\\n\",\n \"* $\\\\bar{Z} = Z_0 Z_1 Z_2 Z_3 Z_4 Z_5 Z_6$ (product of $Z$ on all 7 qubits)\\n\",\n \"\\n\",\n \"#### Minimum distance and error detection of the Steane code\\n\",\n \"\\n\",\n \"Let's see why $d$ for the $[[7,1,3]]$ bit-flip code is 3. You can skip this if you already familiar with it.\\n\",\n \"\\n\",\n \"\\n\",\n \" Click to check \\n\",\n \"\\n\",\n \"\\n\",\n \"### Weight-1 Operators (Single-qubit errors)\\n\",\n \"\\n\",\n \"Consider any single-qubit Pauli error, $P_i \\\\in \\\\{X_i, Y_i, Z_i\\\\}$ acting on qubit $i$.\\n\",\n \"* If $P_i = X_i$ or $Y_i$, it will anti-commute with any Z-type stabilizer ($S_{Z0}, S_{Z1}, S_{Z2}$) that has a $Z$ on qubit $i$. For example, $X_6$ anti-commutes with $S_{Z2} = Z_0 Z_2 Z_4 Z_6$. $Y_6$ also anti-commutes with $S_{Z2}$.\\n\",\n \"* If $P_i = Z_i$ or $Y_i$, it will anti-commute with any X-type stabilizer ($S_{X0}, S_{X1}, S_{X2}$) that has an $X$ on qubit $i$. For example, $Z_6$ anti-commutes with $S_{X2} = X_0 X_2 X_4 X_6$. $Y_6$ also anti-commutes with $S_{X2}$.\\n\",\n \"\\n\",\n \"Since every qubit $i \\\\in \\\\{0, \\\\dots, 6\\\\}$ has both X-type and Z-type stabilizers acting non-trivially upon it within the set $\\\\{S_{X0}, \\\\dots, S_{Z2}\\\\}$, **any** single-qubit Pauli error $P_i$ will anti-commute with at least one stabilizer generator.\\n\",\n \"Conclusion: All weight-1 errors are **detectable**. Therefore, $d > 1$.\\n\",\n \"\\n\",\n \"### Weight-2 Operators (Two-qubit errors)\\n\",\n \"\\n\",\n \"Consider any two-qubit Pauli error $E_2 = P_i P'_j$ where $i \\\\neq j$ and $P, P' \\\\in \\\\{X, Y, Z\\\\}$. By examining the stabilizer generators, it can be shown that any such $E_2$ must anti-commute with at least one $S_k$.\\n\",\n \"* For example, take $X_5 X_6$.\\n\",\n \" * $X_5$ anti-commutes with $S_{Z1} (=Z_0Z_1Z_4Z_5)$ and $S_{Z2} (=Z_0Z_2Z_4Z_6)$ (no, $X_5$ anti-commutes with Z-stabilizers involving $Z_5$, i.e., $S_{Z1}$).\\n\",\n \" * $X_6$ anti-commutes with $S_{Z2} (=Z_0Z_2Z_4Z_6)$.\\n\",\n \" * Let's check $X_5 X_6$ commutation with the Z-stabilizers:\\n\",\n \" * $S_{Z0} = Z_0Z_1Z_2Z_3$: Commutes.\\n\",\n \" * $S_{Z1} = Z_0Z_1Z_4Z_5$: Anti-commutes (due to $X_5$ and $Z_5$).\\n\",\n \" * $S_{Z2} = Z_0Z_2Z_4Z_6$: Anti-commutes (due to $X_6$ and $Z_6$).\\n\",\n \" So, $X_5X_6$ anti-commutes with $S_{Z1}$ and $S_{Z2}$.\\n\",\n \"* As another example, take $Z_1 Z_2$.\\n\",\n \" * $Z_1$ anti-commutes with $S_{X0} (=X_0X_1X_2X_3)$ and $S_{X1} (=X_0X_1X_4X_5)$.\\n\",\n \" * $Z_2$ anti-commutes with $S_{X0} (=X_0X_1X_2X_3)$ and $S_{X2} (=X_0X_2X_4X_6)$.\\n\",\n \" * Let's check $Z_1 Z_2$ commutation with X-stabilizers:\\n\",\n \" * $S_{X0} = X_0X_1X_2X_3$: Anti-commutes (due to $Z_1$ with $X_1$, and $Z_2$ with $X_2$; an even number of anti-commutations means overall commutation for this product). No, $Z_1Z_2$ with $X_0X_1X_2X_3$: $Z_1$ flips sign with $X_1$, $Z_2$ flips sign with $X_2$. Total sign flip is $(-1) \\\\times (-1) = +1$. So it commutes.\\n\",\n \" * $S_{X1} = X_0X_1X_4X_5$: Anti-commutes (due to $Z_1$ with $X_1$).\\n\",\n \" * $S_{X2} = X_0X_2X_4X_6$: Anti-commutes (due to $Z_2$ with $X_2$).\\n\",\n \" So, $Z_1Z_2$ anti-commutes with $S_{X1}$ and $S_{X2}$.\\n\",\n \"\\n\",\n \"Through systematic check, it's found that **all** weight-2 Pauli errors anti-commute with at least one stabilizer generator $S_k$.\\n\",\n \"Conclusion: All weight-2 errors are **detectable**. Therefore, $d > 2$.\\n\",\n \"\\n\",\n \"### Weight-3 Operators (Three-qubit errors)\\n\",\n \"\\n\",\n \"Now consider weight-3 Pauli error $E_3$. Let's check $L_X = X_4X_5X_6$ against the Z-type stabilizers:\\n\",\n \"* **vs $S_{Z0} = Z_0Z_1Z_2Z_3$**:\\n\",\n \" $X_4, X_5, X_6$ all act on qubits not present in $S_{Z0}$. Thus, $L_X$ commutes with $S_{Z0}$.\\n\",\n \"* **vs $S_{Z1} = Z_0Z_1Z_4Z_5$**:\\n\",\n \" $X_4$ (from $L_X$) anti-commutes with $Z_4$ (from $S_{Z1}$).\\n\",\n \" $X_5$ (from $L_X$) anti-commutes with $Z_5$ (from $S_{Z1}$).\\n\",\n \" $X_6$ (from $L_X$) commutes with $S_{Z1}$ (no $Z_6$ in $S_{Z1}$).\\n\",\n \" The combined operation results from two anti-commutations ($X_4$ with $Z_4$, $X_5$ with $Z_5$). Since an even number (2) of components anti-commute, the overall operators $L_X$ and $S_{Z1}$ commute.\\n\",\n \"* **vs $S_{Z2} = Z_0Z_2Z_4Z_6$**:\\n\",\n \" $X_4$ (from $L_X$) anti-commutes with $Z_4$ (from $S_{Z2}$).\\n\",\n \" $X_5$ (from $L_X$) commutes with $S_{Z2}$ (no $Z_5$ in $S_{Z2}$).\\n\",\n \" $X_6$ (from $L_X$) anti-commutes with $Z_6$ (from $S_{Z2}$).\\n\",\n \" The combined operation results from two anti-commutations ($X_4$ with $Z_4$, $X_6$ with $Z_6$). Since an even number (2) of components anti-commute, $L_X$ and $S_{Z2}$ commute.\\n\",\n \"\\n\",\n \"So, $L_X = X_4X_5X_6$ commutes with all stabilizer generators $S_{X0}, \\\\dots, S_{Z2}$. Because $X_4X_5X_6$ (a weight-3 operator) commutes with all $S_i$, it produces a trivial syndrome $(0,0,0,0,0,0)$ and is therefore **undetectable** by stabilizer measurements in the same way that errors are detected. Such an operator changes the encoded logical state.\\n\",\n \"\\n\",\n \"**Therefore, the minimum distance of the [[7, 1, 3]] Steane code is $d=3$.**\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"71c750a1\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \" \\n\",\n \"Exercise 2: Lookup table of [[7, 1, 3]] Steane Code \\n\",\n \"\\n\",\n \"The goal is to create a Python dictionary that maps each possible 6-bit syndrome to a specific single-qubit error code (e.g., X0 is X error injected on the 0th qubit). If we assume syndrome measure results of each syndrome as `s0`...`s5` = $S_{X0},...,S_{Z2}$, the final error if the syndrome is (s5, s4, s3, s2, s1, s0), it indicates an X error on qubit 0, and the corresponding dictionary entry should be \\\"111000\\\": \\\"X0\\\". Each value in the dictionary follows the format \\\"Pq\\\", where P is the Pauli operator (X, Y, or Z) and q is the qubit index from 0 to 6. If there is no error, the syndrome will be all zeros, and the correction should be \\\"I\\\" to indicate the identity (i.e., do nothing).\\n\",\n \"\\n\",\n \"You are asked to complete this mapping for all possible single-qubit Pauli errors to determine the syndrome produced by X, Y, and Z errors, and assign the correct label in the dictionary. When you're done, your decoder will be able to take any 6-bit syndrome from a single error and output the appropriate corrective action.\\n\",\n \"\\n\",\n \"(**Tip**) Try to fill out the commute/anti-commute table below for the X errors! Mark 0 for commute and 1 for anti-commute.\\n\",\n \"\\n\",\n \"| Error Code | Error Pauli String |S5(ZIZIZIZ) | S4(IZZIIZZ) | S3 (IIIZZZZ) | S2 (XIXIXIX) | S1 (IXXIIXX) | S0 (IIIXXXX) |\\n\",\n \"|---|---|---|---|---|---|---|---|\\n\",\n \"| $X_0$ | IIIIIIX | 1(anti-commute) | 1(anti-commute) | 1(anti-commute) | 0(commute) | 0(commute) | 0(commute) |\\n\",\n \"| $X_1$ | IIIIIXI | | | | | | |\\n\",\n \"| $X_2$ | IIIIXII | | | | | | |\\n\",\n \"| $X_3$ | IIIXIII | | | | | | |\\n\",\n \"| $X_4$ | IIXIIII | | | | | | |\\n\",\n \"| $X_5$ | IXIIIII | | | | | | |\\n\",\n \"| $X_6$ | XIIIIII | | | | | | |\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 3: Detect Error with a Lookup Table\\n\",\n \"\\n\",\n \"Now, let's use the lookup table you've created to find errors.\\n\",\n \"\\n\",\n \"Run the cell below to download a quantum state from the grader. This quantum state is a logical zero state of the Steane code, into which a specific error has been injected.\\n\",\n \"\\n\",\n \"Your task is to:\\n\",\n \"1. Construct a quantum circuit.\\n\",\n \"2. Initialize the `qr_data` qubits to the provided quantum state.\\n\",\n \"3. Complete `measure_steane_syndrome` function. We left S0 as an example.\\n\",\n \"4. Compare the measurement results (the syndrome) with your lookup table to identify which qubit experienced what type of error.\\n\",\n \"5. Submit your findings to the grader for verification.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"First, complete `measure_steane_syndrome` by preparing all the stabilizers of the Steane code.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a4a8f077\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Solution\\n\",\n \"\\n\",\n \"def measure_steane_syndrome(qc, q_data, q_anc, c_reg):\\n\",\n \"\\n\",\n \" # Measure X-type Stabilizers (S1, S2, S3 -> c_reg[0], c_reg[1], c_reg[2])\\n\",\n \" # Apply H to data qubits -> CNOT -> Apply H to data qubits -> Measure ancilla\\n\",\n \" qc.h(q_data) # H on data qubits\\n\",\n \" qc.cx(q_data[0], q_anc[0]); qc.cx(q_data[1], q_anc[0])\\n\",\n \" qc.cx(q_data[2], q_anc[0]); qc.cx(q_data[3], q_anc[0]) # S1\\n\",\n \" qc.cx(q_data[0], q_anc[1]); qc.cx(q_data[1], q_anc[1])\\n\",\n \" qc.cx(q_data[4], q_anc[1]); qc.cx(q_data[5], q_anc[1]) # S2\\n\",\n \" qc.cx(q_data[0], q_anc[2]); qc.cx(q_data[2], q_anc[2])\\n\",\n \" qc.cx(q_data[4], q_anc[2]); qc.cx(q_data[6], q_anc[2]) # S3\\n\",\n \" qc.h(q_data) # Restore H on data qubits\\n\",\n \" qc.measure(q_anc[0:3], c_reg[0:3]) # Measure X syndrome (s1, s2, s3)\\n\",\n \" qc.barrier()\\n\",\n \"\\n\",\n \" # Measure Z-type Stabilizers (S4, S5, S6 -> c_reg[3], c_reg[4], c_reg[5])\\n\",\n \" # CNOT -> Measure ancilla\\n\",\n \" qc.cx(q_data[0], q_anc[3]); qc.cx(q_data[1], q_anc[3])\\n\",\n \" qc.cx(q_data[2], q_anc[3]); qc.cx(q_data[3], q_anc[3]) # S4\\n\",\n \" qc.cx(q_data[0], q_anc[4]); qc.cx(q_data[1], q_anc[4])\\n\",\n \" qc.cx(q_data[4], q_anc[4]); qc.cx(q_data[5], q_anc[4]) # S5\\n\",\n \" qc.cx(q_data[0], q_anc[5]); qc.cx(q_data[2], q_anc[5])\\n\",\n \" qc.cx(q_data[4], q_anc[5]); qc.cx(q_data[6], q_anc[5]) # S6\\n\",\n \" qc.measure(q_anc[3:6], c_reg[3:6]) # Measure Z syndrome (s4, s5, s6)\\n\",\n \" qc.barrier()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fed4a432\",\n \"metadata\": {},\n \"source\": [\n \"Let's initialize `qr_data` with the provided statevector (`State`) by using [initialize](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.circuit.QuantumCircuit#initialize) of `QuantumCircuit`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9a55ec14\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Solution\\n\",\n \"State = bring_states()\\n\",\n \"\\n\",\n \"\\n\",\n \"# Logical qubit (7 data qubits)\\n\",\n \"qr_data = QuantumRegister(7, name='q')\\n\",\n \"# Ancilla qubits for syndrome measurement (6)\\n\",\n \"qr_anc = QuantumRegister(6, name='anc')\\n\",\n \"# Classical registers for syndrome (initial & verify)\\n\",\n \"cr_initial_syn = ClassicalRegister(6, name='c_initial_syn')\\n\",\n \"cr_final_syn = ClassicalRegister(6, name='c_final_syn')\\n\",\n \"\\n\",\n \"\\n\",\n \"# Total circuit (13 qubits, 12 classical bits)\\n\",\n \"qc = QuantumCircuit(qr_data, qr_anc, cr_initial_syn, cr_final_syn)\\n\",\n \"\\n\",\n \"#initialize qc with State\\n\",\n \"qc.initialize(State, qr_data)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"92a1a966\",\n \"metadata\": {},\n \"source\": [\n \"Next, add the syndrome measurement by executing the cell below and check your quantum circuit by drawing it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0f4f071c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- AddSyndrome Measurement ---\\n\",\n \"\\n\",\n \"measure_steane_syndrome(qc, qr_data, qr_anc, cr_initial_syn)\\n\",\n \"\\n\",\n \"qc.draw('mpl', fold=-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1cee23bf\",\n \"metadata\": {},\n \"source\": [\n \"Now, run this quantum circuit to find the error injected into the logical $∣0\\\\rangle$ state. To complete this task, you'll need the key (the measured bitstring) from the `counts` dictionary. If the stabilizer measurements are implemented correctly, you will observe a single state with 100% probability in the simulation results.\\n\",\n \"\\n\",\n \"
\\n\",\n \"To get a correct answer\\n\",\n \"\\n\",\n \"If you run the circuit below on a real backend, you might not be able to obtain the error codes accurately due to various errors injected into the qubits. Therefore, please execute the following code using a simulator backend.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b4eca236\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- Run the Simulation using AerSimulator\\n\",\n \"backend = AerSimulator()\\n\",\n \"\\n\",\n \"#make quantum circuit compatible to the backend\\n\",\n \"pm = generate_preset_pass_manager( backend = backend, optimization_level=3)\\n\",\n \"qc_isa = pm.run(qc)\\n\",\n \"\\n\",\n \"#run and get counts\\n\",\n \"sampler = Sampler(mode=backend)\\n\",\n \"counts = sampler.run([qc_isa], shots = 10000).result()[0].data.c_initial_syn.get_counts()\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0a3d5dba\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_histogram(counts)\\n\",\n \"# ---- TODO : Task 3 ---\\n\",\n \"#take most common error code:\\n\",\n \"\\n\",\n \"# Solution\\n\",\n \"error_code='Y3'\\n\",\n \"# --- End of TODO ---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4ad1bbb7\",\n \"metadata\": {},\n \"source\": [\n \"Submit your error code string to the grader to see if you detected correctly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0a72d019\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab4_ex3(error_code)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ee2cf0ca\",\n \"metadata\": {},\n \"source\": [\n \"Congratulations! You've successfully implemented the stabilizer measurement circuit for the Steane code and detected the error.\\n\",\n \"\\n\",\n \"Since Pauli errors are unitary operations and self-inverse, applying the same Pauli gate a second time acts as the identity operation ($P \\\\cdot P=I$), effectively canceling the error. For this optional exercise (non-graded), use this property to correct the error you detected. Apply the gate corresponding to your detected error code to the erroneous quantum state and verify that the error is corrected.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"65b5f314\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"No Grader Extra Exercise: Correct Error\\n\",\n \"\\n\",\n \"Now let's correct this error with a proper gate operation. \\n\",\n \"\\n\",\n \"Complete the cell below to add proper correction, and then do a stabilizer measurement one more time to get the syndrome measurement result: $000000$, which means no error detected. Start by initializing `qr_data` with State again and apply a correction gate, then do a stabilizer measurement. You can use the `measure_steane_syndrome` function you already made.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4f2219ff\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Solution\\n\",\n \"qc = QuantumCircuit(qr_data, qr_anc, cr_initial_syn, cr_final_syn)\\n\",\n \"\\n\",\n \"\\n\",\n \"# ---- TODO : ungraded task ---\\n\",\n \"#initialize qr_data with State and correct error by applying proper gate\\n\",\n \"\\n\",\n \"qc.initialize(State, qr_data)\\n\",\n \"\\n\",\n \"#correct error\\n\",\n \"\\n\",\n \"#your code start here\\n\",\n \"qc.y(3)\\n\",\n \"#end of your code\\n\",\n \"\\n\",\n \"# --- End of TODO ---\\n\",\n \"\\n\",\n \"measure_steane_syndrome(qc, qr_data, qr_anc, cr_final_syn)\\n\",\n \"\\n\",\n \"#qc.draw('mpl', fold=-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f90003a2\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"To get a correct answer\\n\",\n \"\\n\",\n \"If you run the circuit below on a real backend, you might not be able to obtain the error codes accurately due to various errors injected into the qubits. Therefore, please execute the following code using a simulator backend.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"90d4a474\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qc_isa = pm.run(qc)\\n\",\n \"counts = sampler.run([qc_isa], shots = 10000).result()[0].data.c_final_syn.get_counts()\\n\",\n \"\\n\",\n \"plot_histogram(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2730a94a\",\n \"metadata\": {},\n \"source\": [\n \"Did you obtain `000000` with 100% probability in your syndrome measurements? Congratulations 🎉! You now understand the Steane code and how to detect and correct errors.\\n\",\n \"\\n\",\n \"So far, you've explored the fundamental concepts of both classical and quantum error correction. You've delved into the operational mechanics of two foundational quantum error correction codes, constructed lookup tables for error detection and correction, and practically applied these principles by detecting errors in the Steane code.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ce09252b\",\n \"metadata\": {},\n \"source\": [\n \"# Chapter 3: Exploring Advanced Quantum Error Correction Codes & Their Efficiency\\n\",\n \"\\n\",\n \"Having explored basic quantum error correction codes like the Steane code, this chapter delves into more advanced topics. Our primary goal is to understand different strategies for achieving robust fault tolerance and to investigate what improvements advanced QLDPC constructions (such as the gross code and related families) offer over other well-known codes like the surface and toric codes, particularly in terms of error correction capabilities and efficiency.\\n\",\n \"\\n\",\n \"We will explore codes with particular topologies, where the geometric layout and connectivity of qubits influence the code's properties. We will analyze instances of the toric code and compare them with the gross code. For these, our focus will be on aspects like their parity check matrices and the number of logical qubits each can encode relative to physical qubits and distance. This exploration will highlight different strategies for achieving fault tolerance and shed light on the diverse landscape of contemporary QEC research.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Note on Code Representations and a Pedagogical Approach \\n\",\n \"\\n\",\n \"It's essential to reiterate that the primary objective here is to understand comparative principles. The specific examples or graphical representations used to illustrate differences between code families (e.g., surface/toric vs. gross-like codes) should be understood as **illustrative toy models**. They are designed to capture essential characteristics and distinguishing features that lead to different performance potentials, rather than being the exact process of building codes.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"## 3.1 Foundational Concepts and Key QLDPC Architectures for Comparison\\n\",\n \"\\n\",\n \"Before directly introducing the toric and gross codes, this section will give you a high-level introduction to the *Quantum Low-Density Parity-Check (QLDPC) codes* and introduce key QLDPC architectures.\\n\",\n \"\\n\",\n \"Low-Density Parity-Check (LDPC) codes are classical error-correcting codes known for having a sparse parity check matrix, meaning the matrix contains very few non-zero entries relative to its size. This sparsity is crucial because it allows for highly efficient decoding algorithms, making LDPC codes powerful tools for classical communication and data storage. Quantum Low-Density Parity-Check (QLDPC) codes are the quantum counterparts to classical LDPC codes. In QLDP codes, the **stabilizer generators are sparse**; each stabilizer generator acts non-trivially on only a small number of qubits, and each qubit is only a part of a small number of these generators. The aim is to design codes that not only feature this beneficial sparsity but also achieve good scaling of code distance $d$ (a measure of error correction capability) with the number of physical qubits $n$, and ideally offer high **encoding rates** ($k/n$, where $k$ is the number of logical qubits).\\n\",\n \"\\n\",\n \"Now that we've covered these basics, we'll look at and compare the two main types of QLDP codes:\\n\",\n \"\\n\",\n \"* **Surface/Toric Codes**: These are well-studied QLDP codes defined on a 2D lattice where stabilizers act geometrically **locally** (typically on nearest-neighbor qubits). While known for relatively high error thresholds and compatibility with 2D hardware, their code distance $d$ typically scales as $O(\\\\sqrt{n})$ for $n$ physical qubits, which can be a limitation for achieving very large distances efficiently - additionally, for each patch of surface (or toric) code, there is only 1 logical qubit (2 in the case of the toric code).\\n\",\n \"\\n\",\n \"* **Bivariate Bicycle Codes (and related QLDPC families)**: This broader class of QLDPC constructions aims for excellent asymptotic scaling of parameters $k, d, n$. Their construction does not necessarily restrict them to simple 2D geometric locality. The Tanner graphs of these codes might exhibit more complex or **\\\"long-range\\\"** (in a 2D sense) connections, while still maintaining overall sparsity. This different structural connectivity is believed to underpin their potential for superior performance, such as:\\n\",\n \" * Better distance scaling: Achieving a code distance $d$ that grows more favorably with $n$.\\n\",\n \" * Higher encoding rates ($k/n$): Encoding more logical qubits for a similar number of physical qubits and distance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2a5c405c\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0598739c\",\n \"metadata\": {},\n \"source\": [\n \"*(Image taken from https://arxiv.org/pdf/2308.07915: Tanner graph representation for the gross code, illustrating data qubits (circles) and stabilizers/checks (squares) on a toroidal grid.)*\\n\",\n \"\\n\",\n \"## 3.2 Qubit Layout and Conventions for Exercises\\n\",\n \"\\n\",\n \"To concretely compare how different code structures impact performance with simplified models, we will construct codes on the same physical arrangement of qubits by using above image. We will build upon concepts discussed in the lecture \\\"Toward Fault-tolerant Quantum Computing with IBM QLDPC codes\\\" by Patrick Rall.\\n\",\n \"\\n\",\n \"Our focus will be on constructing the parity check matrices for two illustrative code instances on a two-dimensional grid with periodic boundary conditions (a torus), as visualized in the provided Tanner graph:\\n\",\n \"1. One representing a **Toric code** with local connections.\\n\",\n \"2. Another representing a **gross code** that incorporates additional long-range connections.\\n\",\n \"\\n\",\n \"**Key Elements in Our Model:**\\n\",\n \"\\n\",\n \"* **Data Qubits (Circles)**: Store the quantum information. In the provided diagram, these are the blue and orange circles. There are $12 \\\\times 6 = 72$ blue qubits and $12 \\\\times 6 = 72$ orange qubits, totaling $n = 144$ data qubits.\\n\",\n \"* **Stabilizers/Checks (Squares)**: Define the code's stabilizer operators.\\n\",\n \" * **X-stabilizers** (products of Pauli-X) are associated with the **red** squares.\\n\",\n \" * **Z-stabilizers** (products of Pauli-Z) are associated with the **green** squares.\\n\",\n \"* **CSS Code Structure**: We are working within the Calderbank-Shor-Steane (CSS) framework.\\n\",\n \"\\n\",\n \"A clear **qubit labeling** convention is crucial for translating the Tanner graph's connectivity into parity check matrices. The rows of these matrices will correspond to stabilizers, and columns to data qubits.\\n\",\n \"\\n\",\n \"**Qubit Labeling Convention (144 total data qubits):**\\n\",\n \"\\n\",\n \"* **Blue** Data Qubits (Indices 0-71): Labeled column by column (left to right), then bottom-up within each column (0-indexed).\\n\",\n \" * The bottom-leftmost blue qubit is `0`; the one above it is `1`, up to `5`.\\n\",\n \" * The next column's blue qubits are `6` through `11`, and so on.\\n\",\n \"* **Orange** Data Qubits (Indices 72-143): Follow the same labeling scheme, starting with the bottom-leftmost orange qubit as `72`.\\n\",\n \"\\n\",\n \"**Parity Check Matrix Convention (for this lab):**\\n\",\n \"\\n\",\n \"We will define two binary parity check matrices:\\n\",\n \"\\n\",\n \"* **$H_X$ (for X-stabilizers)**:\\n\",\n \" * Each row represents an X-stabilizer (derived from a **red square**).\\n\",\n \" * This matrix $H_X$ is used to detect **Z-type errors**. (Syndrome: $s_x$)\\n\",\n \"* **$H_Z$ (for Z-stabilizers)**:\\n\",\n \" * Each row represents a Z-stabilizer (derived from a **green square**).\\n\",\n \" * This matrix $H_Z$ is used to detect **X-type errors**. (Syndrome: $s_z$)\\n\",\n \"\\n\",\n \"\\n\",\n \"Using the layout and conventions above, we will now define stabilizers based on the connectivity depicted in the provided Tanner graph for two distinct code versions on the *same* physical qubit layout as the exercises.\\n\",\n \"\\n\",\n \"1. **The toric code:** This version will be constructed using only local, nearest-neighbor connections between the stabilizers (squares) and the data qubits (circles) they act upon, following the standard toric code model on a grid with periodic boundaries. Note: the toric code is very similar to the surface code; however, it is embedded on a torus with periodic boundaries in both the $x$ and $y$ directions. In particular, it can encode one additional qubit when compared to the surface code without the periodic boundaries.\\n\",\n \"2. **The gross code:** This version will augment the local connections typical of the toric code by including specific \\\"long-range\\\" connections, the leaf-shaped connections in the above graph. These additional connections modify the definitions of the stabilizer generators, leading to a code with different parameters and properties compared to the standard toric code on the same qubit layout.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c827399a\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"610ae37c\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 3.3 Toric Code Exercise\\n\",\n \"\\n\",\n \"### HX\\n\",\n \"\\n\",\n \"We will now establish the binary representation for the **X-stabilizers** (associated with red squares) for the toric code, which considers only nearest-neighbor connections on the torus. These will form the rows of the matrix you'll be asked to construct (referred to as $H_X$ in the exercise prompt's output variable).\\n\",\n \"\\n\",\n \"Let's consider the bottom-leftmost red square in the diagram. This X-stabilizer acts on four neighboring data qubits:\\n\",\n \"1. The blue data qubit below it: This is blue qubit `0` (column 0, row 0).\\n\",\n \"2. The blue data qubit above it: This is blue qubit `1` (column 0, row 1).\\n\",\n \"3. The orange data qubit to its right: This is the bottom-leftmost orange qubit, which is orange qubit `72` (orange column 0, row 0).\\n\",\n \"4. The orange data qubit to its left (across the periodic boundary): This is the bottom-rightmost orange qubit. Orange qubits are indexed 72-143. There are 12 columns of orange qubits, 6 qubits per column. The last orange column (column 11) contains qubits $72 + 11 \\\\times 6 + $ row_idx. The bottom-rightmost is $72 + 11 \\\\times 6 + 0 = 72 + 66 = 138$. So, this is orange qubit `138`.\\n\",\n \"\\n\",\n \"Therefore, the first X-stabilizer acts on data qubits `0`, `1`, `72`, and `138`. Its corresponding row in the binary matrix (which will be a row in your $H_X$ for the exercise, or $H_Z$ in standard notation) will have 1s at columns 0, 1, 72, and 138, and 0s elsewhere. This 144-element row vector is:\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"The example given in the original problem for the first 6 rows of \\\"parity check matrix $H_X$\\\" (derived from the first 6 red squares) uses this logic:\\n\",\n \"\\n\",\n \"```\\n\",\n \"[[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0],\\n\",\n \"\\n\",\n \" [0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0],\\n\",\n \"\\n\",\n \" [0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0],\\n\",\n \"\\n\",\n \" [0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0],\\n\",\n \"\\n\",\n \" [0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0],\\n\",\n \"\\n\",\n \" [1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]]\\n\",\n \"```\\n\",\n \"\\n\",\n \"### HZ\\n\",\n \"\\n\",\n \"HZ follows the same basic rule as HX for connecting to its nearest neighbor. The only difference is that HZ is marked with a green square, and it starts at the very bottom of the second column from the left of the entire grid.\\n\",\n \"\\n\",\n \"Let's consider the bottom-leftmost green square in the diagram. This Z-stabilizer acts on four neighboring data qubits:\\n\",\n \"1. The blue data qubit to its left: This is blue qubit `0` (blue column 0, row 0).\\n\",\n \"2. The blue data qubit to its right: This is blue qubit `6` (blue column 1, row 0).\\n\",\n \"3. The orange data qubit above: This is the bottom-leftmost orange qubit, which is orange qubit `72` (orange column 0, row 0).\\n\",\n \"4. The orange data qubit below (across the periodic boundary): This is the top-leftmost orange qubit. So, this is orange qubit `77`, which is 5 rows above the orange qubit `72` (orange column 0, row 5)\\n\",\n \"\\n\",\n \"Therefore, the first Z-stabilizer acts on data qubits `0`, `6`, `72`, and `77`. Its corresponding row in the binary matrix. This 144-element row vector is:\\n\",\n \"```\\n\",\n \" [1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 4: Find the parity check matrices of the toric code\\n\",\n \"\\n\",\n \"Following the above convention for qubit labeling and the definition of stabilizers from the diagram, find the parity check matrices for the **toric code** (i.e., the code containing only nearest-neighbor connections).\\n\",\n \"\\n\",\n \"Output the result for the **X-stabilizers (red squares)** in a NumPy array labeled $H_{X}$ and similarly for the **Z-stabilizers (green squares)** in a NumPy array labeled $H_{Z}$.(As per our clarification note, the matrix $H_X$ you generate from red squares will be used to detect Z-errors, and $H_Z$ from green squares will detect X-errors).\\n\",\n \"\\n\",\n \"Please ensure that the NumPy arrays $H_{X}$ and $H_{Z}$ strictly adhere to the expected dimensions and structure (`np.zeros((72,144),dtype=int)`). Modifying the size or structure of these matrices from what is anticipated by the exercise might lead to a failure during grading.\\n\",\n \"\\n\",\n \"\\n\",\n \"**Hint**: Notice the periodic structure in the examples. Can this be used to your advantage to write an efficient piece of code to generate these matrices? \\n\",\n \"
\\n\",\n \" \\n\",\n \" Check the connectivity using a helper function! \\n\",\n \"\\n\",\n \"Once the parity check matrices are finalized, execute the provided code to verify the connectivity of the 5th stabilizer of HXtc and the 66th stabilizer of `HXtc` and `HZtc`, respectively. Please focus on carefully checking the boundary conditions. Success is indicated by generating a graph that matches the following example.\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXtc, HZtc)\\n\",\n \"\\n\",\n \"```\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"**Acknowledgement**: [Prof. Daniel Sierra-Sosa](http://www.linkedin.com/in/dsierrasosa), Qiskit Advocate and a Mentor of QGSS 2025), for the discussion and his contribution on making a helper function.\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"497d1eb1-9a0b-4fc0-9486-9d20a7e1260d\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5dde8af2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Solution\\n\",\n \"import sys\\n\",\n \"import numpy as np\\n\",\n \"from numpy.linalg import matrix_rank\\n\",\n \"from numpy.linalg import matrix_power as m_power\\n\",\n \"np.set_printoptions(threshold=sys.maxsize) # removes truncation of arrays\\n\",\n \"### Defining identity and cyclic shift matrices to be used in code constructions\\n\",\n \"def Sd(m):\\n\",\n \" M = np.zeros((m,m),dtype=int)\\n\",\n \" \\n\",\n \" for j in range(0,m):\\n\",\n \" M[j,np.mod(j+1,m)] = 1\\n\",\n \" \\n\",\n \" return M\\n\",\n \"\\n\",\n \"def Id(m):\\n\",\n \" M = np.zeros((m,m),dtype=int)\\n\",\n \" \\n\",\n \" for j in range(0,m):\\n\",\n \" M[j,j] = 1\\n\",\n \" \\n\",\n \" return M\\n\",\n \"\\n\",\n \"l = 12\\n\",\n \"m = 6\\n\",\n \"\\n\",\n \"x = np.kron(Sd(l),Id(m))\\n\",\n \"y = np.kron(Id(l),Sd(m))\\n\",\n \"# toric code\\n\",\n \"Atc = np.mod(Id(l*m)+m_power(y,1),2).astype(int)\\n\",\n \"Btc = np.mod(Id(l*m)+m_power(x,-1),2).astype(int)\\n\",\n \"HXtc = np.hstack((Atc,Btc))\\n\",\n \"HZtc = np.hstack((np.transpose(Btc),np.transpose(Atc)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e7bd2d52-72a8-4b5b-99c9-b0e9a75b7dc3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Check the connectivity of HXtc and HZtc\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXtc, HZtc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f81fb0f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab4_ex4(HXtc, HZtc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fc9be9fa\",\n \"metadata\": {},\n \"source\": [\n \"## 3.4 Gross Code Exercise\\n\",\n \"\\n\",\n \"Now we consider the parity check matrices for the gross(-like) code. This code is generated by including the long-range connections shown abstractly in the figure (e.g., the leaf-like diagonal connections, though the exact rules are specific to the code's definition). The fundamental qubit layout and labeling convention remain the same as for the toric code.\\n\",\n \"\\n\",\n \"The key difference for the gross code is that each stabilizer (square check) will now have two additional long-range connections on top of its four nearest-neighbor connections. This means each stabilizer in the gross code will act on a total of $4+2=6$ data qubits. Consequently, each row in the parity check matrices ($H_X$ and $H_Z$) for the gross code will have six '1's.\\n\",\n \"\\n\",\n \"A practical way to construct these matrices is to start with the toric code's connectivity (nearest-neighbor connections) and then add the two specific long-range connections for each stabilizer.\\n\",\n \"\\n\",\n \"Let's illustrate this for the first row of $H_X$, which corresponds to the X-stabilizer at the bottom-leftmost red square (conceptually at $c_s=0, r_s=0$).\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f7b4b32d\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2dffcaea\",\n \"metadata\": {},\n \"source\": [\n \"#### HX\\n\",\n \"1. Nearest-Neighbor Connections (from Toric Code):\\n\",\n \"As established in Section 3.3, this bottom-leftmost red square has nearest-neighbor connections to:\\n\",\n \"* Blue data qubit at index $\\\\mathbf{0}$ (visualized as \\\"below\\\" or part of the square, blue column 0, row 0).\\n\",\n \"* Blue data qubit at index $\\\\mathbf{1}$ (visualized as \\\"above\\\" or part of the square, blue column 0, row 1).\\n\",\n \"* Orange data qubit at index $\\\\mathbf{72}$ (visualized as \\\"to the right\\\" or part of the square, orange column 0, row 0).\\n\",\n \"* Orange data qubit at index $\\\\mathbf{138}$ (visualized as \\\"to the left\\\" across periodic boundary, orange column 11, row 0).\\n\",\n \"\\n\",\n \"2. Additional Long-Range Connections (Specific to this Gross Code instance):\\n\",\n \"The problem states that for this particular gross code construction, the bottom-leftmost red square ($c_s=0, r_s=0$) will have two additional long-range connections:\\n\",\n \"\\n\",\n \"* First long-range connection: To the blue data qubit 20.\\n\",\n \" * The text describes this as \\\"the blue qubit three rows above and six columns to the right.\\\" The specific rules of the gross code define how these relative positions map to a precise qubit index on the torus. For this stabilizer, this rule results in a connection to blue data qubit with global index $\\\\mathbf{20}$.\\n\",\n \"\\n\",\n \"* Second long-range connection: To the orange data qubit 135.\\n\",\n \" * The text describes this as \\\"the orange qubit three columns to the left and six rows down (recall the periodic boundary conditions).\\\" Again, these relative descriptions are specific to the gross code's connection rules for this stabilizer, resulting in a connection to orange data qubit with global index $\\\\mathbf{135}$.\\n\",\n \"\\n\",\n \"3. Resulting First Row of $H_X$ for the Gross Code:\\n\",\n \"Combining both nearest-neighbor and the two specified long-range connections, the bottom-leftmost X-stabilizer now acts on data qubits with indices: `0, 1, 20, 72, 135, 138`.\\n\",\n \"\\n\",\n \"Therefore, the first row of the $H_X$ matrix for this gross code (a 144-element binary vector) will have '1's at these six positions and '0's elsewhere. Let's verify this against the provided snippet:\\n\",\n \"\\n\",\n \"**Snippet:**\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"This confirms that the snippet for the first row of $H_X$ for the gross code has exactly six '1's at the global data qubit indices 0, 1, 20, 72, 135, and 138, matching the combination of nearest-neighbor and the specified long-range connections.\\n\",\n \"\\n\",\n \"#### HZ\\n\",\n \"\\n\",\n \"To find the $H_Z$, please revisit the Tanner graph, and take a close look at the `leaf` shape of the long-range connection. \\n\",\n \"\\n\",\n \"1. Nearest-Neighbor Connections (from Toric Code):\\n\",\n \"As established in Section 3.3, this bottom-leftmost red square has nearest-neighbor connections to:\\n\",\n \"* Orange data qubit at index $\\\\mathbf{77}$ (visualized as \\\"to the below\\\" across periodic boundary, orange column 0, row 5).\\n\",\n \"* Orange data qubit at index $\\\\mathbf{72}$ (visualized as \\\"above\\\" or part of the square, orange column 0, row 0).\\n\",\n \"* Blue data qubit at index $\\\\mathbf{6}$ (visualized as \\\"to the right\\\" or part of the square, blue column 1, row 0).\\n\",\n \"* Blue data qubit at index $\\\\mathbf{0}$ (visualized as \\\"to the left\\\" or part of the square, blue column 0, row 0).\\n\",\n \"\\n\",\n \"2. Additional Long-Range Connections (Specific to this Gross Code instance):\\n\",\n \"\\n\",\n \"* First long-range connection: To the blue data qubit 15.\\n\",\n \" * The text describes this as \\\"the blue qubit six rows above and three columns to the right.\\\" The specific rules of the gross code define how these relative positions map to a precise qubit index on the torus. For this stabilizer, this rule results in a connection to blue data qubit with global index $\\\\mathbf{15}$.\\n\",\n \"\\n\",\n \"* Second long-range connection: To the orange data qubit 130.\\n\",\n \" * The text describes this as \\\"the orange qubit six columns to the left and three rows down (recall the periodic boundary conditions).\\\" Again, these relative descriptions are specific to the gross code's connection rules for this stabilizer, resulting in a connection to orange data qubit with global index $\\\\mathbf{130}$.\\n\",\n \"\\n\",\n \"3. Resulting First Row of $H_Z$ for the Gross Code:\\n\",\n \"Combining both nearest-neighbor and the two specified long-range connections, the bottom-leftmost X-stabilizer now acts on data qubits with indices: `0, 6, 15, 72, 77, 130`.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"**Snippet:**\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"This confirms that the snippet for the first row of $H_Z$ for the gross code has exactly six '1's at the global data qubit indices 0, 6, 15, 72, 77, and 130, matching the combination of nearest-neighbor and the specified long-range connections.\\n\",\n \"\\n\",\n \"This process of adding two specific long-range connections would be repeated for *every* stabilizer (both X-type from red squares and Z-type from green squares) to transform the toric code matrices into the gross code matrices. The exact rules for determining the long-range partners for other stabilizers would follow a consistent pattern defined by the gross code's construction on this lattice.\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 5: Find the parity check matrices of the gross code \\n\",\n \"\\n\",\n \"Following the above labeling convention, construct the parity check matrix for the gross code, that is, including the long-range connections. Output the result for the $X$ stabilizers in numpy array labeled $H_{X}$ and similarly for the $Z$ stabilizers in an array labeled $H_{Z}$.\\n\",\n \"\\n\",\n \"**Hint**: Notice the periodic structure of the above examples. Can this be used to your advantage to make an efficient piece of code to generate the matrices? Each row should be weight-6 - that is, contain six 1s.\\n\",\n \"\\n\",\n \"
\\n\",\n \" \\n\",\n \" Check the connectivity using a helper function! \\n\",\n \"\\n\",\n \"Like exerciese 4 after finish compose `HXgc` and `HZgc`, execute the provided code to verify the connectivity of the 5th stabilizer of HXtc and the 66th stabilizer. Please focus on carefully checking the boundary conditions. Success is indicated by generating a graph that matches the following example.\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXgc, HZgc)\\n\",\n \"\\n\",\n \"```\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ab1b4b43-4d81-4e75-aae0-47c24ba7906a\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e46c47ef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Solution\\n\",\n \"l = 12\\n\",\n \"m = 6\\n\",\n \" \\n\",\n \"x = np.kron(Sd(l),Id(m))\\n\",\n \"y = np.kron(Id(l),Sd(m))\\n\",\n \" \\n\",\n \"Agc = np.mod(Id(l*m)+y+np.matmul(m_power(y,2),m_power(x,3)),2).astype(int)\\n\",\n \"Bgc = np.mod(Id(l*m)+m_power(x,-1)+np.matmul(m_power(y,3),m_power(x,-2)),2).astype(int)\\n\",\n \" #B = np.mod(Id(l*m)+y+np.matmul(m_power(x,c),m_power(y,d)),2)\\n\",\n \"HXgc = np.hstack((Agc,Bgc))\\n\",\n \"HZgc = np.hstack((np.transpose(Bgc),np.transpose(Agc)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ac83de2d-bdb3-4bd4-bf20-a1cef7e6640a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Check stabilizer\\n\",\n \"\\n\",\n \"generate_stabilizer_plots(HXgc, HZgc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a180a7cd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab4_ex5(HXgc, HZgc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0f37a761\",\n \"metadata\": {},\n \"source\": [\n \"## 3.5 Counting the Number of Logical Qubits\\n\",\n \"\\n\",\n \"The parity check matrices you've been working with, $H_X$ and $H_Z$, are constructed from the stabilizers of the code. Each row in these matrices represents a generator of the stabilizer group. However, it's possible that some of these chosen generators are not independent; one stabilizer generator might be a product of others. Redundant generators don't add new constraints to define the codespace.\\n\",\n \"\\n\",\n \"To find the actual number of independent stabilizer generators, we calculate the **rank** of these parity check matrices. Since we are dealing with qubits and Pauli operators (which square to identity and whose effects are often considered over GF(2) in the context of their binary representation), all matrix operations for rank calculation are performed modulo 2. The rank tells us the true number of unique conditions imposed by the stabilizers.\\n\",\n \"\\n\",\n \"In general, for a quantum stabilizer code:\\n\",\n \"If $n$ is the number of physical data qubits used in the code, and $r$ is the total number of independent stabilizer generators, then the number of logical qubits ($k$) encoded by the code is given by:\\n\",\n \"$$k = n - r$$\\n\",\n \"Each independent stabilizer generator effectively halves the dimension of the Hilbert space, \\\"fixing\\\" one degree of freedom. Thus, $r$ independent stabilizers reduce the $2^n$-dimensional space of $n$ physical qubits to a $2^{n-r}$-dimensional codespace, which can then encode $k = n-r$ logical qubits.\\n\",\n \"\\n\",\n \"For CSS codes, like the toric and gross codes we are considering, the X-stabilizers and Z-stabilizers form two distinct, commuting groups. This allows us to count their independent generators separately.\\n\",\n \"If $r_X$ is the number of independent X-stabilizers (obtained from the rank of the matrix whose rows are the X-stabilizer generators, which you've labeled $H_X$ from red squares) and $r_Z$ is the number of independent Z-stabilizers (obtained from the rank of the matrix whose rows are the Z-stabilizer generators, labeled $H_Z$ from green squares), then the number of logical qubits in the CSS code is:\\n\",\n \"$$k = n - r_X - r_Z$$\\n\",\n \"This formula holds because the X-stabilizers and Z-stabilizers are chosen such that they all commute with each other, and the total number of independent conditions imposed on the codespace is the sum of the independent X-type and Z-type conditions.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"Exercise 6: Count the number of logicals for the toric and gross codes\\n\",\n \"\\n\",\n \"You will now use the `matrixRank` function to determine the number of logical qubits for both the toric code and the gross code you constructed in Exercises 4 and 5.\\n\",\n \"\\n\",\n \"1. Import the function: Start by importing the `matrixRank` function using the command:\\n\",\n \" `from lab4_util import matrixRank`\\n\",\n \" This function calculates the rank of a binary matrix (here `HXtc`, `HZtc`, `HXgc` and `HZgc`), which is exactly what we need for our parity check matrices.\\n\",\n \"\\n\",\n \"2. Calculate Ranks:\\n\",\n \" * For the toric code, use `matrixRank` to find the rank of $H_X$ (the matrix of X-stabilizers you generated from red squares in Exercise 4) – this will give you $r_{X, \\\\text{toric}}$.\\n\",\n \" * Similarly, find the rank of $H_Z$ (the matrix of Z-stabilizers from green squares in Exercise 4) – this will give you $r_{Z, \\\\text{toric}}$.\\n\",\n \" * Repeat this process for the gross code matrices ($H_X$ and $H_Z$) you generated in Exercise 5 to find $r_{X, \\\\text{gross}}$ and $r_{Z, \\\\text{gross}}$.\\n\",\n \"\\n\",\n \"3. Calculate Logical Qubits ($k$):\\n\",\n \" * Using the formula $k = n - r_X - r_Z$ (where $n=144$ is the total number of data qubits), calculate the number of logical qubits for the toric code ($k_{\\\\text{toric}}$).\\n\",\n \" * Perform the same calculation for the gross code ($k_{\\\\text{gross}}$).\\n\",\n \"\\n\",\n \"**Grading**: Submit the calculated number of logical qubits ($k$) for both the toric code and the gross code (`k_toric` and `k_gross`). You can verify your toric code result against the known fact that it encodes $k=2$ logical qubits.\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b991f749\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from lab4_util import matrixRank\\n\",\n \"\\n\",\n \"#toric code\\n\",\n \"rx_toric = matrixRank(HXtc)\\n\",\n \"rz_toric = matrixRank(HZtc)\\n\",\n \"k_toric = int(144 - rx_toric - rz_toric)\\n\",\n \"\\n\",\n \"\\n\",\n \"# gross code\\n\",\n \"rx_gross = matrixRank(HXgc)\\n\",\n \"rz_gross = matrixRank(HZgc)\\n\",\n \"k_gross = int(144 - rx_gross - rz_gross)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"48f316d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Submit your answer using following code\\n\",\n \"grade_lab4_ex6(k_toric, k_gross)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"100d8c84\",\n \"metadata\": {},\n \"source\": [\n \"## 3.6 Concluding remarks: The power of the connectivity\\n\",\n \"\\n\",\n \"These exercises demonstrate a key principle in Quantum Error Correction: strategic modifications to a code's underlying structure, such as introducing long-range connections, can significantly enhance its performance.\\n\",\n \"\\n\",\n \"The construction of parity check matrices for both the toric-like and gross-like codes on the identical $144$-data-qubit framework has been completed. Your calculations in Exercise 6 for the number of logical qubits ($k$) illustrate how these differing connectivities influence $k_{\\\\text{gross}}$ relative to the standard $k_{\\\\text{toric}}$.\\n\",\n \"\\n\",\n \"However, a more pronounced advantage of the gross code variant is evident in its **code distance ($d$)**, a critical parameter dictating its error correction strength. While detailed distance calculations are beyond this lab's immediate scope, but for the above code instances the toric code would be of distance 6 (as the periodicity along one of the directions is 6 lattice spacing in terms of the number of data qubits) - however, the gross code would have distance 12! \\n\",\n \"\\n\",\n \"\\n\",\n \"This substantial improvement in error correction capability, achieved by adding specific long-range connections, underscores the impact of such structural modifications on code performance, particularly in enhancing protection against errors. This highlights a valuable strategy in the design of advanced quantum codes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3a1b6840\",\n \"metadata\": {},\n \"source\": [\n \"# Additional information\\n\",\n \"\\n\",\n \"**Created by:** Sophy Shin, Tomas Jochym-O'Connor, Sebastian Brandhofer\\n\",\n \"\\n\",\n \"**Advised by:** Blake Johnson, Patrick Rall, Olivia Lanes\\n\",\n \"\\n\",\n \"**Reviewed by:** Henry Zou, Abby Cross\\n\",\n \"\\n\",\n \"**Version:** 2.1\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \".venv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.5\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"state\": {},\n \"version_major\": 2,\n \"version_minor\": 0\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
}
]
![](\"https://qbraid-static.s3.amazonaws.com/logos/Launch_on_qBraid_white.png\")
](https://account.qbraid.com/?gitHubUrl=https://github.com/qiskit-community/qgss-2025.git&api=v2)\n\n\n"
},
{
"path": "community-labs/README.md",
"content": "# QGSS-2025-community-labs\n\n\nAs part of the future leaders in quantum hackathon: https://aiforgood.itu.int/the-future-leaders-in-quantum-hackathon/ the winners in the education category got the chance to provide their created educational labs to participants on the Qiskit Global Summer School as community labs. The labs linked here are optional for the Qiskit Global Summer School and provide additional challenges in case you still want to do more and we highly recommend them if you are interested in learning more about the subjects.\n\nWe were really happy with what the participants of the FLIQ hackathon created and present here the winners:\n\n\n1. Quantum Error Correction with the Surface-17 Code by Dev Verma\n2. Quantum Simulation of GUT Baryogenesis by Mukul Kumar & Vivek Pal\n3. Grover's Algorithm - Quantum Searching with Qiskit by Felix Arkle & Sebastian Gotto\n4. CHSH Game: Testing the Limits of Reality with Quantum Mechanics by Lakshmi Yendapalli\n\n\n# 1. Quantum Error Correction with the Surface-17 Code\n\n## Author Dev Verma\n\nI recently graduated from NTU as a scholar in Applied Physics. I am passionate about quantum computing and cryptography and will be working towards growing my understanding and expertise in these fields. \n\n## About the Challenge: \n\nMy Jupyter notebook is a tutorial on quantum error correction, focusing on the surface-17 code, which is one of the most promising approaches to quantum error correction. The tutorial assumes no background in error correction; it builds the reader's understanding from simple classical repetition codes to the stabiliser formalism, and ultimately the surface-17 code. It also discusses the heavy-hex lattice architecture, which is more realistic for the current dominant quantum hardware configuration - IBM's superconducting circuits.\n\n## Link:\n\nThe challenge can be found on the github of Dev Verma: https://github.com/TheSonOfKrypton/FLIQ-Hackathon-2025/blob/main/QEC%20-%20Surface-17%20Code.ipynb\n\n\n\n# 2. Quantum Simulation of GUT Baryogenesis\n\n## Authors Mukul Kumar & Vivek Pal\n\nMukul Kumar is an 18-year-old dropout student researching nature simulations on quantum computers and he created this challenge together with Vivek Pal.\n\n## About the Challenge: \n\nOur challenge is basically a fun experiment that helps you learn about the beginning of the universe and some unsolved mysteries, all of which we are going to study with quantum circuits.\n\n## Link:\n\nThe challenge can be found on the github of Mukul Kumar: https://github.com/mk0dz/qgss25/blob/main/lab.ipynb\n\n\n\n# 3. Grover's Algorithm - Quantum Searching with Qiskit\n\n## Authors Felix Arkle & Sebastian Gotto\n\nFelix Arkle: Currently an undergraduate student at Newcastle University studying computer science. Sebastian Gotto: A recent graduate of UCL having studied Statistics, Economics and Finance.\n\n## About the Challenge: \n\nThis lab focuses on building and demonstrating Grover's algorithm, a quantum search algorithm. You'll construct the necessary quantum circuits, including the oracle and diffusion operators. This lab is suitable for beginners.\n\n## Link:\n\nThe challenge can be found on the github of Felix Arkle: https://github.com/Bumsparkle/Grovers-Algorithm-Tutorial/blob/main/grovers_algorithm_tutorial.ipynb\n\n\n\n# 4. CHSH Game: Testing the Limits of Reality with Quantum Mechanics\n\n## Author Lakshmi Yendapalli\n\nLakshmi Yendapalli is a former strategy consultant turned computer scientist, currently pursuing her master’s in Computer Science at Georgia Tech. She’s especially fascinated by what mischief qubits are up to when no one’s looking.\n\n## About the Challenge: \n\nEver wanted to recreate some of the experiments that earned the 2022 Nobel Prize in Physics? How about proving Einstein wrong while we’re at it? In this hands-on lab on the CHSH game, we’ll explore the surprising foundations of quantum mechanics. Starting from the EPR paradox and Einstein-Bohr debates, we’ll journey through the development of Bell's theorem, culminating in the CHSH inequality. Then we’ll jump into Qiskit to build and run quantum circuits that experimentally demonstrate the CHSH inequality.\n\n## Link:\n\nThe challenge can be found on the github of Lakshmi Yendapalli: https://github.com/lakshmi-yendapalli/CHSH_game_tutorial/tree/main\n\n"
},
{
"path": "functions-labs/README.md",
"content": "## Exclusive Access to Qiskit Functions\n\nAs part of Qiskit Global Summer School (QGSS), participants with a Premium or Flex Plan have limited-time trial access to Qiskit Functions. Access is exclusive and subject to your organization’s administrator approval. Complete [this form](https://airtable.com/appj8IrSNZGz4l4BB/pag8WgWdUr5uSJGZA/form) to request access.\n\nIf you encounter the error `QiskitServerlessException: Credentials couldn't be verified`, it means your access to Qiskit Functions is not yet active. Please check back later after your request has been processed.\n\n**Note: Running this lab will consume QPU time from your organization’s account. Estimated QPU usage is provided before each cell that executes on a QPU. Please monitor your usage and consult your organization admin if you’re unsure about your allocated QPU time for QGSS Functions labs.**"
},
{
"path": "functions-labs/algorithmiq/TEM_lab_validation.py",
"content": "from __future__ import annotations\n\nimport os\nfrom functools import cached_property\nfrom typing import TYPE_CHECKING, Self\n\nimport numpy as np\nfrom numpy.random import default_rng\nfrom pydantic import (\n BaseModel,\n ConfigDict,\n computed_field,\n field_validator,\n model_validator,\n)\nimport base64\nimport io\nimport zlib\nimport hashlib\n\nfrom qiskit.quantum_info import Statevector\nfrom qiskit.primitives.containers.estimator_pub import EstimatorPub\nfrom qiskit_ibm_runtime.options import NoiseLearnerOptions, TwirlingOptions\n\nUNSUPPORTED_GATES = [\n \"reset\",\n \"if_else\",\n \"for_loop\",\n \"switch_case\",\n \"measure\",\n]\n\nclass TEMOptions(BaseModel):\n tem_max_bond_dimension: int = (500,)\n compute_shadows_bias_from_observable: bool = False\n shadows_bias: np.ndarray = np.array([1 / 3, 1 / 3, 1 / 3])\n max_execution_time: int | None = None\n num_randomizations: int = 32\n max_layers_to_learn: int = 4\n default_precision: float = 0.02\n shots_per_randomization: int = 128\n layer_pair_depths: list[int] = [0, 1, 2, 4, 16, 32]\n\n model_config = ConfigDict(extra=\"forbid\", arbitrary_types_allowed=True)\n\n @field_validator(\"tem_max_bond_dimension\")\n @classmethod\n def bd_must_be_less_than_equal_max_bond_dimension(cls, v: int) -> int:\n if v > 1000:\n raise ValueError(\n f\"Unsupported option: Max bond dimension too large. tem_max_bond_dimension must be <= {1000:d}.\"\n )\n return v\n\n @field_validator(\"max_execution_time\")\n @classmethod\n def max_execution_time_must_be_greater_than_60_seconds(\n cls, v: int | None\n ) -> int | None:\n if v is not None and v < 60:\n raise ValueError(\n f\"Unsupported option: max_execution_time must be greater or equal than {60:d} seconds.\"\n )\n return v\n\n @field_validator(\"shadows_bias\")\n @classmethod\n def validate_shadows_bias(cls, v: list | tuple | np.ndarray) -> np.ndarray:\n v = np.array(v)\n shape = v.shape\n if len(shape) > 2 and (shape[-1] != 3):\n raise ValueError(\n \"Unsupported option: Mitigation_bias must be a 1d array of size 3 or a 2d one of shape (num_qubits, 3).\"\n )\n if not np.allclose(v.sum(axis=-1), 1, atol=1e-5):\n raise ValueError(\n \"Unsupported option: Not a valid probability distribution: the sum of the bias values must be 1.\"\n )\n if np.any(v < 0.1):\n raise ValueError(\n \"Unsupported option: All the bias values must be >= 0.1. for QPU real implementations.\"\n )\n return v\n\n @model_validator(mode=\"after\")\n def validate_shadows_bias_from_observable(self) -> Self:\n if self.compute_shadows_bias_from_observable and not np.allclose(\n self.shadows_bias, np.array([1 / 3, 1 / 3, 1 / 3]), atol=1e-8\n ):\n raise ValueError(\n \"Unsupported option: When compute_shadows_bias_from_observable is True, \"\n \"you cannot also pass a custom-bias\"\n )\n return self\n\n @computed_field(return_type=NoiseLearnerOptions)\n @property\n def noise_learner_options(self) -> NoiseLearnerOptions:\n return NoiseLearnerOptions(\n num_randomizations=self.num_randomizations,\n max_layers_to_learn=self.max_layers_to_learn,\n shots_per_randomization=self.shots_per_randomization,\n layer_pair_depths=self.layer_pair_depths,\n twirling_strategy=\"active-accum\",\n environment={\"job_tags\": [32, \"noise_learning\"]},\n )\n\n @computed_field(return_type=TwirlingOptions)\n @property\n def twirling_options(self) -> TwirlingOptions:\n return TwirlingOptions(\n enable_gates=True,\n enable_measure=False,\n num_randomizations=1,\n shots_per_randomization=\"auto\",\n strategy=\"active-accum\",\n )\n\n\nclass TEMInputs:\n def __init__(\n self,\n arguments: dict,\n ) -> None:\n pubs: list[EstimatorPub] = arguments[\"pubs\"]\n self.ibm_instance: str | None = arguments.get(\"instance\")\n self.options: TEMOptions = TEMOptions(**arguments.get(\"options\", {}))\n if not isinstance(pubs, list):\n pubs = [pubs]\n self.pubs = [EstimatorPub.coerce(pub) for pub in pubs]\n self.rng = default_rng(32)\n for pub in self.pubs:\n self.validate(pub)\n\n def validate(self, pub: EstimatorPub) -> None:\n if pub.parameter_values.num_parameters > 0:\n raise ValueError(\n \"Unsupported circuit: parametrized circuits are not supported.\"\n )\n gate_types = pub.circuit.count_ops()\n for gate in UNSUPPORTED_GATES:\n if gate in gate_types:\n raise ValueError(f\"Unsupported circuit: gate {gate} is not supported.\")\n\ndef validation(\n arguments: dict,\n) -> None:\n try:\n _t = TEMInputs(arguments)\n print(\"TEM input checks out\")\n except Exception as e:\n print(\"TEM input validation failed:\", e)\n raise e\n\n\ndef check_circuit(circuit):\n with io.BytesIO() as buff:\n np.save(buff, np.round(Statevector(circuit).probabilities(),7), allow_pickle=False)\n buff.seek(0)\n serialized_data = buff.read()\n serialized_data = zlib.compress(serialized_data)\n series = base64.standard_b64encode(serialized_data).decode(\"utf-8\")\n hashed_circ = hashlib.sha256(series.encode(\"utf-8\")).hexdigest()\n try:\n assert hashed_circ == \"67b19a21308681a6d3d76e91eb4086773e0d861150f14a186ea30bd12c3aacff\"\n print(\"Circuit verification successful! The quantum state matches the expected reference.\")\n except AssertionError:\n print(f\"Circuit verification failed! Got hash: {hashed_circ}\")\n raise\n"
},
{
"path": "functions-labs/algorithmiq/algorithmiq_tensor-network_error_mitigation (TEM).ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Running error mitigated many-body physics experiments with TEM function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This collection of exercised is based on the following reference - [ArXiv: Kicked Ising](https://arxiv.org/abs/2411.00765). This reference discusses a real simulation quantum hardware of up to 91 qubits. In this lab, you are going to recreate a similar simulation on a smaller circuit size.\\n\",\n \"\\n\",\n \"The kicked Ising model corresponds to the usual Ising model: $$ \\\\hat{H}_{\\\\text{I}} = J \\\\sum_{n=0}^{N-2} \\\\hat{Z}_n \\\\hat{Z}_{n+1} + h \\\\sum_{n=0}^{N-1} \\\\hat{Z}_n $$\\n\",\n \" to which is applied a transverse kick: $$ \\\\hat{H}_{K} = b \\\\sum_{n=0}^{N-1} \\\\hat{X}_n $$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"* [Part 1: Run on local simulation](#part-1-run-on-local-simulation)\\n\",\n \"* [Part 2: Run on real devices](#part-2-run-on-real-devices)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Setup\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Install dependencies\\n\",\n \"%pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\\n\",\n \"%pip install \\\"qiskit[visualization]\\\" qiskit-aer qiskit-ibm-catalog\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You should have Grader `>=0.22.11`. If you see a lower version, you need to restart your kernel and reinstall the grader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.quantum_info import SparsePauliOp\\n\",\n \"from qiskit.primitives import StatevectorEstimator\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"from qiskit_ibm_catalog import QiskitFunctionsCatalog\\n\",\n \"from qiskit_ibm_runtime import EstimatorV2, QiskitRuntimeService\\n\",\n \"\\n\",\n \"from TEM_lab_validation import check_circuit, validation # These are the grading functions to test inputs\\n\",\n \"from qc_grader.challenges.qgss_2025 import grade_algorithmiq_function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![\\\"\\\"](\\\"Title2.png\\\")
\\n\",\n \"![](\\\"qesem_workflow.svg\\\")
\\n\",\n \"![](\\\"active_vol.svg\\\")
\\n\",\n 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sx+71m1CbvphpB3PRUmt9d7oC/P2x4C+/fHaoneMflyFhecwbuIko+dzovkE/N288PP4J3h8qETiuZs+wprjSRJHcxgFKECB/wlolS7Q+nQDPCKh9WgLuLcVfmwHhXtrWZm01SeAmgIoqk9DUaP7Eu5JrT0l/LdTUDQKDUS+ZBdgY1B2UgakAAUoQAEKUIACFKAABShAAQpQwBwCG9f9Av+WLY0OvfTXC3gnpXkfO5jodhqfPmbeYzp1DyDvyBFs+3mjcE9gCjKFewJPll4w+rmYOtHbxR1d2kWhc1w3DJ84Ft3j40wNiVxhfdNnzTE5DgPIL/DDuEcwJCxW/sDNOOLsjR/i1xMpzXiFXBoFKCCHgNbJD1rfXsJXD+GrJ+AVA4XSQY7QxsfQNQuLdgKXdgFFu4UdhsKuQ75MFmBj0GRCBqAABShAAQpQgAIUoAAFKEABClDAEgK6o0R1R4oa+7pU1oBh7+mOHlQYG8Lm53kpGrHruXNQyLzEkpISrP/xZ2TsT0J2Tjayzp+ERquxiofubrnOER3QqXMseg8bhBFjRsLBQd43Lvcf+A33P/iwVdbHpNcXmBU9AAsHzpaN6KBTMS5ZaIOt7reka6UGEfXOaKcWjtyz8Is7By0MznQUsBMBrXdXaEMmQhM4DArdrkAbf2lLU6EQmoQKXZOwZL+NV2u75bExaLvPhpVRgAIUoAAFKEABClCAAhSgAAUocIXAmNGj8OrLL5pkMu11N+Q0GL/r0KTkFpr8+tAkjO4bYFK2+vp67NiwFYeEewJzhXsCUwpyUdNYb1JMYycrhEZux8AIdIruhPgBvTF20k3w8fExNpyked98twJvvbNQ0lgOsoyAq4MzMma+AV8X+e4JrUMT9vR0RIdYyx4xXFNTg7PHT6HubAnangM6acz7/fzHE+LOQct8rzILBWxdQOMSAoRNhib0Fig829t6udevT7ijEOfWQnH2JyiK9wl/WzD/iQn2i3V15WwMNpcnyXVQgAIUoAAFKEABClCAAhSgAAWauUBLPz9sWv+rSat8+bNyrDrX2aQYtj55UkgmXrzT26AytVotDu37DXs3bkNuWiZSj+XgYrX17vUJ8vRF58hodOkZj3G33ow2bdsYtB5TBz/x1DPYsm27qWE4X0aBe7sMx6t9pskY8fdQNUJzcH+iM9rHdJQ9ttSAJ/KOoTH9DIZX+kudYvS4eZs/xupjh4yez4kUoIB9CmiVzsLOwJuhjZgC+Cba5yL0VK2tvwTlmVVQFK6Gojyj2a1P7gWxMSi3KONRgAIUoAAFKEABClCAAhSgAAUoYDaB779ZjvbtdceBGvdavbMIL+40/Q4647JbZlZX5wtY9qT47r5j+UeFewI3IPtQ6uV7Ao8WF1qmwGtk8XRyQ6fweHiEDkbPga1w97yhVqtFl3jE6HEoKi62ag1MfrXA9skvoEvLVmZhqUIjDvVxQ9uoDmaJLzXoxQsXkbv9EGbVmLcRzuag1CfCcRSwfwGNayjQ+g6hITgDcLT8McbWENRWn4Ty9Arg5BfCnYSV1ijB5nOyMWjzj4gFUoACFKAABShAAQpQgAIUoAAFKPCHwIKHH8KM6cbvGqqtV2PEm2Go0Do2W1QnqLHuwRPw93G6ao26ewK3/bIRGXsPIic3B6ln8tGkUVvFwUGpQkxILPzDBqAieAzyvYdAo/z9mUwKFnY8zjdsx6Oci9ixcxceffxJOUMylokCQW4+yLr9bROj6J9epW1E8kAvtG5v3qac2CKqq6uxaeNmhFU4YLLGfPd98VhRsSfBX6eA/QoIhwBAGzAY2tZzhB+HCPcOK+13MaZU3ig0BXXNwROfQdFQYkqkZjeXjcFm90i5IApQgAIUoAAFKEABClCAAhSgQPMV6NO7Fz549z8mLfDutxQ4UBNuUgxbn7wgPhlTh3ljl3A0aNKOPcjLykbSiRxUNtRarfTWvuFoFd4PTcEjcDxgAmocrn2vWlfni8KOxzqr1Tl3/j1IS0+3Wn4m/rvAHZ0G4a0Bs8xOU65oQFp/bxtoDtZg6bKv4O7sgls17RCt9DXL2udv+RSrjv5mltgMSgEKWEdAEzgS2o5PAF5R1inABrNqm2qhOLUcyqOLhAbhRRus0PIlsTFoeXNmpAAFKEABClCAAhSgAAUoQAEKUMBIAVdXF+zdadrdb/9eVoLlJ7sZWYFtT+uqKkZQVSoqjy5Feu4GFFZa7zhMf3cfREX0hmPIUJwRGoEXXKUdAesEDX6+Px/Bfq4Wx969Zy8eevQxi+dlQv0Cr/W9DXd3HmYRplLU4fBgP0S0Mc+xpVIXkZySii1btkLY6oNOPqF4QNVF6lSDxt299TOszD9g0BwOpgAFbE9A49sLmqhnofBt3selmyqvKFgORf5CKOrOmRrKruezMWjXj4/FU4ACFKAABShAAQpQgAIUoAAFbjyBTz/6EAnxxr/xdSCzGHf/1L1ZwLVSVqNDwxFozm9HftZyZJ3NgFb4xxovN0dnxITFwTtsMIoCx+G4RyIUSoVRpTwal4TZ4wKMmmvspPr6etx86zScP3/e2BCcZyaBJcPvwYR2PcwU/e9hS4TmYO6wAIRGhFks518TNTQ04P0PFqGmthYNDY1oFRSCl5x7QQX5jwTkzkGrPWYmpoDJAhqvWGiin4HCf6DJsW6UANqmGiiPvAnFic+hsNLfmaxtzcagtZ8A81OAAhSgAAUoQAEKUIACFKAABShgkMC8O2bj/nvvMWjOXweP+VdLnFW7mRTDGpNbCEcddtYWwLloD84e+QHJR7egQd1kjVKgFO4s6hgYiSDhnsCaoFE46jcKTSoXWWoZ7ZuD1x9wlyWW1CDPv/Qy1q7bIHU4x1lQ4NcJT6J3cKQFMwJFijrkDQtEaHioRfNemWzVT6uRlZ1zuTHY0NiIsMAgvOU52Kz1lNRV4rfzRy9/HRC+DglffFGAArYnoFW5Q9vpBWgiZggbi437EI7trcrCFZVnQZn+MBQV2RZObP10bAxa/xmwAgpQgAIUoAAFKEABClCAAhSgAAUkCHTp0hnx3bujXds2GDN6lIQZ1x/y0LuN2FEu7WhLkxKZONlZOFazu/I8vMsOofjEGiRl/Yhy4Y17a70ifELQJqIPoLsn0H88KpwCzVJKpFMZfniqwiyxrxX0lddex0+r11gsHxMZJrDmpsfRL8T4+7L2Ol9C0NS+cHBwMCyxlUfvP/Abtmzd9mdjUNccDAkIwjt+w+Bkhp2D11vu/nN52FeYh0MXjgnNwnyr3lVq5UfC9BSwCQFtwFBouvwbcDHP/4NtYpEWKkKrUUN5/DPheNG3oFBb7x5mCy33zzRsDFpanPkoQAEKUIACFKAABShAAQpQgAIUkCTQKiICPXskoHevxMtHh3p4eEiaJ2XQopVF+CTb+ONIpeQwZozuM/+xqlKE1mSg9vRGZGR9i5PFBcaEkmWOr6sXosIT4Ro6FIWB41HoFi1LXPEgWnw9IxOx7XzEh5o44unnXsDGTZtNjMLp5hT4dOhduKVDokkp7LE5mJt7BD/8uOqqxuDlY0VDQ/FuyxFwsGBz8Er83JKz2Cc0C3WNQt3OwoKKSyY9G06mAAWkCWidfKGNfQ3akPHSJnCUdIGas1BkPAFl0Q7pc+x4JBuDdvzwWDoFKEABClCAAhSgAAUoQAEKUKA5CXh6eqJnQjz69O6FxJ49ERISbLbl5Z2qxq1f6nYgWf/4rVBlLTo25kF5YSeOZX+FjNOp0Gg1Zlu7vsBOKkd0Cu2CFqEDURY0Bsd9BglE8t9pJmVxd0Ul4f4p5rtnsLikBAueeAoZGZlSyuEYKwr8s9cU3N9tpMkVHHAsgv9tfexm52D+0aP4bsUPf2sMNgo7B3XNwfeDRsNZYf1dkCeFxuDa4yn49WQKDvLoUZO/TxmAAtcS0ASNhVa3S9DJm0DmFDj5BVSHnzNnBpuIzcagTTwGFkEBClCAAhSgAAUoQAEKUIACFLjxBBwdHdFVOB60V2LPy43ATtFRFr0nZ/L/eSG/0fw70v76ZL0VTZfvCXQv3o/C/JVIytuAuqYGq3wDKITGaGRAO+EetX6oD9bdEzgadSpPq9TyR9IZbqcxye0CvFwvIOCOzrLXUl1Tg6+Wf41ly79BXV2d7PEZUH6BSe0T8dmwu2QJbE/NQX2NQd2xou0iWuGj0HFwtNLOwWs9kOLaKnyVuxMfZWxGUa31jj2W5ZuFQShgAwJa4fe3Nvo5aNvdbQPV3CAlVB6BMvkeKKrymu2C2Rhsto+WC6MABShAAQpQgAIUoAAFKEABCtieQPv27YQmYA/06dULcd27wdnZ2WpFPvVhNdYXm/9oTEfhnsBuqkvwr0hB+clfcOjw97hUXWK1dYd6BaK9cE+gImQYTvrfhFLnUKvVooIWY10uYKpbIXo6l8Ff9b8GqW7X5Ol5sbLVduzYcXy/8kes37ARVdXVssVlIPMLeDm54cTc92VLpGsO+k3tBScnJ9limiOQWGNQd6xodLt2+ChsvE01B3UWug87fH1kD95L24AzlcXm4GFMCjR7Aa2jDzQ9lgK+Cc1+rba2QG1THZQ5r0JZ8IWtlSZLPWwMysLIIBSgAAUoQAEKUIACFKAABShAAQpcS8DXt8Xl3YC6ewL79u6NFi0sv0Pvek/mu40X8X+/mefNtg7KCrSuy0Jj4WZkHf4KRy8es9o3iLeLBzqF94Bb6BCcCxyHs+5drVaLLrGnohHDPArwivsZhKrqhF2i1y/nUKIrAmLaGF3vhQsX8OWy5Th46BBOnLTeXY1GL4AT/xT4dcKT6B0cKZtIktAc9Lbx5qCUxqBu52DXqCh8IjQHVTa0c/DKB/Xc/hX4KH2TbM+OgShwIwhovWJ+bwq6mu9Y9RvB0eQ1XtoJZcr9UDSWmhzKlgKwMWhLT4O1UIACFKAABShAAQpQgAIUoAAF7FzAxcXl8k7Anj0S0DsxER06tLfZFVXVNGDwW23QILydbuorSFmH6KajUF3YjRO53yD95H6orXRPoINShU4hsWgZOgDlwp1Ex3yGQKuy3j1kuh2Tka4X0SmoBEN7OKFnpBPUVdVov/KkcJCp/jses7xr4HGL8c3b2to69B042NTHy/k2IDCxXQ8sHn6PrJWY0hzUarXQaDTQCl8a4ee6f7/q53/8N+FH4Rd//3Vd9Vf8/G9zNEJM4c8N3X8vLS1DRmYm0oUvB+Huzya1GromoG6XoO6OwT9+rvtR94rvFItFIWOE1qD170291kPaVJCO+7YvQWldlazPkMEo0BwFNKGToO0u3y7p5mhk0TXVFkJ5YBoU1db7kJfc62VjUG5RxqMABShAAQpQgAIUoAAFKEABCtxgAp1jY9AjIeHyXYEJ8XF2tfpZbzgjoz7Q4Jo9hHsCu+AMPEt+w/mjwj2Bub+iprHe4DhyTWjfsjUiwvujMXgkjrYcgxoHK+7MFLofbZ1LEeV7HomdmjAy0QeujpdbIle9/Jekw004AFHfq1RZj4o53U1iuuve+5GUnGJSDE62DYHtk19Al5atZC1G1xz0mpJo8LHGuqZgXW0tGhsaLjfprvyxqakJV36phabeH18a3c91zUThR92Yyw2+y/N//7GsvALbd+3C6TNn/1yni3DkcqdOneDp5XXdxuCwIYPQN6ozxua7QfH3326ymhkb7HxNGWZt+AApF08YG4LzKNDsBTThU6Ht8jb0bqdv9go2uMCGUqE5OBWKiiwbLM7wktgYNNyMMyhAAQpQgAIUoAAFKEABClCAAje0QHBwEPr07nX5iNBEYWegp6en3Xq8/Hk5VhV2Fq1fKezgiVUVI7g6DZUF65GW+TXOVV4UnWeuAUEefugg3BPoEDIUpwInoMhZ3maJoXW3VNUi2uscurSqwoQBngj0Ed+F2bAiGR2qXfWm0u2xOnZbezi6Gn8X5edLvsSijz8xdEkcb4MCA0Kj8dP4x2SvbG2bSsQOTjQorjkag3X19fj2hx9RXHLtO0i7x8XBxdnlbzsGR48YhnlzZl2uP1hmiJIAACAASURBVHPnQYw75mHQWiw5uLapHrdvXIRtpw9bMi1zUcAuBDStZkHb+XW7qPWGLLKpBspDs6Eo3mf3y2dj0O4fIRdAAQpQgAIUoAAFKEABClCAAhQwr4C7mxt6CA1A3fGguoZgRHi4eRNaMPqGfZfw5Jb4a2Zsq6pC2/ocqAu3Iifraxw5Z71Pibs5CjuGwuLgFTYEF4V7Ak97GdbEkJvUXdgxGe12HtHBJRiW6Ixu7fQ3+K6Vv3hfLuJyNaKlJXdUoGXfaNFx1xuQeTgLs+feafR8TrQtgecTJ+Hh7mNlK0otbK87fHMofHwM22VrjsZganoGtu7cdd21eXp6ITY29qrG4CihKXj7jGlXzbH15qCu2Pu2fY4Veftle44MRAF7F9B0eBjajo/b+zKaf/2aRuHOwXuhOL/ertfKxqBdPz4WTwEKUIACFKAABShAAQpQgAIUMI9A1y5dLh8N2lv46tJFfEedeaowf1Td9V8DXg1GhdYRLZUNiFUfg+OlPTh1ZAVSju1Ek0Zt/iKukUGlUApNt2gEhg1AddAo5LcYjiaVi1Vq0SVVCTv3olwuoWPLi+glHA86tIevcO+ZaeWohaMT2yzPE71nMN+tBk7TjL9nUFflwKEjUFlZaVrBnG0zAt+PfQRDw2NlqWef0yWEzhxocCxzNAbXrF2P/GPXv8NK9+dVYs/Ey/cQ6o4dHTd6FCZPmnDN2jO3/YZxJ217N/cDwp2D3x7Za7A9J1CguQloOr0Ebdv5zW1ZzXc9wh/GiownoDz9jd2ukY1Bu310LJwCFKAABShAAQpQgAIUoAAFKCCfQNu2bdBLOBpUtytQd1+gq6v1mlDyrUp/pAbhbrA9m3dg+5YGnCs4iKTsVSirq7JU+r/laeMbjtYR/aAOGo7j/uNR6ehntVp0icMdKhHlU4iubaoxrp83Wnjqvw/QmGI9v0yFr0b/MaHVaELR3C7GhP9zzmNPPIVtO3aaFIOTbUfAy8nt8pGi3fxNP0J3XVQdYvoYfjeqORqDi5cuR2l5mV7o2JhYuLi64uYJ4zF65HC9Y+2hOfgPoTn4DZuDtvObi5VYXEDT/iFoo56weF4mNF1Akb5AaA5+Z3ogK0RgY9AK6ExJAQpQgAIUoAAFKEABClCAAhSwtoCb8MZy3z69f98V2CsRQUFB1i7JIvnTklKxd/1W5KZmIP1YLk6VW++eQF9XL3QMT4Rz6FAUBt2CC67tLGJwvSRRQiMw3OUi2kRUYHiiCyLD3c1eT9WqZMSUiR9DmjMhDG5+XkbXs3LVT3jt9TeNns+Jtifg6eSKn8Y9hu4BrY0urkrbiMKZUXB2NvwOS3M0Brfv2oPktLTrrsfBwREJwgc3bp00EYMHDZC07szN+zHutLeksdYaNHL1a0g6f/2dktaqi3kpYG4BTfg0aLu+be40jG8uAWH3tjJpLhQXNpsrg9nisjFoNloGpgAFKEABClCAAhSgAAUoQAEK2J5At65dccvNEzF82BA4OTnZXoEyV3Su8Bw2//QrDh9IRlZeLrIvFAiHYgrn8Vnh5eLghJiwbmgROgglgWNwzLsfIBwZaq1XmLIWczxOY6RwRGikYzWcFRqktFLDb6jljo4tOXwC3Q/WihKkhDXCb0RX0XHXG3Dq9GlMvGWK0fM50fYE5sUMwZv9Z5hU2BaPS+gwxfBjRHVJzdEYPH/xIr75fuXl2Nd6tWrVCo8+/CD69+0jed1Z2TmYPWce/N280bllOIZHdMatHXrB29n8jX+pRV6qqUCvFc+hrL5a6hSOo4DdC2gDh0OTsFj4e4CJ53LbvYSdL0DTAOX+KVCUHrKrhbAxaFePi8VSgAIUoAAFKEABClCAAhSgAAWMEwgMCMCCRx/GsCGDjQtgJ7Nqamqw9deNSNt9ADlZ2Ug5dQR1TQ1WqV4pNP2iAjsgOGwgaoNGIt93JBoc3KxSiy6pr6IBs9zPYJzrBcQ4VsFNIdyfqLi6nAJnoUk3I95yNQr39IR9kSXcYai/QXrKqRbamabVNXbCzTh37rzl1sZMZhXYdPOziA9sa1KOLT2V6BAbZVQMczQGdfcGpqRnYO/+A9DqLhS84uXn54u33ngdXQ2883X+vfcjOTnlqli62G4Ozmjt5Y9ewe0xoV0P9A+NNspBrknvp23ASwd+kCsc41DApgW0LXpA00s4gtKKdwfbNJC9FddUBeXem6CoPGI3lbMxaDePioVSgAIUoAAFKEABClCAAhSgAAWME7hp3Fg88fgC6I4PbW4v3ZvzB3bvw76N25GbnonU47korq2w2jJDvQLRNqIPFCHDcSLgZlQ4BVqtFl3iEFWVcE/gOcR3rMeswuOIaNK/S7RROBarcF6sRWt2WpaC4Cb9d1o2CXWdNbGuf/7r/7B6zc8WXRuTmUegnXcQDt72L5OCFypq0DAnHgrFX7rjEqPq/uxpqK9HU1PT1V9Cc0+tVl/1pRurFr50P2p1vyY05rS6/yb8vPGq+b//e8Gp00hNT0d5eQVa+LZAl9hYzJ93B1xcDLv79bDw4Yjb75gncUVASxdPxAq7CgeHxeLWyF4IFHYZWvIVs2wBztfov2PRkvUwFwXMIaD17AhNn58AR8v+/jLHWhjzCoH6S1DuHgNFXaFdsLAxaBePiUVSgAIUoAAFKEABClCAAhSgAAWME3hM2CU4fdpU4ybb6KyjefnYsnodsoT7AjPzc3Gy1Hq7wLxdPNApvAfcQofgXOA4nHU3/rhLObi9hV2BnTzOCTWVYlQvd0SG/e/utJIN6ehe6CiaJmdkoLAeP9Fxcg0o+zUFXS+KNzzSBvmgRdsQo9Nu2rwFTz37vNHzOdF2BJ7pcTMWxI8zqaB1/sWIGd/fpBi2PLm2tha33jYDhcJxysa8dLsKXYQ7DVsJuwp7BLbD+LYJl48hNeeLuwbNqcvYtiCgdQqAesAGKFys+6EhW7BojjVoq45BtXs0FGrbPxaZjcHm+B3INVGAAhSgAAUoQAEKUIACFKAABQSBBx+4D3Nun2X3FqWlZdi46hdk7E9CTk42MgqPCTturn0Hl7kX66RyRExIZ/gJx4OWBY3BUa+B0KoczJ32uvG90IQ73E/Dw+0iWiY4YlBCSyivswGq8lIpYn8RbxKkB9TBZ1ycxdZUdeYSYjZdEs2X1rIWLW4y/jjRsrIyDBkxWjQPB9i+QObMfyPEw9ekQg+N8UNAUPN9c37BE09h+46dJhn9bbJwuqmv8GEI3a7CGVF9MblDb1njF1RcQtw3T8kak8EoYCsCWuHs7ss7BX172EpJrMMcAufWQ5V8pzkiyxqTjUFZORmMAhSgAAUoQAEKUIACFKAABShgGwKTbp6I555+0jaKMbCKRuEovu0bt+Dglt3IOZyF1IJc1DTWGxhFnuEK4Y28yIB2CA3vh/qgUTjacjTqVJ7yBDcmitAPneR+DlPdCtHDqQy+qsbLUS6q6lE7u7toxKAlmXAWbvTT95IaSzSZAQOCFgt1KfTXdUFVh7rZpjUsZ86+A9k5uQZUxqG2JtA7OBK/TjDtz7bDDqXwvL2vrS1Ntno+WPQxlny5VLZ41wvkqFRhXuwQ/KvPNNlyJX73LI6WWW8XuGwLYSAK/EVA0+ERaDs+RpcbQECR8SSUp5bb9ErZGLTpx8PiKEABClCAAhSgAAUoQAEKUIAChgsEBgZi9coVcHb+3zGShkex7Iz0lDRs+3k9clIzkHo0G5eqyy1bwBXZgjz80F64J1AVMgwFgZNQ6hxqtVp0ifu4FGK6azEGOpcgzKFWaFX+/SXcWIZTd3SCcGGa3lq13ySjdZ3+uyalxpIV5etktKrXX5dwOxtOS1ijvrre+2ARvlz2laylM5hlBT4bdhcmtU80KemvEeXoPEze3W4mFSTT5JMFBXjlX68jNS1NpojSwujuItxyy/MIcW8hbYKeUY/sWoZl2TLvdDS5KgaggGkC2hYJv+8WVChNC8TZ9iGgrhPuGxSOFK3Ks9l62Ri02UfDwihAAQpQgAIUoAAFKEABClCAAsYJ/N+r/8TIEcONm2yhWeeEe682CMeDZh5IwuEj2ci7dMZCmf+extPJDTHh8XAPHYyLgeNxytP44yrlWESMy3lM9M1CS7fzKHWsRqDGCTOqg0RDpyS4wK9LW73jinZkIf64/uahLoCUWKIFGTCgeGsm4gr07xjUhUvq7gj/7h0MiHz10AO/HcR9/3jI6PmcaF2BYKHxdHjWWyYVoVZokTu5FTw8PUyKY0uTS0pK8fmSL/Dd9z9YrSwX4Zjl/dNeQYSnv0k1vJH0M95MWmNSDE6mgC0JaB28oBm8C3A27feGLa2JtUgQqD4O5c7hUGjqJAy2/BA2Bi1vzowUoAAFKEABClCAAhSgAAUoQAGzCYSEBOOXn34UNo6JN3/MVsQ1AldX12DTz2uRvHMfcrKzkX7mKJo0akuW8GcuB+H4u5iQWPiHDUBF8Bjkew+BRulolVp0SVuoqjC9ZQbaCXcFVjtXoFp5tYtKuNfrkYpWcLjmXsH/lZ3lXQOPWxL0rqOxth7tvj0qRNL//ZHlXSvEslyDtKGiGu1XnhStK9urBu6T9a9R7EH27j8I9fXWOZpWrDb+un6BZ3rcjAXx40xi2utShLDpA0yKYSuT9//2G35dux7rN2y0iZLcHJwuN269nd2NrmepsFvwUWHXIF8UaC4C6p7CkZIBg5vLcrgOQwROr4Aq/VFDZlhsLBuDFqNmIgpQgAIUoAAFKEABClCAAhSggPkFHn3oQcyccZv5E0nIsHPrDuzduA056ZlIO5GLyoZaCbPMM6S1bzhaCfcENgWPwPGACahx8DFPIglRHZWNmOidgx5ex6BxKUWZQ4PorOlVQYhQu+gdV65sQNmcbqKxfJekwxP6G6FlQqxyCbFEkxkwwG9JBjyE9qe+V7lCWOMd4mvUF+PeBx7EbwcPGVAZh9qKwNE576KFi2k7/dZ1qkdML/H7OG1lzX+to7a2DgWnTmH33r04euwEtFrhkwPXeWUJH8Koq7PsbpWxwTF4t/8co/l+OZGMORsXGT2fEylgSwKa1ndCG/uyLZXEWiwsoEi+B8pzv1g4q3g6NgbFjTiCAhSgAAUoQAEKUIACFKAABShgNwKb16+Fn5+vVerNz8vH+pVrkJWUivT8HJyvLLFKHbqk/u4+iIroDceQoTgjNAIvuLazWi0K4W68fp7HMcLnCFxdLqHYsRZaAzd09qr3xqA6/fd36e4GPHZbezi66r9bsuH7ZHSoEr9nUEosOVEbVgh1VZu/rqVfLce7738oZ+mMZQGB2Z0G4p0Bt5uUqUrbiAuzY+DgoL8BbVISC06uqanB5m07sOKHVWhobLRg5mun6u/VCu/0nGZSHd8e2YsHti8xKQYnU8AWBLQuwcIRorsBlf7/r9lCrazBjAINJVBu6wtFU4UZkxgemo1Bw804gwIUoAAFKEABClCAAhSgAAUoYJMCHSMj8e3ypRarraSkBOt+XIPUvb8hOzcHuedPCa2p6+9eMWdhbo7OiAmLg3fYYBQFjsNxj0QolAZ232Qs0FfYcTfP4xiC/dNQ7lSJeqXGpOgBaifMrQoRjZEUqYB/v2i944qT8xGXLt5ESO6oQMu++mOJFmTAgOIDeYjLbhKdcai9FgEDYkTHXW9A7pEjmD5rjtHzOdHyAm6OTkie/joCXL1NSv7YiV9xWl2FkOAgdOkSiyEDm8eRokXFJfhk8RdITcswycfYyRq1Gs9GjcSkVqYfP/zi/h/wQfoGY0vhPArYjIC659fCEaKDbKYeFmI9AcXJ5VAeftJ6BVwjMxuDNvU4WAwFKEABClCAAhSgAAUoQAEKUMB4gVsmTcSzT5nvjYdGYUfKpnUbcGDLLuQcPoyMU/loUIs3coxf0fVnKhVKdAyMRJBwT2BN0Cgc9RuFJpX+ozbNUccfMR00Gkz1KMStbufQ3bEc3qqmy03ShV6nUa8wrSl4OYfQb324IgIuUOpdxnHXWqhuE3lzXjh6MOKLbNH7/I651sDhNtPu8zPEXN3QiDbL80TrOu5SA9V00+oaMmI0ysrKDCmPY60o8LRwt+BjJt4tqCs/ceub0Pzl6E1HYfegr7DLunVEODrHxqBXjwT4+JjWgLQW1WrhHtfl331v0fQuagW+7X8nwtzk2ak+dd1CbDmVadE1MBkF5BbQCH8v0SYsljss49mxgHL3aCjKrfPhjWuxsTFox99MLJ0CFKAABShAAQpQgAIUoAAFKHClwGOPPozp06bKipJ8KBlbfl6HrOQ0ZBzPRVldtazxDQkW4ROCNhF9AN09gf7jUeEUaMh02ccOci7CLPcz6OtcikBVvdDQ+vtrjdsl5DjKY3ZTjT86NbrrXUcdmnBhbhfRtXosTYWfWv+Ro1JjiSYzYIDnl6nw1eivqx5qnJ/b2YCofx/69HMvYOOmzSbF4GTLCIR4+CJz5r9NTvZi2hqsK8oVjaO7s0931GgLoTkYER6GmOho9EpMQGBAgOhcWxiw8IOPsGffAbOXohE+DNHTMwwf9Z4la67opY/iYm25rDEZjAKWFNAqXX4/QtRVfJe/JetiLisLVORAuWuE8HdFGT4sJsNS2BiUAZEhKEABClCAAhSgAAUoQAEKUIACtiDw6ssvYszoUSaVcubMGaxduRoZ+5OQmZeDU6UXTIpnymRfVy9EhSfCNXQoCgPHo9DNcsdaXqtuX2U9Ogm7AqNDSjAkwRGjdpwV3d2W6ViJtW7FpjD8OTe2wQPjaluKxsqZGA43X0+94yrWpKBzsf4dlrodj7kTI0RjiRZkwICqVcmIKRO/j0nKGvWlXb3mZ/zzX/9nQGUcai2Bj4fciVsje5ucPnHTG9Do33CrN4dSOJrYy9sb4aEhiBKObe6ZEIc2rVuZXJfcAWpqa/HAI4+joqJS7tB/xvOFE57oNBJDQzrJmmP32VxM/MX0JrCsRTEYBQwU0ES/AG27uw2cxeE3hED2P6E6/olNLJWNQZt4DCyCAhSgAAUoQAEKUIACFKAABShgusAbr72K4cOGGh3omfsfxTdrVxk9X46J3SLi0SJ0IMqDhXsCfQZZ6cbC31fiLHyqu6PrBUQHFaF/NwX6xXpBccW2QFdh112AyK67cmUTPvI8IwcNvNQq3FcVLhorObQBLUd20zuu9Fghuu0UP0ozLbQRLUZ2Fc0p14DS3NPotk+8oSFljfpqunDxIkaPmyBX2YxjJoEh4bH4YewjJkdfdyYdL+SsF37/ynvvqC6cp4fn5TsLO3bogIT4bojqGCl7HkMBfl67Hsu+/s7Qadccr21Sw1E4wjjAyR2x3iG4vV1fdPQJkiX2X4Pct+1zrMjbb5bYDEoBSwhoPKOhHbjFEqmYww4FtE3Cce/b+0FRf97q1bMxaPVHwAIoQAEKUIACFKAABShAAQpQgALyCDz39JOYdPNEo4N99M57eOO9d4yeb+hEhbDfLjKgHULD+6Fed09gy9GoU+nf6WZoDkPGuwtHVM7xOIWbhWZgcmstBg8Pg7ODcLnfdV7lv6aiy0X9x17qpn7qcRYlqkZDSrnu2Hsrw+CtcdAb64xjLdSzRO4ZFCKELT4MlXBXo75XoWMdGmfFyVK71CBS6pK6Rn05J94yBadOn5ZaFsdZWMDP1QP7p/wLuh9NfQ3e+jaqtJa7D9XD3R2BgQGIbN8Ocd26okvnGKhUKlOXIXm+Wq1G0JeH4aZwlDzH2gOrGusQJRwjWttUb+1SmJ8CRguo44V7BYNNO7nB6OScaB8CJ7+A6vBzVq+VjUGrPwIWQAEKUIACFKAABShAAQpQgAIUkEfg7vl34u7584wOVlh4Dn379BF26V2/GWZ08P9ODPUKRHvhnkBFyDCc9L8Jpc6hpoY0af5Q4Z7A24V7Avs4l8Bf1fBnrEzfWnhN1N9cqzlbjOiN4ketbnEpRpKz+C44KQsZWeOH7o36m6dqrQZn5sWKhnP6KgXBjfqPE5UaSzSZAQOclgl1NemvSyOs8bSENepL+9rrb2Llqp8MqIxDLSmwatwCDAwz/ajKY+UXMfXgEqvv4nNxcUGgf0u0adsG3bp0Rr/eiWblLPhuJwYI95Lay2vB7mX4MmunvZTLOinwNwGtRyQ0A7fhqqMF6ESBvwpoGqDcEg9FQ4lVbdgYtCo/k1OAAhSgAAUoQAEKUIACFKAABeQTGNC/Hxa+bdr9TCN7DcCR86dkK8rTyQ3R4QlwDx2M87p7Aj0tu/vsrwvxUjTiTvcTmOp+Ae0dqoUdc9dearFwn2DVnO6iDiHCrjtHkV13eQ41WOV+UTSWlAEdGl1xS02g6NC0gT5o0S5E77iSDenoXii+o0hKLNGCDBhQvi4NXc47ic5I6e8Fvw5houOuN2Dbjp147ImnjJ7PieYTmBszGP/uP1OWBMO2/gfl2v81/WUJKlMQZ2cn4c7CMPTsEYdRw4fCzc1NpsjA4T1JGJsnXzzZCrtGoD2FuZjws2n/7zJnfYxNASkC6m4fCFvxb5YylGNucAHFsUVQ5vzLqgpsDFqVn8kpQAEKUIACFKAABShAAQpQgALyCfj4+GDbpvUmBXzszgewcsuvRsdwUKrQOaw9ooWj85R+I/Czci4USv1HXxqdTOLELi4X0NHvAhKiGzGqj7CD5utktKp31Ttbt2vy1B3CbiWxO8kkxKoT7ipc6CU0W2W43sxZq8TDFeFCKP3BMv3q4DVBfxO2urgcndacFVWUEks0iAEDqs4UIWaTeCM101dY40TjG81VVVUYMGS4AZVxqCUEuvq3xroJT8LFQbw5LFbPznO5WJC1WvT3i1gcS/y6bp+27q7C+Xfcji6xMSanrK6uRvT3BVCYbwO4yTXqApTUVWLwj6/gTGWxLPEYhALWENC6hkIz5IDw/3n9x3NbozbmtEGBpioot/aEorHcasWxMWg1eiamAAUoQAEKUIACFKAABShAAQrIL/Dt8qXoGBlpdOCV336Px55+wqD57f3DEBvdCXH9emHsLRPg5+d3ef5P2y7gpT09DIpl8mDhTfD2zqXo6H0O3SNrMaKXD7zdr94VV7w1E3EF4vd9JXd2QMse+i2Lt2Qg7pR443OpeyHOOciza+n2qmCEqPXfbSh1x2Pgkgy4QH/9xSph9+Rs8d2TJj+7KwIELcmEM/Q/I6lr1FfXnHnzkZF5WM7SGcsEgRAPX2y95TkEuHqbEOV/U/tuehMNShvvjP1lpbpqA4Q/Q5996jGEherf9SuGVPzVbsQ1/v7nsS2+KhtqMW7NmzhcLN8udVtcJ2tq/gLqLm8BEbc1/4VyhbIJKPLehjLPcvd6/7VwNgZle5QMRAEKUIACFKAABShAAQpQgAIUsL7AIw/+A7NmTje6kJqaGnTv3BX16sbrxgjy9EWXDtGI7dEdo4VGYIfIDtccW1XTgMFvtUGDSIPH6GL/O7GlqhadvM4hNqwc4/p7ILSl/qZZQ0U12q88KbqLKN+9Fk5T9d8zKDXWHpcy7HEuM3Wpl+f3q/dBvzofvbGk7nhUf5uMtrUy7Z6UZXW/B9EKOzFby7WrU09diz7+BJ8v+VLGyhnKWAF3B2dsueV5RLYINjbEVfOWH9uHd0/sliWWtYJMmjAO06feanT6zI17Me5sC6Pnm3NibVO90BR8A2mXCsyZhrEpYHYBrXMANEMPAVY+HcHsC70igbosFw05i9B0Zh00lcf//BU3v3bwbDsaqnazUe2m/+9PlqzXJnM1lP1+16CmzirlsTFoFXYmpQAFKEABClCAAhSgAAUoQAEKmEegT+9e+ODd/5gUfOrw8fgtP/N/b/Q4OqNL646I6d4F/UcNxaAhgyXHn/WGMzLqxe/EkxxQGOiuaEK0+3lEB5dgaE8ndG9n+D1afkvS4QH99+tVoRHFc7uKluYvxHITiXVKVYdvPM6LxpIyILzJBTOqg0SHJnVzgH+c/h2Pl3ZnIyFfNBSSuwi7JxOM34kqnuHqEUU7MhF/XMKuThPrSk5Jxfx77jO0PI43g8AP4x7BkLBY2SL32Py6+FHAsmUzX6BWEeF4+/VXjUpw9tQZ9NlSYdRcc046LRwbOmvjB8gs4k5BczoztmUE1J1eAtrOt0wyK2fRVJ1C7f770XR6rWglUd0GwCPxNeQ39hbGynCWumhG+xugyHoJyhOfWaVwNgatws6kFKAABShAAQpQgAIUoAAFKEAB8wg4Oztjz46tUKnEmyrXq+C1p1/E/t17EBMTi4RBfTFG2LXi5mZ4800X/7UlZVhxpotJi1UJ9/1Ful5CtH8R+sZqMKi7FxyMX97lWhpWJKNDtfhOufypbeHsrn+clFhNwhre9zqNeoXGJAvdZJVw1uAjFa2EA0D1v9GW61EL1yn6P7HfWFuPdt8eFd89KcRyEoll8sKuCFBfXYsOK46L1yVhV6dYXf0GDkFNba3YMP66GQXeHzwX0zv2lS3D0ymrsKVEQsdbtozmDaS7e/C9t98wKol2yW9oDU+j5ppj0saCdNyz9XNUNNSYIzxjUsCiAlqFAzQjMgBHeY4/tmjxBiZTF6eiev0waBtKRWe2j4zC8DETLo+70NQa+2qnoVbb/I1EYf46oPwwVLtHGjxNjglsDMqhyBgUoAAFKEABClCAAhSgAAUoQAEbEvjs40WIj7PsnXDXW/7W3y7g0Y2G3zMY6lCBaL8LSOhQh/F9vOHhIi9w8f4jiMtRiwZNat0E/yH6G5tSY/3odgH5jvI0oKZUB6Btk/5mbRWahB2P4k1ZX2HHo6fIjsdKIVaJhFiioAYM8BPuP/QQuf9Q6q5OfWn/8fCj2LtvvwGVcaicAh8OmYdpkX1kC3m49CzmJH0FhaJ57VBp07oV/v3aPw12yl69C6NLWho8zxwTXjrwA95P22CO0IxJAasIaIJGQ5vwuVVyWzKppvosqtZ0h7auSFLaWfPuhYen159jK9R+2Fj9gHAOg8x/mZNUjW0PUu4YxGGavAAAIABJREFUDEVVnsWLZGPQ4uRMSAEKUIACFKAABShAAQpQgAIUMK/A/Hl34N677zJvEonRtcLutoGvBqFc66R3RifHcvi4l6Brm2pMHNgCQV7iTTuJJVx7mFqDiKU5ojvSTjrXQjFD5J4cibEOOVVgq2uJSWX/MTmh3hPD6vxEYx2Z0houHvobiLU/JCOqUv+uSF2io7e1g6Or/vsbRQsyYEDD98KuzirxuqSsUV/ar7/5Dm8vfNeAyjhULoHPht2FSe0T5Qp3OU7i5jegaV49wT99BvTrgwfvu9sgr/zDORh2UPiD2Iqv8zVlmLf5Exw4Z/k3v624bKa+AQTU8YuB4FHNfqVVwk5B9bltktd55/2PwNHx6r/3FTZ2wI7auZJj3DAD89+D6ohxO8JNMWJj0BQ9zqUABShAAQpQgAIUoAAFKEABCtigQJcunfHl55/aTGV3vaXAbzXhV9UTrqrFfPcCjHItQjuHKmiEfwrnyXe/mJTFe36ZCl+N/kZXvVaN8/M6i4aTEuuCsh5feJ4TjSVlQIDaCXOrQkSHJrdRo+Vg/fUXpR1DfEq9eKy2GrQcZLlnVJSUh/iMJvG6JKxRX5Bjx47j1ttmiObhAHkFFg+/BxPbGb6bWF8Vt+z6CKcabO9OPTnlnnniUcR1E7/79I+carXwZ8DSTHhB/4cz5Kzxylg/HT2IR3d9xaNDzQXMuFYT0Dp4/X6MqFL/fcVWK1CmxA3Hv0PtjukGRbvt9jvh4/v3Dy8drh8s3Ds9wqBYzX5wbSFUW+X9f6EUMzYGpShxDAUoQAEKUIACFKAABShAAQpQwM4Edgv3DLobeS+g3Et986sSrDjWBTM9zuJWt7Po5lQFd4XQ8PnLrp6ckYFwCxXfBSdXfVWrkhFTJr4jLVuoy12krqqfUhBTqv+ILK1wz+AizzOoVMqwG1LYAHRfZRi8tA56OQqEHY8Q2/EobOuM+CJbdPekpFhyPRxdHAvWNWL0OBQVF8tZPWNdR8BBqcISoSk4tk2crEZf5O/BooK9ssa0xWBOTs745kvDPvhxfMVODK72t+hyyuqr8cD2L7D+ZKpF8zIZBSwloGk9D9pYw4/3tVR9cuWpXB0HTUma5HAOjo6YOfceuLpe+7SCow09cbBuohCvmW7tliz1v4GKvTdDWXrQiJnGT2Fj0Hg7zqQABShAAQpQgAIUoAAFKEABCtiswFtvvo4hgwbaRH3Hc89h0N5S4c4v/eWkB9bDZ6zl7kYszT2NbvsqRY3SA4S6xumvqyz7FLoeqBKN9auwQ/Kw0BiV4zW2xg+dGz31hpK649FjaSr81PLsnpRjbX/E8BB2dfrJtKtTX13Pvfgy1q3n3WdyPrvrxfpq5AMY00be3+fnhKMqx+/9uNndK3g9w0kTx2H6lFslP66sfSkYk2uZu73KhYbgkqwd+ChzE4pr5fmzTvJCOZACFhRQ91sP+Ijf42vBkmRNpa0vQ33GG6jPlH7MpaOTE8ZOvBXBIWF6aznZ2BX7aqfJWq9dBytYBlXm0xZdAhuDFuVmMgpQgAIUoAAFKEABClCAAhSggGUExo4ZhVdeetEyySRkCVt8GCqFUu/I8w51qL9d3l1EYqWFC3UpReo6J9TVIFaXsLst7IssqKB/jZmOlVjrJs/OtNgGD4yrbSm2RGSODYJXoK/ecRU/p6BzkXjjQMruSdGCDBhQsToZnUvk2dWpL+0va9fhxZdfMaAyDjVG4L1Bd2BGVD9jpuqd01u4V7DpBtp84u7qiqWLP5bsWF5egS4/npE83piBqRdPYnHWNnx7pPnv2jTGh3Oal4DWrS00Q3Y3r0X9dzXaphrUZ78nNAXfBBrKDFrjpKmzEBgsfsz51uo7cUHdzqDYzXmwtqEUDpssd1S7zpKNweb8HcW1UYACFKAABShAAQpQgAIUoMANK+Dh7o4N636Bm/AGsi28nL5KQXCj/saTRjhq8/QdnYR3Kyz3Dr+kurQanJZw/6HTMmGNTfrXWCEcoao7TlSOE7S81CrcV3X13Y3Xetapwk5MX5GdmOUnz6PLthLRbxVL7+oszjuNuD3y7OrUt7hLRUUYOWa86Po5wHiBpxIm4vEE+Y2Hbv0PKrQNxhdmpzOfeuxhJMRJ33lZu3gvohQtZF1tTvFZrC9Iw8/Hk5BZdErW2AxGAVsW0LSeKxwj2vw+TFKf/T7qU/8Jbb3hH2By9/DE7XfeJ+mx7amZilNN3SSNvVEGKfZOEI4TTbLYctkYtBg1E1GAAhSgAAUoQAEKUIACFKAABSwrcP+992DeHbMtm/Q62Uo2pKN7oaNoLSk9XODXua3oOLkGlK9LQ5fzTqLhknu5oWWn1nrHSY21xKMQF1XyNDLmV4YKR23qd5W041FYWaiwe9JBjt2TopqGDZBtV6dI2snTpuP48ROGFcfRkgTu6DQIbw2YJWmsIYNu2/0ZjtaLN7QNiWkvY7t26Yznn3pMcrlZ32/DmKogyeP/OvBkxSWkFxUg49IpZAg/pl46idI6HhVqNCgn2rWAOv5zIHi0Xa/hr8VXb52EpoLVBq2pc7d4xPfsA1fhTuuD+3ajZ5/+kuaXqgOxruqhG+b4ZykoiiP/hjJ/oZShsoxhY1AWRgahAAUoQAEKUIACFKAABShAAQrYnoBut+DKFd8gKMj4N4PlWlVNSSWiVp8SNsrp3w2Y5VMLj0nxcqUVjVN9thidNl4QHSelLqmxtrgUI8lZfBecaFHCgKG1vujR4KV3qEbijkfV8mSENejfYaqGBmfuiLG5XZ1y1PXWOwvxzXcrpLBzjAEC49rEYenI+w2YIW3okyk/Ymtx/g37xrKhx4lm7D6E8fnu0nCvMaq6qR4Rn0vbDWR0Ek6kgJ0IqEdmA47edlKteJn1WQtR99uj4gOvGOHr1xJTZ80zaM6Vg7fVzMX5pg5Gz292E4v2Q3VgssWWxcagxaiZiAIUoAAFKEABClCAAhSgAAUoYHmB7t26YfGnH1k+8TUyBi7JgAsc9NZSpmxA+RzLHi8VtDgTzgqV3rpKlPWonCN+bF/QEiGWcNOgvleeQw1WuV+U5Zl0aHTFLTWBorGSEl3hH9NG77jizemIOy2+qzOtpxtaxLYWzSnXgJL1wm7Tc+J1pfZ0hW+s/jXqq2nX7j14eMHjcpXNOIJAQlA7bJz4jOwWH+Zuxxenf7thm4J/gH61+BO4uorfDaobf+nCRSSsLTLpWcQsW4DzNYbdOWZSQk6mgA0KaDxjoB24yQYrM76kyh86QFN5zKAArdu2x+ibbjFozpWD0+uGI6thiNHzm91ETROUGyKh0NRbZGlsDFqEmUkoQAEKUIACFKAABShAAQpQgALWE5g25VY88ZhhnwQ3R7Xqb5PRtlb/jjStcM/giZmRUDmJN4Jkq/HrZLSql6cu7TfJaF2nP1adsOvufa9TUMtwlaKzVol/VIQL7VbTd2JK3dWZ7VMH90lxsvGLBaq4UILOa8+LDYOpddXU1qLfQL5JKQotcYCnowv2TPknwjz9JM6QNmxlQRJez9tywzcFdVpPP/4I4rtL/yBFy8XpcFcY/2fr8FWvIuUij9uV9p3KUc1VQNNmPrQxLzWb5WmbalCxzMPg9Tg5OWHefY8YPE83oUnrgM01d6NUHWbU/OY6SXngNiiKdllkeWwMWoSZSShAAQpQgAIUoAAFKEABClCAAtYVuH3mDDz84ANWLeLSnhwk5GlFa0iOUqJlnyjRcXINKNpxGPHHlaLhpNRVuj0L3U6Id/y+dj+P0w51ojmlDJgu3BsWoda/a0jqjkcpuzqlxpJSu9QxUnZiylHXvLvuRWpamtSyOE6PwGfD7sKk9omyGn1fcAhv5m1lU/C/qrOmTcWEm8ZINq5YvAudFS0lj//rwDkbF+GXE8lGz+dECjQHAXXCF0DQiOawlMtr0GrUqPjSuA8MjBw3EW3bdzTIolQdhF01s1Ct9TVo3o0wWHH0Ayhz/88iS2Vj0CLMTEIBClCAAhSgAAUoQAEKUIACFLC+wE3jxuKlF56zWiGNtfVo9+1R0XsG891q4DQtwWJ1yllXU1Ut2n5/XHSNe1zKsMdZniP5+tX7oF+dj14vqTsxm75NQrtaN1liyfkA1d8koW2d+ev65LPF+OSzz+Us/YaMNblDL3wydL6sa19+/AAWHtvBpuAVqsOHDMLdd94h2fnE0q0YpA6WPP6vA5/d9x0+zths9HxOpIC9C+g+2qQZlQ846P//kb2ts2rdIKjPG75TrV2HjhgxdqLk5Z5s7IoDtZOFcxP0HysvOWAzG6gtS4fDHukf9jBl+WwMmqLHuRSgAAUoQAEKUIACFKAABShAATsTiI/rjscXPILIDh2sUnmLL9LhpdX/yfQaNOLS3K4Wrc9XuP/QU+SNqmo0oWhuF9G6fJekC7H0r7FQVY9lHudEY0kZEN7kghnVQaJDpex4LNqbg/gj4rs6U4RdnX4W3NV5cXcWeuSL78Q0ta6MjEzMufMuUUsOuL5AuHB06N6pr8DdwVk2pmXH9uO94zvZFPyL6KABffHAPdK/X7O/34bRwg5jY1/vp23ASwd+MHY651HA7gW07u2gGWx4A83WF96Qtxi1ewz/MIdK5YC59z4EBwfxRl9JUyg21twnHBgvfkKDrXuZrT6tGsq1rYUPl2nMluKPwGwMmp2YCShAAQpQgAIUoAAFKEABClCAArYnMHLEcNwxe5bFG4S1K1MQVaH/2Evd7rajk1vDycvdYnC1PyQjqlL8nsHciRFw8/XUW1eDEKuDhFgLvU6jXmH6mz8qoY/3j4oIuIi82SZlJ6acuyflfHiWrGvA4GGoqq6Ws/wbKtamm59FfGBb2da8OH83Pjq5l03Ba4iOHDYE8+fOlmydvnYXbrpg/FGiS7K24/HdyyXn40AKNDcBTeAIaHsIR4k2s5emrhiV3/gbtaqhI8chMjpGdG5q3SjkNAwUHXejD1Bu6w9FzXGzM7AxaHZiJqAABShAAQpQgAIUoAAFKEABCtiuQHhYGIYNHYI+vXvB19cXPt7eaNHif8dS5ucfRXJqKpKSknH02HG8/ebraNfO+Df9izOOIy5J/G69lIgm+A0T350nl+zFlDz0SGsSDZcSLtQ1XH9dJSlH0T2tQTTWj24XkO9YKzpOyoBJ1QGIbNJ/tJnUHY/eX6bBR+OkN611dnWK78SUo64FTzyF7Tt2SmHnmL8IzI8ditf7TZfN5bP8Xfjk5D42Ba8jOnXyJNw6aYJk78M7D2HsMeM/cPF17h48uKP5NUUkA3LgDS+gbnsP0On5ZulQvsS4nXxhrTtg/MRJoiZJteOQ19hXdNyNPkBxcDaUF7eYnYGNQbMTMwEFKEABClCAAhSgAAUoQAEKUMB+BXQNw0EDByAhPg6tW7UyfSFaLSK+yBa9g6/ASWiYzYw3PZ/UCHLWJTHWIacKbHUtkVqh3nEJ9Z4YVuend4xuJ6aUHY9VPyYhplz8Pj9b3dVpal0rV/2E115/U5bnciMFaeHigZTp/wcvJ3nu3lp3JhMv5KxlU1DPN9ELzzyBLrHiO3X+CJGx8yDGH/Mw+tty9bFDmLf5Y6PncyIF7F1A0/lNaFvNsPdl/K1+raYRFUuFUxO0RpxioFBgzvwH4Oqm/8/+pDqhMdjAxqDoN0/2y1Ad/1R0mKkD2Bg0VZDzKUABClCAAhSgAAUoQAEKUIACzUjAy8sLA/v3w0ChGdinVyJcXPQf+2nM0l2XpiJArf/+sUbhfpXCubHGhDd6jodQl59IXQ3C/S/n5nUWzSElVrGyEZ95nhWNJWWAr9oRd1WFig5NCW8Udjzqv7+xOF3Y1Zlse7s6L6XmIyG1UXyNEWpht6n4M7peoIJTp3Dz5KmieTjgagHdTkHdjkE5XinFBbgr5Vs2BfVhCh9AWPntMoO4k7fvx6QT3gbNuXLwxoJ0TF//ntHzOZEC9i6g7r0K8Eu092X8rf6m87tQvW6Q0evq3X8wusX31Ds/uW4sjjT0MzrHDTPx5FdQHX7K7MtlY9DsxExAAQpQgAIUoAAFKEABClCAAhSwbYGgoCCMHD4MA/r3Rfdu3cxebNmvKeh6UbzhmDHEF96tg8xezx8JKtakoHOxeF3pQ4UjV1vpr6viZyFWkXisRR6nUaFSy7LGeyvD4K1x0BvrlLATUyu2E1PijsfTQiyNWCxZVvbfIBLrkmO36TjhWLTCwnNyVt+sY3VsEYx9U1+VZY3F9dUYtet9CF1BWeI11yCeHh744tMPDVreoa17MbmghUFzrhzMxqDRdJzYTASahqdB4WzcXXy2TFCz/TY0nlhhdIktg1tj3pP6d7mdqInExqMRRue4YSYW7YXqwBSzL5eNQbMTMwEFKEABClCAAhSgAAUoQAEKUMA2Bfr26Y3Jk27GwAH9LVpg1elLiNl8STRnekAdfMbFiY6Ta0DpsUJ021kmGi7dX6hrvP66yk6cQ9ftpaKxfnUtwmGnKtFxUgaMrfFD50ZPvUObhGPCzs4T34kpZVen1FhSapc6xv3LVLTUXHu3aUGjC7Y3+GNfgyueXaC/QSqW75XXXsdPq9eIDeOv/1fgp/GPYUBotCweiZvegMa4q65kyW8vQfr37Y2H7hfuOzPglbbtACac9DJgxtVDV+YfwN1bPzN6PidSwJ4FtCo3aEbn2/MSrlm7puo0Kr83/aj4kpIS4Y7q63/woEo4iKDrk1oUy/NXnmb3HP5YkLbuPBy2mP8ofTYGm+23EBdGAQpQgAIUoAAFKEABClCAAhT4u4C3tzfGjRmFaVOmIDQ0xGpEIYsPw1Gh/93/i6p61M7ubtEaw4S6VCJ1XXCoQ93t4g3LUCHW/7N3FVBxJFv04i4BQox4iBsJcXeBuLu7u/vPRjYb2d3IZuPu7kpcISFCCMSNCJDgDr+qZyFDYLp7enrQeudw9v+d9169utWwM3Pr3acvkOuxQQhOmgbIssey0eZwjbATzPWwnjVyFOU/+x/HSVfnN+GOx/Ts6vxOuiOvRNrBjcxWdIuyxZu4n/ONtnd7jPKO0iUTz1+8hCnTZghiyRyAxgXKYW/LsbJA0cZtDT7FhsiSK0snId2za/9agZx2/HNFf8XgyeU7cHnNf3mAD7fNXm6YeHV7loaWbY4hoAqBBKvyiK9zOssBFOk+C1GeCzXeV3h4OExMyJxCHjtwBxi4PkHjtbJ6At3TxaATR2Zta9EYMahFcFlqhgBDgCHAEGAIMAQYAgwBhgBDgCHAEGAIZBQESpcqiS6dO6GVS8uMUdJOdxSM4v8CKR4JeN+vdJpKCuptd4dDDH9dcaTr7kP/MoJ1GWz3QF7SxcZnwTqxWGPxgeTS/Fgs4vUwnMiJ6ggke0g6HnMIdDwGvfmM8pcCBYsS0z0pmESkQ3QscPrcZwQ+V5CBnjFW5AlJ3YaUdMfwztLl3oKDg1G/cTORlWVvt5NtpqB6nuIag7Dm+WVseneHzRUUgWQue3usXvm7CM/kLo/P3IDrJ+lSon97nsGcW/vVXpcFMASyAgLxdvWRUH1nVthKsj2EnWmK2E8XNNpXjx49sGPHDlE5qs9KgPcnUa7Z1knnUnXohr/X6v4ZMahVeFlyhgBDgCHAEGAIMAQYAgwBhgBDgCHAEGAIpC8CVas4Y9CA/qhcKW0774R2HXDxMSq91RNyg4ezMWzLFxH0k8vB/+xDVP5oKJjOvbIR7CoU5fX7fvYRKn4UlrTcZP4JX/WiBdcU49AvJA9yqZDaTIz/okc6HvvI0/H4jXR1hmupq5M0ReHByyhcuB+Npx+t4R2eG5EJws8M3WcDy5dYOdZADGQqfXr07odn3t4a5cjqweXsCsCt4xyNt/ky1B9dbm6Ajq4MDLnG1WT8BHOmT0a5suRygprmufcCWodJ7xRffP8Ifr9/XM1VmTtDIGsgEJ/bBQnO/HP0MuNOQ49VQ5z/Pcmlm5N5p76+vqDzqsXY3ZdA099Y1yAfVjpXm0M3+LEYOCX7MGJQMnQskCHAEGAIMAQYAgwBhgBDgCHAEGAIMAQYAhkXgRrVqmHQwH6oWKFChiwyOjgMxQ68Eexue2oVAfMO2p+1kghSeGAISh0RvqX91DIc5h2debGluUoeeSe4xwvGAbhvJI98YqMIG1SJ5p8hJrYTU3eHO/KTeX18lkB69t7J2NX59lssTt0MxeM3FvAiJOf3eGE509Tqy6MXjtPT/Un3mfTH/6/Va7F56zbpCbJB5JqGA9GleA2Nd1r7/DJE6cRpnCc7JChWtDAWL5graauvtl5AgzjpxOCMm3uw7tF5SWuzIIZAZkcg3qEzEiquyOzbSFF/2MUOiH17WPK+2rRpgyNHjqgV3+PvBJx8oFZItnLWud0Fuv7XtbpnRgxqFV6WnCHAEGAIMAQYAgwBhgBDgCHAEGAIMAQYAmmLQM0a1TFk4ACUK1c2bReWsJrdJk+Ygb+r64duNIL6VpSQXXqI/aZHMAF/p993nSgE9xPuwsxFchkL5HqlH459Zl+lF6wUWZjIoHYJzyWY676TAXI6OfL6BZx/hErvhTsexXRPqlroe1g8IQKD8OCFCZ59z40PsfykpuDGlBz+dH2IepVs1AlJ5nvz1m2MHDNOcnxWD7Q3scKzPss13uY/z69gw/vbGufJDgkMDQywcd1fgnO8VGERsfEGSupIlxIdcH4djryU3lmUHc6I7THrIhBfsB8Syv0vy20w+sU2RFztK3lfXbt2xe7du9WKf/gWqD+fdQ2qAk3n/mDofj6pFqbqOjNiUF3EmD9DgCHAEGAIMAQYAgwBhgBDgCHAEGAIMAQyIALOlSth4vixKO7IT/ZkpNKj97rDMUy4I+11z+LQM9RMFlKdfcfsuY9i4aa8IbRT7mW3YjAwMeL1i93tjqIR/HuMJblWWL5FnAbdbYlF6JHv2cYFFyRUJH8yL4twmHXi73gU29XpbREBk07iujpjSVPY2bvfccdLH97+ueBLZgXGa9LWx4N+j0IPMLm3rTpHn8z3x48faNi0heT4rB442bkNpji31nibzucWEQlRXY3zZIcE82ZNR5lSJSRtNYFo8+bZ9BhGOsJkv6oF6uyfA68AMhOVGUMgGyIQX3QkEkpNy5I7D95bEAlhwmoJqW0+X758+PBB/b8LXVYlgCiuM0sNgUeTofdOu/MsGTHIHj2GAEOAIcAQYAgwBBgCDAGGAEOAIcAQYAhkYgRy2tlhwrgxaNqkcabbReAdHzg9jRWs26OkLmxrlhT0k8vh23UvOPsIZ7tPONicdUrzOgbc9EYl73jBZDvNPuO9fqSgnxiH7qG5USCOX4IzWCcG3/sJy8zmJB2PpgIdjyGIQWB/1bnuPQvCFY84eH+2gxeZExiWIJ2YELP/RJ9qpu+xfqJmHQl1GzZBaGioOstmG1+fPqtga2Ku0X4H3toKz7DPGuXILsHjR49EzepVJG/X76Mfqp/9LjmeBubbMAyRsfLMQ9WoEBbMEEgHBOKKTwaKj0mHlbW/ZLTPRkRcHyR5oblz52LOHPXmzT56m4C68yUvmbUDvRZC79Uare6REYNahZclZwgwBBgCDAGGAEOAIcAQYAgwBBgCDAGGgHYQMCCScj27d8PA/v2IrJy0OWzaqUyNrHHxKLD1meAMvpcmEdDvJq4jTY3VVbrGRESh6O4XgnW9MA2HQVf+rru46BgU3uEjmOu68Q9cN/ohR/moHWWN2pHWvLnEdjxGk+5JRzW7J1/7hePszTB4fbCGV2hefIvj75iUZdOpJLHSicaVmZ81mjPo0qYd/PwYcfUrvPUcSuOQ6wSNju57VCiaXP2bnI8MrbIaVZLxgycQUrCGBqQg3eGDK3fQ9qWF5M2+DwlAxZ2EGGHGEMimCMSVngsUkU6eZWTYEmJCELzdSqMS582bh9mzZ6uVo+0fCXDzUiskezi/+At63ou1uldGDGoVXpacIcAQYAgwBBgCDAGGAEOAIcAQYAgwBBgC8iNQrWoVzJw2Ffny5ZU/eRpkpB1YlGx59eIlbK9/BKJjER4ThbBo8hMTSf4ZCX1dPZgYGMJY3xD6+vr4VN4KVjY5YGtnCysrKxQoUAA5ckiflSW0TastD2Edb8jrFoFYfO1fXigVcmz2hGUCvxTqJ70obDP3E8wlxiFPrCH6hAk/G/eKJcC+bhnelP63vFH5GX/HY3i8LtYYm8I9yBbPg/PiVYxmXy6K2SOfj7lOLEqbfEYJ+2+YqIGUKF2jVbsO+Pjxk6YlZbn4ZXV7oV/p+hrtq+/NzXgaLs9sTY0KycDBekRidfrk8ahQvpzGVXrtuYAW4cJ/F1QtdOnDE3Q6sULjOlgChkBmRSC+7BIkFOqZWcsXrDt4X2EkhJLhfxrYihUrMHbsWNEZTj4AevytWWe/6MUyk+PbbdB7rF3ZWkYMZqAHokzp0njqlTEochMTEwwdPJBDx98/ANt37tIqUqVLlYLXs2daXSOrJLe0tMSAfn247fh9/oI9e/dlyK3RLypq1qjO1eZ25SoePPTMkHVKLcrU1ARDBqXd74jUOlkcQ4AhwBBgCDAEGAIMAYZA1kLAztYWkyaMQ5PGjTL8xm7fvg2vh4/h++w53r19i6/+3xAcEowvQYGy1W5mZIJ8OXPDPmdO5HHIiwJFi6BCpYqoXLkyzMzMNFon9OB9lAninzNIF3jaOi/M7fi78yIOeqBkEH9XJ+3gW2n5HlE6wrKjghsj37GNDS4AY/DPbXthTDoeu/N3PCKVrs44kv8hIf8uR9jBLcoWd6KtiZho+s2I0yPYlTb6BkfbL3AuEYVmNe2hryeIkiiHOvUbISw8XJRvdnLy7r0COU0tNdqy8/nFrFuQB0FrcgHi99/mkwsQ/H9fxB5CzD/XUczARqx7Cr9/Hl/A9Bu7JcezQIZAZkcgvhwhBgtmYWJwdz4kRGh+Qektec9HL2+JteLjEvA1WKx39vDTebsDuo+naHWzjBjUKrzikjtVrIAd1XLJAAAgAElEQVQdWzfDwsICgYGB6NStB3x9X4gL1pKXDbmF+fD+XS67j48vGjdvqZWVChUsiIP794DOxAgNC8OAQUNw6/YdrayVVZLmz++AG1cuc9vxePAQbTt0ypBbGzNqBJlzorghMmfeAmzeui1D1im1KDtyS9nj7m2t/45IrY/FMQQYAgwBhgBDgCHAEGAIZD0EWru6YOL4sTA312ymlzaQeeDugeunLsD7sRd8376Cz5d32lhGrZyF7fOhcP6CKFaqBMo7O8G1bWu14gM8X6GSu/DMP/d80bBrVpE3d+CT13C6GyG4/kHTL/A1EPYTTEQc2ofZo3gsP7EZSToev4joeKTdkwFEntQtyg6XI21xjZCBQWk0J1DVXkvohaK0+Qc4OgYTItAadtbyy+lGRkaiZt0GYuDOVj5VchfDmbaadTIseXoGB/yy1gVi2R6ChAS0aNYUA/rKR0B8Jh3a1U5rdiljxKWN2ONzU7ZtskQMgcyGQFYmBhOifiB4p/SLA8pnefz4cbi6uoo+3jn7E7DqjGj3bOHIiMF0POaiRYqgTetWRL+7GnLmtEMOa2tOooQSd4Hfv+PbN3+uC+ro8RP49EkzSYkRw4ZiyqSfuuzzFizExs1b0nH3gI2NDSEGFQSdNonBju3bYfmypUl7Xf/vRvxvkXb1c7UFbHPypq2VS0sUJTdUE5+XmJgYTh7nw8eP3I+7xwMcPnJUoxIcHPLh5lU3LgcjBjWCUqNgRgxqBB8LZggwBBgCDAGGAEOAIcAQUAMB2iU4d/bMJEUONUK15vrxw0ecOHAYT2574PkLH/j4f9DaWnImLpGvMMqUK4v6zZugtRBRSL6cd9jsBT0yHZDP3hEiL6GXwPxDkis/yaUrkOuBQQjOmgbIsuWK0eZoTjr6hOyxSy5Y5rJN4RYUoYtDVwLx4KUZfH7kgV+ccPek0FqavG6nG436Rv7kJwBNjL8hl3403hoRErWH9mZP0s/wg4YO16TsLBk7v3pnjKjYTKO91brwO6IhQ3esRlVkkGDy9yFRSK+4Y1GMGzWc64KW0x5duo1WbzTr8Cy9bTy+hAfJWRbLxRDIVAhkZWIw+uVORFzpJct5PHr0COXKiZc/fkreQtaaw+RElcFnxKAsj6J6SSgROH3qFLW0u6/fuIn5C3+Dt/dz9Rb7z3vwoAHcbIhEm/+/hdiwaYukXHIFpVXHYNs2rfHnij+Syv7n3w1YuGiJXNvQeh4qJzmB6CZ369YF5iJlaii5vJvIf/67cRMhmr+rXSPrGFQbMq0EMGJQK7CypAwBhgBDgCHAEGAIMAQYAr8gkFG6BBPIF9enj5/CjbOX8PiRJ55+eIW4hMz9pb65oQnKFy+FStWqoH2PLihCLgj/akbbPJA7lr8TLZbg8HFAWcFn13jbA+SKNeL1C9KNxVoLeUhWyzg9DA/NL1iXe94o2DV3QlSsDk7e8IeHjyG8A/LgRbRNElkhmEQLDiY6cahpGIgGxgFoQMjA0oQ01fmFo40m2PuJwF5qeWv/WU8+u2+WGp5l4651mofStg6S9/ctKhTNr/4NXX7OXXL+zBJI/qwqrgqQB9vKyhJ9enRD3do1tVL+ly1XUDVeOtn4NvgbKu36+d2lVopkSRkCGRyBrEwMhl8fjBifDRqfQM2aNXHjxg2181SenoCXX9QOy7IBjBhMw6O1tbXBnJkzQImqX41qyX/58oXrFrQjkpe57O1BZ/D9arTLb+my5YiIUE/2I3euXDiwdzfR3s0PHyIh2qlrd3wnXYnpaWlFDFIy7cC+3aAzBv0+f0aXbj3xhugQi7GKFcrD2NgYYWHhePzkiZgQWX1cWjTHgnlzyTOR8mbll69f8ZnM/wsnz46eni6qVqmS6nM1dPhIXLl6Ta26GDGoFlxac2bEoNagZYkZAgwBhgBDgCHAEGAIMAQIArRLcM6sGahVs0a64nHyyDGc3H0Itx+5IzAiJF1r0fbihXLlQ6PmTTFizChORYfaj+MeqPBNWKLyYT0r5Ciaj7fEHyceoMJXfmKQJlhj/h7BenGybHdYiAOs4vVTzUVp3UfRljhFJELPR+WAV2QuRKXjnEBdwpKUMApEiRyfULF4JOoHfETFYGHsnzbJCfP80gkPPqC79eyD5z4+spxFVkliYWCM1wP+JoSWdFZvpsdhnA1kuCY+E40b1kcvcuHczEw7XbmcjOgZIiOqQUMOlRClUqLMGALZGYGsTAyGnmmCuE8XNTregmRk2KVLl1K9aCWUeMbeBKw+J+SVfV5nxGAanXWe3Lmxd/cO0Hl3ifaByKIcPHwEx4gmru+LlykqKU+kR1oTrdz27domI4buu7ujd98B3Ly8zGxpRQxqgtHlC2dBJV8pKejSup0mqdSOHTJoIGZMSz4A9PTZczhCZEIvuV1BVFRUspy0s7BBvXpo17YNmjZpnOy1CZOmYP/BQ6JrYMSgaKi06siIQa3Cy5IzBBgCDAGGAEOAIcAQyNYIFCtWFKv/XMnNYk8P+/DhA7b+tR5uly/D9+v79CghXdc00NVD9YrO6DagD2qWrYQKF4XncnnaRsC6Db+kZfjHAJQ6K3wd/oSJP54YhsqCgUu4LcrFWCTlehtrgstkTqAbmRN4hfx8TzCQZR2pSRz0wlDS4iPKFApGi1rmyGP7kxj57vMBFa8HC6b2tI+EtWslQT91HZ489ULvfgPUDcvy/g3zl8V+l3Ea7bPuud8RoZu5O441AuC/4Lx5cmPUsCFwLJayW1mO/Ik5PI65oZ2/vUYpR13ehF3P1e8C0mhRFswQyGAIZGViMOLGMEQ//0cy4lQ69MqVK9woNil27hHQeZUGtxekLJqBYxgxmAaHQ7v0Du7bw3UBUgsICMSKVX9i246dolannYMD+vXFqBHDkroIHz1+gm49eiEkVJ438qIKkdmJEYOqAR07eiTGjx2T5HDi5CmuU1Rsp2O5smWxYO5sVKrklJRj9LgJOHL0mKhTZMSgKJi07sSIQa1DzBZgCDAEGAIMAYYAQ4AhkC0RaFC/HhbOn8upo6S1uV26jO2EELz44FZaL51h18tvkwsjnFpiUKXmsDH5SbD9WvA3vSiE9/n5GU/VhvJtfAx9HT3e/XoZhOGY6TdZMClAugFNA0tyRKBblC3epPOcQGudaJQx80OJPGRWoLMBKjha8+7TYeMT6Ono8vr46Uciurf8xOCsufNw8tQZWc4hKyWZXqUdJlR2lbylz5HBcL22hqhnSu84lLx4Bgk0NDBAx/Zt0ca1BVGZ4v97IEfJ4euvoZR+SrUrdXI77ZqCd8H+6oQwX4ZAlkMgKxODsV9vIexELcln9vjxY5Ql33lLteCIBBQYKTU668UxYjANzvT4kUNJ8wQpsdOjd1+8f6++nn/lypWwfcumpDlze/btx+Sp09NgB9pZghGDqeNar24d7pwT7X+LFmP9v9KkFBbMm4M+vXpyqSIjI0nXY9tUu1N/rYQRg9p55tXNyohBdRFj/gwBhgBDgCHAEGAIMAQYAkIIDB7YH0MHDxJyk/31bf9uxrHd+3H/lZfsubNKQmN9Qwwm5ODUWp2Rx0IhM6psCUSj712/0tysMD7T3emO/FEpR5Mox0QiHist3/03fEw9BGPjdfExIjfehOYjPw74HGlPKks/AsaI7KW08RcUt/uCauXi0MA5p1pz5Qy3eyBPDD9JHkfW+NCvjCD26iDp9cwbPfv0Uyck2/gebT0JtfOWlLzfpY9PYf+Xx5LjM3tg2dKlMHzIANjn1I787a/4eD94gmYP+Ml1IUy9Az+i1r7ZQm7sdYZAlkcgKxOD9PAi3WchynOh2udIJUTfvHmjdtyvAeUnk/dSARqnyRIJGDGo5WMcMWwopkyawK3y48cPNGraAt/8pd9+oTPvjh0+mFR1z979cPX6dS3vQjvpGTGYElcLc3NcPH8GdCYktd//WI6/Vq/V6AB2btuCOrUVtzHc3T3QrlMXwXyMGBSEKE0cGDGYJjCzRRgCDAGGAEOAIcAQYAhkCwSMjIwwn6iKNGnUME33u2rh7zhy6BBeB/il6bqpLWZG5s+bk89ciT+WlpaIiYnh5rZHREQk+ye9WJmeNqKKK0cQOlgml3q9V1Ef9pWK85YWeOkJnN4If0m/yfwTvupFC26TjOXDtygbvA7Lz5GB78PzIlbFTEHBZHI4kHpKGwahsPVHVCgahpa1bWBhKl2uNPCMJ5w+Ccc/rGqKHGULybEDLgclBSk5yCwlAh8GroGJvvCsTFXYtbu8Gh/iMq/CltRnwtrKCv16d0etGtWlppAU9/mfi6hmkEdSbGLQrFt7scaTDf/SCEQWnCUQyOrEID2k2I8XEHF3POK/PxF9ZtWrV8etW5qrTbRfnoBLT0Uvm6UdGTGoxeNVJr7oMsNGjMLJ05pLREybMgnDhgzmKn9NmPJ6DZtocRfaS82IwZTYzpo+DYPIDV5qDz0foXW7DhofAJ0ZcuHsqST95c7deuD2nbu8eRkxqDHssiRgxKAsMLIkDAGGAEOAIcAQYAgwBLI9Ara2Nlj5xzKUIV0kaWFBQcFYvegPHDt5HJ9DhGfnyVFTgQIF4OjoiKJFi6Jw4cLIRS5b5s6dG3ny5OH+SX/Utffv3xO1n5Q/vr6+eELm0KeFDancAtMIQVjQWnF51MsiHGadnHmXjg4OQ7EDb0gPH38Xn5vxd9w2Cko1V2iMCV6Gktv5YfnwlnQFhsfxdyBqGwsHvUjUN/JHA+MA1DMKgC2RVRXTPSmmrvDAEJQ6Ijzn0ss6Ambt+Wc8ilmP+sxfuEj0qA+xObOKnx2R033eZ6VG26l2dhHi9YTJcY0WyWDBTRs1QM9unWFq+nOGZlqU6H7zLtp7m2u8VMlt4/AtXHjep8YLsQQMgQyOQHYgBhOPIPbTJUQ+XIC4z1cET4VKiFIpUU1t7NYEbLmqaZasEc+IQS2e4+BBAzBz2lRuhRs3b6Fbz96yrEZvet66dgWUNKDWsUt33L13T5bcaZkkMxCDN6+6wcEhHx6TD30urdtpHZ6H9+/AxkYhGUPXo+vKYQP798WgAf0xd/7/cPqs8A0sRgzKgbrmORgxqDmGLANDgCHAEGAIMAQYAgyB7I4AJcQ2/rOWEGTqE2PqYvft2zesmr8Epy+eQ4AWv+CtXLkyatasiXr16qF06dIoVSptCE9lPL5//46bN2/i2rVr3A32q1e1+y3TAKdmWNigN4wtzPCjX0XBo7Hb5Akz8HfBvSNk2y7zz1yuqDh90glIpUHz4XVofgRE5xBcQ5sOVjqxqEMIQEoE1jf0R1HD8BTLeVQ2hm2FIrKUkWvTIxhDnzfXD91oBPUVxl6ooN+WLMWBg4eF3LLt6865i+JsW+ljc75GBMHlxrpsg1++vHkwYsggFHcsmuZ7jo+Px9s1p1HftLBGa198/wSdT67QKAcLZghkFQSyEzGYeGaRDxciymMW7xEWKVIEL1++1PiYFxxKwB8nNU6TJRIwYlCLx3jhzCkUL+7IrdCn/0BcdhNmv8WWM27MaIwbM4pz37N3HyZPmyE2VLIfJawa1KuLAgXyczlCQkLgHxCIL1++4NbtO2rnpfkoEUbNx8cXjZu3VDuHNgMKFyqEK5fOc0s8evwErm20Sww2a9oE/65bw6135+49dOraXbbtUTKZWlRUlKiclAylpCg1jwcP0bZDJ1FxqpxMTExQhXx4p19G5CQa98HBwfhKvjR46uUlad5m4jqjRw7HxPHjuP87e+58bNm2PVkJefPmRflyZTkSPYe1NQLJh/dPn/y4PQUFpX4zVupGGzaonzRL9Pv3H/jk54ePHz9x+tdhRJpIimUG8lzKvlgMQ4AhwBBgCDAEGAIMAYZA2iBQIH9+bFi/Fna2ikul2rQ1f/yJzZs24VvYD9mXcXJyQosWLTgikBKCVAo0IxolCC9cuIAzZ85wpKHcZmpghMk1O8Jl8WjkzKPoIFRl0fvc4RiqussvNkEHd6MtsYiQh69JV+DH8FxkTmD6dVjpkdVLGX9DcZsvqFQ4DMPe/ICewNhCb4sImHSSp4Mvbrc7ikTwd0UmEE3Vd30JCa1BJ9rqteuwcfNWuR+NLJWvg2M1rG+kUMmSYgde38OSl5ekhGaqGENDQ3Ru3xatXJpDT08vXWq/d+kGOr7R/BLBoAvrceiF+t8rpsum2aIMAS0jkB2JQQpp/KnyCPmsukGGXgq7f/++xujPJ8TgckYMcjgyYlDjxyn1BKVKlsTZU8e5Fz98+IiadevLulIue3vcu32DyxkSGooy5Z0E82/esJ6TFIiJjQGdTSjW2rVtg+5du6Ba1SoqQ+jcxD1795M3uJsRGPhdVGpNicG+vXuhRfNm3FqU5Jk4ZRpHOKVmy5YuRn4HB+6lgUOGcaTmrzZ29EjUIHrFiVaCkLqJ3XuhYWGkXTnlHyevZ88wb4H6A1NTq3HN33/CtWUL7qWRY8bh2PETonDUhpNcxGD1alXRu2cPuLqoJn09Hz3Gvv0HsH3nLrW3okwMzpozD1u37+BylCY3hkcR0tClRXOVOS9euowNmzZz3bxSjZKBo0eOQCUn/luj9FLAqr9Xw8PjgVpLafo7otZizJkhwBBgCDAEGAIMAYYAQyBLIVCyRAms/XsVrMjMKW3a/bv3sXjqbNx/5SXbMnT2X5MmTdCyZUu4uLhwsqCZzehnTkoSnj17lvuhFwblsrwWtujctSvGTJ8IHZ3U2bPAO75wehqTbMnnMWa4HGkHtyhbXCczA0MT+Dvk5KpXVZ7C+kEoYfWJmxPYvKY1bCwNk1xtSMejhUDHYwhiENi/gixlfrvmBWdf4VQeJXVhW7OksKMKjydPvdC73wDJ8dkhcGJlV0yrIv1i9uwHR3E6IGvPbixLZKFHDhuUJpc+VD1zQeT7N6udT1DQyFqjxzIoKgylt09EZKzwvFONFmLBDIFMgkB2JQad9A7C7V/VjSnTp0/HwoWafwc/72ACVpzKJA+DlstkxKCWAKZkyP/mz+WyHzx8BOMmTJJ9JdrNRrvaqLVwbcN1X/HZk4fuoB+wqBUoouhkFLI9O7ejphpDi3/8+IFFS5dh9569QqkJ6ZaDdAwqZt2p2zGojC/9wNWley88eap6cqhy92ZF52qEvEw552LNX6t4CazUNkQ7Jbt07ym4VzEOD+7dAZ39Qa1y1RqgZGt6maZSorRDb8Wy31Gvbh3RW/hMOk8nEXL3ytVromNS6xhs3coVf68SL0HhduUqxk2chADS/SrWaAfkov8tQPt2bcSGcH4rVv1Ffv4UHaPJ74joRZgjQ4AhwBBgCDAEGAIMAYZAlkOgYoUKWP3nSpiYGGttb6HkgurCSbNw6NwJIkOZnICSumirVq0wcuRING3aVGqKDBt3+/ZtrFmzBtu3J1c50aTgYrnyY9zMKXAhn4FSWFw8TDe9xNUoO0IG2hIy0A4f4rT3PIjZR24yF5DOCczh8AlNapuhqIPqzs+I/e4oGSI81/BFt6IwMFEo5GhiMRFRKLZbWKLM1zQchl35ZzwK1VGvUdNULysLxWWX1/+s3w89StaWvN1e1zfAOzJAcnxGDrS2tkL/3j1Rs3rVdC/z6roD6GVYWuM6lrmfwKJ7TFpXYyBZgiyDQHYlBvvXB94e6YhDhw6lOMvqpJGHXrJK5DU0Oey5BxKw8rQmGbJOLCMGtXSWtEOtc8cOXPbps2Zjx87dsq+0avky0G4+alRKlEqK8pm6xODyZUvRsf3PW1r/btiE/eSX09v7ObcMJX9y2eeCk1MFsteOqFihfNLyBw4dxviJk3nrkdoN1bVzJyxd/BuXm3ZL9ujVBw89H/GuJYYY/LVj0IjIMlSqpOjE1HbHYG5yA/burevcWtroMFX34dOkYzAPmWFy4thh5LSzS1r2wUNPHD12HN7Pn4PKr+TNk4eTpHUsVgx1atdKdou5a49euHnrtqiSf+0YpORiohwr7R49fPQYuaV7CVHRUbAl0rWFCJFeo0Y11K2d/EOG74uX6EbWpfKmYmzrpg1oUL9ekisltk+ePs3JlPr5fUZ0TDQheW3RoV1bNG7UMFnK5StXYeWff4tZhutYzchyu6I2wZwYAgwBhgBDgCHAEGAIMATSFIGa5MuT5cuWgMrMacsunDmP/02fhTeBivl0mhjtaOzfvz/GjBmDggULapIqU8TSy7SbidLOP//8g+fk85GmpgMdtGrQFItXr0AsDHH25nc88DXB86A8eBFFJP4E5Dg1XZ8v3pzMCaxtFEjIQDor0B8lDMI49/vFgZy1+QkF/wcvUPmBcAeRmFxi9yimSzGcdCl+07BLcfLU6bhAFGyYpY7A9mYj0bKwsCqWKvyaXViBQAg/O5kN/yYN66NX9y6cElh6202362jvawEjPf45pkJ1xsTHoizpFvSPSKnqJRTLXmcIZFUEsisxOKkVMKOtDqZOnYolS5Zwx2tNxkLR94dz586V7bhn70/An2dkS5epEzFiUEvHd/70SZQoQd7tEmvfqSvuu7vLvtKQQQMxY9oULi+VYZwxaw7vGuoQg1QiccvGf7l8dE5a5+49BGfB1SfzBylhR0kuakt+/wNUP1+VSSE9KBFKCVFqdG5bj959RckziiEGf62TElx3biq617Q9Y7Bpk8bY8M9abi0qcdlvoHQ9fTkeNE2IwaOHDsCpokLO5QUZCjt95mzcvqPoDE3N6PPy58rloLKjiefaum17ULJOyJSJwfn/W4g+RF6WEnKLFi/llSalpCWd09mzR7ekJe4RneoOnX/+f1VrT5owHqNGDONe/k5mFg4dMYp3xibF8s8Vf8CZaGEnWpMWLuRLAB+h7TFiUBAh5sAQYAgwBBgCDAGGAEOAIaCMAH1P/Rd5b62teVN0Zvm8cdOw78xRxMbHaQS+o6MjRowYgYEDB8LMzEyjXJk1+NKlS/j7779x+LDm3TKm1o4wrL8PCTbySGtKwVSHzAl0MghCQ0IC1ieEYHWj79DXSUiR6pVJBPS6CcwGJBdKC2z2IrwmP7MpKpfIzYjtUvRulx8mOSxEZk3pRi9S/0Y+szJLHYEDruPRwKGMZHhqn12KKL2Uz53khOkc6JAvL0YPH4IihQulcyWK5YOCgmG0wwMlTOw1rmfT08uYdE0xEoYZQ4AhoEAguxKDlBSk5KC2bebeBPx9TturZI78jBjU0jm9e/VTnL5pS9ekLjs5l6PdSCv++J1LeefuPXTq2p03vTrE4M5tW7hOLmqtCElD58CJMfucOXHm5HE89/FB3wGDQD84qjJ1ZRJdyPy9tWQOH7Xw8Aj06tsflMwRY5oSg4+fPIFLa+ka90I1jhg2FFMmTeDcTpw8heGjxgiFaPV1qVKilDCjxBk1Ortv8NDhXFenGPt9ySJ06dSRc6WkoGubdoiIiOANVSYGX71+zfn26TcQb9+9E7MkN4Nw7eq/knwnTp6KfQcOqoylhKL7XcVMQn//ACIj20MUgUn99+7aQWZYVuNij584iRGjxwrWqO7viGBC5sAQYAgwBBgCDAGGAEOAIZBlEahM1E6ofKi2OgXvkjEKc8ZNwTO/NxphWKtWLUycOBFt2rRROR9PowUyYfCLFy+waNEi7NixA9HRGnQ66RnBuPJvMCwzNs2wzWH4A4XMP6CQ2UcUJD+do6xQmswy5LNI0tv4pf9PxSFVvqZbHyBnHL9MqNhcYh4Lf8+XqOyu+juMxBweBeJg27icmJSp+nz4+BGt2yk++zJLicCptlNRLbe48Tep4Vf97GLE6aVjq6xMh2pkZITOHdrCtUUzrV32ULfUuLg4PNlwCq0Niqobmqp/5V1T8SZYnHKTLAuyJAyBTIBAdiUGJ7gAs9pr/2/3DEIMrmbEIPebwIhBLfxBMDY2ho/XTyKtZt36nDyk3FavTh1s37qJS+vj+wKNm7XgXUIdYjCR2JTSKVe2TBmuUywyMpK3HnU6BpU76ihZ1Kf/QN4utF8X1pQYlIKDOuc9eeJ4jByu6ELbs28/qLRIepqUjkFl0oxKr9Zt0Igjz9SxXdu3onatmlzI/IW/YcPGzbzhysQgdWxEfgd8ye+COjagX1/MmTWDC3n37j1q108u/flrrkQSl3arXrt+Q/RS5cqWxUkisUqNkqVlygtLozBiUDS8zJEhwBBgCDAEGAIMAYZAtkagdKmSnAIJ/SyqDVs8Yx7W7eR/b863rq6uLtq1a8fJQzk7azafTRv7yyg5P3/+jOXLl2PdunUazaDTy10XpvX3QNc0t+xbM9WLQMH/iMDC5u9h+Z88aOJCZaPN4Rrxc6yEqgKetc0PUxv+rrsfx9xRwV94zqCYXKKAENml+NaIXGDtIdDxKLCgK7mATUdRMEuJwOWOs1HeTrqscNUzvyFBXy9TQ1uhfFmMGDIQNjmIHHAGsjOb92NIgvRuTuWtHHl5DwPOq1YZy0DbZqUwBNIUgexKDI4g46UXdtE+MThtTwLWnk/TI82wizFiUAtHY2trgwf37iRldq5WU/TsMnXKoTP9jh1WdDdRAqZS1eq84WKJQTo70OOuYsbbhYuX0H/QEHXKEu0rlvSgs9zoTDdqlGyknYhiZ9AlFqMpMajtjsEF8+agT6+eXLk7d+3BtJmzROOoDUcpHYPK5KaQjKyqmh2LFcXFcwqhZzq3r3HzlrzbUyYGDx4+gnETJkmC4+rlCyj03zyTth07C8rT0t89obmWqRWivI4YOVF1yHNJG2dBDAGGAEOAIcAQYAgwBBgCmR4BR8di2LBuDSwspEsbqgIhlFxoG9NzEC4+VKhmSLHOnTtz3XBFihSREp4tY+i89KVLl2LhwoWS969jnBOmjY9C357/ewKhBfQQh/xmfv+RgR+Qm8iE6vB8b2cZp4fhofmF0uJh3hjkaM4ve/r95UdUvBIkmOsByWUjkEswyX8OYroUYxCPT/3Lik2Zqt//Fi3BIfIZlllKBO50XYhi1tJJbeczC6GjrzxT5QsAACAASURBVJ8poaVEYP8+PVG9asa7QHHjnBs6vbOFga48pKvzrml4Hfw1U54TK5ohoE0EsisxOKAB8EdP7RODU3Yl4J+L2jzBzJObEYNaOKuCBQrgmtvPJ6xK9Vr48lX+/9jROW50nhu1+Ph4FCpWgnc3icRgbGwsihQvpdJXmbDz9n4OKoWqDVMmIFWRQLR7jM46pHI4VFKld78BapOCtHZKNlHSiVpF52oIDAwU3JLyjMGnXl5o4dpGMEaqA5WEpdKw1Hbt3oOpM9KXGFR+hj0ePETbDp0Et/bI4x43FDaB3LKsULkqfvz4IRiTmsOBvbtQtUoV7iUq30pJWVWmTAz+b9FirP93o6Q1laVcN2zaAjqvUBu25q9VcHVRkJ29+vTHlWuKGZaqjBGD2jgFlpMhwBBgCDAEGAIMAYZA1kEgH5k9tW3TRuTIYS37ph57PsaEQSPg81WcTP+vBVSoUAGrV68GlQ5lJg2Bt2/fcrKrBw4oPverbbqGMK23HQaFhT/P/cydwJF/BUk3YCHzj3Aw+UyIAPXmSQ4LcYBVPD8x42cQiehelQS35LDxCfR0dHn9xOYSXIw4fD/ugYrfhDtvnzXLBdN8tmJSpupz4dLldFcKkly8lgM9eyyFg4V0bCuf/Q26evKQV1rearL0Ls2bomunDjAxEX7+0rIuutbzp96ocTMCFkSuWA5b5n4Ci+5pPltVjlpYDoZARkMguxKD3cnbxTX9tU8MTtqZgH8vZbRTT596GDGoBdyVu55oem11DFYoXw7HjxxK2kGJMuV5Z7KJ7RikCZVnJDqWKss7K1AqhEIdg1WIxMzBfbuT0tNOwUuX3SQtl9E7BpUJIykdgyYmJhg6eKBobA4fOYY35EOmKlO3Y1D5rO7eu4eOXfjnXfIVSiVVafchtXkLFmLj5i0q3ZWJwdlz52PLtu2iMVB2LF2qFJmNeYz7Vx4eD0C7BrVhyp2hY8ZPxOEjR3mXEfod0UaNLCdDgCHAEGAIMAQYAgwBhkDmQIAq1WzfvBG5c0vvrFG10z3bdmIRkfYPigpTGwxaD+1069evn1bm3J0+e55cSLRCjWpV1a5NmwGLli7HtMmKzzFy240bNzBixAh4enpKSm1UYSaZPThfZWwhvXAUJfMBzS1fc2SgiZ7wnD2+QpqF28Iphr+DNS4hHh8GCHfd6W13h0MMv5yo2FxiwAt68xnlLwlfJPa0j4S1qzCxqWrNkJAQ1GtEdNOYpUDgcc/fkdfcRjIylYmUqG4mkhItkN8BI4cOQpHChSTvWZuBL3xeoLRbAPLoy9OV/ik0EOV2SFN70uY+WW6GQEZBILsSg9WKAWenaZ8YnEiIwQ2MGOQed0YMauG3Pm/evLh9/UpS5rQiBgsU4R/OrA4xSGcX0hmG1E6cOo3hI0fLjhQf6UGlGnft2AZzM8XQ8gGDh+L8Bel9vhmdGFy6aCG6dlGQUVKIQWUsxRxUr76kW+2q6m41dYnBgf37YvZMxZw+TTvuataojj07FQTf8RMnMWL0WJVbUiYG58xbgM1bt4nZfqo+z58+IjfzTDi52uKlpQ+S5ytg5rSpGDxoAOdCZU+p/CmfKcsSi5FWlbx5FsgQYAgwBBgCDAGGAEOAIZCpEDAwMOBmCpYrK8+sJ+XNzxk3FVsP75GEx7Bhw7BkyRKtyJomFnTr9l0cPX4SixfOk1SjNoLuuXtgz94D+GPpb9pIn5Rzw4YNGD16NO+FYFUF6BfqCNO624jEojGsdGJRxygADYwD0Nj4GwrqR8BHPxyHzORROnIkRF6H8FyCWDysZYkcJRx4/QLOeqLSRwPBXA/qWsKmGH8uwST/OeQlXYoGAl2KXwl5GtFHeG4835r0c/lTr2diy8o2fhp3DGYSYpDOhO3asT1aNm8COoc1I9pL3xcoduELChjJN+uwy6mVuPDucUbcLquJIZAhEMiuxKCJIeC3VvvE4PjtCdjkliGOOt2LYMSgFo6AznZ46umRlFlbUqLKMwbpbbMyFfhvq6lDDNarWwfbt2xK2sO9+/cxe+4C8qbVSzbEVBGDJUuWwP7dO2FlZcWtNXbCRKK9z99ZJVRURicGZ8+YjoED+nHbkCIlmt7E4OKFC9C9W1eufqnzBRPPsHChQrhySTEFVogMk6tjkK516fwZFCuqkJut26Axb0el0POm6nVlYlDMc806BqUizeIYAgwBhgBDgCHAEGAIZG0EZkydgg7tFaMI5DI6EmBUjwE4cVP9a9T29vbYunUrmjdvLlc5KvPQERMt23TEhdMKxY+MYGvXb4Q1+fzarUtHrZfz4sULdOjQAY8ePVJ7rXw5S2NT1yVoZK1H5DmTh0eSuXkrLYlsrAzfyRkl6GJscH6Sij/Z0xyRMG/H/z1GeGAISh15L7jXxzaRsGwrvYMv2QI73FEwmr9LMQEJeNevNMFLOmB/rV6r0eVWQVAyqcOjnkuRz1wDKdFMQAxWKF8WI4YMBJ0pmFHtle8rlHULRE49U9lKPPnaA73PrpYtH0vEEMiKCGRXYpCe5f2FOigmvxBGssdk7LYEbPnZz5UVHyHRe2LEoGio1HNUluKsT+QhXr1+rV4CEd61atbAbtJVR+3Tp0+oXrseb5Q6xCBNtHzZUnRs3y5Zznfv3uMJIQcfPvTEXUIWUtlFqZba/DQqw7p/zy7Q1xJtz779Gmvva0oMPnr8BK5tkmMhdd+pxY0dPRLjx47hXjp67DhGjVVfgqa6gJTOrBnTyI1ihVSLUMegg0M+3LzqxvmKmTG4cf06NGnciPOfMn0mdu/ZKxkeC3NzPH2keK6+f//OzStUZXISg8qzDamUqJRnm85YzGlnyz2/Oql8QOzds0fSjEFGDEp+RFggQ4AhwBBgCDAEGAIMgWyNQJtWrpgzS6HWIZeFhoZiaKfeuP7s5wVXsblbtWqFzZs3w9ZW+hf5YtdK9Bs1bhIG9usNOl4jI9iAISMwddL4pLn22q4pKioK48ePx5o1a9ReqrB1brj1WYwCVvYpYreafYKffrTaOVML6B2aB3nj+OeRBehGIbSvcNed/Sai7gL+mYUBpIMvVMMOvsR9BFx4hErv+Nejvh7OxrAtX0QyXvfuu2PI8JGS47NqYFYmBm3JdwUD+vZEVefKGfr4Xj1/gUo3QmAFeWYK0s3+INLUNffOwpfwoAy9d1YcQyC9EcjOxOCmITpor2Wl+DFbE7D1anqfcsZYnxGDWjqHRx73yNwDxQD41u064KGn+rf5hEpzadkCa//+k3N78vQpWrbivzGqLjFI806ZNAEjhg1VWUpERATu3L2Hy25XcPb8BY6gFGu/EoODhg7HATJTMKedHSeNQmUdE20YkTI9SSRNpVpGJwb79u6F+XNnc9ujWPbpL35eoFhMdm3fitq1anLuchODytKzEyZNwf6DP2dfiq1P2U+ZWOeTyJWTGNy7awdqVK/GldF/0BBcuCh8U7o1+VKmfds2KFKkMAoVLKjWVhkxqBZczJkhwBBgCDAEGAIMAYYAQ4AgUKpkSWzd9C/09YVJC7GA+fn5YViXPnj4zkdsCOdHP69RYqpv375qxcnhvIXMQIwnM+r69+klRzqNcvz4EYQ+A4fg6AFp8quaLH7kyBEO/6Ag9b5oz2thC7fei+Fomy/Z8teNf+C60Q9NSkqKrR1ljdqRiu9EVJnYrruY3fdRLIK/a0lsLjGbiw4OQ7EDb4Q7Hq0iYN5BM4KnVr0G5PuPSDFlZXmfwpb22NliNErkyKPRXjPijEEqFdqiaWN0J13FRkbykW0aAaUi+MHlW2j5xhyGCXqypu94Yjkuf3gqa06WjCGQFRHIzsTg7drPUJJ246trx54A7sLqAjTtu1gTtPnmjNdxivFl2dkYMail0z+8fy8qV1bIWAiRMFJL6NWjOxYuUMxVoLPK6MwyPpNCDNJ8ecjw+Pbt2qJ5s6aCNzKp5Ojvf6zA7Tt3BbelTAz6+wcgJiYaefIo3gB279UHFStUwOSJis65EHKDtWkLF3z8KJ54VC4goxODdWrXws5tW7iSxXToCYKbioNWiUEiO0vlZ6nJTQyWc3JW+UFXW8QgnWtI5xuqsg7k92Hi+HHIly+vlKPgYhgxKBk6FsgQYAgwBBgCDAGGAEMgWyJA50/T9/T0IqVc9umTH/q07Qzfr+K+TElc18nJCXv37oWjI/+ce7nq/DXPk6deWPPPBqz5c7m2lhCd98LFy7hFPv/Omj5FdIycju/fv0e7du3g7u6uVlobEwtc6rUIFXL/7Hh7pxeJXeaf1cqjyjl/rDF6hAnrgbmX14edc3HeNb9ee4oqvsKSnR4kl61ALrGby7nJE6bgn234QzcaQX0rik2Zqt/IMeNw89ZtjXJkheA6+UpiW7MRsDTUXLYyoxGDBfI7YMyIoShYIH+GPioq0+yz7zJcouWv86+HZzD39v4MvX9WHEMgoyCQnYnBRzbnUGA6+X45h5r/LfhILkitvyn6CIPj9dDNvzJuRP9ULBQdnIUcGTGopcOkhB0l7qj9tngp1q3/V/aVflswHz17dOPyzl/4GzZs3My7hlRiUDmpmakpihYtgrJEkrJyJSdUKFcOxYun/DAopuNKmRhUXqN3vwFwu6Lo6VXu4nInA93bdeoiCceMTgzS7lLaZUotLDwcpcpWkLRPviBtEoNrSOeqK+lgpSY3MZgeHYPKz+CvmNJZkHQmZKJ5ez/H1h07cJd0zvq+eMl7bmzGoOyPNUvIEGAIMAQYAgwBhgBDINsgsHH9WjhV1IyEUAbrx48f6OnSHk8+vlILw9GjR2PZsmUwMOAnTdRKKsG5mWs7HNm/K5nSjIQ0GocsWrqcXGothxbNmmicS2oCKi06aNAgbN++Xa0UlkamHDlYOa/iM30smZu3wvIt4oQ5OMF19BKAccEFiQAofzJfswgYduHvuouJiELR3S8EO/h8zUmuzpp18CVuLHrPfTiGC3cpvu5ZHHqG0n8Xtu/YhRV//iWIZ1Z2GFi2IZbU7iHbFp3JjEEdfXm73aQUZ2JijK6dOnCdgrRjMCNbwDd/6B97hvI68ktCu395haaHF2bk7bPaGAIZCoHsTAy+sj0Nm7pEkc21jPpn8icZHBgQLjoujrxPGfW9LHaFO4iOyWqOjBjU0okqd/OdOnMWQ7WgG3/21HFOSoZat569cePmLd7dyEEMprYAJQvr1KmNRf9bQOZKKJj28PAItGrbjpcosbHJgYf3k3cW/koo5s6VC+fPnIQVGeRObdVfq/HHipVqn5qmxODjJ0/g0lp7Mwbphq67XUKB/26QNSHdkc+fqyflIwSKMskq1MWan9xou3HlMpdSTAfj0kUL0bVLZ85/1px52Lp9h1A5Kl+3sLDAU0/FbBP6ZUX5SlVU+srZMXho/x44V1Z8iGzTviMekDmav5pTxQo4euhA0r+mXbq0W1esMWJQLFLMjyHAEGAIMAQYAgwBhgBDQBmB/n37YORw1SMe1EUrJiYGXZu2hvvrZ2qFHjp0iOtOywg2Y/Z8omrTBHVq1UjXcjp07Yl1f6+UtZNT6obWrVuHYcOGqRVuZmCMk93moV4hxbzGvaZf8NogQq0cqpw7h9mjSCw/uRaKGAT0F74Ym2OzJywT+Ak4sbnEbM7/pjcqe8cLurqX1IVdTcX3MlLMx9cXXXv0lhKaJWKW1e2FfqXry7qXjNAxWK1KZQzq14eMGFJ8l5VRLTIyCr6X76PxFysYx8tPXoZER6DO/jl4HxKQUSFgdTEEMhwC2ZkY9LM/SS58Ebn8SY2IZr2al25uvwFOq/e+lh7++pCCmBJUilyNyn7GiEEtnXnJkiVw7tQJLjuVyaxUtbqsK+XIkQOe7gpSLTQsDJWqVEdkJL8uvbaIwcSN2dnZYte2raB7p3bx0mX0GzhY5b5/JQbpjMGz586n8G/WtAn+XfdzqHrnbj1ESZUqJ8oMxOBfK5ejTetWXNkzZ8/Fth07ZX1mLp0/g2JFi3I55SYG6RxKOo+S2so//8bylask1+7gkA83r7px8V7PnqG5S2uVueQkBpXxqVazDvw+p5TQ2bNzO2rWUPwuSzkjRgxKfixYIEOAIcAQYAgwBBgCDIFsi4CjYzHyOWsL9PTk6YChpGCfVp1w0/uhaEzpRc3Tp0+jRo30JeGUCz589DjevnuPsaOGi96H3I5v3r7D7HkLsW3TP3KnlpyPSoq2bdsWHz58EJ2DkoO3+i9HuVyFcM8wGBdNAkXH8jk6R1mgcSR/BxKdDfiiYyEYWvLP+gnbfx+lQ4Q7+Hy7FIGRmYnG9cdFx6DwDh+VXYq3onLALcoWRqZB6DA4p0br0YvBAQHyYK5RIWkYbG1kxkmH1sqr+P5ITktPYjAHUYMaMWQg10WckS0+Ph733W6i6kt9FNSz1FqprY8txY1Pz7WWnyVmCGRFBLIzMfgt1wmiSEGIwQZEyaB+MfWONzIGWHoRoK2Aapobea/SK8AJIQnyzfBWs4R0cWfEoBZhVyajaMcg7RyUy0YOH5Y0f2//wUOcfKOQaZsYpOvTjrfzpym7r3gjXrx0OZWEpTIx6OPji8bNW6rcgrI0KyVsmrZwVWvAemYgBhs1bIDNG9ZzGNBuQfrhQE7z8XoMY2NjLqXcxCCdP7l+7Wou9+49ezFl+kzJpVevVhX7ditI0Z279mDazFkqc8lJDHo/eQRTUxOVRL5yJyPtiC1Ztrzae2TEoNqQsQCGAEOAIcAQYAgwBBgC2RoBIyMj7N6xFYUKElklmWxQ+x4473FDdDY6B/7cuXPcOImMZJQUnDlnPrZvln9sh9h9Hjx8FB8/fsLokep16YnNL9UvICAArq6uuH1b/Ow6ezNr3B2wAsY21ths4Sd16WRx9nGG6B8qPJfdo2AcbBvxEynfPHzg/DBWsC73InGwqy8PKWOx5QFs4o24NZ9FmxMi0A5ukTbcTKLQ/748rGEYgHVTwwTr4nOYPmsOzpw9p1GOzBRcIkce7GkxFgUs5ZuXqrz/9CAGqVSoS/OmRDq0Pejf7YxsD2/dR+5HQahmkEerZQ6+uB4Hfe9odQ2WnCGQFRHIrsSgDrko5E86BvUMCTln/F/XoL6ancwHiPrb40+SHotXROGg7bcqeBen+eUiSQWkQxAjBrUI+qCB/ckA8mncCvfu30eHzop5gHLYvds3kMvenkvVo3dfXLsu/MEuLYhBWo+yZGWjps1VyomqQwxSQuv0iaMoWkQxGJ12FtIOQ7GWGYhBuhePu7dBOy+piZGHFbv/xo0aYtO/P2+xyk0MKs+LfP3mDeo1lD5fY8yoEZgwbiy3NaF5hcrE4Pz/LcSGTVvEQpLMr5JTRRw5qBiErarTtXSpUjhz8hjnQ2cJ0mdbXaN/D+jfBWpjJ0zEIfJFAp+p8zuibi3MnyHAEGAIMAQYAgwBhgBDIOMjMHH8WHTvKm3Oemq7mzJ4NPaeU7ynFWOOjo64ePEi8ufPL8Y9zX06deuNv1f9QT4ba9axJbXwqTPnoLVLS6IqUk1qCq3FhYaGonHjxrhzR/wX88Vs8uLOgOXYbR+EEN04zWsjl/aHhzgQCVD+G/hvjYh0aQ+B2YAJCci/2Qu6AjML35BcOkK5ROzs7oP38LtphKcReXCFEIKf41InewwRjxODvJErj7mIrKm7HD95CnPmLZAcn5kCGxcoh41NhsKcdKlqy9KaGCxcqCBGDx+C/ET9KKMa7RT3uHYHjj4xcDLMpfUyF9w5hJUPTmp9HbYAQyArIpBdiUE7RMDb7jz0jQ0Vx9qKzBl0LqDeEb8mssVbko8tUyfB93gDdPevhFvROdQJy7S+jBjU4tFRsuTqpfOwtFS05Y8aOx5Hjx3XeMWJ48eRG4kKUuyZtzeatVTITwpZWhGDyt19qma10VqVySShjkHqX7y4IyjBl2jTZ83Gjp27hbbNva4pMfjo8RO4ttH+LI1pUyZh2BCF/KqP7ws0btZC1P6EnHZt34ratWomuQkRg8pynmJmDNLElFijBBs1PkJYqFbacVqiRHHOraJzNQQGqpZUUSYG//l3AxYuWiKUPtXXp0+djKGDB3GvqZqRWLFCeRw7fFBxNgIdrqqKWLV8Gdq1bcO9zIhBSUfFghgCDAGGAEOAIcAQYAhkGwScK1dKUuWQY9OrfvsdK9YrVD7EWKFChXDr1i3kzp1bjHu6+Cz+fTnKlyuLlqRTJz2scYvWOHX0AAwN//sSKz2K4FlTCjlY3aEkJg+ZjOem/KNKxG7VJdwW5WIseN2jEuLweYBwl5/JVg/Yx/ETSmJz/VrQizdfce1eNJ5/soV3WD68jhVP9M0rcR9tuygubkuxr9++8Y7QkJIzI8aMrtgCc6p31Gpp6x9fwJoPt4kUnZqzqSRUZUqUsrp16YjmTRpBR0dHQgbthwT9CILfAx8Ufh6J4ob8sr5yVbP92VWMvbJVrnQsD0Mg2yGQXYnBEviOa1aXYWjxn2y4NencG1sP5A+ses/A8stAkPT3MLEJOhgWWA77I4QVD9QrLON5M2JQy2fSq0d3UKKM2o8fPwhh0gLf/P0lr+pUsQKOHjqQFN+73wC4XbkqKp86xGArVxd8//4d12/cFJVb2Wn1nytB46nVrFufzDb4mGoOKd1QfXv3wvy5s7l8dKaiCyHrfAmBJmSaEoOPnzyBS2vtE4OURKaz7uxzKm68rv1nPRYt+V1oe7yvd2jXFiv+SJ5DiBjMn98BN66QP6TExBKDXTt3wtLFv3ExtBOOEl/qWrWqVbB/zy4uTIwkqTIxKJWso92od29eI0PBrbl1yzk5pypTq4wJJSspaamu0dmJlHSlxohBddFj/gwBhgBDgCHAEGAIMASyDwLmZmbYv3dXkkqMpjs/tPcAJpHLcHEJ8aJSOTg4cKQg/WdGtguX3HDz1m3MnjE1zcv0fPQYGzZvw18rNPu8pu3CpZCDjUpVRcP+vUHlETW1skSC0zVCWDLSq1kumOXjJy6+H3VHxQBhiS8xuQJ/hOLCjUA8e2MJ32AHPCXdAfHqfvn4Hzg9LV9g0ljNyGGqMEXVd7KqrW44AF2L/7ysrI19DrmwHvt9b6NgoSLQ1zIxWIOMQBnQpyf5HsFKG1uRnJPODnz5/AWCfT+iUIAeKiUI/+5JXiyVQCodSiVEmTEEGALSEciuxGDNBD8cNLsGExuluaddKwGl1OxyvvaSdAj5SD+A/yL/CimMWUHyz8HVuDAZEzBiUEYwVaU6fuQQKpRX3H578/YtJ/35/r34QeCJeZ0rV8a2LRtBPyRSu33nLjp36yF6B2KJwSmTJmDEsKFcXvrmlMqgijVKbN27dZ2bMejvH4BKVaurDJVCDNJkWzdtQIP65MYAMUoGUXIwKiqKt8TMQgzSTTRsUB9bNv6ck7Hqr9X4Y8VKsUeQzI/iRPEKCw8nEkCX0LqVK/e6NohBmldZ4rZP/4G47HZFdN0W5uY4efwINz+Fkr505uQ7MjeEz5SJQeo3bORonDx1WvSa1PH3JYvQpZPi1iLt6KWdvarsqacH6KxBanQGJJ0FKdZ6dO+KRf/7KQ8zZvxEHD4iJCVqg4f3FdI/UolPsfUxP4YAQ4AhwBBgCDAEGAIMgYyDwOSJ40Ev3slht67fxJABAxEcFS4qHe0QpKQg7RjM6EY/N9A5fz26ySe3KnbP7h4PoaunCyeiLJLRjZKDTZs25c5VrNVr2JR0sbUV667SzzJOD8NDhaVoH9lHwcrViXe9AJ/3qHQ9RLAmz1xRsHZJnisqKhpud7/C87kxXgTmwaMoe0Qk6AnmEuNQTD8MB6cT+TINbOmy5dizTzHeIitZTlNL7Gg6Es65i2ptW3Hxcai3fx6efVdcSi9QsLDWiEFbogw2ctgglCtTWmv7USexn99nvHv9FuF+AbAJBpxiciC/fvqQlRufXsLkazvVKZ/5MgQYAqkgkF2JwVYJr/G3zjVY5sv5sws7P5H0HKiaW0jtAUoIi0bcUiJJSsTHNbVz5GJTv8CKCBOQRNd0nfSKZ8RgGiBPh7WfJoQHlc6kRgmzP1auxM5de0StbmZqys0lGz50CGh3E7UXL1+iY5fuvDKLvyYXSwwqE2/h4RGYMHmKaLKFEh+UAKG2YeNmzF+o6CBLzaQSgxTHc6dPJHXVbd2+g5N/5LPMRAzSfdAvAkYO/znA/gQhu2bNmYuAANWymr/uX1lydvnKVbC1tUWfXj05N20Rg82aNsG/69Zwa9Bnp/+gwdwNXiGj8zI3bfgH5cqW5VwX/LYI/27YJBTGSerSfVK7QIjP6tWroUu3nqAdnmJMWfaWyrc0beHK+zu18o9laN9OIQV6390d7TspnnUhc2nRHGtX/8V1UibGs45BIdTY6wwBhgBDgCHAEGAIMASyJwKOjsWwm4wCkKNb6xt5j9uucUt8CPomCkz6WYvOpCtWrJgof+aUeRCg5GCdOnXw8OFD0UX3HzwKjiVKifZX5TgoJB9sydwePvusH4mo3qQzQMAcNj6FnkBnnx/JFU1yuT/2w80H5PuTb/Z4QiTB/ONTnxMotKaY1/e090SpstJnEl25eg3jJk4Ws1Sm8SlrWwC7W45GXjPpuAht1ve7H5oc/h9Con/KxmmDGEwgMy4r5i0E13oNYKDHPzNTVc2bLh5HSHio0JZSvE67AEHmdfar1QK6cQmIj46FDYyRL8oIjqY5YaKvWbeq2gWlErDk/lEsvS9+fq0ca7IcDIGsikB2JQb7JDzDvIQ7MLOzhoGpkmz44BpAPoXKm1h7uf0Sir7gbyISm8srxhwd/Z3xSUDKXGy+jOTHiME0Og069+DA3t1JxB5dlnYN7j94EMeOn8Sr169TVEJlQ1u5uHBEQiKpSJ3evnuHzl17wO/zZ7WqF0sMUiLy0IG9KFWyZFJ+Sr5tIz++L0g7biqWh9wqnUTIrI7tFXKbX75+5WRTg4PJlSUVpu6MQeU0tWrWwO4d25L+1YDBQ3H+wkWVGb7ifAAAIABJREFUa0khBmlnGO0Qo/by1StMmzErRX4fX19CJH1X6xzEOo8bMwrjxoxOcg8KCsL6DRtJB6c7Hno+4rrqlM3U1AQFCxRE7do1MaBvH+TNq9BCTpQCXTBvjmhiMBlpS6RaZ86ek7RUOOk+pDMXVdnMaVMxeNAA7mVa4z//bsTGzVs4Kd1fjcp3du/ahcxVHAQrK8WtNnqO9DzFmDIxSMnh0qVKohvJt2v3HmzbsQtez56lmsalZQsO2+LkS5dE69S1O+7cvce7rPLsRep46/Yd/P7HCo4kTM3q1a1Dbi93RfNmTbFn7z7QD3uUIKT21+q1uHb9Ove/o+kgcA/yifUXo523z58+4v4t/XtBSfpEi46O5s6WGUOAIcAQYAgwBBgCDAGGQNZCYCtRDylHPj/KYX1cO+LKE3EKMGZEmebatWtwcuLv2pKjLpYjfRD48uULKhMloo8fUx/38WtVZmbmGDNxBiwsNetAahRhgyrRStJgqWw/jjAfH/qRLiwB0k9nuzsKxKQuJxoUr4+rUbZwi7TBJfJPdeYEanoiUwt4oFtf6bKNVOWnTv1GmpaRYeJbFa6MtY0GENJKe2Tsidce6HduDeIJaadschODRvE6WObUEdVzFpGMb6ODC/Dw2xvJ8TQwp4klPHsuhZGe9ucnqlPohGvbsOWpeLUodXIzX4ZAdkQguxKD4/AQYxI8oWegD4vcStLiVEqUSoqqYW+8XqDQXl81IvhdA+MM0IGQgw9iNHs/JFtBMiVixKBMQIpJU6JEcaxYthRly5RJ4R4aFgb6Jp3O9bOzs0PuXLmSkYiJAdt37sKixUtB/dU1scQgzUtn3NG5dHVq10q2zDNvb0KSvcZXQvxRoooOdKb7qV+vbpIfJYOoBKlQ15bUjsHEhaaTGRlDBw/i/i8lnWi312eCYWomhRikeei8v2JFVUteDBsxCidPn1H3KET70/mAs2dOR44cKW/Y0e5BegM4guBdIH9+0hGo6EhVNtqtN5CQbPR5UYcYpDluXbuCfPlSDlqlz0Czlq149zBpwniMGvGz45E+E5fJLEwqvUlv2uXNmweOBNdKlZJ/6UDrpRKkQtKwiYsrE4Oz587HwUOHsXXzBlDZXWpPnj7lZnDGEOKNdkwWKlQQVZ2dU/xuDR0+EqfOnBV1LsqzFBMDIiIiOLL2qdczhIQEc/MKWzRvxv0eU6O/Cz1694Mh+X25fyfl3E763FatUVvw2VV2oLM76QxPZgwBhgBDgCHAEGAIMAQYAlkHgVYuLTFvTsoLiVJ2uGrh71jx72pRofr6+jh79iwaNmwoyp85ZV4EvMnnuWrVqvFe4lXeXaHCRTF4xPifsl4Stu5IiLwO4cIzgjycjWFbnp988T/zAJU/Kcim6AQd3InKATeODLTFg2gryXMCJWwrWUhL049YNDFOozT9Bw0lnys9NcqREYInO7fBFOfWWi1l5o09WPvoXKpEslzEIO3Ua21fGrMrtNbo+W9zbCmuf3ouCx4ZiRwMiYkEnet49m3mf2ZlORyWhCEgEwLZlRhciFvomaAY2WRmZwV9E6WuwTFkpJiNqSiE6UWbsxfcUOluCArp8V9KEpXwPyf6nmNQYAUcjcitTliG9mXEYDocD5UF7d2zB+nuKiB69eMnTmLz1u0qO5PEJFKHGEzMR0mQiRPGJcl2Cq1DyZUlS5eJGpqtScdgYh0njx1Okp+k3VtduiukMn81qcRgdTJQetOG9UlzHX/Nq21ikK5HSaYpEyckSbQKnQF9nZJGe/btw59/K2Q9qalLDFJSeD2RBaUdpMpGu/Cauwi/ya9apQqRRB0H+k8ho92vq9esI11+6unR/0oMbtm2nVtq2dLF6Nyxg9CynMzpb4uX8HZAppakaZPGmD1jOgoUEJ6VceLkKdLpNxWUPKQ2sH9fQvbOSJaWjxik+G0mMquJsw0TAxkxKHi8zIEhwBBgCDAEGAIMAYZApkKAzpI/dvgA9/5fU3tA1Ch6dumGMPLFrZDpkA6tvXv3olMneWYaCq2XUV9/8+YNRo8eDXMy+7x9+/bo2FExhzwr2tWrV9G4cWPuAqUYa9zMlSgCtRTjmqqPUYIuRgXnJ/N+dHhzPLWOgHl7xSXP1CyeNIZdu+mHoPvWXFfgzWgb2eYEStmcg14k6hv5o4FxAOqQf0YMJIo0Ah2PfOtQtZ1//t0gpZQME7OpyVC0KSr8HYDUgulF45ZHFuPOZ1+VZJ0cxGAuPVOsq9oTDhrKoI6+uA47ffmVidTFIpepNR6RzkF9XXlmZKq7PvW///kl+l/4Bx9DNZutKWVtFsMQyOoIZFdicB3c0ALvuOPV0dWFea4cZJbzf3/nqhAOxTVlo5Xys+BPGmguXL6Km7fvITYuDkbQwwCTUihroNR9KMPDsyy4KP4X7ChDpvRPwYjBdDyDalWroEmjRqCdhI7FiiZJP9KS6BzCW7dvc91OZ89fEH2bj287dEahkZEh4sgvhzJhJAYC2hFIu9fovAvavUalQxPNh0hN3rh5EztIN6MqqdHU1qAyiUMHD+Reovul3ZDqGiVXE2e20dijx06kKsvaq0d30omp+EOwbv2GJJJGzHpUkpNKQubOZZ/CnZI+6uxZzHqqfOgXBS3JrDr645AvH6xzWCcjbCm5RDvyKLmWmqxqg/r1ULFCeS794SPH8ObtW8FyqHRmXTKHIpd9ziTfb/7+5Kx3C8YmOtBnu3WrVqhN5F/z5MnNPee02/Hrt6+kk+4pKIb0OZdi9HeoZg3FENrLble4rr1Eo52elISrSCR5c5IO2JykE5fOEfz48RNuk7kpx0+c4joKNTFKPlYlNdDnkJKE9PeC4uNPfjxJLRuIhKqPT8rW9TKlS4OSzpaWFtzyIWTeB53JqcronFL6DObJ/fOWbXBwCCfRyowhwBBgCDAEGAIMAYYAQyBrIDBx/FhOZl9To5/32tZtiscfUx8D8Wv+pUuXYtKkSaKXDQgMhC2ZRZjVrCQZpfH8+c+uHiqrWrt26qoeWWHvlAzu2lXczHRKHg8ePg6FikifPdk9NDcKCMzn+a4bheC+yVVlHr8MxuX7MfD6ZAOv0DwISki/eWpWOrGEAAzgiEBKCBY1CE/2KHhUJh2PFaTLTT569Bh9Bw7OlI9XXnMb7GkxGmVshS/PSt3gl/Bg1Nk3GwGRIbwpNCIG4+Ix3rEBuhVRfM+giW19chb/euzEh0gdBMfpadR1+Gsd9iZWeNzr93QhB1c+OIkFdw5pAg2LZQgwBHgQyK7E4D6ds6iGn0qAukTNwiInUdDTJZeK9MjPJCK3bZJSSjnS7zt2XziL+w88OZU6ZdMhF5I6GBdFQ8N8sj5zpyLsMYB0D0YkpN8FDTk2xIhBOVCUKceBvbuSuqsaNW2eZoSTTOWzNAwBhgBDgCHAEGAIMAQYAgwBhgBDgCEgAYHChQrh4D7xl+/4lpg+fDx2nRL3pW2HDh1w4MABtSpe+dcacmm0NfI7OKgVl5Gdabdg4cKFk5W4aNEiTJ06NSOXnay25av+RuNGDVC+LP+NeuWgOXPmYP78+aL2aGVljfFT58LQUBoxVzvKGrUj+bth6Rd6bo1yw+1BLJ6+t4J3SF58iDMXVZ82nPTI3MPiJt9QOlcABoX+QBWjIO67SVXmbRkJk47qzUH6NVfdBo0ljY7Rxv7F5qyRpzi2NxuBHMbaO6urH5+h88mViImPFSxLCjFIZUPLm+XGuhq9YKSrL7iGGIfGu0ciMiaYc/0UpYsfsbpJ5CBtLK2WyxEV7QvBKWdBNMpfFq+Dv+G23wscfXkfnmQeYUwCjzQt+V2pameP050WiylFFp9vhJgddmkDLn/Q7IK1LMWwJAyBLIxAdiUGL+kcRTEo/mYmmp6hPkxtrUkHIfmj2YB06dVXuqD0JhC4/gqRPn6YEHKT/Bc7OSmonKejESEHjeR93/oo2gKd/Svjc7yS5Gkmey4ZMZiBDowRgxnoMFgpDAGGAEOAIcAQYAgwBBgCDAGGAEMgjRBY9L/5aNa0icarXb96DX379kVsvPCsM9ohd+/ePU46U13r0XcgFi2YS2ady/sli7p1yOX/8OFDODkl71Rbu3Ythg4dKtcSWs0zedosoqTTGtWJmok6RskQKil6+fJlUWF16jVCy9bCIxtSS5Y/1hg9wlLO5YkkMqO3leYEesZY8Xy1J6pMjZwKGAShlN0XVC8di2bOFjAzUnzRaLPJExZI2amgvFiITgwC+1XQaP3xk6ZIVtTRaGGJwUPKNcaCml0IYaorMYNw2AqPk/jfXXGXHWg2dYlBw3gdLHfqgGo5iwoXo4ZHza29SKPLTyb5CyEHAwg5SInUQ64TYKSv+nmiJPlv9w5jpccpxP/6G0FeK2gcC1PSqOJaojmmVuuuRlXSXHd6X8fMm3sRHJ28S1ZaNhbFEGAI8CGQXYlBT919yIGoFNDoGejDJIcldM3JfGHaNfjiG3CFqGJ8CkryXRLmgbdx/N3kM80qI6+e+u95+c7KP84Q7fyd8ThGvlmGafnbwYjBtERbYC1GDGagw2ClMAQYAgwBhgBDgCHAEGAIMAQYAgyBNECgSOFCOLBX825BOi+uVZ3G8P4sPDKAkoGUDCtK5PelWs++g/DbgjlZghw8cuQI2rVrlwyK3bt3i5balIqhHHFSScHEtQMCAlCmTBl8IaMphEyXzPwZM3Em7HOlJPiEYvUIvzYquAAMoYtHUZZwi7IjcwJtcSuafg2oPVJJqC5bvQiUtvJDxSIRaFXLArmsUo+I3u8BxxD+rgDarfCyWzEY/J+9q4CrIvvCH53SpaIoINiiYndid7sW6qprt39r147VVdfuzrU71lZsxcBuUARBuut/77xFQWFm3sx7SNyzP37r7jvn3HO/mYfvzXfPdwzIw0uJtmvPP5i/4C+J0VkXpquljZX1+6GNGucJ0t30OLkMx97cVUqGUywxmEwOUDSzKYnp5VorlV8Mykkkd+2tvdPlpTMyf634C3qXbiwmBefzPMQfDffPIPNi/3tYnoYUpK9rahngUvfVovMp6/gmPBDDL2zC1Y/fZJaVzcH8GQIMAeUQyIvEoAb5+/Ot5jbeScR6xobQsyXKAzE/zkc+E+eLA3GveYEuoWWOoUaKEVuqtLBkbbT8XBkPciA5yIhBVd4JMnMxYlAmgCycIcAQYAgwBBgCDAGGAEOAIcAQYAjkMATmzZ6JRg3JCWiZNm3MRGzcK25uu6pIr9xCDi5evBgjR45MdwUOHDiANm3ayLwq6g0fP3Eq2rRugWpkfrkcu3r1KmrXrg3aQShkDkWdMHDIaCG3dK+HErmtt5GFkBDmiLsxtghJ4e+8Uyq5ks76GklwNfqEMvZh8KhigLIO4uYDfbn7EuW94wVXu+2iAeuaJQT9MnN4++4d2nUUN/tR8iIyA+0MzbCj6VCUsy4iM1Pm4RGkM63u3ml4Gx6k9BqFHIpCR4f/HrPS0MfqKt1R2NhK6fxiA6pv7kM6BhXd28mE0Otbvgv6lm0uNvyr35eYCFTcOQHhcdGw10tCvjRKp2QkIq722kTWUT25rmynptIbYwEMAYZAhgjkRWLQGjG4o7VP8I7QIAeU9IwNoGtkkM43MiUeE8OvI1EjczlRGrA0Xy21dLiHJOugXkA1vE0yFNxDdnJgxGA2uhqbN6xDvbp1uIpGjh6LDx8/pqsuICAQb8jsA2YMAYYAQ4AhwBBgCDAEGAIMAYYAQ4AhkPMRKFrEgcwW3CV7I/e976MjmRcYnyQ8f6t169agHXKqsh59+mPWtKkoXLiQqlJmeZ4RI0ZgyZIl6dY9ceIEmjRpkuW1iF1wwqTf0bplc1SrKo8UTF2PzlScOHGiqOU7dOmJipWqZuobk6SHd5H2hAwsiLdR9gj9iafoNQkh46QfjJJ2n1G3vA5qltQFGVmkvJE8hTY8TicNmVGSVwbR0O7qrnz+NBFNW7RGQGCgrBzqCna3c8IOj2GwNFCtHFvaeu8HvUPzg3MRkyhMxGa0T15ikDBpI5zrortTNXVB9DVvz6Mz8frLc44U7FmuIwa6tZK8ZkhsBFrvm4TkpNB0OZJI7qs9N6uUGNzz/Br+vHMEr8OEu4glb4gFMgQYApkikBeJQVeNUPyreVT0XUEJQtqdT380idQotW0xz+CV8CnTHE5aphht5CZ6DWUdXyYYom5gdUSmSPmQoexqqvFnxKBqcFRJljGjRmLYkN8yzbVl23ZMnvqHStZiSRgCDAGGAEOAIcAQYAgwBBgCDAGGAEPg5yKwYP5c1P/vcKicSjrWb45br30EU9ja2uLJkycwNzcX9FXGgZKDM6dNgUPhwsqEZRtf2hl46NChdPVcu3YNVatmTn79zOInTP4drZo3Q/VqVVRaRuPGjXHmzBnBnEZEinb0hD9gYKA4GR9PZLQ+RtsREpAQgYQQ/BRLu7C+zVYTTKhiByftKNTTD0JdvS/IV/QTijQrpZIVjDffgyUhPfksFokI8JQnVTZ95mwcPHxEJTWrMklnl2pYWs9TLd0WqXXufHYFw85v+nGunhIbyYgYpN2wpQ1tsLZaL1AZ1Kww3/AAdDowDl1Lt8awiu1kLxkcE462+8cRcvDbnL9kDR149VgvOzeVwT3w8ibm3DrECEHZaLIEDAF5CORFYrC6xifs1jorCThNbW3oEoIwWDcR02PuZJqjlJYFBhuVkbSG2KCd5HPQoBD1riG2FjF+jBgUg1IW+RgbGeHA3j1wdXXJcEVGDGbRhWDLMAQYAgwBhgBDgCHAEGAIMAQYAgwBNSPgUqwYdm3fInuVbes3YfKMP0TlOXnyJDw8PET5KuvUw/NXzPh9Moo45Dxy0M3NDffv30+3ZUqgFi9eXFkY1O7/vyl/oEWzJqhRTfWk5Zs3b1CyZEnExsYK7qNMZQ841JvOkYEfCCmY/BPnBJprJKAO6QqsS34a6X9GQa1v9b/XiUFKj4qC+xHjEH7oLsoE888Z/EJI0tctCsLazkhMygx9Tp0+g/9Nnio5XtWB2ppamF+zO3qVVChcqctGXNiILU+ukLl88lb4nhjUIWqef5Zrhxp2xeQllhAdGB0GG8NMBldKyPeZ5Gu3bwxSUhQzB0vblsUqjzESMn0LoYTg/DuHuXmGzBgCDIGfj0BeJAZbarzDCq0rssHfbuCHlxqRiCefShLJT1ozIQcp5uarLnsNoQQtP1fC5ThLIbds8TojBrPFZfhWhL6+PurVqQ0Xl2Kgg73Tmvf9Bzh/4WI2q5iVwxBgCDAEGAIMAYYAQ4AhwBBgCDAEGALKIjB7xjQ08WisbNgP/o0q1cSLz36CeXr06IEtW+QTkXwL9fQcgD+m/A+ORYsI1pOdHMzMzBAWFpauJH9/f9jZ2WWnMjFxyjQ0a9oYNaurTwZRtKSohhbydXgOzXxFsxwjPfKwr6R+ABrrhaCpXijK6oZnSksmpiTjQ9/SKqnxy0s/lL8Uni5XbIomrseb40KsJfdzP8EUY5280b27heQ1v3wJQcMmzSTHqzLQyiAftjcZBndbR1WmTZcrKTkJDfbNwMNgX5WskUoMJicloamVK2ZWbK+SvNklyYeIQHQ+OIn0+KXgZOelyKebftaWMnWe9X2ETscWKRPCfBkCDAE1I5AXicHems8xU+u2ypC9ovMFN7WSyUzWZASnhKGFngMsyGxZF20zla2RWaLLsRZoGaQamXd1F8uIQXUjzPIzBBgCDAGGAEOAIcAQYAgwBBgCDAGGAEMgDQKmpqb49+QxaGlpycJl4fQ5WLphtWAOQ0NDvHv3DlZWVOJRvda730BMnTQhx5CDoaGhGUqrxsTEgB7czS6WFaQg3WtCQgLKlCmDZ8+eCW5du0gHGNXfI+gn14E2kDnrBqO45SdUdI2HRxVzGOppIvToPZQL5Jf2pGt71zGDuVMBuWVw8fbrHuFhohkhAa04IvAaIQXjvqMlWxu/w/RR8treunTviecvXqikZqlJ3KwdsKPpcNiqsOPt+1r8IoNRe88fCIv/Jo8ptd7UOEoM5tcxxqqq3eFgbC03XbaMf09kSgOiQ1DJTl5X88r7pzH52u5suUdWFEMgryKQF4nB0VoPMFLzkUoveafEBvBKtkVNIiv+i5EfWhl+gqFG+i5ClS6YJlmFT7XwOlG6coC66vo+LyMGswpptg5DgCHAEGAIMAQYAgwBhgBDgCHAEGAIMAQIAv379sGgAb/KwoISWg2r10ZQdPoOpoySzp07F+PHj5e1njLB02bOQb8+vVCwoGrIGGXWVtbX29sb5cuX/yEsJSVF2VRq81+6YjVq1qiG8uXkza4TW6CXlxdq1Kghyt24zX1oWah+nk4BrWgUM/NHBacYtCXEnqnBjw/zIv2CUOp0oGCd3taxMG9ZQdAvM4dP4Vo4eDEE998Y4Xl4AQQl85ORhbRicHTSZ8nr0cBFfy/F1m07ZOWQE9zaqRI2NBooJ4Vg7PG33uhzajloV6eqTEtDE7Nqd0f/EnVVlTJX52l3dCEu+j3O1Xtkm2MI5DQE8iIxOFv7Fnpqqu4wTDKZc+wS3wmx+HYAz1gjEe0M/NGTkITueulVIlR9j8wMc8aCCGdVp1V5PkYMqhxSlpAhwBBgCDAEGAIMAYYAQ4AhwBBgCDAEGAIZI0BHRpw6dgSWltKlBmnmyUPHYNuRvYIwOzo64tWrV4J+edXh4MGDaNu2bbrt6+npiZqzl5sx8/T0xMaNGwW3qF2gIYyanBb0E3IwJQ/saukFox6dFagXBFP9EET1+pGw/T6P3YaH0Evz4C+jdQK0YxHbUzwxGB6rgRPXI3DzmTaefLHDhwQTofJ/eH1zy/twK2+udFxqgNf16xgybKTkeDmBrR3dsaHxIDkpBGNnXN+HxfeOQfZAwf9WokR+jQLFSYfjUFnSmoKF5yKHqMQ4FF73Wy7aEdsKQyB3IJAXicE12lfQTFM1ctL0LniQYoFmCZnP1HbWjsIvhh/QhfzYaStmtqrSqKJAm6BKqkypllyMGFQLrCwpQ4AhwBBgCDAEGAIMAYYAQ4AhwBBgCDAEfkSgYf16mD93tixowsLCUadKdYTGRgrmOXToEFq1aiXol1cdFi9ejJEj0xMwtra2+PTpU16FhNt3cHAwChcujOhoYYlHo6ZnoZ2/nlJ46ZA5gVV0Q1GXEIH19IPgphMGrTTqm3R+2vs+JYWJo+134BDHP2MtmZBGvp6Z54pPBC75xOLSvSQ8DrDGK/JAL1lDnhTo0IIP0a+vqVKYpHWOi4tDtVp1JcdLDWzrXBnrGg6QGi4qrtWh+bjy8SnhBOVhnLqYsY4BNnsMQl37UqLWZ04KBA6/voM+p1cwOBgCDIFshkBeJAb36fyLqpryOu3TXsb1SS6YmlhR1JVtoh9ICMKPaEOkRlVlQUm6cPavr6p0asvDiEG1QcsSMwQYAgwBhgBDgCHAEGAIMAQYAgwBhgBDID0Ca1Yuh3tF8d1LGeE3e+IfWLNjkyC0NWvWxOXLlwX98rLDiBEjsGTJknQQuLq64unTp3kZFm7vU6dOxYwZMwRx0LJyh3Grm4J+NoQELGLoi6oGnzGBEH9Cs35uu2nDuoILb96giz6o+EqYYLpbUR+W5Ry/5nrwLgmnbsTgoZ8pnkXZITZF3rzP74tsSOTKFo5NEMSEz6FHb0/4PH4iK4cywcXM8uN6l5nKhCjl+zk6AvX2TYN/VIhScXzO3YrXxN91equMZFRZYTkgUY9Ty3D8zb0cUKlqSixiWwqli1ZH0fxlUMDSGRb5bGBpUgBRsWEIifyMzyHv8eDNZXi/PAffz6qTNFRN9SxLXkIgLxKDF/SOo5iGsDS+2PtgYEJ1HEkqLNad8zPTTEAnQhCOyfcKNlrxSsVm5Gzm10R2DnUnYMSguhFm+RkCDAGGAEOAIcAQYAgwBBgCDAGGAEOAIUAQcCxaBHt375SFRVJSEmq4VcKniC+CeW7dugV3d/dM/f766y/MmTMH2tramDx5MgYPHiyYM7c5tGnTBrSrMq1VqVIF14mUY163iIgIODg4ICREmMgxbHwMOvZN00FmTKS6ihj7wYFIdTnmew8jIulJTYuMbxwZ7gBtMgOIzx7ni4ZRx8zvXy42KRkOm4VJ3MtkRuFp08Lwfm2Ix2H5EZzE32Uo99qbaiTg4qSP0NAUJi0zW2vG7Lk4cDD9vSm3Lr74U20mwt3OSS1LXP/4HG3JPLv4ZNKeqQIrZGyJvc1HwdncTgXZ8l6K+5/fov4+YdI/NyDjSIjArvXGo3wx8d077wKe4N8723DxwT+IjhPuzM8NOLE9ZB8E8iIx+EhvP8w15JNxqVexYlxrfEqR9vf8GevrqKQXKvuGKODXCNECUueyF5GZgBGDMgFk4QwBhgBDgCHAEGAIMAQYAgwBhgBDgCHAEBCDwMhhQ9Hjl25iXDP1Wf7nYvy5fLFgjqZNm+L48eOZ+nl5eaFGjRrpXqckEO0Q69Gjh2D+3OLg5uaG+/fvp9uOh4cHTp48mVu2KGsfs2fPxqRJkwRzaBdqCXOPvShMSEAH4w8oauwLK54Ha90i7VA4SZ83bxh5SBjax01wbYsN95EPOun8IpO1cDXeAueJLOj5WCs8SzQWzKNqh1W1b6JaXenE1fKVq7B+42ZVl5Vhvk4u1bCyfj+1rLXe5yzGXd6hktzaGpqYVKUdhrmlJ6FVkpwnSUR8DHS1tKGnlf4+U/e66srfjpC0F/0eqyt9tsirr2OI3h5/oGHFXyTXExkTiiUHhuDei3OSc7BAhoCyCOQ1YlCDKAj4GewROCokHsUPKYaoFNtSfMB3nr4FziCfZpLk+NRAG7/GiIem7DzqTMCIQXWiy3IzBBgCDAGGAEOAIcAQYAgwBBgCDAGGAEPgPwROHDkIOr9OjjWqXAsvAn0FUzx48ABlypTJ1G/16tUYOHBghq+XK1eO6ySk5GJuNzMzM4SFhaXbZuvWrXHw4MFDrmYaAAAgAElEQVTcvnVR+4uKiuK6BunMQT7TIITNuMmzYWZmIipvzTgz1Iw1E/R92dUJOgZ6vH5x/9yBS4QBbpGcZ+OscCHOEtfjzAVzq9thYtF76NzDUvIym7duw5KlyyXHKxN4t9tcOJhYKxMiyrfvmVU48PKmbKnPFDInspKdM+kSHIl8utK6QEQVnIHTvhc3MNlrN2rbl8DqBv2lpsk2cVc/PkOrw/OzTT3qKKR4ocoY2WElkQrNr5L0h7xWYOsZ9cnsqqRIliTXIJDXiEErjVg80Fddd/xBIiH6W3w1SfeDOen2f1PwrKTY74OYlKgCEQ0Lm/xEKIIZQ4AhwBBgCDAEGAIMAYYAQ4AhwBBgCDAE8iYCZcqUxub1a2Vtfv/uvRg1foxgjrZt22L//v28fs+fP0f58uURHR2dqV+DBg2wYMEC0K663GihoaEwN/+RQOrcuTN27dqVG7csaU9Ucnb06NGCsQ09mqNB4+aCftQhf6IuekUVEPS9XQywrlUyQ797z8Nx4VY83ny0gXesHcJStAXzqdPBRTsCFY0DULpwKGrUMSH3Fn9HpFAtK1evwdr1G4XcZL/uZu2As+2nys6TNkFUQiyRqpyOl6EBsvMaaetjVYN+aFa0vOxcyiTwiwjG0AsbcenDtzmPs2t0xYAyDZVJk618w+OjUW/vdLwN/5yt6lJlMS2q9iedgtNUmZLL9fjddczf7QnaRciMIaBOBPIaMeiqGYbz+qpTaZgUXwEbE8mHBwlWSTcUZ2zkS8m/SjBExYDaEirI2hDWMZi1eLPVGAIMAYYAQ4AhwBBgCDAEGAIMAYYAQyAPIjB65Ah079pZ1s77tu2Ks/euCea4dOkSatWqJejn4+ODvn374saNG7y+Xbp04ToIixQpIpgzJzl4e3tz5Oj3NmDAAKxatSonbUXttVpaWuLLF/65lqamZpgwdba4Wsjx8RHhhaEvILP12oBM6OmqmDMYEJqEY1fDcP+1MZ6GF8CnJENxa6nJy1YrFpWMAlE6fyhq1zFAQTv+zkZly5g1dx727Vd/5+rM6l0wqGwjZcvL1P9FiD/q75+B6IQ4eTnJPdLepQrXpaehMpE5cSXNv30Y825n3MGyvclQNCmSMw9LtDg0D9f8n4sDIYd56ekYYHDrRaheqpXaKv8c6odZ27vBL+il2tZgiRkCeY0YrKEViL16F1R24RvFNsKjZGmqAV2IHPoqi4eyazkRY4OuwRVk51F3AkYMqhthlp8hwBBgCDAEGAIMAYYAQ4AhwBBgCDAE8jQCGhoaOH38KCwtLSTjEBAQgNrVayIuKYE3R4kSJfD4sfjZUVSib82aNfjf//6HkJCQTHPr6OiAEmZTp06FtbXqJQclAyMjkMqF0u7K7238+PGYO3eujMy5L3TUqFFYtGiR4MZ69h2EEiUzl7BNm6BdlA1cEjMn92JTNIksqBm26JjiSbAN3lB5UA3BEtTmYKCRhGq6waiv/wVV9T7BeqCj2taiiYcMGwmv6/I7F4SKvN5lJoqZqUZycd/L6xh4dh2Sye8VOWZvbImdTYehpKW9nDRKx17+8BSjL23Fq7BPvLG/V+2Q5XMOld7MdwFDzm/AzmdX5abJlvF25kUwocsm2Nu4qL2+2IRo/PXPANx9oRq5QbUXzBbIcQjkNWKwlbYvVusJH3oTcyFjoQWnqHZIlvhhYbLJC4wxeSVmKV6fJRFF8XuYq+w86k7AiEF1I8zyMwQYAgwBhgBDgCHAEGAIMAQYAgwBhkCeRqA8keJcv2alLAxmjZ+Ktbu3CObYsGED+vTpI+j3vQOdIUfJny1b+NcwNjbGmDFjuB8jIyOl18lOAYsXL8bIkSN/KIl2R06YMCE7lfrTa6HSs66uwg+5ihNSsBchB8WYW7wxmsRYfXVNJn96EG+CC7FkTmCsJa7FmyNOoKNQzDpSfTQJuVWWyIrV1f+MenohqKYXCl2Nb4TXYw8bGBX8Vr/UdTKLq1W3AaJ4pH5VtZ5fvxUw0Jbf7Tjj5j4svntcVlma5BDFhEptMLpCC1l5lA3+HB2Oydd2Y+8L8URsW+fKmFujG6wM8im73Fd/Ku0588YBrPc5h9/KNcaMavK6yjMrZM6tA1hw56jkOrNzoJtTXYzquAaGesZZWub2s3Nw4MrSLF2TLZY3EMhrxGAfnZeYrXtPJRf3UpINOsfWkZxrk4U32hjyHwwRk3zIl9LYFp21B1vE1PW9DyMGpaDGYhgCDAGGAEOAIcAQYAgwBBgCDAGGAEOAISASgfFjR6Nzxw4ivTN2q1uhKt5+4X9YYWFhgY8fP0JPT/pDfi8vL3h6euLZs2e89drY2GDKlClcFyHtJsyJNmLECCxZsuSH0leuXImBAwfmxC2ptea6devi4sWLgmv87/c5MDExFfQzSdJC81AXnI9TEIEXyU9Iys+9lwpqh6O02Sd0TolAA90vMNVKynQfD/LHw7SpeiQln794gS7dewpiKNdBS1MTgb/Km31Ka+h8fDHOvHsA2h0txWjnsrutE3Y2GwZLfelEm7JrpyAFmx5fxLTrexERH6NsOIwIoTrErQmGlGsCQx1d0fHxSYlY9+gcFt49itC4qK9xHg7lsK7hQKVy8S0amxjPdXAeeXNHdG05ybFLvXHoUHvETyvZ6/FRLDs4DPFkpiYzhoCqEMhrxOBYXR+M0hWvdMGH88L4klgQX0rypbhkc5UcCIqQHJ8a2DiwCm6Sw03Z3RgxmN2vEKuPIcAQYAgwBBgCDAGGAEOAIcAQYAgwBHI0AqdPHIUVmdEm1Y4ePIwhI4YJhqtKAjMxMRF//vknZs6ciWiBjiUnJyfOr3PnzpJJAcGNqcmhTZs2OHToxzliGzduRO/evdW0as5Nu2PHDnTv3l1wAy3adESNWvUy9ItN0sWbSHu8Iz9vo+wRmmAimE+dDiaacShp8glli0SiVQ0jFLLUBAhJZb/RhwiSkT/zmL9OLOJ7qGeG0NLlK7Fxs3CHsFxs9LV18aGf9G7mhOREVNk5Ce8igiSXYqClg5VkjmBLx4qSc0gJ9An2xfCLm3Av8K2U8HQxxjr6aFa0PNo7V0HDwplL6V7w88G+Fzdw/K13OkIwbTIX8/zY4jFYtrzr67AA9Dy9nMjwfpC9v+yWwEjfFCM7rATtFvzZ9ubTI8zd0RPBEfK7jH72Xtj62QOBvEYMztW7i946r1UCfueYWriYZCs5V0DBU9BLowwgNVHBDw0RlaItNTzL4hgxmGVQs4UYAgwBhgBDgCHAEGAIMAQYAgwBhgBDIK8h4Fi0CPbu3ilr26P6DML+8ycEczx58gTFixcX9BPr4Ovri/79++PUqVOCIW5ELpVKc9apI13CSXARFTvQmu/fv/9D1p07d6JLly4qXi3np0tISODmS4aFhfFupoijMwYMHsX5JCZr4kOMHd5GFiQ/9vgUa0N6tKR1lakCQT0yecjVMAClCoagQSU9VHTSgWYG5ehuvYv8Cfq8SyalJMOvb2lVlJUuR3RMDJq2aI2ICPldC0LFGerowbfvCiG3TF/f8/waBp1bJymejiFsR+Q41zTqTyhYfhJW0gKZBEUnxGMmkT1d/fBfVaZNl8vGwBQWBsYw1TVEREIMvsRE4lN0qFLr9S5VB/9zb6u0VCntQJx76xDWPsqdM/CK2JbCeDJP0NqsoFJ4qtM5LCoYc3f2wIsP3upchuXOIwjkNWJwrcE1tNSWf4CBypG7RLZGpETlgfyasXhS4ILsuyyIHIBy9q8vO09WJGDEYFagzNZgCDAEGAIMAYYAQ4AhwBBgCDAEGAIMgTyJQNfOnTB29I9z7JQBo0Y5d3wI4+/IqVy5Mm7cuKFMWtG+Bw8exLBhw0CJQiFr2LAhFi1ahNKlVU+YCK2t7OtmZmYZklwnTpxAkyZNlE2XJ/zpbMmFCxcK7rXJsMMgfXjwjS5AyMGfd2qecn7ldMLInMBg7qeqbgge1TCAdXEH3j2EHruHcgHCkrze1fPBvHghQTyUcVi9dj1Wr5VGtimzDvX9WcRgQSML7Go2HCUts3YG07E3dzHhyg58jApRFqqf4k9nP3Z0qYp2TpVRq2Dmhz78yX6ufHiKSx+e4MjrO4SMzJ3SljVKt8bI9tI7XNV9Ef8+MBSXHuxT9zIsfy5HIK8RgwcML6CalvSu89TbwSfZFA2iGkm+O2oS+fCjNjclx6cGesWao1lQFdl5siIBIwazAmW2BkOAIcAQYAgwBBgCDAGGAEOAIcAQYAjkSQQWL/wTtWvVlLz38/+eQ59+noLxy5cvx2+//SboJ9WBSor+8ccfHOlHpUb5jM4Z69atG+bMmYNChVRLmkit//u40NBQmJtnPP/l2rVrqFq1qqqWylV57ty5A3d3d8E96VdbBr0S6rsf+Qqw14pFXb0g1CNEYH39IJhrJqRzf2geA5O2/LKVUR+CUfJUgOA+fcxjYdxWdXKiHz58RIcu3RAXFye4tiocspoY1CS/GyZWboOR5VuoonzROShxNvbydpx4e090THZztCQdiOWsisDW0JSbw5hEul8fB/vh2ZePSncjZre9iamnf/M58HDvJcb1p/oc8lqBrWdm/tQa2OI5G4G8RgxeNj4NF035HfIb4x0xIba85Ivf29AXiy18JMenBm6KKoQRIdLnHMouQIkEjBhUAizmyhBgCDAEGAIMAYYAQ4AhwBBgCDAEGAIMAWUQuHzhLIwMDZUJSec7afBobD/G34Ggo6ODwMBA0A44ddvjx485eVEvLy/BpXR1dTmycurUqZmScIJJ1OTg7e2N8uUzfoCkaklWNW3hp6V1cHDA+/fvedfXsqsL42bnsqRGE40ElDT0Rwm7IAyICEdxXf5uqS9krmBEb+GHh3YbHkKPTBrks2CtOET2Es4lFoj+A3/DnbtZR15lFTGYQnRDK9o6cl2ClNTKSltDJENn3TyAyFzaRZeVWP6MtSzy2WJMp/VwsVcdAa/ufdx7cQ5/7R2AmPgodS/F8udCBPIaMfjY5AgsNeJlX8lB0ZWwP6Gw5DwzTJ9haL43kuNTAyeFumJ5ZFHZebIiASMGswJltgZDgCHAEGAIMAQYAgwBhgBDgCHAEGAI5DkE3MqVw4a1q2Ttu0nVOnj66R1vjqZNm+L48eOy1lE2eOPGjRg3bhyCgoTln0xNTTF+/HiMHDkS+vr8c9uUrUOqP5VHbdu2bYbh/v7+sLOzk5o618fR6/7nn38K7FMD+bp/hqaehcrx0KFzAg0CUdwmCDXKJKOOmxm0/htRl7TjNhxj+Yn4FDLl8E1PV2hp80ucpmy/gyJxBrz101zv+5QESCecXBs9bgLOX7goN41S8VlBDBpo6WBlg/5o6cjfpalU4SKcHwW/x5DzG/EwiJ/EFpGKufwkBEo6VMGYjuthQqRnc5p9CHqJWTu6IzBEWII7p+2N1ateBPISMahB/g71Nz2gksnD7hFN4Jss/SDeDsu7aEY+W8i1jp8r4kyctdw0WRLPiMEsgZktwhBgCDAEGAIMAYYAQ4AhwBBgCDAEGAJ5DYEB/fthQP++krf9/NkzNPbwEIxftmwZBg8eLOinagcqx0lnzm3YsAG0I0jIChYsiGnTpqFPnz7Q1PyPyREKUtPrixcv5ojKjCwkJCRLui/VtDW1p7116xboTEsh06++EnrFBwi5Cb9Obq3SuhFwNfZD2fKJ8KhiBgOdjO+3wEs+qPRSmKS7WVYLtu6uvGuHnH8EtzfC9+mdstqwcncR3kcmHrGxsRg/cTIuX7kqOYfUQHUSg/RXQlvnSljb6Fdokn+yyqIS4zDn1kGsfvAvklOSs2pZto6KEWhRtT96e0xTcdasTRcVG4b5uz3h8/Za1i7MVsvRCOQlYtBKIxY+pvIPtn1K1ke58GayrvtN28tw0ZHf5evmXxtvk6QTlLI2oWQwIwaVBIy5MwQYAgwBKQjUq1sHmzcoBsjv3X8Ao8aMk5KGxaRBwKFwYVBpLmp373mjTfuODB+GAEOAIcAQYAgwBBgC2QqBtatWoGIF6TKDKxf+jXlL/xLc04cPH1CgQAFBP3U53Lx5kyP7qMyoGCtZsiTmzp2Lli1binFXi8+IESOwZMmSDHPHxMRkm85GtWxeBUnFyIlqF24No4YHJK1mRyQ66ZzAunrBaEjmBFppxeOFYTR0u/DPN4yLikGx3a9J9wE/OfjMmFzjTvwdbPHhUXDe+1Yw1wuSS1cglxAIteo2QBSZ45nVpi5i0NbQDLubD0cZS+myblKwOPP+AUZd2oqPkV+khLOYbICAno4BBrdehOqlWqm1msDIN/iAa9COtkAZmyZqWys5OQmbTv+B4zfWq20Nljh3IZCXiMHiWuG4lE/xXE+OHU4oiH5RwgeW+NYILngSWsLninjLjEvRgO0H4QN9cvaqylhGDKoSTZG58ufPj/z57ZDf1ha25MfIyBChoWEIDSM/5MTlnbt3ER0dIzIbc2MI5A0EChcuBDtbOyLpYwsbG2vo6eqR90wo994JDg7G9Rs3szUQdevUxpaNig+C/+zbj9Fjx2frenNCcYUK2ePqxfNcqT+LGNQm8kP58xeEiYkpjPPlQ1wcmTESGYGQL8HkvhSW1ZKDs7GRMWzzF4ChoRH3k5iYgKioSISHh+PjB/XKlVhb23JzgvKRfWtoaiCc/N0VHh6GL2Tf8fHyteHl4MJiGQIMAYYAQ4AhkJ0QuHLxHAwN+KUI+eod0q0vjnrxPzApXrw46Ey87GALFy7kOgIjIiJElVOjRg0sWLAAVatWFeWvSqc2bdrg0KFDP6TU09MD7eBixo/A2LFjuWvHZxr6VjDpJk6Wy0gzEbV0v3BEYD1CBLpmcGo/CokI8iwreGksN9yHMXR4/SJJrmAV5Yogub6IyMVX0LCRo3HlqvDcTsHNK+mgamJQk0iqjijfDJMqt1OyEnnun6JDMeHyDhx5c0deIhb9UxGwMy+CCV02wd5GegeumA08D/JCuNETJP0310wnygYVLNuLCZXsc957N1YdGYuk5ETJOVhg3kAgLxGDNbQ/40C+K7Iv7OToMlgT5yw5TxGtaHjnvyQ5PjXQJz4fagTWkJ0nqxIwYjCLkC5TujQ8+/RCsyYeMBD4YhgZFUW+oBzBpq1b8ezZ8yyqkC3DEMh+CNSsUR29evwCj8aNBIv7TGab7PlnL7bv3AU/vw+C/lntwIhB1SP+M4nBEiVLo5xbRRQqlPkJ2JiYaLx48Rz37t5CYMAnlQFQqJADKrpXRjGX4pnmDCNE3YMHd3H37m3EE7JSFUbJQPfKVeDs7EJO0Gf+gPPduze4ef0a3r59JWvZBg09UKGivBNftIA48mDv7yVCM3BklcqCGQIMAYYAQ4AhkCECBQrkx9GD+2Wh06hyLbwI5D/wM2rUKFBCLrvYp0+fMHToUOzdu1d0Se3atcP8+fPh5OQkOkauo5ubG+7fv/9DGnp4l+6BGT8C165dQ/Xq1QVhMu7wAlomP15XDTInsKBhAIoYf0ARIz+0JXJiDeLNePPReX4vOjtCz4ifbI/ZcwfFI4VnA4rJFU9yFROR61VXZ+gY6AnikZnDjl27seCvxZLjpQbKJQb3v7yB/v+u4ZYvZ+2AnU2Hw9bQVGo5SsfRe2L9o/OYeWMfIhIYoa80gNkowN21MYa1XQZDPWO1VnXv8yHE5/Mna6SXI9aJN0dx/SYw1OX/PSSnuGe+tzFvd2+ER7GOVjk45vbYvEQMdtZ9h6VGd2Vf0sYRdeGdaC45TwO9z9hnLf9gycFoO/T+4ia5jqwOZMRgFiA+fOhgjBg2FFpaWkqvtmv3HkybMeunSEooXSwLYAioCAFdXV1MmzoF3bt1UTpjUlISli5fiSVLl4H+ObsYkxJV/ZX4GVKiBQrao1nz1qRbTrnh57du3cCFc6dlgZDPxAT1G3jAhYcQ/H4BSk5eJHKrDx94S15bV0cX9Rt5oEwZ5T7c+Pq+x/FjBxFOuuGlWHOCc8nSwifChXIzYlAIIfY6Q4AhwBBgCKgLAfr5b+H8uZLTB5GDb+7u/LKJNPmxY8fQrJm8uSqSi+QJPHfuHPr27UsOC70VlZ4qMfTr1w/Tp0+HtbW1qBg5TmZmZgjL4HOKq6srnj59Kid1nohNTExEPqKYIdRdaVB7E3Sde3KYmOuGEiLQjxCBlAz0hZ7Wt84ZmyRdeEYKy+HedkiEdQP+z4if7z6Hu7dwV86dokmwqleG93oF336OCg9E5HJKgVWdUpKv/atXr9Gxa3fJ8VID5RKD+15ex9Dzm7C8Xh8yT7CK1DIkxT0J/oChFzfgXqC43zGSFmFBWYJA1/oT0L7WMLWuFZMQhifRJ5Gglzkpp5NsBPvkmrAxdlRbLcHh/pi9owfeBYiT3lZbISxxtkUgLxGDy43voJOuPMWr2BRNOIS0JMeNpOuADjB6i3nm8j/7zQ93xmzyk1OMEYNqvlIrli5Bi+bpv6RRybsz/54l0nf3uAHtVBKRSiNaWlqiWtUqoF8g05qvrx+GDB+Be94/nmZUc/ksPUMgyxEwIeTH5vVrUbFihXRrU1mV02f+xdNnz7j/T7+EWlpYwMrKirxnaqPSdw9NHjx8hMFDh+Pd+/dZvoeMFqxTuxa2btrAvcSkRFVzSeztC8Lr0gUuWVZIiVJCrnXb9HMMaXfck8c+CA35Ag0im2NqagYzIrFpY2MHR6f0HwaePvHBkcPSugbyGedDb88B0E/Tcf7m9Ss8uH8XQUGfOQlPQyMjmOQzgRPp6CtbrjyMSUyqXb50HtevKS/PYGBogC5de5H32beHc1Qy9LHPQ9C9pySnQIcQ+XQtKqdK1zUidaRaeEQ4du/YQiR/Q5S+6G3bd+a6E6m9f/9O6fjUgAQibbp/3y7J8SyQIcAQYAgwBBgCUhH4tZ8nBv7aX2o4Nq5ah2lzZwrGUxlx+tmYz86cPYfAwM9wJ5+xXV2KCeZUlQOVWZ81axbmzZsnWm6cfpYYPXo0xo0bl+5zhapqonnoCA8qi56RValSBdevX1flcjkm15eQENy+cw8PH/lg9IihgnXXr18f588rpP0zM5syXVC/9a8oauwLkwzkQb/GkeadEeGFoQ9N3nzvdMnYlV/4ZwOSBy0ovPGx4GzAd3okV/cszCWAaJPmrRD4+bMg7qp0MNDRg1/fFZJTBsVEwspAvR1e3xcXl5SA2TcPYtn9k5LrZoHZAwEjfVOM6rgK5RzTPwdVdXV+YU8QoHMLiZpRgqm1UnRhFl0azpbqI7rjSXfrkgODcePJCcF6mEPeQyAvEYM+ZidgoylP5epqohXahNeUdaPMN32CX/NJf+6Uuni/4LLYGyN8yElWsSoMZsSgCsH8PtXSxX+hdatvw9T3HziEhYsXgxJ9fEaHxnv26olf+/f96kblRTt16Y5HPj5qrJilZgj8fAQO7vsHFcp/60xav3ETlq1YSea18UstFHN2wgDy4KVTh2+68P5EAqhN+07w96cyET/XmJSo6vHPSilRhyKO6NT52wleSsqdOXMcVLIzM7MkZFrTpi2Rv0DBry7nz57C7dvKz8Ps2r0X7O0VsqW0e+Dk8UPkvv6Y6dpaWtqoUbM2qlT9pm2+c8dm+JEuPrGmraOD7r/05khOalHk76Gz/57Es6eZn2ykJ/0rV6lO1v72xS6IPNzYtHE1dxBGGetG1i5YsBAiydzElcsWKRPKfBkCDAGGAEOAIZAtEPhz3hw0qFdXci3j+g/FnjNHeOPLlClD5MMfCK7x6VMADh89jlt37oJ+RnavUB4VyY97xfLIb6f4u16d9urVK3h6euLSJfHzW2xsbDB16lQMGDAA9DOGKs3b2xvly5fPMGWdOnVw4cIFVS6XbXPRrr/bd+9xZCD9d2hIKEceV63sjoYN6gnWTa/PjBkzeP3syEzs4WMmC+aiDq2irVEy4dshs4yC4pEEf0/+Lj8aZ7j5HqyT+KU941KS8KmvcC7jTfdgmSyQi9T1SURdfEBM/WM6jh7PWqJAV0sH/v1Xibo+2cHpvJ8PRl7cDN+I4OxQDqtBBgJFbEthPJknaG327fuyjHSZhj7+fB5R+V6RbqIE0ek1yAEFg/AiKGPjITpGiuOeCwuw5+JfUkJZTC5GIK8Qg8W0IuBldk72lVwU44LZ0SVk5dlvdRv1yXxjuVYnoDruJ5jITZNl8YwYVBPUdCba2lXfTl0N/G0Ijp88pdRqTo6OmD931tdOqKCgYLRu30GQWFRqEebMEMhGCNAT1RMnjOMqoqd4+w34DTdv3VKqQkoqLvxzHuj7h9qLl6/QrmPnDGWClEos05kRgzIBzCA8q4hBYyNj9Ok38OtcvYcP7xNi7rDoDaXtfIuLj8PqFX8jLk78/Iuq1WqiVm3Fg5nAwADs2LYRCQnivtSUJlKcTYkkJzXavbdu9XIisSsshUT9aRyNp0ZJwW1b14uWBXUj8xcbeXzrlj9z6ji8vZXTa+/TdyDXqUiJxY0bcs7DCtE3BnNkCDAEGAIMgVyPwMF9e1C4UCHJ++zWpA28nvLLgVPSbNUq5f6eDA+P4NQWKElICSFNLU1UImRQxQpuHClkZGgouWahwB07dnDdgMrM8HN2JrJMs2ejY8f0yg1Ca319PTIe8HoN1CJz7gx0uP998OBBtG3bNsMUHh4eOHky93YiPX7ylCMB75Cfh48ecyQxve7uFd3gWLSoaFip4+nTp0Hx4jNNTU1Mm7OIkLsK7PmsdLwxWsRYCbnhsYctjApa8vqFHrqDcsH8cwZpgofN7WBiyz8mIOrAPZQMEZ4f+KRNIRha8Hfv8hV97PhJTPljmuD+VemgqaGJzwPWqjKlWnIFk87EsVe24dAr5Z4PqKUYllQ2Ag0qdMOglgtk5+FLkEyowPvBBxFvFCB5Hd1IG5SzaktoQv5OZskLkMBrT45h+cFhiI0nHczMGAIEgbxCDPbVf425Rg9lX/MuEVVxNt5WVp77dhfhoC3/PX5eUtMAACAASURBVGjj1xjxavx9IWuTGQQzYlDViP6Xb/uWTahVU9GpsW79RkyfNVvSSnp6epz8YNUqlXH4yFFMnTaDSMaxIbWSwGRB2R6BW9evwpacDKY2ccpUbNu+U1LNdF7I3t074ezkiA0bN2P+wr8EZ19IWkiJIDZjUAmwRLpm1YzBxoTgKkeILmp+fu+xc/tmkRUq3HSJXPTAQcOgp6/P/Ted+XfzhpeoHAYGhug/cAgnOU3JwHVrVyCSEHzKmIdHc5R1U0jznj9/BrdvCktj2drlR89e/b4us3XzOvIAT7nO21at28O1eEkuRyiRpVq7ZpkyZWPQ4BGcRCmdVbiLdDsyYwgwBBgCDAGGQE5CwMBAH1cv8kssCu2nZrlK8AvjlxXcsmULevToIZSK9/UPHz4SkpAQRWTUxW1CFtLPWLSTsGH9eijioFAsUKVR6dOJEydi5cqVSE5OFp2azltcTBR4atT4pojAGxxODmJdIYTgHTK7JpGsU5fIvNdTyKjSPCNHjswwvHPnzti1K/fIkMcTWfXjJ0+T6+vNXd8i5PpWJNfXvUIFuJUT7pbjwzgyMlJQxpbGDx8zCXb5hbuCTJK08FukMJl+3yYWZi3Sj574vs4vL/1Q/pLw5+Z7dnGwaJZx92hqzpDnfnC7IiJXgQRYNCkn+p7+oeYvIWjYJOvnhd7oMgvOZurvHJYKzMbHFzD9+j6Ex0dLTcHishECbWsMQfeGE9VaUVhsIF4l/IsEHWnz7tMWp5NgiuJ6zWCoa6a2mt8G+GDOjl4IDs9cFUhti7PE2Q6BvEIMbja5iea6n2TjXyS4GSJTpCtLaCIFX+yVa+bKqOgPSfoo5V9X1H4sgjaJ8lO3k0boA0S+3AeqIKEu07Cwya+cfpi6KsmivMZkJsLjh99OdlYiUm4BgYGSVzcgM6Uqky9BFy9flpyDBTIEsjsCrq4uOHPiGFcm7RYsW6GSrJItLMzJgwwH7jR0djDWMaj6q5AVHYO6OroYPmr81+I3rKOytsrLC9Sr3wjulapyeei8vN07t4gCpG7dhqhUpRrne+PGVVy6oLzMAiXXKMlGLTo6CmtWLRXsOGzSrCXKlFFI+t4iROIFQigqa+bmFuj36+CvYesJqUlnIYq1seOncK7Pnj3B4YN7xYYxP4YAQ4AhwBBgCGQLBIq7umLH1k2Sa6Gfh93cvsnrZ5bIh4yaKFlScRBHVfb46TOOQKIPCTx7ySMd+Wq6e/cu+vfvD/pvZaxZs2ZYsGABSpTgkY2KTwIWkM9NcWkedOiTh0ZjGgA6mhgxYgSWLFmS4bJSujCVqT+rfV++eo0jx45z4xrU0RFKOzqpVCyfde7eB24iv98NirCHaTL/A75P2rGI68lPDNJ67Nc/ghbpiOMzf5IrXlW5dEiuHsJ18dXTqdsveElUb7LSltbzRDdXkYR7Fhb2JPgDhl7cgHuBb7NwVbaUOhGo59YZg1urd0zE6y+3EWLwCIka8rt/UrHQSTZCgcQasDMhnedqsrCoYMzf7YlnvqwrVk0Q55i0eYEY1CBk3FvL4zDSIJ/XZNjTpHyoGSIsfc63hKt2JG7YXZFRhSL0Qqwl2gQJP8vWTIqC04sWstdTVQL/D36IiVHfwZs8RwwWK+aMs6cUuvBPyZeqxs2yz8VW1U3D8jAEVI1AE4/GWLNyOZf22ImThMgQHnav6hrUmY8Rg6pHNyuIwUKFHNClW0+u+NevXmLfXmldrE7OLmjXvvNXEP6cxz+LJdVxICH08hFij9rypQsJsSftL+umzVsRWVDF6eUD+/fg5YtnvBek36+/wdxcIc+0avliRERGSLqAfTyJHKi1NRd76tRRPPC+JzpPKjF4n0iQniZSpMwYAgwBhgBDgCGQkxCgahEL58+VXPKRA4cxdOQw3ngq0UgVBei/c6rRGcQrVqzApEmTlJL+p3vu2bMnZs2ahQIFCmS8/RNPgOtv07/WohRQqTDatGmDQ4cOZRg3fvx4zJ0r/drl1Gshte6WLVvi6NGjvOF1GzSBR7NWopbwiLZE+QR+Oc7klGT49i0tmE9r6x3YJ/DLiYrNpbv1LvInKBRAMjOxufhyLFy8BNt3ZG3HaqPCZbGr2XBBPLPKIS4pAfNuH8aSe+w7QFZhnhXrFLMvjzl9FYfB1WUPPh9HbD5fQjmI70YXW4sWdGEWVRrOllXEhkjyCwh5h/uvLuLB60vweXcNEdEhkvKwoJyLQF4gBstrh+CMufwGqM2xDhgdIb1Tn94lzfUDsN1K/LOqzO6sNREOGBcmPOuQEYM5970pqvLKlSoRGcMdnO+16zfQmZz4YsYQYAjwI9ClU0cyU1MhuUu/CP1vsqJbKLcYIwZVfyWzghikVRuSLvASJUsjnJzcfyFAqGW2S1NTc/xKJEFTbdXKvxERzi9rYmNjh159+nMh79+9xe5dWyWD6FzMBW3bKYjJ+/fuknkwwl/IHB2dUdihqKRuwdRC27XvAidnhWSX19XLuHrlgqg9GJG5jr8NUch73SASw5cuKt8pKWoh5sQQYAgwBBgCDAE1IUA/244bM0py9j//mI3lm9bwxtOOucePH0teIzsFfiYzhWkXH51BqIzpE6n2YcOGccSiiYlJ+tAIIiW6+KJCRjTVLMn8xMG14EZm6t2/fz/DpebMmYMJEyYoU0ae9h07dizXwclnJUqVRU9yYEyMFSNEXvto4VlBt6sYwLoU/0zEoFPeqPhBV3DZe9WMYVGCXzY35OR9uH0UnpPoXcME5q72gmtm5nDV6xqGjpD+u0Pqwk97LoK14XfvIanJZMSd9/PByIub4RshXmlExnIsNAsRWDzoAuxtXNSyYnxiLB5FHEaCgXrvGw0yO0wv3B7lbJqrZR8ZJX3t/xCP3lzliMIn728gLkF1nZBZtgmBheytnGFmbIMEciggNDIAASHvs0tpP6WOvEAMjjB8gSlGT2Xj+1uEG3bHCkuQ8y00LN9rTDd9LruWMaElsC7SQTAPIwYFIcrZDu4VK2L/P4oTXg8ePkKL1hkPNVfHLvv17UO6S4y51IuWLFV6CQsLC/Tq0Z2L+/jRH7v/EZZva9miOTfLjdqu3f/A/5NCH5hKOTYhg8jpbIqGDep/rSUiIoKTptm1ew9OnDrNW2OVypXQvm0bNG/W9OvsglevX5OH8y9x7sJFHCOSKBFkroGy1qN7N1hZ/dcNs2YdaZlV/MVqb18QjRs2RNMmHqBrp1pQUDC8rl3D9p27OLKXz+jpYFpzq5aKTtHo6Bgyl8wPj3we4+z58zhyVPiBvNj9NGrYAM2aNkEp8lDAmnTlWFpagM4I+ej/ERcvXcbBw0eIbCGZqaGkWVtZ4ZfuXbmot+/e48DBH0/TVq9WNR1GFJfrN24qudI3904d2mPBf6eqDx46jGEjR0vOpYpAur8WRKbIza0sbMjcQ5v/up7ovXDq9BmC7WHcuCle4qFO7VrcvFBq/+zbj9Fjv8lTli1TGv08+6BE8eLkGlpy9ya9jq/evMaVK144evw4uYc+qGJbyJ8/P1qR92z9enXJ6er83ExHOnfkU0Ag3r17R7o1T5DOrjOIktiZ9n2RdN4jfT81JR2hlMiztbWFhoYGAgIC4EveFyfJ74Bjx09y8rHKGn2/el26wIVRydg27TsqmyLL/LW1tTFy9P++rrdty3r4k/cpn1WpWhO16ygkEc6fO43bt/h/9/Dl0tHRwYhRigdc4YSQXE2Iyayw5s1bo2TpstxSD7zvkntL3O8/S0srePYbpNi7yLmIWbEftgZDgCHAEGAIMATEIjBy2FD0+KWbWPcf/Eb1GYT95xUqNJlZx44dsWfPHslrZMfAS5cuwdPTU1Ca8vvazc3NOXJw6NChZL5zGiIoo67BDm4wq1Ui0w7FRYsWcSQlM3EIbNy4kbtmfGZpZY0x/5smKqFeCpF6DS8EDfIPn/mYxcC4nWIOeGYW/SUCJQ4Kfx9WZa7H5nEwass/s5CvZvrdrGbdBmqd95PR+n1K1sWC2uqTDha6+J+jwzHp2i7seyH9O4/QGuz1n4dA08qe6Nt0ploK+Bz5Br4aXkjQEp4DqqoCdEhns5t5e2hqaKkqpeg8j9/dwMM3lwhReCXHyY4WsHRCIUIO21sVg721C/mzKwpaOkNHW++H/fsFPscT35t4SAjR2y/+RXwCOeyTRywvEIP7za6hjq7yY3q+vwUqBdfH6yQjWXfGErNH6GXsJysHDW79uRIuxim4Bj5jxKAQQjn8dTrX7NL5f7ldhIWFoUx59yzb0Q2vy8hvpxgaXdhR0aWhjDk7OeHcmZNcyK3bt9G+k4Ic4rO1q1bAo3EjzoU+mKcP6MuVLYP1a1d/JVMyi3/95g2GDh+Fh48epXOhXZezZ0yDiwv/Hii+fy9fgbXrFISLWPv35PGvud3cq5C5V1/QuFFDLP5rAeiMSD67Q0jNYaNGw9c3/S+N5oSgmzLpf5lL2fyXlBKnM2bNwVFCako1z969MOS3QV/JTb48VJbzzwV/gWIt1koSovHkscOcOyUYe/RWfNGjZO+MP35HPUIqfY/ToiV/SyKjU2uiRNWm9Wu5/7z/4CFatmkntlyV+lFSd8TwoXAozH9ilC76lhBpCxctwSFCwApZRsSgoaEB/l70F3fvCdnOXbsxc/ZcSUQ4zU3f26NGDEOL5sLD7ClRvn7jJqwipHl4uLQP15RwHDJ4EDloIK5jetv2nfh72XJCUAYIQfH19ZxEDOrq6WH4iHFfa9+8aS0CA/iHLLdt1wnOxVy5mF1kJqEvmU0ox/p4DiCynjZcihVL/yLkb5ScdKJiW7RogxKlynC+d27fxLmz4gY6Fyhoj+6/9OHiTpDfRY8eZXyiX1QRzIkhwBBgCDAEGAI/AYG5s2aI+oyXWWm9W3bChYf8h+6mTp2KadPEkS0/AQLJS1JiZP78+ZxMaGyscg8CC5PP8DNmzECPHj24w2jIoGsw0cYIOkPqZFrfypUrMXCguO42yZvMRYHXyAHa6tWr8+5IS0sLM+eLP7jcMzI/CiT9+KA47SJfNOMQ0VuYgLPZ8AAG4J9ZKDaXLcmlL5ArWCsOkb2E6+IDrP/A33DnrnxZM2Vvs6udpqO4RUFlw2T7b3l8Eb9f34vweGljE2QXwBKoHYGVw2/B2kz199azoCuIMHqGJI14te/h+wV0EkzhotsUxnrmWb526oKxCdF4/PYaIQqvEKLwMt4FZA8VAdoZWtjKBQWti6GQNSH/SEegg630ecix8TE4c2cLdl+YD/rn3G65nRjUI3MF31ifgp6GPMnfL8k6KPbZQ/btcMz6BmroyZfsLfGxLvyT+SXHabGMGJR9ybJ/Ap8H97527vXo5YmLl+Xr5orZdXYgBsk3MOzYshmU9PC+/4DrDoyIUJALWlrapCOvAYoXVzzspkZJCEpAPvLx4f67QoXy2L5lE4wMDbnuwytXvbiOu1RzcXEBJeHSGu1oGz5qjBiIOJ/viUHaHbZiqWL4PO18u//gwdcuQgMDA7JeU67bKdUCidRNi1Ztv5IYtJ6VyxVfdGhHI5X/CA7+JmFQyd0dNWuk/7K04K9FhAhZIbpm6kgx3bF1Czc4Pq3dvnOHrPsGQUFBKFq0KCoSDCkxk9b6/joQZ/49K2q9jIhBV1cXruPNjnR8ZWRyiUHakXf7htfX1NVq1eG65rLK6HVeu3oFatesmW5J2vVLh7/TLkxL0lFLO+6oLGham/L7NGzeuo231O+lRH//Yzq2b92M8m7lOLLv8pWrXCdscrJi8K41OVXbskUzmJqafs377v17jnwPDv6iFCx1apFuxc3pyfMXZE+0Q/A1uW8MyH1VuHAhODs6oWDBbzNaaLdp2w6d8JncV8oYvVf279n1tcuXxtLa6Vr034mJSXByLIoiRRxQtEiRr6kpCdmhSzduNqsYyyopUTG1CPnkz18Av/Ts+9VNjJRo3/6/ETJecdpoJZnzFylxzl/qoq3bdISLa3HuP3du30R+rwqfnhbal9Drnbv2JPeWQkrh4oWzuJnmPc4X6+hUDO07dOFc9u/bjVcv5cs6CNXKXmcIMAQYAgwBhoAqEdi0bg3KksOSUq11rUa47/uCN3zTpk3o1auX1CWyfdzbt2/Rt29fnDunvKR42bJlMW/ePDRpQr43HiGHUG+n/9zTdPsUnHx1J0MMdu7ciS5dFJ9DmAkjQL+DUvUaIft95kLok+9cYqxmnBlqxprxuqaQKWJvfnGBli6/vGfCrttwjiYSsjzG5erpCi2i8sFnSTtvwzFGONf7PuQBOCWmJdq6DZuwYtVqidHSw8pYFcaFDr9LT6Bk5PMQfwy9uBG3P71SMpK55yQEHPOXxfxfFQ0IqrR7nw8hPp8/SZmiyrRK5dJJNkKBxBqwM3FSKk5dzhHRXziS8D4hCb1fnkdwuHqfqdGOP0r80Q7AwrYlUMDCkfxb8cxBHRYc7o/lh0ZwJGhuttxODNYmnYIHzK/LvoTH4uzQM1R+M9ZTu/Ow046TVU9kshbsPyqapoSMEYNCCOWC1ynJlNqZ85w87KfdT6lylerc3s8mBunJtrmzZiIkNISTSrznnXGXB5VbpadoUzsCX756hfqNmnBSmJfOnwUdJj9t+kzs2vNPhnBRqcXRRN6le7dvX9ioDr+Yzi2aMC0x2PWXnti5bQtHCI6bMJEjLjIyKts5b/asr11654mUaS/PfihdqhSOHzlISLlgTJwylZNGzMhoB9rvUyalk1Vt3a5DphhllGPX9q2gJCY1ut7ylauwZ+8+QrxG/OBOicjxY8dw3Zup5tl/AP49K/zF+nticNLU33F4/15CUlhwp3a9rl3Hk6dPOQnKVJMrJUrznDp+hJPTpEbJdEqqZ5Ud+Gc3KpJZI9So/OvqtWs5su/Llx9PjVBylHbfdenc6Wt5Qvff9x2DgYGBGDxoIP4iw+VpZ15mp6G7de3CvVdSjcrvDhg0WDQs9L22fctGUOKTGu1UnTBpSqadgJS0X/73EhRzVnywff78Bdp37pqp1NL3hdCO6f0Ey1Sp3pu3bhFZ2DFEmjjjD6QUyyWLFqJaVcUAb3pfUzIys/dh2vVyUsdgKdI114x0z1GLI++hv5f8KXgNx47/Nmfzz3nf7gHBwEwc6tZvjEqVFDifPHEEDx94S00lOm7Y8LHQI7N/qB3YvwcvRc5opDMdW7RUyHBv37YRHz/Il3UQXTRzZAgwBBgCDAGGgAoQOEG+H1AJdalWs1wl+IV95g0/deoUGjduLHWJHBO3b98+DB8+nBwaVF5av06dOlgxcwFKniUH3ZK/PTw+RUjBJoQczMhOEGl9jlBkJgqBlJQU6BF1jISEBF7/keOmwsZWoW4kZIUS9dE9Stj3TnFNWFXnfwj9+fJjuPNz7Fw5t0trw7oy//wzsbnulNWGlbv0WWoPH/lwzxp+hg0s2wizqquXGI8jc8QW3DmCpd6nkJCc+DO2ydbMQgS61BuPDrWHq2zFmIQwPIk+iQQ95Q5Mq6yA7xJpQRf5Il3hapX+kLm61lMmr/+XN/Ah8wm9X12Az7triIiW1hVFOwDtieRnYdL1x8l/WjiplQDk22NySjK2nJ6Go9cVimO50XI7MTg53zOMMnop+9JNiSiB5VGK0WZSTRfJCLTnH3MmJve9eFPUC6wmxpV1DIpCKYc7OZKurQtnv91YlOT4H3kYr6o5YZnB87OJQUoCUFJl4OAh3L/5jHaIHaNfmP/rbKNkRWHSldepQwd069lLVNcQnUlHZ9NRo91NNet+m2XIt3ZaYpB22/k8fgLa9SVklLQ6tP8f0CH31Dp17Y7evXqSTitHdOvRC7STUMh279j2lQS5dOUK6SJSyOUJ2fQ/pqJ3T4XmPyXmBvw2RBRZM+l/4zGgv+JLBSVd6jXyEIz7nhikczoocUNPLS5bvhKRUeqRIKTdetsIiZVqlPScPXdehuScEF7KvD5syG8YM2okF0JJ6t59+4uazdimdSsiBbrw61J1GzTOVLI1bccgncXoROZy/jZkmKg5hVSql0r2pppYQpm+t06fOAo6c4Xa9JmzQE+fChn9Yk/JumZkNiC1s+fOo0+/X4XCuPfF0YP7vxL+dC26phibMvF/6N9PQQQ/e/YcLchhirg4/hM7OaljsHVb0q3nonho8YKQYwcJScZn5mYW6DdAQQCLJRKFcK5WvRZq1qrLud24fhWXLgofEhDKyfe6E+n6a/df119ycjKWLJ6PRIEHRqn5KlSsjAYNFfff+rUrye8A5bpW5dTNYhkCDAGGAEOAIaAKBO7evCY5DSVaijk5I/E/JYnMEj0gKidlykjvSpRc4E8IjCbzrydPnoy///4bSUkKhQ2xRiVFLwz/G7XTdHRQjCutG447/j8+mKLSmFWrKg5jMhOHAJVw9fXlV6PoP2gEHJ3FkWVahMMdGe5ARDv5u+5eGEZDtwt/t0BCTBycdr4UnFn4wigGup35ZxaKzmVMcnXizyWEbM069RFN1JV+hi2r54murjXUsvSVj08x/MImvA0XfnailgJY0ixHYF7/k3AqoJg7L9f8wp4gQOcWEjXV80xKen0a0CezUcvZNJeeIgsi33x6RGb2XeY67p68v4G4hPS/Ywr9J/3Jzf8jnYBcNyAhBbOj7b20GLvOz8+OpcmuKbcTg6ctr8JdJ1Q2Th7B1XErQZ6UbzmdcFy0/aZeJ7Wo3VEFMCBE3O851jEoFeUcFkdnwI0bMypd1avWrOW6g+hMO3XYzyYGA0gXVH3yMJdKI4ox2oW3fs0qzpV2FFFiYdz/JuH0GcWMRiGj/hfPnfk6V5GSbJRsE7K0xOBdot3fhnQoibVf+/fF5P9N4NwfP3kCSqI1aNwEVJpRjFGy5hZ5KJ9qNerU+2Fe4fd50nabvSGSOo2bthAkTdLmmDd7Jrp26cz9LzrLjXY28llaYpDOcaTE4K8DB2eJJG5aspfWGEUeAtCuuh07dnF/VrVR6cxrly9yaWnXXn1yLZUh8CmZRUktapTIHDNOcW98b2mJQfqaUIfh9/Fpr+Huf/Zi7HjFmnxGSdZUaVQxcqff59pBpE5TJXDpPUPvHT5LS17TbksxZHvafDOm/f51JqEYUjGnEIOWlpbw7Pfb163u27sLr1/xH1tOKz0aHRWN5cu+EdBC1z2z193dK6NeAwXZ9oB0C54iXYPqtPYdusKRPNSk9ubNK+zds0P0cmlJzGV/LyQd94r3vpGRMUqULEWIdReYEJldIzITNjkpmZOrDgoOwgff96TL9als2VXRhTJHhgBDgCHAEGAIZIAAncWdOnNeCkAvieJMQxEzqD+TQ4lWVlZSlsixMY8fP0afPn1w8yb//MXvN+hknh9PB6+BtqbW15d2+1xCl31zf8DiCfmOV/w/FZMcC1QWF16lShXBa9LlF0+UKy9e8qtTlA0cE/llOyORiGBP4QdxZhu9YZqiy4tKJBJIrnKCyFlsuI984JcvjSC5vojIxbcY/V55jqgU/SwbUb45plRpp7Llg2MiMfnaLux5Lv3QhMqKYYmyDAFjAzNsGqeauXePAs8gxuQd6e/h707Oss1lsJBOtCXczNoRFTR+WeKfWWPatZ/53kI4kR/Nb1402xKAfFhtODkFx2+szy5wqqyO3EwMGmkk4r3dKYFjP8JQJpIMBT41QWKKprAzj0dbA39stMxY7VCZxDPCimFhhDhJYUYMKoNsDvedNWMaenTv9sMu6Nw8Sn7dvHWbI5dUZT+bGFyydDkWLlqs1Hbu3rz+VXaQEjLVa9dVKj5tR9zK1WswZ56wRF9aYnDI8JE4fOSo6DWpnKb37Rtf/ZXp+ksNWr1yOZp6KGR/KBG6azd/91Bagod2KdKOM2WMEqg3vS7DzEwxp6FshUoIDc38dEZaYpD6/z5tBjZu3qLMkpJ96WzJZX8vRoP69X7Icer0GdAfOgNSLBErVMjI4UMxcvgwzo0SkIv/XiYU8sPrB/fuwcHDR7Bpy9ZMY9OSu3R2ZhXSvaWMlSldGscOH+BCKAFfqSr/CU4qBXr2tELHn94v9L5R1qzJg6Y7/512p3hTAjwzy2dszM2IpJKldDYknREpxShpTslzerjAvUp1XgnmnCIl2qp1e7gWVwza/vIlmHTACc8WtbcvhK7de3MxdLYgnTEo18qUK09ksVpwaSh5duhAxlLNcteh8YULF0HnrooOZ2r/7N6Ot29fi05dr14juFdWnNRPlVF1JzKo9Ygcqhi7fu0KvK5eJh0FTJpIDF7MhyHAEGAIMARUi4C5uRnOnjohOenlC5fQo3dPwXja9ZYXje573bp1mDBhglIHbje1HoVe5Rp+hYx2ZBZe3Av+kekP7b5584bMwi6SF6GVvOe2bdvi4MGDvPEtWndAjdriFH5oIve4fGgYq5i3nZnR2YAvOxSBrokRr1/k3tsoFS48G1BMrph/7qB4BP+sRFrXq67O0DHQk4zp3v0HiHrOz+2Gcbdzwqr6/VDUxEbyPmjgtieX8Pv1vQiNy25dXrK2xYJFIFC9ZAuM6rhGhGfmLsmECvQO3o8Eo5zRZaqbYIZiuk1grCevk0kWaHkkmH4emLalIx69ld/xlZ0gy83EYDP9AGw3z3jGszLX4Fq8OZoFi5Pu5Ms71uQlJpEfudYz2A2HY4Ql0Ok6jBiUi3YOi6cEx6zpf6BAgQKZVk5lMB/6+ODEyVO4eOmyoNRjZolukoehdGYXtcKOxZRGytnJCefOKMiEW7dvo32nroI5qMQhlTqkRnXw6ew9ZWzJXwvQtk1rLuTYiZMYNHioMuGoU6sWtm7ewMXQOXedu/0iGJ+WGBTTsfd9wkNk3l55N8VpwmUrVmL+gr8E10zrQMliShpTE+rgS9sVdePmLXTs8iPRLGbxtN1cM+fMxZq1mZ+qSUsMUgKREolZbXS23oSxo7+SmRmtT2U/Hzx4hBNkvsqly1ckzfG8evE8KMZ0BmilajUzPEDz5QAAIABJREFUnb0nd/9pOwaPk/f5QCIFq6xRQpoS09Qqk1o/BQRkmmLq5Eno59mbe52+p+h7S4qlnZfatmNn3LlzN8M0dN7nnJmKOXh/LvwLS4nkrBQbNWI4RgxTYENPydIuzMwsJ3QMFiez8lr+NyuP7uP40UPw8XkgCE2RIk7o2FnxXqfdcKtWLBGMEXIoXbocmjZvxbm9e/cGe3ZtEwqR/Lpnv0FkZqyig8HP9x127lDuYIFH05YoW9aN6xSkHYMtCbla/D9y9TMhxqm0KMUlhcwKMifvCTNzC3LAxDpdvf7+H3Fg325ERYnrYJe8WRbIEGAIMAQYAgyB7xCgh5xOkL/zpdr5M2fRh6iU8Fn+/PkzneEsdd2cFhccHIwxY8Zg8+bNEEOSZtQ1uOTGIYw4tfq7zxD+sLMT93Anp2GmrnoHDhyI1avT4/j9Wo3IAbX6jZqJLsEmSReekZk/Q0lNdLdwEiwb8kvqBnm/QsW7/GMKaL57DsmwaFCat0axue44pcCqTinR+/3e8T2RZm3TXryykeSFBAL1tXUxzK0p+fGAgbZ4opMS77tJd+DCu0fxjsmGquvyZPu8vzafh8bu3w6MKlvw5zA/vE05j2T9nPWdTjvZGHaJlVHQxFXZLTN/JRGIjAnF0GU1yfxE9SjzKVmOStxzMzE4z9QHvxq9k43T4khHTAvnnzEsZpHV5g/Q2eijGFden6qfauJporGoPIwYFAVT7nKiM7voLLyO5MetnLDUBZ3ptXfffqUf5v/sjsE27Tvi7j1vpS5ev759MHXSRC5m155/MG6C4s9iLW1X07v371GrbgPB0LTEoJt7FaVOmtLks2dMxy/dFaTprDnzsHrtOsE10zpUrFgBB/7Zzf2vCxcvoWefzL/49yEzDKf9PoXzpfKRVEZSilWvVhW7tis62i5fuYruPXtnmiYtMSiWIJZSk1AMnY3XtXMn8r5pBycyx1HIDpGuvR27dnMEsRhL21V38tRp/DpIMdNNHZa2Y/Af8t4ePXa80stQApwS4dSo/C2Vwc3Mrlw4R7q2CnEvFytRWinp2bQ5mzdripXL/ub+Fx8JvnnDOtSrq+gSrN+oCTerUYqlvfdOkGsygOeaZPeOQSp12cdzEJHiVUgNffjgix3bNomCxbmYK9q2UzwIUFnHYBk3NGnWkssZEOCPLZuU+70lqnDi1KhxM7iVV8xUSUiIJwc31iOYyHwqY63bkJmMrsVJ52gE3hP55FKly+Cxz0PcvHENnz9nTIjr6uiictXqoDKkqUY7NLduXof4+Hhllme+DAGGAEOAIcAQkIWAfcGCOHxA2md2uvCp4yfJPPGBvDUUJGv4+fnJqjO3BHt5ecHT05PMqX4muKXvuwYpofg+LBAHn13HlPNbEBEfwx0YTJ0pL5iQOXAIjBo1CosWLeJFow5RfmjSvI14xEhD7G8R9jBJ4Zfke6dHZmR1F5jnR65zoY2PoSkgXiY2V2GSS0Mg11v9GGh0kzdnsFmrtvhE1Gayg1kbmqBVUXfUsi+OWgWKw0wv4y5Nn2Bf7HzuRSRDvUDlQ5nlbQRWDr8JazN7ySBM39AFvuHe5JkZ+V6txy8HLHkRNQVqEslhk8jicLWqqaYVWNpUBA5cXYbt/87ONYDkZmLwms1lFNeW/3dDty8VcCJW0Rglx/61vgZ3vTA5KZBEPq9Yf/Agvc38c5FTF2HEoCy4c36whYU5KlaoACoNWKVyJVSrWiXTTVGSbfzESeRLznNRG//ZHYNCZEVGm2jftg0WLVTIf1JJTSqtqay9f62Y1xUZFYWS5OG3kMklBkePHIHhQxUkkhRi0LFoUVw4e5qLf/joEZqTD/yZ2dLFf6F1K8XD/Jp164N2l0oxKvH47L9OJTqrrwTpHsrM0pIzFy9fRo9enlKWVGkM7Q4rW6YMR6xXqlgRFSqUzzQ/JdYnTvkd/v7+vDVQsp7ONKRGZVroDFB1WdqOQanEIH2f0PcLtX4DBmU6i9PS0gL3binIUakyoqk42FhbcxKh1Pg6ch/euw1TQoQFBQWjwn8SkFKx9HlwD1SaNJDMzaFyoplZdu4Y1NbRwS+/9IG1jeKDCp0TuHnTGtGz7xydiqF9hy5crDo6BgMDA7B5ozxJl4yuSwnSIdkiTYfksaMHOUJPWevSrRfp5C3MhcXHJ+DwoX/w5rU4spnKmNLT1Xq6ihPN6pZNVXZvzJ8hwBBgCDAEcj8Cjo5FsXeX+Nm63yNy/PCx/7N3HmBRXUsc/9M7SFVAEbuxoth77y2o2HvvNSZRYzQ+E1OMPZrELrbYW4yxd40K9l5AxYKASu+8c3YFUdl77967S3PmfXzJy87MOee3y7J7/3dmMHy08A1rHh4erAOA8juu88qzkZSUhF9//RXfffcd6xaguV0hrxq8M/JPGBp8PJMm8PULNNvwDe6EyPu+lVdYyjnH5MmT8cMPPwiG1qrbEG3ZzV/aWGs2r6t8oo1gSGJqCp4OEK7y4wnM1/gjf5K5YK741GQ8HyBcfcgTWK4OgHOycPVcPFiu/uK5hDY08/vZ2L5DfvWxNqy19bVkN+UZGbyb2clF9rjkRPBKQTIiwAnkt/fA4tFnFcHwnVIIoa+fqL5X9+w7BC75c1s1twHMIgrCy0U90oNMPwTiEmMwbF5VVjX4Sj8LZHHWvCoMuhjF43aBwzqhWexZY4SnKL9Z4LHbAdgYKvu79YDNQ678vJ7kc5EwKBnVp+NYtUoVVm1TDy1bNM+0Mmr8xEngPebFLDdWDDZr2gTLfle3HZQrDKaJEjyHlBaqSoXBIYMGgs825CbWljOz54xXwl2+qJ4TyCureIWVJjtx9BAKsy/+Us8m9Bo5e/JYekvbhk2a4/6DzGd+vScMsta2vfpmvzD44bl4FW4VVnnZpHEjtGK/N7ydUkbjLVCHshaap89o/iA6c8a36NNL3Xp27ISJ2LZdf1+6dFExmLE96JjxEzV+SeRMVvypbuXDqyhHjR0v9tYh+DgXlLmwrElQ5tWcRw7uV+UQE7qlbOSfvbvAX4PchFqm5uSKwfafs4q3ku/aGqxftxrBTx5JOb7Kp5BHYXTtpp4tpA9hUJvqRambdnVzR89e794rzrF5kcePyfvQV7VaTRQtWhwu7Avgnt3b8fChNFEwba+enkVZK9Z3czXX+a3E02CqqpD6XJIfESACRIAIKCNQulQprF+7SnaS3dt3YdQ49QxsTVaE3Wj4QMNnedkL54HAx6z94qhRo7BzZ+af6xsX8cLBXpqrCrbcOAm/xOv46aef2Ge5knmASNYcYebMmZg2bZrgYlWr14aPr3Zzz8slWKNNrLo9vZDdbJ4flu7C8whf7faH10thYZCvcYPlstJRrpsdCsHSQVjYFDrXvwcO4qsp6u5BZEQgtxFo6t0TQ9oom5PZZJQxUt6KzWZm5kwcHIziGb5n5xYmJux9zMvucxgaCldA55bz5MR9+h2chR2nFufErWm9p7wqDPpaPsUfrHWnUrubZIWqL951ipKbz8EwAQ/c5F2zyrjmP7HO6BomvUMACYNyn7FPJI5XEnbt0hl8Dl1Ga8FmQ924eVOQQm4UBvkMxpXL1JUrcoXBjLPXskIYHMxmfkz9+ivVnuUIg7a2trh2ST1sVUwYvHPjanorGylnE3qB7Nm5nVXdqe+m9O3WQ1VNlpnlBmHww33XqF4NfVnbVS4SptmbN2/QsGlzVRVbZvb7ksVo2byZ6iElc/ikvDXpomKQv+b4a4+bkJCZcYbluvUb8fVUZV8mM7Ylzaz1bsY2tUorFPnZ/NasRL066nYbfM4pb2ebmeXUisHadeqjVu13dwvt2PYX7t4Vb22V8YxuTGTr8VZki2Iz8pYsEm7NJOU1WL5CJbRoqb5T8eHDe9jy1wYpYZJ8HB0d0b1HP5gzAZnbZfb+9u/+vyXF6svJp2NXFCuunrV7+9YNVnWoeV6lvvZAeYkAESACRODTJFCefd5evVx+J4odm7dh7BfCN3YVL16cfb5Qd00h+5jAfjaDfNCgQeBCYUb7sJVo+mOsDRTvAJXMLkAX+LUHXsVHq9qTcsErf37lrary+nP0888/Y9KkSYLHrMjm1ndlnxe1MdtkIwyPUo9HELLLLnHI16ayoM+bwOeocFh8BtXl/PHI11pzdxq+iNRcAW6JcGihuVOP2LlevXqNxs1birnR40QgRxKY4Psnan7WWvbeTlzZjm9/93kv3tDQEM1atUP9hurrOLnJTBPzoahJE9iZO+embeeavb58HYxh86vmmv0KbTSvCoO/OVxFd8tgxc+RX7Q7Rr5SVpHPN1HV9DUOuCirauZ5FkQUwbQI6fNESRhU/BL4NBLwlonr1qyCjY36DrNz/51H567vi4UfksiNrUQzVjflRmFQTitR3nKRVzlyExMG09qk6vpVLySE5cRWolLPn7EClccIvaYyClB5SRgcNmQwvv7yCxWy1Wv98M23M6Tiy9Rv/9+78VlpdfVbkxatWGvG9y9CNW/WFH8u/U31+MlTp9G9Vx9F6y1ZvBCtW6qraPmMQT5rMDPLicIgn29Xp26D9O3KbaXp5OzC5hMOUeWJT4jHgrnK7rTkebzZPNVGjdVfoO7cvoWdOzYrep7Sgp1Yu1nfLr1gZaWeM3L79k3s2iF/rpJONsWS8IpNXrnJjbdyXbxojq5SUx4iQASIABEgAoIEKlfySu+IIgeVFGGQV7NJmaknZ/28EhMXF4fp06erWowmJiaqjnW2/6+ozmakAW+VwEwO+9lvQ3ArVC0oWlpaYty4cfjyyy/Tv5fnFT66PMeiRYtUlZpCVo7dpNajzyCtlx0U6Q7HFPXMbk323DgO8b2FhUEe67b8GkwyaSObMe8zlitBQi53lstYJNdTkzgk9hLfl9DZuvXsg9t3pI2W0RouBRABPRJY8+UtWJrbyl5h0qLmuHAz82sBVarVQscu6u5PuclsjVxR2q0C4kPtYJ4qXOWcm86VU/b63douuPLgRE7Zjux95FVh8I7rYbgYJcjmkhY46lU5rI2WP7s0LU83JlIuYWKlUtN2PyQMKiX+CcVnrDLixxarGiRhMGtaiWasGNSnMJhRQNT1y57PcuSiWWaWm4VBfp4B/fri22/ezaqs6F0Nr1593Gt8x5a/0mcV5iVhcOL4cRg9crjqqdW1MNile0/VrMGMlnFOqK6FwSlsVuTadZnP6MlpwmA9NgO0OmuRlGZ/79mJ62/nemr7+2ttbcOqWMemh/3840xtU3zkX6NmHdSt11D1369c9sf+f/YqzuniUgBduvZMrxTkLT+3/CV/ppLiDWVIYG5ugVFjJqb/lz9/X4TXr/PGzAFdcqJcRIAIEAEioHsC5cuVxeoVy2Qn3r93H4aMGCYYTzMGpePlAiqv/jt9+jR2dpmGdqVqZB78VissuWgQ7oa/f0e7k5MTvvnmGwwbNgwmbJY02fsEFi5ciNGjhdvfVvKuDl82R1pbaxzrgKoJwuJCMlLwpF9ZVvXJyj6FzO8iCieoO1xoMqm5jNZeRMFEkVxs/uETCfMPhfazYNFvWLVmrbbYyJ8IZCuBYkz8+nHQP4r20GGSEyKiM+/+xBN7FC6iutnA1i6fonWyKriATVHWuashLK3Vc9Eiw5MQdDsMzx7GIPWVM7tpwTqrtpJn1zlzfTfmbFHfYJ2bLS8Kg8WMo3GxgG5E22rP6+BOkvLfl29s72KCrXZjazJ7XTUPqY5zCfaSX3IkDEpGRY6cAG+zydttcps7fwH7WagRDAmDeUsY5E90xopB3v5TV/bgwUOEvHyZabrcLgzyQx099C+Kstkr3PiMPT5r70PbuG4teBtMbsNGjsbev/fpCu9HeRo2qJ9+gWjz1m2Y8IV6RqU2lrGV6LgJX2Dr9h2ZhnNRkIuD3Nb4rcPUadO1WeYj33//3oPSpdVl8Z26dMd/58+/58Or+3iVHzddC4NCAraHRyHwNqfc/AMuoUNHdXVYdlhjNie0MmuPlGY7WcXcHVY5p8TGjv8q/cLT/Hk/ISE+Xkk6NGzUFFWqql/vx9js0v/OnVaUj88U7MzmxPB5n9x4u1TeNjUn2ajRE9NFy00b1+JRUGBO2h7thQgQASJABPIogZIlSmDjujWyT3fsyFH0YTe6CZmLiwtevHghe41PMXD16tU4OHct1raf8Pb4H1QNpqbi8ouH8PpjpEY8Rdls7VmzZqFLly5MgxIRoT4hyL/88gu++ELdsUST1ahVD+1Zq3dtrQQT3zrGiLdzDahmAYdy6u9/mizswBVUfiw+4+tyDSvkK1NYJ7ku1bGFfUn5lQ28cxT/rkpGBHITAZ+6o9G9kXr8jhxLSUkBny+oru7WbDa2dug7cDjc3MVbDsvZh25iDFCpeHU0b6e5RTH784Owp3EIvB2KkKAEmMa4w8hA/T2bTDsC/X8pxwRl8bbR2mXNWu+8KAz2t3qEX+1vKAYZmWqEQsFNFefhCVY5sOuIls8V53IPboLoVPHPFmkLkTCoGPmnlaBL5074+ccfVIc+zL4k9h2guf0GzRjMemEwN80YlPqbkxtnDH54tq8mTcTwoeo7hZYtX4nvZn3/0fEzzhgcPmoM9uzV30y0+vXqYu2qFao96EIYHDN+Irbv2JnpU5pxxuD6DRsVD6w/uH8fSpYorlqrcbMWuHvv/TtqMs4Y5NWEvKpQifG2pLw9KbdBQ4dj/78HMk1XsKA7Th8/qnosO4XB1q3bo0y5Cqp9JCYmMHFsMwIDHyhBoIrt228InNlFP27r1q7A06fKerF38u2OIkWKqfLt2L4Zd+/ckr1H94IeTBTsxoRL9d2ON29cA2+bmsq/0eQg69d/KHirU257dm9X7ZOMCBABIkAEiIC+CXgWLoxtmzfKXubCfxfQybeTYDwfNxERESF7jU81sEHdevhf8Xao48GqyzKxMf/8jgX/Zf4ZO6N706ZNsWHDBvA5y2RQiaVTp04VRFG/UTPWAamD1rjMUg0xKqIQjPkQSAG7ni8W1j7egj4x4ZEoveMRy5R1uW7Yx8Pqc+GZhUKbTkhIQH02DiBe4U2CWoOnACKggMD0PltQzrOW7Ax++2dhxS7h95S05LyK26dzD3ixTlE5zUwMLNCyQTOUqeSm1dbYuFs8D4xC0J0wvGKXASwS2Y0KOeurtlbnkeIcEvUQxoZmcLDUjtWHuf0OzsKOU4ulLJljffKiMLjaMQDtLZTf0LYvzhndQoX/1kt9Yk+4nEJ500ip7pn6hbFW58WeNtYqBwmDWuEi5yre3ulfLK/fuIGWbdprhEIVg1kvDOqzlSh/ojOKvWXKeyEqOlrvvxR5oWIwo6DOBT8u/H1oP/0wC127+Kr+81dTvgEX0fRlGdsC60IYHDthIrZtz/yiRds2rbF4wTzVUTSdXZtzXmCVZS5vxZVKVasjLOz9u6/KlimDfaxtJjex9ygp627euB7Vq6mr7zoy8en8BfU8zg8tJ7QSbd2mA8qUVQ89jo+PwxZ2EfBpsHomjVJr274jq9Qso0qzb+9OXLt2RVHKocNGg99RyW3FsiXseQyVlc/Tsxg6+HROr2b0v/gfDh3cLyuXvoN69x2I/Pld3zLcxRhe1veSlJ8IEAEiQASIAFxdC2Dvzu2ySdy8cRMtW7UUjOcXQrlgQKYdAU9PT4Q9C8HUul0xoaYPuwhpyBK8E4n237+IFuu+kZS0RYsW2LdPfx1HJG0ihzhNmzYNM2cKt75v2qItGjUVfl1rOk73qALwSDYXPO1rwwS86eslSsRpxRVYMZlRyMIN4xHZV1zMc2a5LEVyhRnFI6qPeC6h/fCKQV45SEYEcgMBUxNzrJ+s7EbZwbMr497jAK2O27BJSzRt0SbHVHPnM3OFb5dmcHCy1OocmTknseZBT+6/waO7rxD1zBQWKcrEM8UbUpAgLOoxQuMeIjblNVLYHFaYJiLZKJY1hE6EcawjvO3V1+jk2svXwRi+oFqOu2lZm/PkNWHQgKnaD90OI5+het6zEpvxpiTmRhZVkiI99oX7fpgZKFPcz8bZo0Voda32Q8KgVrjIuVSpkjiwTz0L6sbNm6o5g5rs3Knj7Iuo+iJo2YqVERmpnfJdvFgxHD6g7gPOL8bzi/JilrG6p0MnX/j7a/fHu0njRljx5++qZfjMO946UFu7dOEcHBwcVGEeRUuIhh/852+ULKn286pSHeHh2pWZZ9WMQb6/NSuXg4tK3Oo1bILAoCDR8yl1yAvCYMb2lprEsYH9+2LaVPXr7ec5v2Lh4iVK0WmMz0phsASr7jvEqvy46aKCL62dbWhoGCpX+3gmi7m5Oe7cUA/sffb8OarXqquI4xEmMhVjbZq4lfPy1ng3fHYKg4bsIlI7JtyVKFlatc+YmGjwVpWhGtrzygFStVpNNGC/89yuXLmE/fs+bocrNa8Nm1k49O3Mwvi4OCyY/7PU0Pf8ipcoyc7dCUZGRqr/fvrUMZw6eVxWrqwIGjJ0NJs5oRZDt23ZiPv372bFsrQGESACRIAIfOIEHBzswb9vyLXHjx+jbl3xz1M5rVJf7nmzKi4pKQkWFhbg/+RmxS5etytVHes+n5R+IZkzrbpsDC4+uydpWxfYd2ZvdiPvp24TJ07EnDlzBDG0YZ8ha9drJAtVnfh8qBMnPEcslV947FkSRqbCMyATNl5AiRjhC/W6zvWoH7vZT0HrWT5jkM8aJCMCuYGAV7EGmNpT2dz51uNtERuv3fVMzuazshVUs0z5vPnstGIu5dGxW20YGumn5XRcVIpKJORiYfxLO5in5rzq9fDoYITEPkBcKhMAjbgAmIBkY7UAqMkMWTvG0gY+sDFXdp6Zfl1x+X7OvU4h9trMa8JgRdM3OOZyRuzYkh5v9bIaTserr/8rMVfDONx0O6okhSp2TXRBjH5VTqs8JAxqhYuc69SuhfVrV6tAHD12HL37DdAIJWPLv5p16yM4+KlWAEkYlIYrK4XBcWNGYdwY9UwBofaR0nYuzSsvCIMZ22muWLUa07/730eH51VpvDqN2z/7/8XgYSOkAZLhlXHG4JZt2zF+4iSts2ScMcjjeZ7MjM87ucUqo/iFjzgmApViH47lXjjKWLHMW3ry1p6Z2d+7d6BcWXVLpqo1auNFSIjW5+MB+fLlwxV/9d2wvGUpb12qyQp7eOAEm5XHLStbiXJRzKdTV3h6qsXLiIg32LRhLV6/fiXrzJqCnJxd0K+/uh1udFQ0flv8q+z8FZnA2qx5K1X8rZvXsXvXNq1zlSr1Gdq08wEXRfnraf/+vbh6WbsbQbReVEGAMaukGMfmNKbZ6lV/IuSF8v7xCrZEoUSACBABIvCJELC2tsbxw5m3QpeKgFe2idlzdkNW/vzis9fE8nwqjwcGBrK26h/PoNvR5Ru0L1UzHcOm68fRdetsSViWLFmCoUOHSvLNy069evWCn5+f4BE7dumJKtXktRYslGSOHtEFRBH6lzaEYy31jXuaLOz0LVS+lSKaK6CsMRyql9RJLv+KJnD0Fr+BWdNiN2/dQo/e/UT3TA5EICcQ6N10GtrVkv++GB4Vgk5fyv/b5uySH/0GjYS9gzJxSQ5LQ5igftWGqF5XPcIjqywyPAlBt8Pw7AHrMBbhAuNU66xaGuHRT/AyJhCxeJUuAHIhMNlAXlcDs0h3eDlrLoiRcrDTN/bg182DpbjmSJ+8JgyOtnmI7+xuK2adxDo8FGDzBZNYi3GlZmWQjEomb1RpYk4MQCprZStkRs7VYeY1DQbG79908CjZAvxHGyNhUBtaudTXw6OQqm990KNHik+QUYRavnIVZsycpTHn1r82oGqVKqrH+/QfiCNHj2m1fvly5bB3l1pskFUx2LGz6gK9Nta4UUOsXPaHKiQ3Vgzqe8ZgxvltSttCciFMSguS7Jox6FWxAh4GBuHNG/WbsxKbPWsmundTD7ef8s23WLvu4zvWLC0tmICmbs/46tUrVNRjT/qsrBjk51m9Yhm4GMnNt1sPnD33nyycX0wYj1EjhqliubjKRdbMbNqUyRg4QP1lVUlb1k4+n+PXX35S5eHPGX/uNFl2VAwaGxurRMHChdUXlUJDQ7F5kx+iorS/m1HKEzJk+BjY2tiqXNf5rZLdprR7j75wL6geyP43mwV4/bq6wlOqla/ghRYt26a783mCN7TMIXWtND8zM3NVe1a5VrRocXTsrK56j0+Ix4K56tcVGREgAkSACBCBrCDg/5+yO6Mrli6LN3HCIwQCAgLg5SXeOjErzpsb1jh69CgaNmz40VZrFCyNM/3f3YCVxIY7eczrg2dR4l1l5s2bhzFjPh5ZkBt46HKPvK3q/v3CreW79RqACuxmNTlmxDp9jYrwgDmELwbetYqFaRfhNZITElHE747onEGd5rJm+/KVd/Y0Xs1atkFoWJgcfBRDBLKUwJyhB1E4v3okhhxb8NdI7DimbEacpaUVeg8YhsJvb+aVsw9tYyyM7NC5Qyu4FRaubtY2r9b+7P0y/Hk8Am+HIiQoAcZRbjAyMNM6zYcBL6MeISwuCHEpTAA0Zr1NWQVgijETACFPANS0IZMUG1S27Klov8kpSRj0qxciosX/jitaSE/BeU0Y3Op0AY3N5Y2yyYj4fEI+NA35uIuZ0qfhzRp2zS0pSjSNgbUnLOsuh7Hrx58lRYMzOJAwqA2tXOjbonkzzPlpNl6yC8Y+nbtq3abywyPv3LYFlbwqqv5z1x69cPrMWY1UZs74Fn16qd9AxUTEzJK0a9sGi+bPVT1EwqDmF19GsVbfwiDfRcbWivUbNWXiWaDWvxlcrD559DAuXLyI0eMm4MkTNsFYg2WHMNi/bx9MnzYV/50/j05dumt9vg8DLrKLMc5OTqr/XK1mHTx/kfmQ298Wzkeb1upKKt7GlovT2hqvXOMtcfnvnCarX68u1q5aoXpYFzMGxapHmzZpjOV/LFWtt33HTlW1qRxL4xgbG4tqrEWoJtF5zU5nAAAgAElEQVSWt/7kr1Nuly5fQbvPO8pZDts3b2LtmCqrYpu1aoNbtzTfVVSwoDtOHz+q8s2KikFTU1N08u0Od3e1wPb8+VNs/ms94hgbfVn9Bo1Rrbr6zupgNrtwPRMHtbVixUvCp2MXVVgCuxCy9Ld5WgluXpWqoGmzd/NgtrKWnA/02JKTt31p07YDTBjvDesyF6KlMPjcxxfFS5RSud65cws7t2+WEkY+RIAIEAEiQAR0QuAIG89g97adtZyEjarUwoNQ4e4ve/fuRatW6s+xZOIEVq1ahX79Mq+6Otr7R9T3VM+N5jb/3E6M3a8edyFkJM6q6VSuXBmchZD1HTgCpT5TdxiRYz7RLiiZJNwCNIa1qHvZX33tRMhsVgXAIUX4Qnk0yxUqIZfdqkvIl2IquF4kyxUuIZdQkinTpmPfPzlzrrcYb3r80yFga+WAFROvKTpw7xkl8SRE+QgI3umnbQdfxaNOpBymgE1RdkN2Q1haC78XSMmlax92rwueB0Yh6G44Xj1JhUViYbDOyxrtRdQDhMc+QhzeIJULgCZsBiATAFN0LAAKndMp2hvFHKspQrHu4PfYfmqRohzZFZyXhEFjgxQEux1ks/zEK/XFeC+O8sSU18JdAcRyfPh4avxrRKzTrjWpERMGTUsPgUnhjjAwVI/Z0cZIGNSGVi7ztbGxwbFDB+DkpC5ZD7h0Gd179kZ0TIysk3CR8Y8l6jtlnj17huq11bPmNFlGYY/PF6xZt4HG+VyZ5cgoQpIwqJlzVguDGdtinjh5irUR6av164mLUlyc4ibUhpI/ntXCIBfWDvyzl/WBVw+UV1oZOWLYUHz5xQRVLl4pxyvmNFnGitUQNh+uPpvppu3v65ZN61GtalWcOn0GAwYPYfPmPhaKsrpikJ/3+JGD8CzMPvQx69y1u6Rq0YycMlYLrl7rh2++nSH4uss4D1PsNZZZIp/P22PenF9UD508dRrde/URXC8rKwZNzczQjc0qcGFtSbg9efIIW5gomJiofHiy0CHNWTtYPiePi5Lcjhw5gAv/ab455MNcVvxOyX6DYM1mDHI7dfIYmwsovde+N5vB2qhxM1VsQkKCSgh9ygRKfVr/QcPg6KAW9S8FXMSBf7Wf0cTF2+49+6Zvk7d6ffQoUJ/bptxEgAgQASJABN4jsHnDOhQrpm47Lsc6NmyJiw9vCoYuW7YMAwZoHjMhZ928HDN9+nTMmJH559kGhSvgSJ937UNT2Fwon1MLsVPgc4iPjw+2bt2al5FJPpu7uzuePhUWskdPmAxXt4KSc37oWCXeBk3ihFsD8tmA9zp5wtTWSnCdqG0XUfa1cOsvXee63604TCzkV+3s3L1HsHuUbLAUSAR0SKBueR+M8VEmxjQfY47EJCZI6cj4jeLtfbqqRmLo2gxYFXPlErXRtK12M8Z0vQ9t8nG0wQ/eIOjOK9y6/gRRiaFINXkrABqyGYAG6jm82WnGsY7wtvdVtIXnrwIxcoG89tWKFtZBcF4SBuuYhWOPs7wOZh+i7BXmhd2x4m3FtXkKksMuIWqnujhBazO2gnH+ujCrNA3GLtIrGUkY1Jp07grg7Tj/2rgOVpbqu9kePHyomst19660AeZpp+VtH1exdoBpeaayO8TW+K0ThMFbI54/cwpcoOSmTWtOLqYMHTww/c5WEgY1o85qYZDvJOMMt9+W/o7ZP6kFFCn20+zv0dW3s8r1zp27aNJC+M7irBYG+b6aN2uKP5e+G6jOX3/DRowGF+u0sfbt2mLhvHetgLi4xEUmIdu03g81a1RXuexjswaHaDFr8H/fTUfvnmrh0d8/AB06Zf7hJasrBvl+6tapjXVrVqn2Fhoahl59++P6jRuScPLKY16BzI1XPzdq2kK0xWvGGaW8wnDI8JGquahSjFdc8urNNHGYr3fv/n3B0KyqGLRg76tduvQCn1XA7d69O9i+dZOUY+nEp2atOqhT912rgh3b/mJ/T8T7s3NR0bdLDzZ7yFW1Dz6n8M8/FkoWM2vVro/addQ3o8TGxrCWqevx4sUznZxJKEn5CpVY29I26S63bt3Avr93IUmiCOvk7MxaiHZPb8EaHPyEVVqu1Pu+aQEiQASIABEgAhkJLFowF7VqSL9I8CG9/u274PDlc4JQZ86cialTpxJ4iQT69u2L1as1dyP4sGoQDYpj/bPzqlahvH18RitRogTOnTsHe3t7iavnXbfk5GTwdvtiNvPH+czPRMxN4+MOySYYHOUuGu/vkQTHJhUE/cKvPUSl/8S7fgR4psChkfAF/7CrD1D5vHj7+4tFU+HUQH7FJP9O1rzVu9b+oiDIgQhkA4ERHeahYUX5gs6FWwcwaaH6xlRdWjHWSYa3M7ay0t3sPRMDC7RjnXVKlJU/D1GXZ5STa8/W87gWdEFOqF5jDFONUdrABzbmyuZEzvTrisv3pV2T0uuBtEyel4TBybZ3MclW+NqeVDxFnzZCuEiFvtRcaX6Jj3Yh5mAHbcM+8DeAZZPtMPGQNhuThEGFuHNDeI3q1bBy+Z/poh6vIPpl7lxs2bodr1+/FjxCfhcXjB09Cj26q+ejceOVSN1Y5aEU+2rSRAwfOiTdlVdffT11msaL+vxC/IB+fVUVVrwt5tSvv1LFkjComXZ2CIOflS6N/X/vTt8Unx85cdJXKtFGk3HhZAp7Plu3bKFy4SJb2w4dVdWnQpYdwiDfT8fPO2DunJ/Tt8bFrNk//Yw9f/+daRVexjNwUeqLiePRklXZpplUYZz/zv27b0/6F/sbN2/i2xkzBSvsKleuhJnTp4HfCMCNzyhs79MZgUFBmaLNDmGQb+SnH2ahaxf1B3P+PrRw8W+qlqdxcZl/eeXVm/y9IK29Ko8bMHgoDhw8JOXth91cMAiTv5qU7rtsxSrVmpxPZubo6KB6v0trgcx9vp/9E5b+8afoeu7ubjhzQj1HVV+tRPmcu569+8Ph7eDy69eu4u+9O0T3pmuHTr7dUKRI8fS0YpV/rq5uaNehE2xt7dJj/NauwLOnmlsIZ9xz7ToNUKu2usI4MjJCdbNLeLjynvBSudSoWRt16zVKdw8LC8W5MycFZyNasptx+L69Kr2b3xLB9u63ejmio8X7xUvdG/kRASJABIgAEZBCYMa336Dt23b1Uvw/9BnbZwibsSTcNrB///5Yvny5nPSfZEyDBg1w7Jj6s2Nm9mHVIMyZ2DWxEeKSE7Fhwwb8zb6TvGTfp6qyTiGTJ08mUfAtxNu3b6M0+64qZNZsZvaU6e8qMuW+AIdFFoRdirAI+diUVbz0FJnnl5qKgitvwIhNGhSyQLNYGPQQnw3oseK64MzC83H54G+egBb9PeUeXRXXkX0nkDNWRNGiFEwEtCDw+3h/ONrIr+iZ+nt7nL6yS4sVpbvms3dAv0Ej4ZJf/v7SVrM3d0Vn32ZwcBJubyx9d9njGfI0Eis2+mXP4iKrmkW6w8tZmtCiKdXpG3vw6+bBOfJ8QpvKS8LgfpezqG4qrINIeYIeslbilZ4Ld1GUkudDn/jrCxB3bqyc0PdiTIp2g2UD4WKutAASBhXjzh0JShQvhiWLF6FkiXcXc/nO+QX2Cxf9VRfKw9kPb8/mUaiQ6qcE823YoP57B9yxc5dqJpxU40IfFznSWgjyOC6wHDtxAjdv3WLVLveZQBADB3Z3Y3UmYHIxxtbWVjX3bMnSP3CYzcPgRsKgZuLZIQzy3WRsLcv/P38NbWPz43h7Uf6c8tYIjo6OyO/irJrz0KbVu7lgkVFR6NKtJ65dvy76UsouYZBvjFfKLmaVYy6s6ifNuIi1d98/qtfvq3D1742BgQF7jXugMGuVyefb1an9fouARb8twU+/vKscFDt0hfLlsIm1fEqr0OX+t2/fwRF28eDBg4esNc4z2DvYs+orFzRt3Fi1zzSLiIhQtb28clVzL/3sEgb5HnnVIK8eTDPeKpVX8l1k8yYfPAwEF1SKFS2CenXroGqVKu+hmv7d/7BilXZz3mbPmonu3d7d2MATciH71JkzePL4CZLYXcVcyK1Zszrq11WLT2m2dt16TPlGXakoxR49UM8e4K/va9fevbaDmEDLZ0YqtfwFXNG7z0ClaTKNDwx8wKrwpH1w4K1Eu/Xom97KlCfksw2vXbuMsLAwRLx5DTNWIWjHhMCSJUuzNk3v3029a9c23L4p/rufttFefQagQAE3vZz796UL2H7fiOb28vJG0+bvVzfHRMfg8eNAdlEuhP1de6m6lMO/4HkU9mTCabH3cnJRcHMWC5qihyIHIkAEiAAR+GQIjBoxDP36SLuxMzMo//vyGyzbtFaQV61atXDq1KlPhqnSg3p6eoJ/RhSy6IWHYfkyww10bViFV1UPpUvn6fh9+/aJzroszD6nDR0p/ZqGJmCtYxxRPlHdHUmTJaWyeUYDxNv6ma7xh2uSepSFJktguZ7JyHU/0RJH4x1xhLU+PRHvhDes+qWEyWts+TpC0Wvh5zlzsWHTX4pyUDAR0BcBd6fimD9CWXWW75RCCH39RF9bBL/x15eNCClTTnwWqaZNfObujbadq7Lrb8I3FujtEDpOvGzpHoTG6HdciJwtm6TYoLJlTzmh6THJKUkY9KsXIqLDFeXJ6uC8Igxasba0Qe6HYCw01FIi3I0xbhgaLtwNQGKq99xi/5uIhGvSrx1rWsO0zBhY1JgraQskDErClDeczNhcqkkTxmPQwP5aHygqOhr/+3421m/YqHUsrxTjswnLlZXWqmLnrt0YNXa86mI9CYPiuLNLGOQ749V/37EWj85O6hlcUuwuE4OHDh8B/k8plp3CIN8fF6q/nzkDfGamtvYiJERVSXns+AltQ8ErfX/5cTY8PApJjuXr9ezTTyUiCll2zBhM248FE4x++fEHtG3TWvK5uND2C/viuXL1GskxGR2nTZ2Cgf37ahX7x5/LVVXL2ticn39E544+H4VwEVkXrXZyijDID2hkZIzGjZujYiXp/c8jIt5g986tTNiWVimYBrJ334HpLUi1eT6k+EoVBnkuLvi1aNEWdvnySUmd7hMS8gLbNm9ggnGkVnHkTASIABEgAkRAVwS6sY4NX0wYJzud3/JVmDpzumC8tbU1q+ynv3VSIfO2n2Lde16cvQmXvx++S+nIqkFGsBvZjHQ/m0rqvnO63+LFizFy5EjBbXpXrYFOXeUL5WnJyyVYo02s+Pfga02cYOPhIrin13v8UTFEWBjkCW40c4FVQeE1720OwOsXnioh8CgTAp8kZ5Y3FX/1uYlSheW3MuTfccdNfNedJae/Nmh/nxaBllX7YUCrWYoO3WSUMVJSkhXlEAvmN5k3b90e9Rtq17LUECZoUqMZKtfKWzeLXDj1EAfPqQtEcpo5RXujmGM1Rdtad/B7bD+lbO6log3ICM4rwmAL8xBsdPKXQeDjkHGvymJltPRrtVIXjT7cGUmBCuZFGxjDmLUQtai1GIYW0toKkzAo9dnJQ36uBQqgX9/eaMTal5QsWULwZEGPHmHHzt34Y9lyxV/0evbohj49e6JUqZKZrhkc/BS8QofPrOPm5uaGub/8pPr323fuYNr070SfhYnjx7EqI3V7janfTtd6lqK3d2WVeMrt8JGj+P3PZaJrfuiw7Pcl6XMVu3QXv6OEiwkF2YB0bgOHDNOaM2+z2KtHd1W83/oN2L1nr1Z7traywvI/1cyfsrae4yZ8oVU8rwrlz61Ph/aC4i+v+ty6fafW4jKvNv2RtaDkduXqVcz64Uet9qcrZ15124vN72vSqBG42C1kXAj6a/NWVZtMpdataxd08unwUfVcxrxnzp7DDiao72I/vAJPzCp5VcRXk9TPM/9Cl/Y7JxaX8XH+mktr8bn4t6U4fvKkNuGq94HBAweoKk9t2IWkzIzPROU3CixfuRq8ElKJ8feTQQP6qQTJjBWgGXM+f/ECu3fvxZ/LV4D/u7bGfxemTv4KJYq/X5nN30cnfTVZ23Qf+fMWok2bSxdUtVnwxfNnOHrkgDYhKl8ullVhF1eKFdP8t+QNa1l95Yo//C9eYFXp2g9ub8Yq9ewdxC+6aL15FrCHVS9q29qzYCEPVK1WE8WLZ/63LG0ffJ6g/8X/cEuL6kg5Z6AYIkAEiAARIAJiBPhNYb+y7xxy7VLAJXRgnV3E7Pnz5+xmHmkXIsRy5fXHi7GbYB88eKDxmA4ODqpODFjBZjsGZagu6OQFlFfPbCb7mAAXBbk4KGTN2I1eDZu+62Yjl6NNihGGs3aiBiItQK+4xMOuTSXBZaKDw1Bmv/j3jysFEmDXir0GMljoqygcPP0KtwLtcDeiIK4n5pNUDzG49EWM8H3XGUdbDvx7Z90GjbUNI38ikCUEvuq2GlVKNpW91v5zq/Hjmr6y47UNrFi5Knw69wDvziNmVsYO6Ni+OdwKa3fDqljenPB4UmIK5i9ajcRU8VmpWb1f41hHeNvLn1nJ9/vydTCGL6iGVNZCOrdYXhEGf8h3E8OshTs1SH1Oar2ojRsaOgaYxV6HR5DwDUpC66SkpIC9QFSvEdUP/9/bf3/vv2fy38zMLWDKisJysz1j19FiY8Wva8s9o4GDi2vu+e2Te0ot4rgoxNsfcqHDzdUV/MJ2CKs64pVHV1krvDcSWqxpsZzKlV+g560SC7KZXDY2NoiPT8A/+/9lbQQz3A2pbVLyzxEE+Ey4Mp+VhjNrvcnvGn70+DEeMVHkzt17GufI5YiNa7kJ/kW9EPud4YKuq5urqm1qSMhL8IshXLzk8/N0bVzQL89+b5ydneDEWrTyNV+wNQ8cOCg421HX+9BHvlo1a4Cfz4XNV+TtjLkod59dKLl167Y+lkMVb2/wmYAurM0tv0PvxYsQ1es04NJlvaz3KSTls1oKsOfQwtIKlhZWSEpKVAluXNB9GpzzWoHo4jkxMTFh8x6dYGdnBxv2w5u3RLHqVn5mLrQmJyfpYhnKQQSIABEgAkRAMQH+GX37lk2K8pRiNwHFs/l2QnbkyBHw2Xm6NP639fzFAPa5IpqNJlDPKs8LNn78eMydq7nNU8eOHbFlyxbg7kt29+eFd0d2ZzObB78/tiA38QgKeoRT7KbGKmxG+odjTnRxDt7S9gwbGSBkQ3oMhmfl98U1uWv3i3RF/hThi3AvjOMR11tYGOTrF1h+FWYGRoJb4bmie3rh8LkQXLhhgnuhBXA13gXx0L6KtIHtfcwfayL36Kq4/oOG4tJl+g6lCCIF64XA2q/vwsLUSnbusfPq48pdZa1ItV3c1a0g+g4cDls7zYJfAZui6NqrMbt2KzzfVNu1c5L/zr/O4uaTgJy0JdVeDFkb5tIGPrAxd1S0t5l+XXH5fta+tpRsOK8Ig6fyn0RZkyglKFSxkalGKBSs+aYDpcKg4g3m8gQkDObyJ5C2TwSIABEgAkSACBABIkAEiAARIAI5h4CRkRFOHTssqRJB065b1WqIG0+Fb+RctGgRRowYofjg/qxC8YI/+7nojyesq0xV70po37Y1KlfSjZijeIM6SMBnsteuXRuXLl36KBuvujx//jwKFWJtqnhVwVI2u/F5hjatfasDRRx0sIusT8FHlPit38ie2wDVjZXeTCCswp5fLhTy+elKjN9lz29OjWWzr4Vs8YjvEFFUN90oGsc6oGqCreB6Kexu/8f9yoDdkSh8vHUXUTjeIlOfYNYO9ECcM47GObAfR7xOFa8qEmNZwCgG/0wOFd2WUB7eeWbZilViS9HjRCBLCZQuVA3/679D0ZodJjmzWXChinLICbaxtUOPPoNQ2LPoe+EGTPyvUqo2GrcWn1kqZ92cFBPyNBIrNvrlpC2l78Us0h1ezu0U7e30jT34dfNgRTmyMjgvCIP5DBMR6HZIJ9gOxjmhU2gVjblIGFSGmYRBZfwomggQASJABIgAESACRIAIEAEiQASIwHsE/lrvx9pgF5NNZcDn3XAoQLgSq3v37li3bp3Wa9y7/0AlAqaJgV4VK6iEIi4YldYwhkLrRXJgABcHp02bhlWrVqnahhYtWhQtW7bElClT4Mq6+aTbTdZicmOGuTjFmajVq2oOPJF2W3rNuhNdfCsAX/APgBlroVeFjfbgYiEXg3k3I23sxo0bKFu2rGjIlSlrsddBN/MwiyRaoEuMePvcgGoWcChXRHBvYYeuonKQumLwTYoxTsTzGYEObFagE+4nya98Elp0fpsANKgsv/rl6LHjGP/Fl6LMyYEIZCUB3/oT4NtgguwlExLj0XKshaqFX3YYv5mnU5de8PJWz7MzM2DzTJs1Q4my4u812bFffay5bOkehMbkvM5DJik2qGwpPjJKjMnAORXwOirrhWexfWX2eF4QBjtaPsNyB91Ut8+KKIGfIzR/niZhUM6r7F0MCYPK+FE0ESACRIAIEAEiQASIABEgAkSACBCB9wjMnjUTzZo2kU1lxsTJWLllvWA8H1ERGBgougavGjt+4pRKDLzIqgMdWZt+LgiliYG8ZT5ZBgKZVQ3ydqK8rWgeskePnzChMEBVTXievTb4jHcuDteoVhWflS4lelIusPbr10/Qr0L+Irg4ZBHm2gYhWaSAT3RB5mDEdINxEYVhLDJn8JZdHCw6VtaYMjQsgs37fonYx4VUQmBAgh1SxCoMpWxQxKenZwC+6C1fGLx37z58uyu/SK6Do1AKIpBOYFb/nShVSP7NE/+eX4PZq/pkO9HadRuyFtpdMXRQL+RzzLyaONs3qacNXDj1EAfP/aOn7MrSOsV4o5iDWrSVa34HZ2HHKeF5uHJz6zouLwiDC+2voZfVE52gafuyqurGHU1GwqAyzCQMKuNH0USACBABIkAEiAARIAJEgAgQASJABN4jMGTQQAwZNEA2lb07d2PEmFGi8cHBwaqZ8kK28a8tCGSz5tLEwHz58pbAJQpJjsOHVYPlWEVh57zTWjUzJFev3VAJhXxe/fczvxWl1qtXL/j5CbefG1m1LRa2HIZ1Vs/x2DhONKcUh+5RBeDBWn0KWYRBIl71q5juwtueHjn3HBdvmOJemCuusDmBsWxuUVZbZYunWPmF/LnYL9hs+JZtO2T1tmk9IqCRgKWZNdZ8dUcRoZ/XD8C+UysU5dA22NjYGM4uBVDA1Q35+U8BNxRgP/UqN0Krz2tqmy7X+yclpmD+otVITNXN+7QugRjHOsLb3ldRyhevgjBiQe54XvOCMHjV9SgKGSl/LaWwZ92NzReME/h7TcKgol8NkDCojB9FEwEiQASIABEgAkSACBABIkAEiAAReI9A0yaN8eP3/5NNJT4+HhXLlkdcUoJgjvXr16Nbt26y16FADQQ+rBo0ZOVu4xoAttq128zLfPlsxpCQEMEjbur4FXzL1sNJ89c4afZaJzjqxOdDnbh8grl4S8J/67niyMVE3H7mgGtRBfEyxUwn68tJ4sIukJaxDkZFz9fo395ZTgpVTFh4OJq2aC07ngKJgK4JVC3VHF92Xako7bCfquF20HlFOTQFG7BKYCfn/Ez4c1WLgEz840Kgo6MzMquWtzV1wfCRHfWyl5yedOdfZ3HzSUCO26ZhqjHKG3eFuYmNor3NWOOLqw9PKsqRFcG5XRj0NI7BpQLHdYLqEpsp3CCEdWwQMBIGlaEmYVAZP4omAkSACBABIkAEiAARIAJEgAgQASLwHoHCHh7YvmWTIiq+jdvgv/vXBHMMHz4cixfnjvZYimBkR/CHVYM1PIGWn2XHTnLcmjdv3kSZMmVE9xX2xSY4WNjgqVE81lg/E/WX4uCaZIo+0R9XyUalGOFUAp8R6KhqD3o7yVpKOr34WBskoazFc5R0eYlaXqmoVYHNqdSB3WXVnF169NJBJkpBBHRDYEDL/6Fltf6yk4VHPEO3b4ogMSledo60QLt89qqqvzTxjwuBvCrQxMREq9xdWndGkVK6+Z3VauFsdg55GokVG4WrwLNri2YRheDl0kbR8meu78acLUMU5ciK4NwuDPaxeoz59td1gmppVGF89Vr4cxcJg8pQkzCojB9FEwEiQASIABEgAkSACBABIkAEiAAR+IjA4X/3IV8+4comIWxTR06A356tgmR5G1HeTpRMDwQ+rBo0ZrMYJzYCLLS7yKyHnWV7ygULFmDMmDGC+yjp6I7bI/5U+fAKvnm2jxFvwBuDKTQ2Z3BshAeMWWuxi2w24JF4Jxxl84fOs0rCZJHZgwpX1hhuyF4rpczCUMrhOSqVjEfLWg4wM9V9q9Kz5/7D8FHC3PV1RspLBDIjMG/EcRR0Ki4bzuYjc7Fky3it4i0sLOHqXvA9EZBXBJqb62YuYDGX8ujcs45We8orzsuW7kFozOMcdxyTFBtUtlQ+X7X/L+UQER2e486XcUO5XRhc6XgJn7MbY3Rh/cIrYnsMa+UuYCQMKiNNwqAyfhRNBIgAESACRIAIEAEiQASIABEgAkTgIwK//vwjGtSvJ5vMlg1/YeLXk0TjAwIC4OWVt+ffiULQl8OHVYNNSgF1i+prtVyTt2nTpjh48KDgfvt7NcPydmPTfbZavsBdk1hFZwxl4t/DqEJIeFMUAXHOiGIt5rLLPIwjUdo2GOU8I9Gith3y2+u/zezylauxeMnS7DoyrUsE3iPgaFMAv4/3V0TluxVdcfRi5tX1pqZmqhagqgrAt//kVYA2tvqdk2sMc4wY2hMWlp/eTSAXTj3EwXP/KHpO9RXsFOONYg7VFKX3OzgLO07l7C4LuV0YDHQ7hHyGiYqep7Tgz541wDORmcIkDCpDTcKgMn4UTQSIABEgAkSACBABIkAEiAARIAJE4CMCvXv2wNjRI2WTiYqKQvVKVRCdGCeYY8aMGZg2bZrsdShQgACvGlzAZuWEx6idbNicujH1ARPdV4PlluchNjYWNjY2SE5OFtzyynbj0NerabpPgEkk9luGaXXM6CRzBEUXRCCbERjI/hmRmH3tQR0N41HGKhil3V+hSXVzlC6sbN6VVkYByfcAACAASURBVCDeOg8dMQr/nb8gJ5RiiIDOCTSs6IsRHeYpytvj22IIefWItfx0YbP/3N9WAarFQHsHR/AZgdlhdSo0RJ0mpbNj6WxdMykxBfMWrkYShD93ZMcmjWMd4W3vq2jpF6+CMGJBTUU59B2cm4XB8iYROJH/tE4QPWaCYHkmDIoZCYNihIQfJ2FQGT+KJgJEgAgQASJABIgAESACRIAIEAEi8BGBChXKY9WyPxSR6dnSBydvCldkeHt748IFEgsUgRYKDngC7Lj6zqNNWaCqh96Wy+mJt23bho4dO4pu8+HolfDMlz/d741hEpbYMJYClpBijCfRrkwIdEdgTCGEsHmB2WVmSEFN01doYB7GfkKR5BkK5xbZV5kbGRmJ+o2bZRcOWpcIfERg9OcLUa+C+HuBJnSX7x7HqefL4F015wk1tqYuGD5S/tly88tlx6azuBUckOOOYMgqxMsbd4W5ibKbMmas8cXVhydz3PnSNpSbhcERNg8xy+62TthuYS1EB7JWomJGwqAYIeHHSRhUxo+iiQARIAJEgAgQASJABIgAESACRIAIZErg7MljMDU1lU3nl+nfY9EqcXGRzxnk8wbJ9EAgmc3FW3TiXdWgoyUwoi5gxGYOfoLWp08frFmzRvDkFfIXweUhH7dr+836MSKM3lUaprCCzOdxLuqKQPbzJKYAk+OyhyuvS6po8uatEBiGGkwUNM8wE/EJa4Oa3Ms7257xdes3Ys68+dm2Pi1MBD4ksHziVdhZyRfvv1vRBfdfsrmZY8RbZmcHfd/WnVC0lHN2LJ2ta4Y8jcSKjX7ZugdNi5tFFIKXSxtFe+OtRHlL0ZxquVUYTE1JRpfACZhc0Ruedsp/b754/Rn+jCos+jSRMCiKSNCBhEFl/CiaCBABIkAEiAARIAJEgAgQASJABIhApgSW/7EElRTM/7t54yZatmopSnfJkiUYOnSoqB85yCTwYdVgJ1Y5Vt5VZrLcGxYXFwcnJydER0cLHmJWwz6YXLfLRz57LEJxEgZ4yNqCBqnag7ojTmR+kD5puRlFobT9M7TDK7Q3jYS9wFyk5NQUPBlQTp/bEcz9eacuCHr0KNvWp4WJQEYChfOXwZyhwnNGxYh1/aYwQsIfYcYP8xTdQCO2jtzHi+cvj0496sgNz9Vxy5buQWjM4xx3BpMUG1S27KloX6+jQjFwTgVFOfQZnFuFwZiTg5F4Z5kKTQlHd7QuUQ1tyrVAXddCMJXREbjui1q4mmgripqEQVFEgg4kDCrjR9FEgAgQASJABIgAESACRIAIEAEiQAQyJTBk0EAMGTRAEZ0WNerj1vMgwRw1a9bE6dO6meuiaLN5NfjDqkF3O2Bwrbx6Wo3nWrt2LXr37i167kdjVqPQ24qBVykmOMZagh5lPwfiHRCcbCUary8HW4NElLZ6hnKFXqNlTQuULMhmRjJ7feAqKj4Wnxt5qb4d7Iu562t7GvNu2rwFP/48J8vXpQWJgCYC7WoNRe+m8mfbprL5rU1HmyCFVRnVrd8Erdr55DjYxgbmGDGkJywsTXLc3vS9oQunHuLguX/0vYys/E4x3ijmUE1WbFrQmMX1EBx6T1EOfQXnRmEw/sZCxJ0dkykSK1NzNC/bAm0rd0WLAs4oYBQvii4u1RBuwU1ZBwFxRZGEQVGcgg4kDCrjR9FEgAgQASJABIgAESACRIAIEAEiQAQyJVCqZEls8FutiM7Xw8Zhw77tojlu3LiBzz77TNSPHGQS+LBqsG91oIiDzGS5M6xJkyY4dOiQ4OYru5bAnN5+KiGQ/1xOtAPrGJotxmQHVDYNRyHnp6hZPgUNqzhk2gE27nUUSm0Tr8a76hgH2/aVs/QsoWFh8GHVglEiVZpZuila7JMnMLXnengVayCbwz9nV+Gntf1U8RYWlpj2v19k59JnYJ0KDVGnSWl9LpEjcyclpmDewtVIQlyO259xrCO87X0V7Wvh9tE4dmWLohz6Cs5twmDik/2IOdAaYFX1QmZcsCWsmu1FRdM3aG7+Es3YT2X275k1Dz8a74gOL6tKQkzCoCRMGp1IGFTGj6KJABEgAkSACBABIkAEiAARIAJEgAhoJLB353a4uhaQTSjAPwCf+3wuGj9+/HjMmUNVRaKg5Dp8WDVY3AnoJe3Cldwlc1Lc48eP4eHhIbolm6qzYVg+m2aGMQWyHGsJ2sAsFA3Nw1DTLByxRrGI6ltJdN8uK67AAsaCfmGs0iGqj3gu0cUkOsTGxmHoiJG4eu26xAhyIwJZQ2Dd5PswM7GQvdiEBY0QcPtIevywURPh4VlUdj59BdqaumD4yI76Sp+j8+7YdBa3ggNy3B4NU41R3rgrzE1sZO9t3aEfsP3kQtnx+gzMTcJg8qsbiNrNbpJKEm4vznlZ1F8L02I93kPnaJigEgibWbxEI/Z3284wSfX47Ijiqh8pRsKgFEqafUgYVMaPookAESACRIAIEAEiQASIABEgAkSACGgkMH7MaPTs0U0RIZ8GLeEfeFMwh7OzM4KDg2Fi8um1PVMEV5vgD6sGR9UDnLKvNaY2W1fq+/XXX2P27NmiaWx8g2BoXUjUT1cOBY3i0oXA+mZhcDJKeC91KqtXfNSvDGAg3JIsdYM/PGPNBbclNZcuzhYdE4MRo8fiypWrukhHOYiAzgiU9ayJGX22KsrX/gtHRMaEp+co5OGJ4WOy6YYCkZP4tu6EoqWcFZ03NwaHPI3Eio1+OXLrltEeKO/IqtRk2r7/VmD5vqkyo/UblluEwZTYl4jaVQWp0dJmUVp3ugsj22KC8Gqzm3l4NeHu2Pw4n5BPEmgSBiVh0uhEwqAyfhRNBIgAESACRIAIEAEiQASIABEgAkRAI4GKFSpg5bLfFRFaMHsOfl0qfnf75s2b0alTJ0VrUbAAAV41+OtRIOrtjBwvNm/u8wp5Hll8fDzc3NwQHv7uQn5mhzZyrgHrtvqddWlqmAgPy2AUtg5GTYsQTEwUr1q6WMEYTlVKCj5P4SdvotId8aanUnIpfUG8DA1ViYL37t1XmoriiYDOCXRv9BV86o6WnTc+IQ4tx338ezvjh3kwNTWVnVdfgcXzl0enHnX0lT5H5/1jyU6Exz7Ntj0aGhrCPp8tnBwd2I89HB3sVf8e88oADw/Kb+X974W1+GPvl9l2LqGFc4MwmJqSiOg9dZAcel4yQ9tekTAw0f2NVCQMSn4KMnUkYVAZP4omAkSACBABIkAEiAARIAJEgAgQASKgkYABq1Ta//dudjHLUTalyMhI1K5SHRHxMYI5mjZtin///Vf2OhQogcCZQOCft9WbhqwKbVwDwFa40kxC1hztsmbNGvTp00d0j+bV58Ks7BhRP20cDNicQHfLF/BkQqCn1RO4mr9gcwLVAp4R+8e4iMKsAahwNeBdq1iYdvEWXDY5IRFF/O6wTCK5rFkuX+Fc2pzvQ9+Tp07ju/99Dz5bkIwI5EQCswftQ3G3irK3tmbfTKzaM+2j+Fp1G6BtB2Wz42RvSiDQ2MAcI4b0hIXlp1eNf+7EPRw5f0AfWN/LaWRkBAf7fCrxT/3joBIBHeztwMXBD+30rueIfyK/Rfu2Ewuw/rB4BbzeD57JArlBGIw52hOJD9ZrhcemazAMLV21ipHiTMKgFEqafUgYVMaPookAESACRIAIEAEiQASIABEgAkSACAgSmPzVJHSSMCdQKMmIbv2x98xhUdL+/v6oVCnr5qCJbiivOSQmA/OOvasarOEJtPwsr53yvfPw19OlS5eEz2hkDtuuT2FgJq39l1AyR9NXKGL9WCUGFmLVgWZG6rlDmVn3qALwSBYWZqOQhLD+4pWd9isvwzZV+OJ/JMsVLiGXti+Iy1euYPGS33Hhor+2oeRPBLKMgJW5HVZ/KdzWWmwzQ3+sijuPLnzkZm5ugW9n5cw5uXUqNESdJqXFjpbnHk9KTMG8havZu16cTs5mylqdO6aJfw5M/Hv773a2Nqzbs/BNGRk3cOQ3Zdv5bdd4HA7YqCyJnqJzgzCYcG8tYs+MAhIjJFOwbLYPJgWbS/aX6kjCoFRSmfuRMKiMH0UTASJABIgAESACRIAIEAEiQASIABEQJFCzenUsXjhPEaUTR4+jV9/eojk6duyILVu2iPqRgwICZwLfVQ0as2qGiY0Ai7xZTXLw4EHwSlQxMy07DhbV5V3UtzSKZa1Bn7CKwGAUsw6CtUms2HLpj9eIt0ODOHtBfz4b8F4nT5jaCrcxi93qj9JvxOcM3u9WHCYWZpL3qMkxPPwVjh47ho2bt1DbUMU0KUFWEKhVpg3Gd/5D0VKtxlkjLiE60xw9+g5GufJeivLrI9je3A1DhrfXR+ocn3PHprO4FRyg1T7Nzc3etv1UV/+ltQG1tbHWKk9mzjGRSTi31lhRnglLGyPohTKBW9EGBIJzgzDIt58SFYSY432Q/Py4JBSmpYfCopZCRTeTlUgYlIRfoxMJg8r4UTQRIAJEgAgQASJABIgAESACRIAIEAFBArxN1pGD+2FtpWy+yuf1myMg6LbgWvyu+1u3bqFkSeGZavSUKSDwYdVgk1JA3aIKEubc0MaNG+PwYZFKVQNj2HQJZG3C3CQdxNwgGa5MBOQVgUWsHsPZLJxVi0gK/cjJJdkU/aPE1w3wTIFDo3KCi4Rfe4hK/4mLkheLpsCpgXAuoYXCwsJVMwTv3L0r79AURQSyicDg1j+iWZVeslePinuNdhM0C/mu7gUxevxk2fn1GditrS8Kl5DfElyfe9Nn7hfBEVi5aV2mS5iZmcKZCX8uLk5MCGStQFkVIBcBraws9balB9fDEXRM/nzBmLgI9PnpM6Smis+U1dshBBLnFmGQH4EzTLg+D3EXpwDJIlWlxpaw6XRX5+1ESRhU9iolYVAZP4omAkSACBABIkAEiAARIAJEgAgQASIgSuCbyV/h8w7KKg6W/roQsxeIV2XxeXCrVq0S3RM5KCBwJvBd1aANqx4bUx8wMVKQMOeF8ra03t7i8/RMinaHZQM/jQcwZBcPK5m+QUPzUDRgImBV9vOb3SPEG6QoPzS7tjs2wgPm+HgOVcbkQWZM8Oshcha2z0Irb7BMwiqlpFwiJ2vv0xmPnzxRfn7KQASykMBvo8/Bxb6Q7BW3H1+MhZtGCsZPmT4b1ja2stfQV2Dx/OXRqUcdfaXP0XnXrNmNJINYODs5sh8mBL79p40OKgC1PfjxTS+RHOasbVi6/7lb+/DzpgGy4/UdmJuEwTQWya9vIuZYT6SECVeWGrs3h1XzfTpFSMKgMpwkDCrjR9FEgAgQASJABIgAESACRIAIEAEiQARECZRiFXwb/FaL+gk5xMfHo653dYREvRbMY2xsjKCgILi5iVdSKdrQpxz8YdVgm7JAVY88RaR9+/bYtWuX6JmsOwTAyKHie36eRjFMBAxDA/MwlSBoZ/j+nMCdli9x0yTzdoKiC37g0C7GGWUShatx41OT8XxAedHUVqsD4JQs3CZUai6hxb7/8Sds2bpddD/kQARyCoH89h5YPPqsou18t6Irjl7cJJijSvVa6OjbU9E6+gg2NjDHiCE9YWGZN9tGa2J27WIwzl8/g1YtGiA/qwzMTuNFfkeXKNvBn3u/xv4Lyj6LKduBcHRuFAb5iVJTkhB/aSbiL8/i/0fjIU3LT4JF1dk6Q0jCoDKUJAwq40fRRIAIEAEiQASIABEgAkSACBABIkAEJBFYvWIZypdjApICmzJiAtbt3SqaoWfPnli7dq2oHzkoIHAm8F3VoCNrnTaqHmT3xFSwDX2Enj17FjVr1hRNbezWBFYt/kU+gwSUtXqGUq6hGBkRgaIm8YKxV00isdcyTDS/FIdyCdZoEyt+wfp6M2dYFxSuNInYHYDyL8XnB95onh9W7vLbCh44dBhffs3ar5ERgVxCoKl3Twxp85Oi3fabWRZBz28I5visaFX0HtFP0Tr6Cq5ToSHqNCmtr/Q5Lu/+nZcQcJ+LwangNxy1b90EJYoVybZ9vgmLh/8m8fdnoQ2OWlgbz8IfZtsZxBbOrcJg2rmSXv6HWF49GHFP41Gt2/vDyFE3s0RJGBR7RQk/TsKgMn4UTQSIABEgAkSACBABIkAEiAARIAJEQBKBtq1bYca330jy1eT07NkztGjQBG/ixautTp48idq1aytaj4IFCPCqwV/Y/L24t9VwXSsDn+XPE8gqV66MgADhtmD8oG26LEL7z6uhcXUXGL7twGmy1h9uieaCHN6wCsIlNrpppWmbbIThUeLtDS/nj0e+1pUE9xX1+CXKHngp+hxeKZAAu1byL2xGRkaifuNmouuQAxHIKQTGd/4Dtcq0kb2dB0+vYtD3FUVnu/E5uV9O/Al2BZTN5JW9UYFAe3M3DBmurCW4Pval65wxMQnYsuEYnr75WNypW6saatcQbzGt6z3xfFdPhyD0kovs1GERTzFkbhXZ8VkRmNuFQc4oNSkOcRe+QsKNBZkis6jzJ0xL6qadKwmDyl6VJAwq40fRRIAIEAEiQASIABEgAkSACBABIkAEJBEwMzPDgX/2wtpK2QXPaWMmYc3Ov0TXLF26NK5duwYjo7w1+0704FnpcOQu62329uKpux0wuJZOV4+IiIStrY1Oc4olW7duHXjFqZh5uRfHthP/wtDw/fl+4f9cRqWn4u32frN+jAgjJq7qwIZFFoRdirFgpmfGcUjozcRbEXNffhXGBsK/M1JzCS3Vs08/3Lh5S2w79DgRyBEEVn95E1bm7D1Opq3cOw1r/54pKbpl/Z6o106376WSFpbg1K2dLwoXl18tLGGJbHUJDnyNrbv2ISZJc8vyEsU80a51U5iwKsKstEPLImGYIP/v4SH/9Viye2JWblnrtfKCMJh26KSnhxBzvA9SY56+42BgDJuOt2BoW1RrNpkFkDCoDCMJg8r4UTQRIAJEgAgQASJABIgAESACRIAIEAHJBMaNHoVePbtL9s/MUVVtVK0WwmMjRfPMnz8fo0ePFvXTlcNqv/WoU6smihXNvnZjujqLpDy8WnDuEcCNXTBvWALwsJcUJtVp/abNaFi/LlwLFJAaosgvKioKXFAODg4WzTPnux/QsXe3j/xiwiPx2Y7HovH/WITikmmUqJ8Uh+YxjqiUKHzBOBkpeNKPtfJlFUlCZuh3EYUSLAR9UtgMpccDyknZmkafhYuXYOXqNYpyUDARyAoCxd0qYvagfYqWmry0Dc5e3Ssph62VI76YMh3GZjnvppbi+cujU486ks6R25z8zwTh0NkDSE5NFN06nzfo064F7LLoxpXkpFQc/0P4vVts03O3DsOpazvF3LL18bwkDHKQqQkRiD03Fol3V8HIuRrMKs+EiXtTnTEmYVAZShIGlfGjaCJABIgAESACRIAIEAEiQASIABEgApIJFHR3x67tWyT7a3L8/qtv8cfG1aJ5bGxs8ODBAzg5ic9gE00m4jDhy8no6tsJVb3Fq7KUrpWj4qMTACtTvW2pS4++mPvLbLi56l8cHDp0KH7//XfRs7QoVwNL92zU6Jd/xRWYQ7ia5I5xDLZZhYiuJcWhRKIFOsaIt3G9XN0K+coWFkwZdugqKgeJCxKXatnAvrR4C1NNi5377zyGjcw60V4KR/IhApkR8Kk7Gt0bfaUITtephRHy6pHkHH27TUKpKp6S/bPK0djAHKOH9Yapufh7RFbtSek6KSmp2LP5PG4EX9Qqlbm5GTp3aAV3N/3/bQp5EoXru6y12t+Hzv1+LofImHBFOfQdnN3CoFFiGEwTpP+e6puHWH6ThCDkfzFfzI0e10CAhEF6aRABIkAEiAARIAJEgAgQASJABIgAEchCAosWzEWtGjUUr1i7YhUEvwkVzdOlSxds3KhZxBFNIMHhi6+/QeeOHVCtSvbMHpKwxVzt0rVnP8z56Xt2AdZVb+fgMynr1q0rmt/MyAR//vY76jVvpNE3df1FeMYJV93FsQq+ebbsAqSyIhDVHsxSDTE2ohBLJZzsRr44WPkIC9cJEdEoviVQNNd1+zhYf65MBK9VryHi4uJEmZMDEchOAtN7b0a5IvLn1V68fQBfLNBupmZJz8roN2pgdh5b49r1KjVFrYbFc+TetN1UxOs4bNzwL+tAIF4lnllu3kq6acM6qFSRVWPr0c7vf4ao+/L//gW+uI6JS3VXqaavo2a3MGj7ag8T2ubo63iUN4cRIGEwhz0htB0iQASIABEgAkSACBABIkAEiAARyNsEGtSvh19//lHxIf+Yvxjfz/1ZUp6tW7fCx8dHkq+2TpMmfwOf9u1Qo3pVbUPJXwsC3Xr1wy+zZ8Hd3U2LKGmusbGxKFeunKq6VMyG1f0cX66dK+gWevQ6vB+IK36rrZ7imTGruNSB9Y5yhVuymWCmcMN4RPatJLqaC6t4tBCpeJSaS2ixEaPG4sy5c6L7IQcikF0ETE3MsX6y+PuC0P4WbR2LbYe1r+r5YsKPcHCTP1NOX8zszd0wZHh7faXPsryBd8Kw/e+/EZ+ivKVzpQpl0axxXdapWfx9X84BDy1NgGGK/Mr8XaeXYM0BaTMu5exPVzEkDOqKJOWRQoCEQSmUyIcIEAEiQASIABEgAkSACBABIkAEiICOCPA77LdsWg/PwsItDaUs59OgBfwDb4m6Ojg44O7du+D/1KWRKKhLmuK5uvXuj59/mAneklaXNm7cOMybN080pbOlHQ5OXgK7nrUEfRNi4lFi433RfCfNX+Ok2WtRPykOdeLzoU5cPkHXVKTiUZ/PACNDQb+kjRdRLEa44lFqLqGFVq/1w/yFi6Ucj3yIQLYQ8CrWAFN7rle09vj5jXDpDpvFqqU1ruWLJh0baBmVNe7d2vmicHHHrFlMD6sc238T566fYHXbyTrL7ulREO3bNIWFubnOcvJECXEpOLVC+D1bbMGZa7vh8oNjYm7Z/jgJg9n+FHxSGyBh8JN6uumwRIAIEAEiQASIABEgAkSACBABIpATCDRu2AA///iD4q2cPH4CvXr3ZnJHqmguXbcUJVFQFLleHLr3GYAfZ32HQgV1Iw6eP38e1atXR2qq+Gvozzaj0aVyY4T3ryh6NocVl2EDE0G/R0ZxWG/9XDSXFIdCSeboES0+68q/tCEca5UWTBl2+jYq3xK/YO5fxhiONUpK2V6mPrdu30b3Xn1lx1MgEdA3gd5Nv0G7WsNkL5OQGI9OXxdAVKz2NwCYm1phyrezYZwD5vk55suP4p6ecHZxhL29FYIevMSbsDiU+qwQipbS/wxf2U/AB4HJySnY7HccgWE3dZXyvTx2tjbw9WkDRwfhmzS0WfzR7de4f0hZvh7fF0N8Yqw2y2aLLwmD2YL9k12UhMFP9qmngxMBIkAEiAARIAJEgAgQASJABIhAdhJYv3YVSpcqpXgLI7r1w94z0qox9uzZg9atWyte88sp09ChXRvUrF5NcS5KoD2BHn0GYvasGToRB0uWLKmqJhWzSgWKwX/wQpUI/cC3KIythSvqYjdfROlIYZ8klmuubRCSddB9zojpmuMiCrMGoMLJ7lrFwrSL8CzM5IREFPG7IzpnUEouMa6Nm7fEq1faiyZieelxIqALAnOGHkLh/KzKVqb9c3YVflrbT2Y00KvzeJSpkXXz/OxtnZkAWAQuTADMxwRAGxtzWFlZwMTE+L0z+K3biycv2IxUZsYGZnCxKQhPj0IoU6EQnApYyz6vPgPDXsbgr7/+wZv4F/pcBqamJmjbsjFKFCuik3VObn2OxBfiN31oWuxa4GlMX91JJ3vRdxISBvVNmPJnJEDCIL0eiAARIAJEgAgQASJABIgAESACRIAIZAOBWjVrYNF84VltUrb15MkT+LRog5AocXHB3t4ely9fRqFChaSkztSHREHZ6HQayMXBH/43HR6FCsrOO3bsWMyfL2321/VhS1HG2UO11qWiqbBvUFZw3ZcBd1ElIFF0b5ssX+ChiW4qOXyjXVA0yVJwzRgk4qWEike7VZeQT2SmVRSSENa/gugZhRy+njoN+/89oCgHBRMBfRDIZ+2EZROuKEr98/oB2HdqhewcRdzLYfD44bLjNQU62DijaGFPuOR3QD4Ha1hbqwVAMyZqiVlsXDzm/6b5TBbGdnBzYJWExZhQWNEdFpbiOcXWVPr4tYvB2H/iIBJTYpSmkhxft1Y11K4hfBOGlGRHfpPipdln/eHZ2HZigbIkWRRNwmAWgaZlVARIGKQXAhEgAkSACBABIkAEiAARIAJEgAgQgWwisOz3JahcyUvx6utXrsXkGd9IyuPt7Y0zZ86wCgjtL1Z+NeVbtGvbCrVqVJe0Fjnpl0DPvgMx67tvUZhVqmhrmzdvhq+vr6Sw2Y374cvandN9A81jYdBd5IIva03qsfKGaNXdedMIHLIIl7QPMacq8TZoEic894tXPN7r5AlTWyvBdNHb/FHmtfCsLKm5hBbavmMnZn4/W+xo9DgRyHICdcp/jrE+ymZgDvrBC/efXFa090kTfoa9m/Dvq6YFihT0hEdBDyaIxSMkNARR0dF4/eYN+nTryFqC2sna15MnofD7a7O02FQD5DPPj4IFCqFkqYIoXiY/DA11UCItbXWV1/6dlxBw/yz7N/F20VqkleRatnQJtGjWACbG71dcSgpmTtERifjPT/vPKhnzf72sFe4GX5K6ZLb6kTCYrfg/ucVJGPzknnI6MBEgAkSACBABIkAEiAARIAJEgAjkFALly5XF6hXLdLKdsb0HY8fxfyXlGjFiBBYtWiTJN81p6rcz0bZ1S1SvVkWrOHLWL4Fe/QZh5bKlMDYykrzQpUuXUKNGDcTHx4vGeLsWx38D58HQwDDdNwHJeNa/vGis9eoAOCabCfq9MIzHSptnormkOLgkm6J/lJuoq79HMhybCO8//NpDVPpPvJLRvzDL1VichaZNhYaFYeu27bh37z5Onz2L2Ng40f2TAxHICgIj2s9FQ68uspcKef0YJzqXkwAAIABJREFU3aYWljS/VGiRJrW7oLFPfcF9FHYrzATAQmwGoD3s7KxUFYCWluYwMFCLcH4bt+PJU/U8UxtrK4wY3Fv2uc6euYGjZ47JijcyMIWzlbtqr2UqeKBAQRtZeaQExcclYdO6I3j65p4Ud7355Hdx+j975wEV1bWF4Z/epdlQwS5i793Ye++9t2hiotFoErsxxq6xEgtgwd4VsWJvUYpUBWmiCEjvnXcHH0iM3jtz7h1A3Octl3nh/P/e57vjmDV79tkYNrA390wUL+6+cI5E6GP2GY7JqfGYsM5K9GtQaXA+MqbCYGGRpjgyAlQYpNcBESACRIAIEAEiQASIABEgAkSACBCBIiSwY+sWtJagAy+J64QY1KkXfCPezz0SWrKOsaFDv4y5O0Jn+dp/Hh0TAxPumlh5VlhYGBo3bgzZ70JLR10LnjN3opqx2X+2+vQoB92K/N158edcUD9KuOtup8FrJKhmCaUj/HOuIWZWQiWUyuHvTgnW5Ap+Y4U7Hs25jkdVgZmFwVqc1xjx1+XJDifrZjp79jwO2h/Gu8hI4fPSDiKgRAJ//+QCUwP22W7HnTbC+tR80Rlqa+phyfL1UOW+Y1CpvAUqc1dhlyljDCOjvAKgDm8XXnpGBjZv35dfHGpYzwq9uC42lpXDdULvsD6MxJR4Fvl/NNqqpVDepBKqVTGHVYOKMDDi/yKFvEHfBMXizIWrSMyIklei1H36eroY1K8HKlZQ7PV02z4a2XEmzLk98rmEDcenMusLW0iFwcIm/nXHo8Lg1/386fREgAgQASJABIgAESACRIAIEAEiUMQEalta4vBBO0myuHvrDqZPnYaUTOFOMB0dHTg7O8PKykqS2GRS/AnIisetW7eGh4eHXMn+1WMGfmg54JN73cunw7A3/zW4MQGhaHRLePblRZ1IeGomypWT0KY+yaaon8HfhZOBbIROridkBe0DriiXyf9BfXpONt5OEfYSDFZgQ2pqKvbss4Xt/gOKyGgvEZCMQAXT6tj6/V1Rfkv3DMI9t7OiPPLEr0LCuKJSGa4A+KFzWV5j/4BgnDh7KX/7gD7dYGVZQ175v/bFJyRh5x4l/bnkvthQSrMMKpY1R41alWBZzwzqGoqf1+VhMG48uoasHOEZr0wQGEWyZ9enRyfUtaollwP31opb1nJt/eym3Q6/4OpTJT0vcal9Uk2FQSVAJcvPEqDCIL04iAARIAJEgAgQASJABIgAESACRIAIFDGBVSuWoXevnpJk8defG7D5b/muCa1QoQIecdcXmnMdGLRKNoGsrCz07NkT169fl+ugbcytcH/Sxs9/oKSeivTxTQS9Ku3zhFqBa0g/JfDQSICDrjSdLfXS9dE3RfjqOffOJjCswt+9EnvRFQ0jhDt45OmeFAT1iQ3P3N0xe848JCZKUzRlyYE0XyeBXi0mY0qvVaIOP2KROd7FvhblIROXKV0OYeGhTEVBmf76rft46uKen8ecWZOhrS385/pTiQcEvMXxs9IUO4XAqEIdproVYFGRKxLWMYdFdeHOuQsn/4HXK2ch6yL9ecvmjdCxXav8a14/l4zHg3BEupUTlet3W1sjPCZYlEdhiqkwWJi0KRYVBuk1QASIABEgAkSACBABIkAEiAARIAJEoIgJGBkZ4cyJo9xsJENJMpkxbDyuPLkjl1e1atXw8OFDlC1bVq79tOnLJDBlyhTY2NjIlbyBpg48vt2Jykaf/1A2i+u6ez2pLrhPd3k91Q46o1KGDu+eONVM7OKuE5VilcpSw6xE4UL3szKpMOrHX9hMfhMFqyvhgmk9K8t59RUukgoafWKDbPbgjO++R0yMcOcliz9piMCnCPwy0g7NLLszw3nx6ilmrm3OrC8o3Lf7KCZPY591uHf/MURGRedali9bBhPHsl+h7XTdBf+4P5bkXIqaaKroopyROapWroS6jcxhaPLhfTU+NhUnjjvhXeKXUQSrYlEJg/r3gJam5mcxXD0UAI34aopiyt8fGfcG326R5jXInISCQioMKgiMtosiQIVBUfhITASIABEgAkSACBABIkAEiAARIAJEQBoCfXv3wsrlSyUxS0hIwLBuffE8TL4PCevWrYt79+5xM5uMJIlPJsWLwNKlS/H777/LlZQq1923b+IiTDRvLbj/WSs9GNWpzLsv6tozNAnREPTarf8G0WrSXH03LaEiTLP5Y0aopSFlQmPBvMy4jkdNgY7Ht3J2TwoG+8wGXz8/TJ0+M3cGIS0iUBgEDv32EtoausyhbB2W4uAl+d5zhIL4+r1CzRrCxf5P+SQlJ2Ob9f78H7Vq3hgd27cSCvnJn2dnZ2PD1j2Q/V4clr6GKSqUNkdpU0M4ezsjLfvL6iw2MTbCkAG9YGry6f/uuGGdAtVs/i+V8D2HGy722HXh5+LwqOTOgQqDcqOijRIQoMKgBBDJgggQASJABIgAESACRIAIEAEiQASIgBQErHdsQ4vmzaSwgquLKyaMGoP4tGS5/Fq1aoUbN25AV5f9w2C5AkmwKScnB1eu3UDP7l0lcFPMwtfvJdTU1FC9WlXFhEW029raGjNnzpQ7+qy+ozFn9SLUPClcVPYySoH+4Ka83snRCah99hVUuP/xrevaUXiqlSB3nnwbu6SYoHl6KV6vHOTg1aQ6gh2PsHdG5TT+D6fl7Z4UczgXVzdMnSH/cxQTi7RfNwFL8+b4Y/I5URAWbO+Opz7XRHnkiRMSk6Gvx1Yg8vLxxQXHG/l5jBraH5UtKjLlFRUdjz129kxaZYpunb0HDQ1NtO7VnLtulf99Vpl5sHhrampgcP+ekHUQFlyRr1PgcZ7tmef5bDw5Aw+9LrCkVWQaKgwWGfqvMjAVBr/Kx06HJgJEgAgQASJABIgAESACRIAIEIHiSKBixQo4efQwtLTY5h99fKYTh49h4aJfkZ0jX4dD27Zt4ejoCAMDg+KIJz+nx/88xckz57D+T2k6UhQ5rKubO+wO2uOvjWsVkRXJ3iNHjmDMmDGQFVLlWf0btMOGkzbQ5K53K2PzDLrg77qLVU1H3MRGgtblbNyhzc3L4lu+6sk4rRch6CXPhprc1aVDkoVnU7k004ZpA/6r6qJueKBJsJpgWNcWOjCpp9xi8cpVq3H2/Jf1QbcgONpQ7AgM7/AThnecz5yX7P2m/88mSEoRf/1ty6bf4NHT28y5XLzsBE/vF7l6NTVVzP1uKtTVhf88fyqgj88rnHN0YM5FWcLtazfhTcRLNLRsjyHjh0FDm/+9Vll5sPqqcNdRd+/cHo0bcldT/3/9cy0ESX5sXaJ5HpPW10VCcgxrWkWio8JgkWD/aoNSYfCrffR0cCJABIgAESACRIAIEAEiQASIABEojgTGjR2NuT/Mliy1P35Zij1HD8jtV79+fVy/fr1YzxzcYb2Huz7NBCOGDZH7XFJu7NitN5yuXOS6M1SltJXUy97eHmPHjpXbs2mlWrA+bY8y3Awu2Uo/+hQ1k/m7R3O77iZYcZ+483PIPPIU1VP4vVK5mYXbSr1ClgQNL1o5qpgdb86VIvnNnpdKhc5Q/tmAaXGJqHXqlSBHb6NU6A1WzpzBvOCxsbHoN3AIZNcj0iICyiLw+6QzsLJoyWz/wOM8FlsPYNYXFF48dxN9+ndk9tq6yw7JKSm5+qqVzTFiSF9mr7Pn7uC5vxezXhnCpOh0rPpjTr51pXK1MHbCdBiWK/6d/x/zaNygLrp2apdbwL1sGwytFP5rqvl4BoZ54ue/2WdkKuNZyeNJhUF5KNEeqQhQYVAqkuRDBIgAESACRIAIEAEiQASIABEgAkRAAgKyayoP2O6FVe3aEri9t5g5fAIc/5G/66JKlSq4c+cOzM3FfWNfsgN8ZDRp2kws/nVBkV3nOX/hIgwfNhgtmvFfo6ms8wv5rlmzBr/99pvcnYLl9Y2x59B+1G/UIN866sFzNHku3GnqUlsVpm34X6uR933Q9IVw16K9XhhCuHl9UqzRieVhkaXNaxWvkoGYSQ0Fw0nZPSkYTGDDth27YLtf/kK/2Hik/7oI6Gjq4eCvfqIOveXYLJy/s0uUR544JCQclSqVZfKKio7lrv48kq/t/E1r7j1buMP5U8HSMzKwadtepjyUKfJ9Egzbo//uXtfVLoUJo3+ERV0zZYZWinfFCuUxsFcvPDmsAZUcts5OWWLnHuzEwWurlJKjMk2pMKhMuuT9MQEqDNJrgggQASJABIgAESACRIAIEAEiQASIQDEjUKtmTRy1l+7D/wzuQ81hXfrA7ZWv3Cc1MzODk5MTaktYoJQ7OM/GqOhobtba9zhz4rAUdkweR46dQFxcPL6dPoVJryyR7Aq/7777Drt2yf+hvJaaBrZu2Yoe/Xr9K62s9AxUPeQrOBvQTzcZmiP552JmpKSh+pGXgl73tGNxT0v89YOyg7RLM0K7VCNe1LKOR/9RNaChw391b/oxZ9RM4p93JfMKHFsLatzMLGWu6OgYdO3ZW5khyPsrJtDMsjt+GWknisDMdc3xIvipKI88cUpqGrS1NJm8nN08cM3pXr528rjhKFvGlMkrLDwadvbHmLTKFJ20PQlnT6f/hJBdzzmo53Q07yr8xQdl5sfi3btTd0Q9rM4izdf8fnAUngXI/2UoUcEkFFNhUEKYZCVIgAqDgohoAxEgAkSACBABIkAEiAARIAJEgAgQgcInMHH8OPzw/SzJAkdGRmLSwBHweO0vt2epUqVw/Phx9OjRQ26Nsjdeu+GER9yMwSVcx2BRLV+/l1i3cQv2Wm8vqhT+EzeZu15y2LBhuHTpktw5qauqYfniZRg7efwnNQZ2rjDJ5i+apSIT4ZM/dBp+LrihnRuMsvk/4A9VS8MB/bdy58+30SxTExOSKgh6Pa0FlGlXh3df1MMXaOKTJejlbKmC0m25q1WVvObOX4Dbd+4qOQrZf40EJvZYgb6tpjEfPT0jDf3mGyIjM43ZI084Yey33DxX+b/k8HHAk+cc8dI/KPdfa3HFxbnfsX+Rw9X1Ja7cvCb6TJIa5KjgjyW/IpFnlmP7Zv3QfUh3qGuyd99JmjOPmamJMcaNHIRQ/yQE3zZhDpuRmY7xay0leQ0yJ8EopMIgIziSMRGgwiATNhIRASJABIgAESACRIAIEAEiQASIABFQPoEdW7egdSv2WU8fZ8hSHJR1HixfvhxLliyB7J+Leq1euwFNGjdCz+5dizSV/oNHcFe+7oaRoWGR5iELHhoamlu89fT0lDsXWVFw0c+/YNLMzxcBkk67oE4s/3Wcsk655wMtoGtiwBs78ZQz6sYJd91tKRWCNBXhK0wFD8rdXDon3gLa4J9/GKCTArVR/FfCyts96a+TDPVR/N2TgnnLseHU6bP4Y82/rw+UQ0ZbiIAggc0zb8K8rKXgvs9tcHxki/UHJzPrCwrv3XZB228aM3llZ2dj8/Z9XHEoM1dvZVkDA/p0Y/KSiez2n0dY1BtmvTKEMaEp3BdU5glaV6tUn/vyx2ToGPJ/yUPQSIkbGtSzQq9uHXL/G+Pe6QhkhLFdHytL0T3gLlYeHKHEbJVnTYVB5bEl5/8SoMIgvSqIABEgAkSACBABIkAEiAARIAJEgAgUUwIGBgY4eugAzMzKS5YhS3FQFrxv376wt7eHrIuwKNeg4WOwd9c2mJqydxRIkf/y3//EN+3boHPHDlLYMXu4urqiZ8+eiIiIkNtDTUUVixf8ylsUlJnFeAah0T/Jgr4u5pkw7cbfNRjlEYAmT4TnB57SDYefRopgTHk2DE4qi1qZurxb5e14lLJ7Up7c+fa8CgnBwCHDxdqQngj8i4CRfmnsnecuisrq/eNw/Z9DojzyxOER0dzVn8ZMXm9Cw3Dw6Jl8be/undCgHtvc3uSUVGzdZcuUhzJFzk7uOOlgLVcIU0MzjJ8wG2Ur81+vLJeZxJv69OiM+nXfF6O527Bxi71JNNfD/vpqnLlffLr5FcFFhUFFaNFesQSoMCiWIOmJABEgAkSACBABIkAEiAARIAJEgAgokYBlrVo4aLcP6urqkkVhLQ5Wr14dDg4OsLRk7ygRc4jAoGCsWPUn7PbK92GomFhC2ouXLsPb5zkWzJsjtFVpP7e2tsbcuXORmipccMtLQpUrCv4yZx6m//idcF7cp7SVbL2gJtB195or5GWN4++6k33iK4/XE8143NCJFs5Njh3N0gzQNZV/ppi8HY9Jp5257knhjkd5uiflSF1wS69+AxEeHi64jzYQAXkJdGw4DN8P/Eve7Z/cN/F3K7wKey7KQyaWdY6lp6cz/7137+FT3Hv4JD+PWdPGoZSBPlNeISERsD9xikmrTNHBnfvh7f9Y7hAa6poYP/In1GhsIbdGmRtlsyPHjxoCE5MPxcr4qDQ4HxPX2bhgd08EvBVX4Fbmufm8qTBYVOS/zrhUGPw6nzudmggQASJABIgAESACRIAIEAEiQAS+IAIjhg3Fwp+FrwxT5EisxUE9PT1s3rwZ06axz6FSJM+Ce0+cOoOw8AjMnjWD1UIyXVhYOH6YtwDH7fdL5imvUWJiIiZOnIhTpxT/sPrn7+bgu5/lL2ZqHnCBWSb/daKZOdl4M6WeYPryeEWpZmCPgTRX9plkaWB6YkXBvFwrZMCkZ0PefdGegWj8j3Ano1vFDBj34PcSTEiODctXrsL5iw5y7KQtREA+ArMHbkOHhkPk2/yJXRGxIRi1uDL3HQCu7UvkWrViExYtncvscojrFnzNdQ3KlrGxIWZMGs3s9eC+F+48vsOsV4YwJ1MFyxbNYZqj16fLeLTt1bJIrwavYFYOo4cN4Aq//5596PPkHcKelGFGlpwaz80XZOsMZQ4qoZAKgxLCJCtBAlQYFEREG4gAESACRIAIEAEiQASIABEgAkSACBQ9gbWrV6Fb1y6SJiIrDn43ciIev5R/Nl1eAv369YOtrS13pSd/R5aUCS/8bSkGDuiL1i1bSGnL7DVmwlSs+WMFzCsJF5+Yg3wkfPLkCYYPH46goCCFLHU1tLBk8RKMmjBWIV2sgysahgt3cLh3NoFhFf4rb+MuuqJBhLDXTv0QxKtlKZTn5zbPTKgEw2z+btu3GqlIH9eEP56cHY+f8/J97ovr5y/B44kLPHx9cNzxPCpUMGM+4yXHy1i8bAWznoRE4GMC++Z7wFCP/f3c/upq7Du3SBKwz575oUGDGkxe6RkZufMF8wqUTRrVQ/fO7Zm8ZB47rA8jMSWeSa8sUZh/LP7a+Ruz/YzpC1CxSiVoaEl3E4G8ybRu0QQd2n16dvLdw/HIjGW/rvy+5zlsPjVT3lSK3T4qDBa7R1KiE6LCYIl+vHQ4IkAEiAARIAJEgAgQASJABIgAESgpBLS1tbH3712oYyXtt+GzsrIwb9JMnL1zVWFUZcuWxf79+3Nn3BXG6tyjL65eOgd1tX93GRRG7E/F2Lx1O6pWqYKB/fsqPYWUlBQsXrwYf/31F2TPTJFlrKOPNRvWo0efXorIcvcmv4mC1RXhKyvdyqTCuB9/cU1er4s6kfDUTFQ4108J+iSbon6GAa9XFtfx+FqOjkctrnuyvED3ZJ7Xm9dvcOO8I9wfPYX3Cx/4hL+C7NrSvLXg+58wa/4PzGeMjIpC917Kf90xJ0jCL4qAeVlLbJ55U1TOv+7qi8ee0nSxxsQmwMiQ7erPl/5BOHnOMf8sQwb0RM3qVZnOFhefhF17DzBplSm6d+kxHG6wdavr6etj8Yp1SExIgFqOJnRKCX9ZQ6qzjBjSF1Urm3/SLpv7a+323+Ii7To/Dzdcj4gzKUI1FQaLEP5XGJoKg1/hQ6cjEwEiQASIABEgAkSACBABIkAEiMCXScDY2AiHD+5HOa4gJ/XasHI1ttvsZrKdMWMG1q9fDwMD/gIMk/n/Ra7P3LH/gD22bFwrxkZS7Z2793H1hhNWLV8iqe/HZk5OTpg6dSoCAwMVjmNuVBYbrbejRSv2LkszGw9ocpMG+VaEWhpSJjQWzM9snyc0uTmHfMtbIwnndd8JesmzoV66PvqmlBbc+qx9KRjVrMS7L9bBjeue1PzknuzsbLiF+eN6oBvOBz6F86sXSM1M/6xf92btsPvkIcG8+DYMHTEKAYFBojxITARkBPq2moaJPcR1oI5YbI53Ma9FA61gVgWv3wQwX3V57eY9OLt65Ofx0+yp0NTQYMrL3z8UJ86dY9IqU/T3xh0ICvViCtG0eSsMHTk+X/s26B3MqrBf3ylPEvp6upg0bjj0dD8/pzUyNAkeZ/Xksfvsnm+3NEdknDRXUYtKhFFMhUFGcCRjIkCFQSZsJCICRIAIEAEiQASIABEgAkSACBABIlA0BKpVrQI7m73Q52b9Sb1OHjmOlcuXIz4tWWFrMzOz3G62YcOGKayVR3DBwRGampro0U3a61Tlif25PampqZj/yyJs37JRjM1ntSEhIVi4cCGOHGHrgKhdvjJ22tuiWvVqovLLsXdGlbTPf6ArM8/muuFCJtUB92k+fyzOq7KAVyrntqXUK85LVNq5YoNsNczirhNVETDzMkmD/kD+wmZcUBgaOEXnJxUUE45rgS645u8Gp6BniFLgusFKhmVw79kTUQdct2ETjh4/IcqDxERARmDRGHs0rtGJGYZ38CN8v641s76gcL/NKYyfNJjZa4/dEURFx+bqZbPsxo9i97p+zRlPPf5hzkUZwqwUFSxZMot5luOIMZPQqEnz3NQyMzOwYtF8jBo0G3VasV3dKnTGGhbVuCvAu0Fdg/8LIW63wxHjVU7I7rM/D43yxw/b2a6MZQ4qsZAKgxIDJTteAlQYpBcIESACRIAIEAEiQASIABEgAkSACBCBL4xAs6ZNsHPbX1BXl34+0JN/nuDnb2cjKDqMiUrnzp2xe/duVK9enUlPIiCBu+Jt1apV2Lp1K2TFR5bVp3VnbLLbBS0t8dfERTt5onEQ/4e6shxdmmnDtAF/ETL6BucVLOxlox+KCLXPd9wpwmRSghnKZfNziOI6HhMFOh5DQ0MRuPEcbge6c52BrgiIYfszkpf74YP2aNO+rSJH+dfeW7fv4KefFzLrSUgE8ggc+tUf2pr8xX8+WrvPLcTRq+skAfryZQj39wd/9+7nAiUlJ2Ob9YcrNtu2aor2bdi6pWVXNm/aZoOs7ExJziWVyUu3EOw7+Cez3aLla6H//+5+3+fesN2zPdera9vh6DSwA1RVJfhGxv+za27VFp26NYCqHP+pctsmBdmp7K9Bxye22HdJmhmXzHBFCqkwKBIgyRUiQIVBhXDRZiJABIgAESACRIAIEAEiQASIABEgAsWDQK+ePfDHyuVKSebdu3eYP2kWbnuydzQtW7Yst9tNR4f9gz6lHK6Ym27btg3Lua7N6OgPnWmKpFxKSxezv/8B02Z/q4iMd286N2erxskg4a47w2ToD2kmidd17Sg81UqQ5AxdUkzQPL0Ur5ds/t+rjzoeY2JicJWbE+h67zG8fbzgxV1vKJshKNWaNmI8Fq1dyWwXGxuLzt0VnxvJHJCEJZJAncqtsHLiaVFnm7ulI5753RblkSdOTErmvXKSL4iH1ws4XHHK3zJmxECYVzRjyisyKg579x9m0ipTdOnoZdx9cp4pRHmzivhx/ofi2aULp3H31vV8r4aW7TFwzFBo67FdvZpnpMZVAvt3HAzLRqZy5ZmWkoUHtuJmB685OhFPXyg+K1muBAtpExUGCwk0hcklQIVBeiEQASJABIgAESACRIAIEAEiQASIABH4QglMmjAes7+bqbTs1y7+HTZHDiAtK4MpRunSpfHTTz/h+++/V+r8QabkipEoJycH9vb2WLp0KdMcwbyjyK4OXbPrLzRq3Ejy05nauEMf/G0fsarpiJsoHNvU5hnnxf/Bc4B6Mo7rRUhyjqoZOhiRLHxF3T+1c+ASHgjn2/fh4+kN1+DnSM5IkySHT5m0rFkfx65dEOXfu/8ghIWJ61wUlQCJv3gCIzstwNBv5jCfQ/b+1f9nEySlvL++U8zq0aUfLl9nK3rJ4l64dB1ez/1yU9DgOurnfj+F64AT7lD+VM7eXsE4f+WSmOMoRbv5jzWIiOauWmZY7Tt0Qe/+Q/KVWzeuxtvQf8+FLGdaGZOmzIZhOV2GCICRvilGcO9LxuXlLy7+c+01kvzYukTzkhz3Z02kpCcx5VxcRAa1x0JPQ3l/5widUz0zCpoZ4ueECsWhnxcPAlQYLB7PgbIgAkSACBABIkAEiAARIAJEgAgQASLAREDZxcHrl69hBTdHLySWvUhTqlQpzJs3Dz/88AOMjIyYzllSRefOncOiRYvg5eUl6ogDv+mOLQd2i/LgE6cfd0bNRP7uT1mB4NVEK0CN/4P49GOcVxK/VybXwbe5VDCyJLjVTi0HmBtfmStr/tcsLDEaji+dcT3Albse1A0RSeKLG/I+BC01Dbh7e4q67nXNug04fvKUvCFpHxH4D4E/p15CzYrCBf3Pobvlys2m3TtCErKODnfQszf7nLitu+yQnJKSm0v1qpUxbFBv5rxOnnLCy+AXzHplCFNiMrFy1Q/M1pOmfYdatevm6lNSkrFy8fxPeulql8LUSfNhVqO0QrGqV7BC//4doKWr2Bu3w54g6GZUUShWwc0vQp5gkc0AZn1xEZpa9oNhVvF6zRUXNpSH9ASoMCg9U3IkAkSACBABIkAEiAARIAJEgAgQASJQqATGjBqBeXPZOz6EkpXialFZDH19fUycOBGTJk1CkyZNhMKW2J9HRETkzmHcu3cvgoODRZ3T3KgsFi5bjL6D+ovyERJHP/ZDYy/hzlFnSxWUbssVB3lW5BNfNPUQnttlrxeGEHW2GYsfhx+dWB4WWdpITE/GrSAPrhDohmvcnEDvd2ydN0K85Pm5vqY2dvxtjQ6dOsqz/ZN73N09MHHqdGY9Cb9uArpa+jjwi68oCOvtp8LxwT5RHnni128iULFCGSavd5FR2HfgeL62a8eZpqOXAAAgAElEQVS2aNakAZNXekYGN19wL5NWmSKvh344dHIzUwhZ5+SKPzdzs4nfd/I9c3mCo/a2n/VSVVXDqIGzUa9tLbnidWjUHa06VoeKgg2amVyD3O29WVBVYb9K9Pgt7gsStzfJlWdx3kSFweL8dEpeblQYLHnPlE5EBIgAESACRIAIEAEiQASIABEgAl8hAWUXB2VIN61cAzv7A4hPSxZNuF69epgyZQrGjx8PExMT0X5fgoGDgwP279+PEydOSJLusG59sX7Pdkm8BE2ysmGx30dwzqC/TjLUR/HPGQTXWWhh6y3odU87Fve0xHXwZWVl4XVIMNK9A+Hr9xyPXj9HZnaW4HGVsUGN+8R8XIPOMDe3gGEbSwwcPoT7kJ7/elZ58ujeqy8io6Lk2Up7iMC/CLSy6o35w8UVwGaua44XwU9Fk1VRUUFqWho0NeS/grJg0H+cn8Hp9oP8fzV1wkiUNjVmyuttWBT2H/5QZGQyUYLolO1pPPX8MBNQkRDVqtfCtFkfvkB08uhBOD95KGjRrlk/9BjW7bPvVZrqWhjaYxgsLA0EvT61Icg7DoG3DJm0eSJZt6Csa/BLX1QY/NKf4JeVPxUGv6znRdkSASJABIgAESACRIAIEAEiQASIABH4LIHCKA6+fv0aq+cvweXHt5Gdky3J0xgyZAjGjBmDQYMGSeJXnEwCAgJgY2MDW1tbhIaGSpJatdIVsHDFUvTo01MSP3lN9O1cYZqtxbs9FZkInyzcpSOPV6haGg7ov5U3vfx9EeFv4ef7HP7crwB/P6SlSdN1qHAinKB79caY0LAb2lrUgblBaa4r5v/tNNz1pp4mqTAYJL5zdtHS5XC8fIUlPdJ85QSm91mL7s3GMVNI52Zw9ptviAxZ25fI9f23C7Bt11pml+OnHRAQ9L4DWFdHBz/MnMjs5ezsh2u32QpwzEGFhDkq+GPJr0hknOXYvVc/dOraKz/KqmULkJSYKBQ19+e1qzbH8HGjoWP47/d/U4NyGDFoAEqVZu/2u3chCBkhVeTK41ObZHMFZfMFS8KiwmBJeIpfzhmoMPjlPCvKlAgQASJABIgAESACRIAIEAEiQASIgCCBgQP6Y+miXwX3id3gdO06Niz7A96hgWKt8vWy+YOyAqGsi7BFixaS+Ra2UVhYGI4fP47Tp0/j9u3bkoWXXT05ctAwLF77u2SeihglnXFFnRj+wqDMz2dAJeialuK1Tjzjgrox2rx7crg5g1tKhSBNhb8AnZiYAN/n3vDnOgJfcsXA+Pg4RY4l6d4m5WtgetOe6Fq1EaoYlePGLfJ/YP5KIwU545qKyuGCwyUsW1E0rwlRiZO4yAns+OEhyhlXZs7j/L1d2HJkFrO+oPDhg2do1Vr4SwWfCpadnY2N2/Ygi+tslq16dWqhb88uzHnZ2Z1FWLTiX0pgDiiH8F1wHDZtZf+7fdaPC2BuUSU30ruIcGxau0KOqB+2mBqaYcrUOTCu8L4z0MqiIXr3bQMN/rdxwRgO1qHQza4guO9zG576XsOaIxOY9cVJSIXB4vQ0Sn4uVBgs+c+YTkgEiAARIAJEgAgQASJABIgAESACXxmBtm1aY+O6NdDU1FTqyXO4KyG3rtkIe/tDiEgUd+Xjx4nWqlULXbt2Rdu2bdGqVStUq1ZNqWcRa+7t7Q1HR8fcYuCDBx+usxPrm6fv1bIDFnIf5FapUkUqS4V9Ynxfo9G9eEGdi3kmTLvxf8Af8zwEjR4kCHqd0g2HH1c8K7jS09MRFPCSKwL64KXfC4S9fcPdTsq14BXB0tXQwje1GuO3FkPR0qwmNP8/v0uRVFxa6MC0XlVFJP/aG/HuHXr2Ue6MSebkSFhsCZQ2rAjrOeKuX1xpMwK3nKW5cjPiXQzKlDZi4vXqdSgOHz+Xr5UVBWXFQZaVlJyCbdZ2LFKlah5dfYpzV2yYYmhr62Dpqg2QXdcqWw/v3cL5M4o/N21NXUwaOw+TJk5Gs3ZVOD+mdPJFKfHZeHRIwaGEH4W0ubwElx5LM+NS3GnEq6kwKJ4hOchPgAqD8rOinUSACBABIkAEiAARIAJEgAgQASJABL4YAnXrWGHr5k0wNmb7oFWRg4aHh2Pb7+tw4dplxKUlKSKVe2+ZMmVyi4QtW7ZE/fr1c39ZWFjIrZd646NHj3D//v3cjkDZ79HR0VKHyPVrUqU2vl80H527dVWKv6KmlfZ5QjYrj2+FaKYge6xwF5w8Xq4aCXDUfoc3r1+9LwT6vkBwUADXGZSpaOqS7JddBVrbrDLq1q2L5h3aolPbb9DoZgzUwf7hdpBWClTGCPPiO8CQ4aMQGBQkyRnJ5Osg0KXJGMzst17UYSf+XgevwnxEecjEhqVMEBUdATU1tisp79x/jAePXfLzmP3tBOjp6jLl9epVOA6fPM2kVabo0M4D8PJ/xBSibv1GGDtxer72oK01vD3dmbzq1WwJD1+2PD4O+MIlEqGPSjPlkSeau7MDQt75ifIoLmIqDBaXJ/F15EGFwa/jOdMpiQARIAJEgAgQASJABIgAESACROArJFChghmsd2xDpYoVC+X0eQXCi9evIDZVvtlFYhIzMDBAw4YN0bhxY1SvXj33V+XKlXN/12X8ULhgPn5+fpDNCPT19c39/flzbm6dvz9k/17Zq17Fahg0eiSmfPfhw1xlx5THX/OgC8wy+O+Oy+RmT76ZUk/Qjs8rKCYcji+f4lKQC64HuSE15d9dg4LmEm6wMC6HepZWaNSmBfoMGYiKFT9ce2dk6wbDHHGduek5WXg7pb6ojNdt2ISjx0+I8iDx10Xgp2G70aZOX+ZDB4R6YNrqhpJ0627esBtz5k1jzmX/4VN4GxaRqy9taoKpE0Ywe9294477T+8z65UhzMlUwbJFc5hnOQ4YMhKt2nyTm5qsu3rZb3ORwXVes6wfpy3Dlt3LWaT/0Vw74g/1mOrMXlEJYZixSfycVuYEJBZSYVBioGTHS4AKg/QCIQJEgAgQASJABIgAESACRIAIEAEiUIIJGBoaYsfWLahjVbvQTplbIFy1Ho43riIqWfjqSWUkJuswlF0/KptbKCsS6unp5f/S19fPv1KtYOx33JWMeYU/WSGwKFabWg0xcPxIDB87qijCC8aMc3RDg7fChTD3TiYwrFqe1y/a8Rkav9XgPqkGolMS4BT0DNcCXHE90BUBMWGCuShrg4mOARrUsEK9po3QdUAvNOIKz59a77yD0OxRsiRpePQsg1IVyjB73bp9Bz/9vJBZT8Kvi4DsSsn9C3ygq80/C5SPit2lZTjgsFIScJ6e/lwXLtt10ampadiy88MVm82aNEDXjm2Z8pIVzTZvt0F6BlvRjCmoHKLXLyKwQ0Qxbv6vy2FaumxuJFnHtfW2DXJE/fSWCyduou/Qjsz6gsJL22Kgo2bM7HXr2QlsP/sjs764CakwWNyeSMnOhwqDJfv50umIABEgAkSACBABIkAEiAARIAJEgAhAS0sLK5YuRvdCvo4yLS0NO9ZuxhWHS3gR/oqexGcIyK7m7NCgOSbP+x7tvmlfrDklh0bB6nK4YI5uZVJh3O/znRxhb9/i5rGLiHvixxUDXeAaFoBsrtOwKJammjrqW9REHe562tZdO6Bn315QVRW+GjTqmjuahKhLkvKzsqkw6sve+ZKUnIz2HbtIkguZlHwCNSo0xJppjqIO+uuuvnjs6SDKI08cG5fIXSeqx+T1wi8AZy5cydcOG9Qb1atWZvKS5WG97yCTVpmiW+fu4cqdw0whDA2N8MvS1fnaG1cv4fqVi0xe+rpGiHgXBh1dLSZ9QVFMeDrcTgl/yYQv0NYzs3HH/ZToXIqLARUGi8uT+DryoMLg1/Gc6ZREgAgQASJABIgAESACRIAIEAEiQAQwYthQzJv7I9TVpSlmKIL05OFjOGt/DA+83YqsAKRIvoWxt7yBSe6MunGzp6NO3TqFEVKSGOVs3KHNTdXjW+/U05A8/kOnnawTx/GcA5443cVzTy+4vvJFambRdOWoQAWW5S1Qp04dNG3XGn0GD+A6Sw0VZhNx2xPN/YULiPIYh6ulIXXCpzsT5dHL9kycOh3u7h7ybqd9XzGBQe1mY0yXX0URGLnYAhExIaI8ZGLLGnXh4+vxyS5uecyvXL8DV3ev3K2yTsi530+BpgbXicywfH1f4/TFCwxK5Up2b9qBwDfvz6joat6yLQYPH5Mv+3v7RgQF+itqk7u/U6sBcHp4lkn7sejZvbeIdjcT5TV1YwPEJkaK8ihOYioMFqenUfJzocJgyX/GdEIiQASIABEgAkSACBABIkAEiAARIAL5BOrXr4ctG9bD2NioSKh4enri+O79uHPvHoKi3xZJDkUZVFaUal69LroN6IPJ3PxANTW1okyHKXbOYWdUSdXh1WZz94M6Ns/GtTMO8HZ+BreX3niXFMcUTwqRkbEJatSsjfo162DclNG518yKXYmRsah7PlSsTa5exitkcl1RXta792D33g9XKooyI3GJJrBs/HHUr9qO+Yyufjcxb0tnZn1B4ZFD5zFyTD9mr79t7BET+/7KavOKZhgzYiCzl8OlB/B4/oxZrwxhcmwaVq36iXmW46hxU9CgUdPc1NK5Lv4Vi+chO5utO3vZz5uxfN0cSY55eX8AtJLY34eDw30wz7pkdUlTYVCSlxaZyEmACoNygqJtRIAIEAEiQASIABEgAkSACBABIkAESgqBMqVLY8um9bCqXXhzBz/F7uyJ07hy4hzuuj1GYnpqScH7yXNUMSmP5k2aYez309CwUcMv+qyRt73Q1F/lP2eIS03EzSB3bkagW+6sQN+oN0V2Ti0tbVSrXhPVa9VGTe5X2XIfOlPq16iMeg3FFeHyDiZP96S8EFya6cC0QVV5t/9nn4urG6bOmMmsJ+HXQUBDXQsHFr6Ahjr7NY47Ts3BKae/JAHmH/AG1apWYPKKT0jEzj0frv5s36YF2rZ6XwRTdGVmZmLD1j2KypS+//kTf+w/upE5ztJVG6Cjo5ur9/H2wIF9u5i97ju5ok2nRsz6PGF2FuD0dzrUwP4avPhoD+yuLBOdS3EyoMJgcXoaJT8XKgyW/GdMJyQCRIAIEAEiQASIABEgAkSACBABIvBJAr8u/BnDhgwucjpJSUm4ePo8nty6B08vT7wIe8X1T+UUeV5iEpB1BtatWBVNmjVDj6H90bY9e3eOmDyUoc1ISUP1Iy+RlZWN+yHeXCHQNbcQ+DTUD1lFNCdQNhPQonJV1OCKgDVqWaGSeeXPdmOaaeqgY79OkqDJOvIU1VLef+gudnkbpkBvCFtRIy92uw6dkZySIjYV0pdgAo2qd8TisWzz6vKwzP2rE5753pKEUlJyCnR1tJm8nnn4wPHahzzGjxqMCmblmLzecR3A+w4cYdIqU3Tp6BXcfXKOKUSFiuaY/dOHK2Mvnj2B+3dvMnmVMzHH28hg5itfCwYNC0qGzyVx75t/HB4LVz8nprMUVxEVBovrkymZeVFhsGQ+VzoVESACRIAIEAEiQASIABEgAkSACBABuQg0a9oEvy9finLl2D5MlSuIgpsCAwJx9vAJPH/mAb8AfwRESXNdo4JpKLy9kmEZ1LCoilr162DQ2BGwqmOlsEdxF1y7cBlPb95DiKcv7gZ5ICG96IpQsi5AWTegrBBYtXoNyLoE5VkmmWroMaKHPFsF90Td9UYTP8Ftcm2IVU1H3ERx3Tg/zJ2He/cfyBWPNn2dBMZ3W4r+bb5lPnx6RhqG/loeiSmxzB55wl7dB+PSlVPMPmcvXsVz3/fz8jS42bk/zZ7KXLjycA+Cw3VH5lyUJdzyx1qERwcz2X/TqRt69R2Ur9287ndEhLNd4T2w+wScuWLHlMfHooeOwUgNrCzKa+zqGkjNSBblUdzEVBgsbk+kZOdDhcGS/XzpdESACBABIkAEiAARIAJEgAgQASJABAQJ6OroYN7cHzFo4ADBvUWx4c3rN7jheAWeT9wQ+NIfvqFBiEtNKopU8mOW0zdGVbNKqMLNqqvTrCE69+qOSpUqFWlOygju6e6BG+cc8dzFPXdO4NuEaGWEkcuzVCnD3I7A6tysQFlB0ID7/yxLjWtGHTKohzTzHbmuycr7n7Ok8R9NTk4OXk3kislqqsx+9keOYePmLcx6EpZ8Ahu/vY7K5eowH/TqPwewZv8EZn1B4bUr99G1exsmL9mfly07bZCWlp6rr1WjKgb378nkJRMdOXYZwW8CmfXKEMZHJOPPtfOZrSdPn42alu+/oJKYkIA/li9k9tqy2hY//jqRWV9QePHvYOhlsRcGvYMfYald0d92IAmMAiZUGJSaKPnxEaDCIL0+iAARIAJEgAgQASJABIgAESACRIAIEIFcAq1atsCKZUsgm0FY3NeL5y/g8vgJfN298DrwFSLeRSA6IQ5RSXFI5jpapFqyLsDypmVQoUIFVKpWBZYN6qJF21YwM/sws06qWMXFx/GCQ+7sRy8fb/i9e11kaelqaKFh1dqo07A+2nXvhJTYTMSpcsOpJFiWlczQpGVjCZwAY9tnKJWjIYmXcy2gdDv2os1LrnA+fPRYSXIhk5JHQE/bEPsX+og62PrDU+B430aUR5449G0kzMqbMnmFRbyD3aGT+drundujSaN6TF6pXHFxy459TFplip7d8cHRc9uYQqipqWP56o1QV3//3uTy9BFOHDnA5KWiogJf72DUqG3OpC8okv31fE8k6qM31+HknZL3BQgqDIp+eZGBAgSoMKgALNpKBIgAESACRIAIEAEiQASIABEgAkSgpBPQ19PDT1z34MD+/b7oo7q6uCL0VQhio2ORkpSMZG6OYSo3y0r2Ky0lFbJuk4+XqqoKTMuVRaXqVbgPQGuhcRNpCkeFDTIuLg6GhmzddLJcVy1cgr3HDhZ22lBTUeXmMnIdmHXrolmHNug/dBA0NTXz83ByuInwVGmuLi2nponOA7vynjHi3TvocX8e9HT5Z2Eln3SGVbyOJLz8dVKgPkrcnMHuvfoiMipKknzIpGQRaFd/EOYM3iHqUDPXtcCL4CeiPGRiWbEpPT2dK1ypM3k9euKKW3cf5WtnTBoNY2O2973Q0EgcOHqCKQ9lik7bnsETz2tMIarXtMTUb3/M1x4/bAdX53+YvGpWbgjfIDcm7ceiIO84BN5ie055Xr/t6wvf1y6S5FOcTKgwWJyeRsnPhQqDJf8Z0wmJABEgAkSACBABIkAEiAARIAJEgAgoTKB69Wr45ef5aPqFFscUPnAJEAS/esVdO7cebVq3wvixY5hPdOPadUyZNpVZr4iwmqkZ6lnWQcNWzdFraP/czszPLS9PH7i/kOaqP8MsNfQe/u85g+kZGfB080b06wikZWYhXiUbVcqYonWHlrxHinrmjybO0nSppiATEZMbKILwP3sXL1uBS46XRXmQuGQSmNV/Ezo3Hsl8uOj4txi1pCoyMsW/3hfMX46165cx53Lk5HkEv3qTqzfQ18N308czez1+7IOb928x65UhzOLeg9YsX8I8y7Fnn4Ho0Ll7fmqrli1AUmIiU6oThv0Iu+PSdOjdPhOI7LdVmfKQiZLTEjF+DddaXQIXFQZL4EMtxkeiwmAxfjiUGhEgAkSACBABIkAEiAARIAJEgAgQgaIm0OGb9pjzw/eobGFR1KlQ/M8QSEtLw14bW+yz3Z+7o3/fPli+dDEzL1k3ZbuGzfEmPpLZ43NCEx0DNKxuhbpNG6Jr/95o1FT+rkxZXqdPXUa6yn+7PRVOlLPo0ak14rm5WwHPA5CSkIpErhCYrfJvpzJQR9chHz5c/2QcLi8LW2+ocP8Tu3KQg+cDLaBrYsBsdcHhEpat+J1ZT8KSS2DffA8Y6rFd3SmjcuLmZuw6+ZMkgP557IHmLdiu/szkimabd+xFFjfjU7Ya1KuN3tx1wyxL9r5iveco4hJjWeRK04T6R2LbzqXM/t/P+QUVzd//vR32NhR/bVjF7HV8vyOGjWef31gw8MWdYdBDeeZcHvs4Yv3xKcz64iykwmBxfjolLzcqDJa8Z0onIgJEgAgQASJABIgAESACRIAIEAEiICkB2VVvI4YNxbQpk1CqVClJvcmMnYDsGr6Tp89wBUE7xMR8+FBb1u154og9uzGnnD5kLK463xPlIRNrq2uikUUtWNXnZjN2/gY9+vWCqqoqs+/lE1cQI2LOoFqOCoyy1SHLIFY1ExkCRUYDrrOw70edhZ9KXme/C8pmaTOfq6DQtVIGTLo3ZPaSXYHas09/Zj0JSyaBSqVrYMt3d0QdbqXNCNxyPi7KI08cGRUHUxO2v08Cg1/j2KkL+XkM6NMNVpY1mPJKSEzGjt3vv1RRnNajqy44d2UvU0ra2jpYumpD7nWtsnXv9g04nD/F5KWhrgXZ9dQ6ulpM+oKi5PgsPD6kJspn76XfcPmJnSiP4iqmwmBxfTIlMy8qDJbM50qnIgJEgAgQASJABIgAESACRIAIEAEiIDkBAwMDjB45AmNGjYC+vr7k/mQoP4Gjx0/AhusQ/NwsuWuODjA1NZHf8KOdW1avx5bdis8iU+XmBA6r0w51LWpArYkFeg7ux+XB3qH08QFuX7mDUEWuw+M6Aw2z1aCVo4ZkrqCYyFBUbNeiIczNK/KyjLvgigbvxH9wLgvyRiMVmeOaMD87mXDI8JEIDAoW5UHikkWgV4vJmNKLvWtMRmPMsup4GxkgGoxhKRNEx7xj/pLAzbsP8fjJh5l3c2ZNhrY225+/wMAwHDtzRvSZpDaw2boHfsGuTLb1GzbB6PEfroO227sDL3y8mLya1u2Ap563mLQfi7weRSDCpaworx+2t0NolPjXoKgklCSmwqCSwJLtJwlQYZBeGESACBABIkAEiAARIAJEgAgQASJABIiAQgSoQKgQLkk353YI2tghPCKC13f17yvQs4fAFZg8Dj7ePujduzd3saXwtZ3NKtTC9MY90bVaI1Q2LPuvD/tf6qZAY2RTyRi8evUa95+4f9aPuxUQ+jmq0OO6AtO5q0FlXYFib/isZKCP9t2/4T1DbHAYGt6IluScmcjGm8lsVyzmJbBuwybIise0iEAegV9G2qGZJft7wsvXbpj+p/xX//KR3/aXDb7/YRLzw7E9dIJ7D3x/1XG5sqUxaewwZq+bN13x2PURs14ZwvSUTCxf8iNk15yyrEFDR6NF63a50qysLKxYPA8ZXIc5y/px2jLuSyLLWaT/0Vw55A/N+OrMXlHxoZixuRmzvrgLqTBY3J9QycqPCoMl63nSaYgAESACRIAIEAEiQASIABEgAkSACBQaASoQFhpqnL/ogL/37MXbt2FyBR3Yvx+WLv5Nrr2f29SzZQc8D/9v11kt04qY0aQ3etVoihrGFaDBXTXLt6JU05A4UZqCgizOueOXkaz2fraYbGlygwENc9S5ctr760GzpJhBWOBAplyRsfsw4YJKhX0e0FARd01eXli3bwxhXIO/S5GP+e07dzF3/gJRz5/EJYeAmqo69i98AW1NHeZD2Tosw8FLK5n1BYU+PkGoXbsyk1dqahq27LTJ17Zs3gid2rdm8srOzsaWHbZIz2ArmjEFlUMU5PkWf9uyzwlduOQPGBkZ50YK9PfD7p2b5Yj66S3XHR6iS+9WzPqCwis7E6AJ9vmpN1yPYNf5eZLkUhxNqDBYHJ9Kyc2JCoMl99nSyYgAESACRIAIEAEiQASIABEgAkSACBQKAVmBcMiggRg5YhjKlilTKDG/hiDvIiNx4uQpnDl3HlFRinWjlS9fHpfOi7se78cJ03Hu9lVoqqmjnkVN/NZ6GHpXbgIdDU2F8TtXzUbpTuK64PKCXuHmDKpkq0KNawWM5wqBqaofioQKJyaHQJObSzh4SM/8eV2fk6gccoZFOnvhpaCvp2kqDAawXyealJyM9h27yHE62vI1EKht3gKrJp8VddTfdvXFI08HUR554viEJBjo6zJ5+bx4iXMO1/K1I4b0Q9XKlZi8omMTsNvmEJNWmaJb5+7jyh22ObEmpqXx828fCrhXHS/g5nVHpnT1dY0Qnxgt+N4nj3lMeBrcTrFd95rnv+XULNzzFPc6lifXotpDhcGiIv91xqXC4Nf53OnURIAIEAEiQASIABEgAkSACBABIkAElEKgR/du3AzCkahXt45S/L8GU1c3N+4ayJO4dv2GqOOePXUcFubmzB63b95CSEAwevbqiVpXwmCQo8HsJaZrMD09A89cPRAdFo0U7p9TVNmu12NOnhM2rF0NderW5rWIuu6OJq/4uyflzSGS67JMEtllOXHqdLi7e8gbkvaVYALDO8zD8I7snVayKy1HLDZHZOwb0ZQa1G8Kt2dPmItNl67ehLvn89w81NRUMfe7qVBXZ+vUfeHzCme4eazFbW1fuwlvIl4ypSW7QlR2lWje2vnXOoS8CmLy6tRqAJweSlOIc771BvHe7F3QsgNMWGuFpNQ4prN8CSIqDH4JT6nk5EiFwZLzLOkkRIAIEAEiQASIABEgAkSACBABIkAEig2B+vXrYejgQejUsQP09fSKTV7FOZGz5y/g6LET8PXzkyTNRb8sxJDBA0V7aRxwRoVMcZ1wslmFgeMsoaYhXDjLzMyEp7s3Il9HIjUtHYncrECuaa9IV3ktHXTq24k3h7TEZNQ8Hsj1MYpPVsbr1SSuuK7C7mW9ew927/1w5WKRAqTgRUrgj8nnYGnenDmHR14O+G1nX2Z9QeHpk5cxaEgPZq8duw8gITEpV1/FohJGDu3H7HX+wl14+3ky65UhTIxOwx9/zGW2Hj1+Kuo3fN9tnJ6WhmW/sXv9uWQXfln5LXMuBYWXbAKhk1qV2Sso3Avzrbsx678EIRUGv4SnVHJypMJgyXmWdBIiQASIABEgAkSACBABIkAEiAARIALFkkDbNq3RvVtXKhJ+4uk8evwPnG7dwpWr15GQkCDp8+vauRPWrVktyjM5Mg5W5xJ6v4MAABWvSURBVMV3CcmScK6vgdLNa/Lm89zHF55eL5HBXg8Tdd7PiY2z1NBzuHAxo4yNO3QhXPyUJ0nnhhyvpvy8+HxknadTps+UJxTtKcEEtDV0ceCXF1BVZeuqk6HZcWoOTjn9JQml4OAwWFiUY/KKiYnjZu8dztd2bN8KrZqzzS+VfQFhw9Y9THkoU/TiSTDsjq5lDrF01Qbo6Ly/ptXLww2H7HYzez33CIRlvSrM+jxhdhbg9Hc6d/2z4tdQ53mcf7ALB66xz10UfYhCMKDCYCFAphD5BKgwSC8GIkAEiAARIAJEgAgQASJABIgAESACRKDQCMiKhF27dEaXTh2hr69faHGLU6Dbd+7Cibum8xb3u9TFwILnNDQ0xM1rl0UdPdLZD02fZYjyyBP76qVAa0RTXq+srCycOnMFWcWsMKjG3V46ZFAP7upC/uJKxlFn1EgW112ZB+iFfgq0h/PzEnowsjmDsnmDtL5eAs0te2DhSFtRAGZvbAuvgAeiPPLEKSlp0NZmKxC5uHniqtPd/Dwmjh2K8mXZ5tqGR8TA9tBRSc4kpcn5Qxfw0JVtJmBF88r4fs7C/HTOnTqKRw/uMKVXvnRlvH0XxKT9WPTaLwF+1wxEea06NBpu/rdEeRR3salZDRjqqRb3NCm/EkKACoMl5EHSMYgAESACRIAIEAEiQASIABEgAkSACHxpBFq1bMEVCDtxhcJOkBWxSupKTEzE3Xv3c4uBDx49QkpKaqEd9dB+W9Sx4p+Nx5dMtFcwGj9+f22f2JWIDERNbihoc/n4FcSocS0mxWxZmldAkxaNeLOKfPQCTb2lyT1BJQPRk4R58SX0w9x5uHdfmoJOMXsclI6cBKb0WoVeLSbLufu/29Iz0tBvviEyMtOYPfKEgweO5gr/9sw+p89fhu/LwFy9lpYm5syazDyr0M3NH5edrjLnogyhbJbj6iWLkJgSy2TfsUsP9Og9IF+78c/liIyMYPIa2H0CzlyxY9J+LLp7IQiZIVWYvTIy0zF+raUkr0HmJEhIBIiAQgRUTMqaFf5EaIVSpM1EgAgQASJABIgAESACRIAIEAEiQASIQFETqFWzJho1bID69eqhfv26sDA3L+qUmOP7+wfA58UL+Pg8h7uHB7y8fZi9xArHjx2DOT98z2yTlZGJqgdfSDY3z294VWjpv7/m7nPr5kUnhKUVXvFUXjhlVDXQdRD/jKui4MWXv/2RY9i4eYu8R6R9JZDAX9/dQcXSNZhPdvmxHdYdmMSsLyi8dfMJOnRsxuQlK5pt2raXKw5l5upr16qOgX27M3nJRHb7zyMsSpprkpmT+EgY/SYR6zctYLab+u2PqF7TMlcfGxuDtb8vYvay3nwEM+aMZNYXFF7aHQKdTPa/0z0D72P5gWGS5EImRIAIFA4BKgwWDmeKQgSIABEgAkSACBABIkAEiAARIAJEoEQRMDExzi0SWtaqidqWlqhWrWqxLBa+CglBQEAgnnOFQA8PL3h4eiIxSZoOOykeaNkyZXDZ4bwoKwM7V5hka4nyyBO7VMmCaef6vF7ens/x7EWAJPGkNCmVrYo+w3oKWpbieBlLxMu5ajZKd6onGPNzG2RF6mGjxjDrSfhlEzDSL42989xFHWL94alwvL9PlEeeOCw8CuXKmjB5hb4Nx4Ejp/O1vbp1RMP6VkxesutM/9plw6RVpsj97nMcObuVKYSamjqWr94IdXWNXP2Tx/dx+jhbd6aKigoi3kajdDkjplwKiriGU9wT+fI57LQGp++ycRF9ADIgAkSAiQAVBpmwkYgIEAEiQASIABEgAkSACBABIkAEiAAR+JiAlpYWanGFQvNKlVCpYgVUrFgRFStUgLl5JZQpXVppwNLT0xHx7h3evg2D38uX8OcKgf7+/njh64e0NPHX6ykt8f8bXzx7GhUqmDGHSTjrgnrR2sz6gsJgrRRgDP/cPFln0OlTl5GuUswuoeLS6dmlLYyN+a+9TTzjjLox0swZDOJ4qQjwEnow3Xv1RWRUlNA2+nkJJNC58UjM6r9J1MmmrW4E/zfPRHnIxLJik+y9VF1dncnr/iNn3H3wT7521rRxKGXANqf29et3OHT8JFMeyhQd3XMUz56zzQSsaWmFydNn56d35OA+uLs5M6Vbs3JD+Aa5MWk/Fr10j0bIPbZicJ7XL3t64WWo+NegJAciEyJABOQiQIVBuTDRJiJABIgAESACRIAIEAEiQASIABEgAkRALIFyZcvCxMQERty8wjJlSuf+s6yYqNjKQVR0dG4RMDIyiisIRiAmhm3ek2Jxlbd7zR+/o3u3rswBYp6HoNGDBGZ9QWF6ThbeTuHvGJTtv3riKqJU318ZWJxWFVNjtO7YmjelaB9uLuNDabpG5eXFl9CS5SvgcOlyccJIuRQSgTmDd6Bd/UHM0SJiQzBqcWXIivVi19Il67Bi5c/MNvbHziLkzdtcvbFRKcyYzN4J++iBN249us2cizKEOZkqWLZoDvMcvV79BuObjh/e51csmofUVO6LGAxrxriFsD6whkH5X4nTiQCovKvG7JWcGo8J66wkeQ0yJ0FCIkAEFCZAhUGFkZGACBABIkAEiAARIAJEgAgQASJABIgAESAC0hEYPnQIflkwX5ShuY0XVLlJg1Isr97loF/elNfq7pU7eJ2YKEU4ST1K56ij21DhuWaFzYvvkBccLmHZit8l5UBmXwaBAwufQ1e7FHOyJ5w2Ydepecz6gkI31+do2Oj9/DtFV3pGBjZv35dfHGrcsC56dPlGUZvc/bIi5w7rw0hMiWfSK0sU5h+Lv3b+xmw/+6dfUaHi+zl+oW9CsG3Tn8xeF07cRN+hHZn1BYWXrSOhlc3e0f/QxwEbj0+TJBcyIQJEoPAIUGGw8FhTJCJABIgAESACRIAIEAEiQASIABEgAkSACPyHQGULC5w5eUwUGY0DzqiQKc31mM/KpcGoT2PefF69eo37T8TNRhN14M+I9bJU0X+48JxBzYMuMMuQ5vpVN46XsQAvvrO+i4xEj979lIGDPIsxgSrl6mLDt9dEZbh0zyDcczsryiNPHB0Tz3X6GTB5+QcG48SZS/naQf16wLImWxdafEISdu45wJSHMkX3Lj2Gw439TCG0tXWw7I+N+drbN6/i8kW256ahroW4uDjo6Crabf/f1BOiMvH0GNvVsXluux0W4urTg0xcSEQEiEDREaDCYNGxp8hEgAgQASJABIgAESACRIAIEAEiQASIABHIJeB44SzKlSvHTCPG0Q2N3moy6wsKw9RTkTa+iaDXxeNXkKCWJbivsDe0bloPVapY8IaVlJcax2uCMC++hIaOGIWAwKDCRkXxipBA/zYzMb7bElEZjFhkjnexr0V5yMRlSpdDWHgoVFVVmbyu37qPpy4fvigwZ9ZkaGuzFa4CAt7i+Fm2ohlT8nKK/t64HUGh3nLu/ve2Bo2bYdTYyfn/ct/f2/DS14fJq02THrjvLM3Vw8/uhSHavTxTHnmi77a2RnhMsCgPEhMBIlD4BKgwWPjMKSIRIAJEgAgQASJABIgAESACRIAIEAEiQAT+RWDp4t8wsD9711hydAKszoZIQjUbOQiZXFfQ6/rJq3inUvzmDFbQ00OHnh14808Mi0LdS+GCZ5Rng7y8+LzWb9yMI8eOyxOO9pQQAovHHkaj6h2ZT/Pi1VPMXNucWV9QuG/3UUyeNoLZa+/+Y4iMis7Vm5UviwmjhzB73bjhjCfP/mHWK0OYxY0CXLLkO+Y5ekNGjEWzFm1yU8vMzMDy3+YhK4vtvXPZz5uxfN0cSY559VAANOLZOjtlCUTFh2LG5maS5EImRIAIFC4BKgwWLm+KRgSIABEgAkSACBABIkAEiAARIAJEgAgQgf8Q6NmjO1b/vkIUmfI2HtCCmiiPPLFLCx2Y1qvK6/Xw9iMERb4vBhSnZZKthh7DegimVH6fJ7RU2DqkPjZ3acnxqsvPiy+hO3fvYc68nwVzpg0lh8CBX3yhq6XPfCBbh6U4eEma2ZS+viGoWbMSUy5JycnYZv3his02LZvgm7YtmbyysrKwaZsNsrLZimZMQeUQBXuGw9qW/f154ZI/YGRknBvppd9z7LPeKkfUT2+57+SKNp0aMesLCm9Yp0A1m/0K6uvOh2B9cYEkuZAJESAChUuACoOFy5uiEQEiQASIABEgAkSACBABIkAEiAARIAJE4D8ETEyMcf3yhxldLIiyDzujair7h7wFY3obpkBvSFPeNOITEuBw5S6gwpKt8jSaOSoYPKQnVFT4E8uxf4oqabqSJOJtnAq9QezXicqKK+07dpEkFzIp/gTKG1fB9h8eiEr05+3d4ewjbkZhXgIJicnQ12N77/D09sXFyzfyzzJ6+ABYVKrAdLao6HjssbNn0ipTdO2UE5wenGQKUbp0Wcz7dXm+9rLDWdx2usrkpa9rhPjEaMH3NnnMI1+nwuO8uDmrG0/OwEOvC/KEoz1EgAgUMwJUGCxmD4TSIQJEgAgQASJABIgAESACRIAIEAEiQAS+TgJH7Q+gVs2azIePvO2Fpv7SVOliVNMRP1G4K+XS8cuIU8tmzllZwgY1qqBuwzq89kXBiy+hSVNn4Jn7hzltymJDvkVPwMqiJX6fdIY5kZycHPT/2QRJKbHMHnnClk2/waOnt5l9ZEVBWXFQttTUVDFv9jTmWYU+PsE45yjuCxLMB+ERbv5jDSKiXzFZt2rzDQYMGZmv3bb5T4S+Zrv2uWeHEXC8dZQpj49F/1x7jSQ/ti7RPK8Ja62QlBonST5kQgSIQOESoMJg4fKmaESACBABIkAEiAARIAJEgAgQASJABIgAEfgkgXlzfsSY0R8+QFYUU0ZKGqofeck18IkvDuZwcwYDx1lCTUOdNw2ns9cRnpWuaKpK319OQwud+/N34BUFL76D/71nH/7es1fpbChA0RNoUrMLfht9kDmRBx4XsNi6P7O+oPDi+Vvo049/JidfoK277JCcwg3h41a1KhYYPrgPc15nzt7BiwAvZr0yhCkxmVi56gdm67ETp6Nu/fdfskhJScbKxfOZvbastsWPv05k1hcUXrYNhlZKZWavgLfuWLC7J7OehESACBQtASoMFi1/ik4EiAARIAJEgAgQASJABIgAESACRIAIEIFcAm1at8L2vzaLomFs+wylcjREeeSJn9ZWQZk2VrxeLv+44UVIqCTxpDQxylJDr+HCcwaNbd04XpqShHaprQrTNrWZvZ46u2D6zO+Y9ST8cgg0rNYBS8YdYU74RYgzZq1tDlnnoNgV8joclSqWZbKJjIrG3v3H8rVdOrRB86YNmbzSMzK4+YLFrzDu9dAPh06yvy+vWL0ZmlpauUzc3Zxx5OA+Jj4y0XOPQFjWq8KszxNmpgF3bLKgksM+k/bM/e2wv75adC5kQASIQNEQoMJg0XCnqESACBABIkAEiAARIAJEgAgQASJABIgAEfgXAS3uw+N7t25w1/Gxf1ibctIZtePZZoV9/Dhe6iZDY2Qz3qeUnp6Bs+evIUt8k6KkrwY1rl7Sr09n6Ojwz9BKPeEMy4TC48V3yMTERHzTuZukHMiseBKoXM4KG7/9MJePJcvZG9vCK0DcnEJZ3LT0dGhqsH2Z4KmLO67fup+f/pTxw1GmtCnLcRAWFg27wx+KjEwmShCdsj2Fp55sz8rcogpm/bggP6vTJ+zx5NEHXoqkW9nMEkGhzxWRfHZvsE8cAm4aivJaeXAE3AO4GbO0iAAR+CIJUGHwi3xslDQRIAJEgAgQASJABIgAESACRIAIEAEiUBIJ7LHeiaZNGjMfLcr1JZq4SnO1ZzIy8W5yA8FcLh+/ghi1LMF9hb2hullZtGjDX9iMdPZD02cZkqQmLy++YF169EJMjPi5cZIciEyURsBQzxT75nuI8j/htAm7Ts0T5TF96o/c9bVbmD1OnLkE/8DgXL2ujg5+mDmR2cvVxQ9Xbl1n1itFmKOCP5b8ikTGWY6duvZC91798lNbs/I3xMWx/fmeMOxH2B1nf1YF+dy7EISMkCrMyDIy0zF+rSUyZK2HtIgAEfgiCVBh8It8bJQ0ESACRIAIEAEiQASIABEgAkSACBABIlASCUybMgkzZ0xnPxp3taCFrbdkcwafD7SArokBbz63HG7iber7GWPFaZVWUUe3wd35UyoCXnwJDR42EkHB7wsttEo2AZv5niilZ8J8yKysTIxeWhXvYl8zezy454bWbdmu/szOzsbm7fu44lBmbvy6tWuiX++uzLnY2Z1FWPRbZr0yhDGhKVi3kb34Om3WXFSrXjM3tajICGz4czlzmvutz2D8jIHM+oJCB+tQ6GZXYPZ65n8Hvx9in4fLHJiERIAISEaACoOSoSQjIkAEiAARIAJEgAgQASJABIgAESACRIAIiCPQoH492O3bI8pE184VZbLfz7QSu1zNs2DSrT6vjY/3C7j5+IsNJbm+VLYq+gzrKegrihdXWIxVy0CoQRZSaxrDtH41cFVZ5jVg8DCEvGYv9DAHJmGhE5jZbwO6NBktKu56+ylwfGDD7BHxLpq7+tOYSR/y5i3sj53N1/bp0Rn161oyeSUlp2KbtS2TVpkiZyd3nHSwZgqhpqaOFX9uzr8a+vGDuzh7im2upIqKCiLeRqN0OSOmXAqKUuKz8eiQqiifg9dW4dyDnaI8SEwEiEDREqDCYNHyp+hEgAgQASJABIgAESACRIAIEAEiQASIABHIJyD7APjeLSfB2Xh8yOLOu6JBpDSFwRCNVGSPa8L7hHK44tiFk1eQpJpdvJ4kN2ewZ5e2MDbmn6WlKK8UZCFUJw2xlfVg2KQaNLSlYS2D16JNe2T+vwOreMGkbKQm0NqqD+YNF/clAP83z/DtmmbIyn7ftafI0lDXQkpqEvNM07sP/sH9R875IWd/OwF6urqKpJC/99WrCBw+eYpJq0zRoZ0H4OX/iCmEZe26mDjtu3ztIbvd8PJwY/KqV7MlPHzZ8vg44AuXSIQ+Ks2UR55owe4eCHgr7ipcUQmQmAgQAdEEqDAoGiEZ/G+jITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhQL0QCAsJZqgoK6HIQOm5VxhYGCnbFQJzwEVnIQYBeQm87jm86xDDk89fKHIzLTQL/WdhcA8hcJwo0GLpecDwYoCE138gfMP0i+E58JTH/9piDALK0rRwGoaZZ8+dZ0jNyKKLXaOWDI4QSHBrYPCxpODoYKA3Vu/vZ5i+pohkD12+dJtBR1eFZH0gDb9+/2bomzwHrtfYUJfB1dGGLLNACwv6p8wDmkmdu1HJcgQWTU9uvmKYOquBbOPyS6oZJCQhZcfjRw8Ypk3sItuszav3M/iEOJCtH1njrpkfGFj/kr/z8NjVTQx9azKo4pZRQ0ZDYDQEBi4EAAUM3SY9gQ/eAAAAAElFTkSuQmCC\\\")
\\n\",\n \"\\n\",\n \"# Lab 0: Hello Quantum World!\\n\",\n \"\\n\",\n \"# 목차\\n\",\n \"\\n\",\n \"* [Qiskit Global Summer School 2025에 오신 것을 환영합니다!](#welcome)\\n\",\n \" - [Lab 0 개요](#overview)\\n\",\n \" - [Qiskit 설치하기](#install)\\n\",\n \" - [오류 발생 시](#troubleshooting)\\n\",\n \" - [IBM Cloud 계정 세팅](#setting-ibm-cloud)\\n\",\n \" - [불러오기](#imports)\\n\",\n \" - [기능 점검](#sanity-check)\\n\",\n \"* [Qiskit pattern에 따라 3-큐빗 GHZ 상태를 만들어봅시다](#ghz) \\n\",\n \" - [Step 1. 정의](#map)\\n\",\n \" - [Step 2. 최적화](#optimize)\\n\",\n \" - [Step 3. 실행](#execute)\\n\",\n \" - [Step 4. 후처리](#post-process)\\n\",\n \"* [축하합니다!](#congratulations)\\n\",\n \" - [보너스 챌린지: 실제 하드웨어에서 GHZ 회로를 실행해봅시다](#bonus)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2144e6fe\",\n \"metadata\": {},\n \"source\": [\n \"# Qiskit Global Summer School 2025에 오신 것을 환영합니다! \\n\",\n \"\\n\",\n \"안녕하세요, 올해도 새로운 Qiskit Global Summer School(QGSS)으로 여러분을 만나볼 수 있어 정말 행복하네요. 이 2주간의 여름 학교에서는 처음 시작하는 학생과 연구자, 이미 Qiskit에 익숙한 전문가까지 다양한 커뮤니티의 사람들이 양자 컴퓨팅과 양자 프로그래밍 지식을 배울 수 있도록 이론 강의와 실습 문제를 제공하고 있습니다.\\n\",\n \"\\n\",\n \"특히, 이 여름 학교의 실습 부분에서는 여러분이 몇 가지 흥미로운 주제들을 만나볼 수 있도록 만들어진 주피터 노트북(\\\"lab\\\")들을 공부하고 풀어보게 됩니다.\\n\",\n \"\\n\",\n \"각각의 랩은 앞의 이론 강의에서 배운 내용을 보완하고, 관련 문서, 튜토리얼, 참고 자료 등을 확인할 수 있는 링크도 기재되어 있습니다. 여기 양자 컴퓨팅에 대한 수많은 교육자료들을 찾을 수 있는 [IBM Quantum Learning](https://quantum.cloud.ibm.com/learning) 플랫폼처럼요.\\n\",\n \"\\n\",\n \"## Lab 0 개요 \\n\",\n \"\\n\",\n \"이번 입문용 랩의 목표는 여러분께 랩 노트북을 사용하는 법과 답안을 채점하는 법을 보여주고, 여러분이 앞으로 풀어야 할 양자 코드를 실행할 수 있도록 컴퓨터를 잘 세팅하셨는지 확인하는 것입니다.\\n\",\n \"\\n\",\n \"QGSS의 랩을 완료하기 위해서는, 모든 노트북에 수정해서는 안 되는 미리 작성된 코드와 직접 채워야 하는 챌린지 코드 블록이 있다는 점을 확인해주세요. 특히, `### 아래에 코드를 작성해주세요 ###`라는 주석을 찾으신다면 그 아래에 필요한 코드는 직접 작성해야 합니다. 만약 노트북 커널을 재시작하시는 경우에는 모든 셀을 처음부터 순서대로 실행하여 노트북이 올바르게 실행되고 채점이 되도록 해야 합니다. 이렇게 함으로써 모든 코드가 수정된 내용에 따라 원활하게 실행되고 있다는 것을 확인할 수 있습니다. 물론, 패키지를 설치하는 셀이나 계정을 저장하는 셀과 같은 몇 가지 예외는 있을 수 있습니다 - 이런 셀들은 다시 실행하지 않아도 괜찮습니다.\\n\",\n \"\\n\",\n \"이번 랩에서는 Qiskit pattern에 따라 GHZ 상태를 만들고, 양자 회로를 최적화하는 법을 중점적으로 배웁니다. 마지막 부분은 여러분의 코드를 실제 양자 컴퓨터에서 실행할 수 있는 보너스 문제입니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b3df2ea\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskit 설치하기 \\n\",\n \"양자 컴퓨터는 암호를 푸는 쇼어 알고리즘, 더 빠른 검색을 가능하게 하는 그로버 알고리즘, 배터리 설계 등에 적용되는 양자 위상 측정 등, 기존 계산 방식의 혁신을 가져다 줄 기술로 자리매김하고 있습니다. 이 모든 것들의 첫 단계는 이러한 알고리즘들을 구현하고 실행하기 위한 소프트웨어 도구를 선택하는 것입니다. 이 여름 학교 챌린지에서는 양자 회로를 설계하고 구현하여 시뮬레이터와 실제 하드웨어에서 실행하기 위해 Qiskit이라는 도구를 사용하겠습니다. 다음 코드를 따라 Qiskit 2.0 버전을 설치해봅시다.\\n\",\n \"\\n\",\n \"첫 번째로, 현재 사용 중인 파이썬의 버전이 최신 Qiskit 버전과 호환 가능한 3.10 버전 이상이 맞는지 확인해주세요:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a35543d4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from platform import python_version\\n\",\n \"\\n\",\n \"print(python_version())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47d097d3\",\n \"metadata\": {},\n \"source\": [\n \"만약 3.10 이전 버전을 사용하고 계시다면 각자의 환경에 맞는 방법으로 파이썬 버전을 업데이트 해주세요. 아래에 여러분이 시도해볼 수 있는 몇 가지 방법을 링크해두었습니다:\\n\",\n \"\\n\",\n \"- MacOS: [Homebrew](https://brew.sh/)\\n\",\n \"- Linux: `sudo apt-get update `\\n\",\n \"\\n\",\n \"각 OS별 파이썬 업그레이드에 관한 자세한 가이드는 여기에서 찾아보실 수 있습니다: [How to update Python](https://4geeks.com/how-to/how-to-update-python-version)\\n\",\n \"\\n\",\n \"![](\\\"data:image/png;base64,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\\\")
\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03041cd7\",\n \"metadata\": {},\n \"source\": [\n \"다음의 순서를 따라 계정을 설정해주세요:\\n\",\n \"\\n\",\n \"1. [새로운 IBM Quantum® 플랫폼](https://quantum.cloud.ibm.com/)에 로그인하세요.\\n\",\n \"2. 위 그림과 같은 화면에서 오른쪽 위의 **Create API key** 버튼을 누르고 안내를 따라 키를 생성한 뒤 안전하게 저장해두세요.\\n\",\n \"3. 다음 셀로 가서 `이문장을지우고API키를붙여넣으세요` 부분에 API 키를 붙여넣으세요.\\n\",\n \"4. 다시 플랫폼의 위 그림과 같은 화면에서 **Create an instance** 버튼을 누르고 안내를 따라 open plan으로 인스턴스를 생성하세요.\\n\",\n \"5. 인스턴스가 생성되었다면, 인스턴스의 CRN 코드를 복사하세요. 인스턴스가 보이지 않는 경우에는 화면을 새로고침 해주세요.\\n\",\n \"6. 다음 셀로 가서 `이문장을지우고CRN을붙여넣으세요` 부분에 CRN 코드를 붙여넣으세요.\\n\",\n \"\\n\",\n \"IBM Cloud® 계정 설정에 관한 자세한 사항은 [이 가이드](https://quantum.cloud.ibm.com/docs/guides/cloud-setup)를 참고하세요.\\n\",\n \"\\n\",\n 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\\\")
\\n\",\n \"\\n\",\n \"# Lab 1: Recreating famous experiments at home!\\n\",\n \"\\n\",\n \"# 0. 셋업: 라이브러리 불러오기\\n\",\n \"\\n\",\n \"모든 좋은 실험에는 장비를 준비하는 과정이 필요합니다. 이 경우에 그런 장비는 파이썬 라이브러리, 그 중에서도 우리의 양자 컴퓨팅 툴킷 Qiskit 라이브러리가 되겠습니다. 여러분이 lab 0를 완료하셨다면 모든 설정이 완료되어 있을 것입니다. 만약 lab 0를 완료하지 않으셨다면 아래 셀의 주석 표시를 지우고 그레이더와 Qiskit 2.x 등의 여름 학교에서 사용할 필수 라이브러리를 설치할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"519fb94e-18f2-4ade-a40f-58c1078424e5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0e8bb120\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3fee413b\",\n \"metadata\": {},\n \"source\": [\n \"Qiskit 버전 `>=2.0.0` 그레이더 버전 `>=0.22.9` 이상이 설치되어 있어야 합니다. 만약 버전이 다르게 표시된다면 커널을 재시작해보시고, 그래도 안 된다면 그레이더를 다시 설치해야 할 수 있습니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"53e15069\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 계정이 잘 저장되어 있는지 확인합니다\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"51102304\",\n \"metadata\": {},\n \"source\": [\n \"## 불러오기\\n\",\n \"\\n\",\n \"아래의 셀을 실행해 필수 라이브러리를 불러와보세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8ae038b9-2181-4c10-8018-833b52ff17a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 필수 라이브러리\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import ipywidgets as widgets\\n\",\n \"from IPython.display import display\\n\",\n \"from PIL import Image\\n\",\n \"import io\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.circuit import Parameter\\n\",\n \"from qiskit.visualization import plot_histogram, plot_distribution\\n\",\n \"from qiskit_ibm_runtime import Options, Session, SamplerV2 as Sampler\\n\",\n \"from qiskit.result import marginal_distribution\\n\",\n \"\\n\",\n \"from qiskit.transpiler import generate_preset_pass_manager\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab1_ex1_1, \\n\",\n \" grade_lab1_ex1_2, \\n\",\n \" grade_lab1_ex1_3, \\n\",\n \" grade_lab1_ex1_4, \\n\",\n \" grade_lab1_ex2, \\n\",\n \" grade_lab1_ex3,\\n\",\n \" grade_lab1_ex4,\\n\",\n \" grade_lab1_ex5,\\n\",\n \" grade_lab1_ex6\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"39f36676-bdd1-4ba2-8486-2b4796766eea\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# 목차\\n\",\n \"\\n\",\n \"1. 중첩, 간섭, 그리고 측정\\n\",\n \" 1. 이중 슬릿 실험 - 중첩과 간섭\\n\",\n \" 2. 슈뢰딩거의 고양이와 이중 슬릿 실험\\n\",\n \"2. 얽힘\\n\",\n \" 1. 앨리스와 밥의 이길 수 없는 게임 - CHSH 게임\\n\",\n \" 2. 양자 텔레포트 - 마법같은 작용을 이용한 비밀 전송\\n\",\n \" 1. 시뮬레이터에서의 텔레포트\\n\",\n \"3. (보너스 챌린지) 실제 하드웨어에서의 텔레포트\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d36937e1-9cc0-4c1b-8240-f87504825196\",\n \"metadata\": {},\n \"source\": [\n \"2025년 세계 양자의 해 QGSS의 첫 번째 랩에 오신 것을 환영합니다! 이 첫 번째 랩에서는 양자 세계에서 가장 중요한 4가지 개념 - 중첩, 간섭, 측정, 얽힘 - 을 Qiskit을 이용한 실험을 통해 공부해보도록 하겠습니다.\\n\",\n \"\\n\",\n \"# 챕터 1: 중첩, 간섭, 그리고 측정\\n\",\n \"\\n\",\n \"여러분의 여정을 시작하는 최고의 출발점으로, 그 유명한 이중 슬릿 실험을 통해 양자 중첩, 간섭, 그리고 측정에 대해 알아봅시다. **중첩**이란 전자나 광자와 같은 양자 입자가 여러 상태에 동시에 존재하는 것을 뜻합니다. 두 위치에 동시에 존재한다거나, 두 가지 다른 에너지를 동시에 갖는다거나 하는 것 말이죠. 이것은 동전을 튕겼을 때 앞면인 상태와 뒷면인 상태가 동시에 존재하는 것과도 비슷합니다... 여러분이 그것을 확인하기 전까지는요! 한편, **간섭**이란 이러한 양자적인 가능성이 파동처럼 작동해서 서로 겹쳐지는 현상을 설명합니다. 만약 이러한 가능성이 같은 위상을 갖는다면 서로 보강하게 되고(보강 간섭), 서로의 위상이 달라지면 가능성을 약화하거나 상쇄하게 됩니다(상쇄 간섭). 이러한 간섭 현상은 바로 이중 슬릿 실험의 결과를 설명하는 주요 개념으로 등장합니다.\\n\",\n \"\\n\",\n \"이러한 중첩과 간섭 현상을 잘 보여주는 **이중 슬릿 실험**은 1801년 토마스 영이 처음 보고한 이후로 전자와 같은 여러 입자를 이용하여 반복적으로 검증이 이루어졌습니다. 특히 재밌는 것은, 실험에서 입자가 어느 슬릿을 통과하는지 아무도 확인하지 않는 경우에는 파도가 겹쳐지듯이 아름다운 간섭 무늬가 나타나게 되지만, 누군가 입자가 통과하는 슬릿을 **측정**하는 순간, 간섭 무늬가 사라진다는 것입니다. 마치 입자가 누군가 보고 있는 것을 알기라도 하는 것처럼이요!\\n\",\n \"\\n\",\n \"이런 이상한 아이디어는 1935년 에르빈 슈뢰딩거에 의해 **슈뢰딩거의 고양이**라는 유명한 사고 실험으로 발전하게 됩니다. 박스를 열기 전까지는 산 고양이와 죽은 고양이가 동시에 존재한다는 바로 그 사고 실험이죠. 양자역학에서의 측정은 단순히 현실을 관측하는 것에 그치지 않습니다. 그것은 현실을 빚어냅니다.\\n\",\n \"\\n\",\n \"양자역학의 이상한 세계를 경험해보실 준비가 되셨나요? Qiskit을 이용하면 측정이 있는 이중 슬릿 실험과 없는 실험을 모두 구현하고, 중첩과 간섭이 어떻게 아름다운 간섭 무늬를 만들어내는지, 그리고 측정이 어떻게 이러한 양자적 시스템에 영향을 주는지 확인하실 수 있습니다. 양자 세계의 미스테리를 탐험하는 재밌고 놀라운 실험실로 여러분을 초대합니다!\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"309c6569-6875-447e-a514-cc049e7a37f0\",\n \"metadata\": {},\n \"source\": [\n \"## 슬릿을 통과해: 양자의 신비가 시작되는 곳\\n\",\n \"\\n\",\n \"![](\\\"data:image/png;base64,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\\\")
\\n\",\n \"\\n\",\n \"# Lab 2: Cutting through the noise\\n\",\n \"\\n\",\n \"이번 랩에서는 양자 노이즈의 세계를 탐험하며 현대의 양자 컴퓨터에 발생하는 노이즈를 극복하고 원하는 데이터를 얻는 방법에 대해 알아보도록 하겠습니다. 작은 Max-cut 문제를 예시로 양자 컴퓨터에서 양자 알고리즘을 실행할 때에 노이즈를 어떻게 줄일 수 있는지 차근차근 배워봅시다. 양자 컴퓨터에 노이즈가 있더라도, 다양한 트랜스파일과 오차 완화 (Error Mitigation) 테크닉을 통해 오차를 최소화하고 올바른 결과를 얻을 수 있습니다. 랩의 마지막에는 랩에서 다룬 모든 테크닉들을 실제 양자 하드웨어에서 구현해보는 보너스 챌린지가 있습니다.\\n\",\n \"\\n\",\n \"# 목차\\n\",\n \"\\n\",\n \"0. [요구사항](#requirements)\\n\",\n \"1. [서론](#intro) \\n\",\n \" - [이번 랩의 목표](#spirit)\\n\",\n \" - [양자 노이즈란?](#quantum-noise) \\n\",\n \" - [노이즈의 원인과 지표](#sources)\\n\",\n \" * [연습문제 1: T1, T2, 게이트 충실도가 높고 측정 오류는 낮은 큐빗 찾아보기](#exercise_1)\\n\",\n \"2. [Max-cut 문제](#max-cut)\\n\",\n \" - [문제: 그래프 정의하기](#the-graph)\\n\",\n \" - [정의: 양자컴퓨터로 그래프 옮기기](#the-mapping)\\n\",\n \" * [연습문제 2: Max-cut 문제를 Ising 해밀토니안으로 대응시키기](#exercise_2)\\n\",\n \" - [QAOA 알고리즘](#qaoa-solution)\\n\",\n \" - [해 확인하기](#checking)\\n\",\n \"3. [양자 노이즈 시뮬레이터](#noisy-simulator)\\n\",\n \" - [백엔드 선택](#choosing-backend)\\n\",\n \" * [연습문제 3: 오류율 예측](#exercise_3)\\n\",\n \" - [NEAT를 이용한 오류율 예측](#neat)\\n\",\n \"4. [트랜스파일러](#transpiler)\\n\",\n \" - [이중 큐빗 게이트의 수 최소화하기](#min-two-qubit)\\n\",\n \" - [최적의 레이아웃 찾기](#opt-layout)\\n\",\n \" * [연습문제 4: 모든 적합한 레이아웃 찾기](#exercise_4)\\n\",\n \" * [연습문제 5: 트랜스파일러를 사용해 최선의 레이아웃 찾기](#exercise_5)\\n\",\n \"5. [오차 완화](#em)\\n\",\n \" - [무잡음 추정법 (ZNE)](#zne)\\n\",\n \" * [연습문제 6a: 양자 회로에서 전체적 접기 구현](#exercise_6a)\\n\",\n \" * [연습문제 6b: 양자 회로에서 국소적 접기 구현](#exercise_6b)\\n\",\n \"6. [결론](#conclusions)\\n\",\n \"7. [보너스 챌린지: 더 키워보세요!](#bonus)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## 0. 요구사항\\n\",\n \"\\n\",\n \"이 랩을 실행하는데 필요한 라이브러리는 다음과 같습니다:\\n\",\n \"\\n\",\n \"* Qiskit SDK v2.0 with visualization support (`pip install 'qiskit[visualization]'`)\\n\",\n \"* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\\n\",\n \"* Rustworkx (`pip install rustworkx`)\\n\",\n \"* Qiskit Aer v0.17\\n\",\n \"* Pylatexenc\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install -U qiskit \\\"qc-grader[qiskit,jupyter] @ git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import qiskit\\n\",\n \"import qc_grader\\n\",\n \"\\n\",\n \"print(f\\\"Qiskit version: {qiskit.__version__}\\\")\\n\",\n \"print(f\\\"Grader version: {qc_grader.__version__}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Qiskit 버전 `>=2.0.0` 그레이더 버전 `>=0.22.10` 이상이 설치되어 있어야 합니다. 만약 버전이 다르게 표시된다면 커널을 재시작해보시고, 그래도 안 된다면 그레이더를 다시 설치해야 할 수 있습니다.\\n\",\n \"랩 0에 따라 환경설정을 완료했는지, IBM Quantum 계정이 잘 저장되어 있는지 아래의 셀을 실행해 확인해주세요.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 계정이 잘 저장되어 있는지 확인합니다\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"\\n\",\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"service.saved_accounts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 불러오기\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import rustworkx as rx\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from rustworkx.visualization import mpl_draw as draw_graph\\n\",\n \"from qiskit_ibm_runtime import QiskitRuntimeService\\n\",\n \"from scipy.optimize import minimize\\n\",\n \"\\n\",\n \"from qiskit import QuantumCircuit\\n\",\n \"from qiskit.providers.fake_provider import GenericBackendV2\\n\",\n \"from qiskit.quantum_info import SparsePauliOp, Statevector, DensityMatrix, Operator\\n\",\n \"from qiskit.circuit.library import QAOAAnsatz\\n\",\n \"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\\n\",\n \"from qiskit.visualization import plot_histogram\\n\",\n \"from qiskit.transpiler import Layout\\n\",\n \"\\n\",\n \"from qiskit_ibm_runtime import (\\n\",\n \" Session,\\n\",\n \" EstimatorV2 as Estimator,\\n\",\n \" SamplerV2 as Sampler,\\n\",\n \" EstimatorOptions,\\n\",\n \")\\n\",\n \"from qiskit_ibm_runtime.debug_tools import Neat\\n\",\n \"from qiskit_aer import AerSimulator\\n\",\n \"\\n\",\n \"from utils import zne_method, plot_zne, plot_backend_errors_and_counts\\n\",\n \"from qc_grader.challenges.qgss_2025 import (\\n\",\n \" grade_lab2_ex1,\\n\",\n \" grade_lab2_ex2,\\n\",\n \" grade_lab2_ex3,\\n\",\n \" grade_lab2_ex4,\\n\",\n \" grade_lab2_ex5,\\n\",\n \" grade_lab2_ex6a,\\n\",\n \" grade_lab2_ex6b,\\n\",\n \")\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. 서론 \\n\",\n \"\\n\",\n \"### 1.1 이번 랩의 목표 \\n\",\n \"\\n\",\n \"*이번 랩에서는 적절한 트랜스파일 및 오차 완화 기술을 통해 IBM Quantum 하드웨어의 성능을 십분 활용하여 실생활의 문제를 해결하는 방법을 알아봅니다.*\\n\",\n \"\\n\",\n \"### 1.2 양자 노이즈란? \\n\",\n \"\\n\",\n \"노이즈는 양자 컴퓨터를 만들고 사용하는 모든 과정의 중심이 되는 개념으로, 현대 양자 컴퓨터의 주요한 특성 중 하나입니다. 하지만 양자 정보에서 노이즈란 정확히 어떤 것을 의미할까요? 노이즈(에러, 오류, 오차)라 함은 주어진 양자 계산을 수행하여 측정하였을 때 예상되는 결과를 얻지 못하도록 방해하고 왜곡시키는 작용입니다. 우리는 이러한 노이즈를 크게 두 가지로 구분합니다:\\n\",\n \"\\n\",\n \"- 결맞음(coherent) 오류: 이 종류의 노이즈는 결맞음을 해치지 않으면서 양자 계산 자체에서 발생하는 오류입니다. 주로 양자 게이트가 가해질 때에 존재하는 약간의 오차가 누적되어 발생하며, 이러한 오류는 정보를 파괴하지 않으므로, 이론상 원래 상태를 찾는 것도 가능합니다. 전형적인 예시로는 원래 각도 $\\\\theta$ 만큼 큐빗을 회전시켜야 하는 게이트가 부정확한 조정 등으로 인해 실제로는 $\\\\theta+\\\\varepsilon$ 만큼 큐빗을 회전시키는 경우가 있습니다. Hadamard 게이트가 이런 식으로 잘못 조정되어 있다면 완벽한 중첩 상태를 만들지 못할 것이고, 결과에 편향성이 생길 수도 있겠죠.\\n\",\n \"- 결어긋남(incoherent) 오류: 이 종류의 노이즈는 양자적인 시스템이 주변 환경과 상호 작용을 하기 때문에 발생합니다. 이 종류의 오류 때문에, 우리는 대부분의 양자 컴퓨터를 mK 스케일의 극저온으로 냉각하여 주변 환경과의 상호 작용을 최소화하고 양자적인 결맞음 상태를 최대한 유지하려고 합니다. 이 노이즈는 큐빗의 상태를 혼합된 (순수하지 않은) 상태로 만듭니다. 이것은 큐비트에 통제되지 않은 무작위성을 부여하기 때문에 더욱 치명적입니다. 구체적인 예시로는 들뜬 상태 $|1\\\\rangle$ 에 있던 큐비트가 시간이 지나면서 바닥 상태인 $|0\\\\rangle$ 으로 떨어지는 현상을 들 수 있습니다. 이것을 이완(relaxation)이라고 하며, 양자 시스템에서 이완이 일어나는 시간을 이완 시간 $T_1$ 이라고 합니다. 만약 어떤 이유로든 이 시간 이상 측정이 지연된다면 큐빗이 이완되어 제대로 된 결과를 얻을 수 없을 것입니다.\\n\",\n \"\\n\",\n \"### 1.3 노이즈의 원인과 지표 \\n\",\n \"\\n\",\n \"앞에서 잠깐 언급했지만, 노이즈의 원인은 다양합니다. 양자 게이트를 전자기 펄스로 완벽하게 구현하지 못하는 것을 포함해 측정 (판독) 오류, 양자 시스템의 결어긋남, 그 외 많은 것들이 있을 수 있겠죠.\\n\",\n \"\\n\",\n \"이러한 노이즈에 대한 양자 컴퓨터(또는 큐비트)의 저항력을 평가할 수 있는 지표 중 몇 가지를 논의해봅시다:\\n\",\n \"\\n\",\n \"* T1: 이완 시간은 $|1\\\\rangle$ 상태에 있는 큐빗이 $|0\\\\rangle$ 상태로 떨어지기까지의 시간을 나타냅니다.\\n\",\n \"* T2: 위상 어긋남(dephasing) 시간은 $|+\\\\rangle$ 상태에 있는 큐빗이 $|+\\\\rangle$ 와 $|-\\\\rangle$ 를 구분할 수 없는 혼합 상태로 떨어지기까지의 시간을 나타냅니다.\\n\",\n \"* 단일 큐빗 게이트 충실도(fidelity)는 하드웨어가 구현하는 단일 큐빗 게이트 $\\\\mathcal{E}$ 가 이상적인 유니터리 게이트 $U$ 에 얼마나 가까운지를 나타내는 값입니다. 충실도가 1 이면 실제 게이트와 이상적인 게이트가 오차 없이 정확히 일치하는 것입니다.\\n\",\n \"* 이중 큐빗 게이트 충실도는 하드웨어가 구현하는 이중 큐빗 게이트 $\\\\mathcal{E}_2$ 가 이상적인 유니터리 게이트 $U_2$ 에 얼마나 가까운지를 나타내는 값입니다. 이중 큐빗 게이트는 단일 큐빗 게이트보다 복잡하기 때문에 충실도도 좀 더 낮아지는 경향을 보입니다. 단일 큐빗 게이트에서와 같이 충실도 1이 완벽한 상태에 해당합니다.\\n\",\n \"* 측정 판독 오류: 큐비트가 부정확하게 측정되어 실제 양자 상태와 측정하여 판독한 정보가 불일치할 확률을 뜻합니다.\\n\",\n \"\\n\",\n \"특정 IBM Quantum 하드웨어의 노이즈 정보는 [IBM Quantum Cloud Platform](https://quantum.cloud.ibm.com/computers) 에서 확인하실 수 있습니다. 원하는 디바이스를 선택하여 기기의 전반적인 속성과 각 큐빗의 자세한 특성을 확인해보세요. 아래 이미지에서 `ibm_brisbane` 디바이스의 특성 요약을 예시로 확인하실 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"플랫폼에서뿐만 아니라 이 정보를 코드로 직접 액세스하여 확인하는 것도 가능합니다.\\n\",\n \"\\n\",\n \"어떻게 할 수 있는지 함께 알아봅시다!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"![](\\\"data:image/png;base64,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\\\")
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# Lab 3: The Power of 'good sampling' for simulating a chemistry Hamiltonian with SQD\\n\",\n \"\\n\",\n \"Qiskit Global Summer School 세 번째 코딩 챌린지에 오신 것을 환영합니다. 이번 랩에서는 양자 컴퓨팅의 가장 유망한 응용 분야 중 하나인 양자 화학을 탐구해보도록 하겠습니다. 실제 양자 화학 연구자들이 사용하는 워크플로우를 기반으로 양자컴퓨터에서 분자를 시뮬레이션하는 실제 예제를 소개하며, 각 단계가 목표 달성에 어떤 역할을 하는지 전체 워크플로우를 단계적으로 알아보도록 하겠습니다.\\n\",\n \"\\n\",\n \"**추천 학습 자료**
\\n\",\n \"\\n\",\n \"Fig 7. IBM 양자 하드웨어의 중 육각 격자 큐비트 위상구조를 나타낸 도식\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"8a20c704-0564-4bc3-b24a-64ce11179489\",\n \"metadata\": {},\n \"source\": [\n \"## 샘플 기반 양자 대각화(Sample-based Quantum Diagonalization, SQD)\\n\",\n \"\\n\",\n \"자기 일관적 전자 배치 복원(self-consistent configuration recovery) 과정은 노이즈가 포함된 양자 샘플로부터 가능한 많은 유의미한 신호를 추출하기 위해 설계된 방법입니다. 이 과정은 반복적인 루프를 통해 실행되며, 각 반복은 다음의 네 가지 단계로 구성됩니다.\\n\",\n \"\\n\",\n \"1. **전자 배치 복원**: 특정한 대칭성을 위반하는 모든 비트열에 대해, 현재 추정된 평균 오비탈 점유율에 더 가까워지도록 설계된 확률적 방법을 이용해 비트를 뒤집어 새로운 비트열을 생성합니다.\\n\",\n \"2. **부분 샘플링**: 원본 비트열과 새롭게 복원된 비트열 중, 대칭성 조건을 만족하는 비트열을 모두 모으고, 미리 정해진 크기의 부분집합으로 샘플링 합니다.\\n\",\n \"3. **부분공간에서의 대각화**: 각 비트열 부분집합에 대해, 해당 비트열로 정의된 기저 벡터에 의해 생성되는 부분공간으로 해밀토니안을 투영하고, 이를 고전 컴퓨터로 대각화하여 부분공간 내의 바닥상태 에너지를 근사적으로 계산합니다.\\n\",\n \"4. **최저 에너지 결정**: 계산된 모든 바닥상태 추정값들 중에서 가장 낮은 에너지 값을 갖는 상태를 선택하여, 이 정보를 바탕으로 평균 오비탈 점유율의 추정치를 업데이트합니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"204b5142-3f9b-4d8f-8408-d6d32e9c9a13\",\n \"metadata\": {},\n \"source\": [\n \"SQD 알고리즘의 전체적인 워크플로우는 다음의 도식에서 나타냅니다.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Fig 8. SQD 워크플로우를 나타내는 도식\\n\",\n \"\\n\",\n \"SQD 알고리즘은 목표로 하는 고유상태의 파동함수가 희소성을 가질 때 특히 우수한 성능을 나타냅니다. 즉, 파동함수의 진폭이 0이 아닌 값을 가지는 기저 상태들의 집합 $\\\\mathcal{S} = \\\\{|x\\\\rangle \\\\}$ 의 크기가 문제의 크기 (size of the problem)가 증가함에 따라 기하급수적으로 늘어나지 않을 때, SQD가 효과적으로 작동하는 것으로 알려져 있습니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"1dce8aba-af4c-4a9a-a33a-3d8175085da8\",\n \"metadata\": {},\n \"source\": [\n \"### 전자 배치 복원 루프 1: 전자 배치 복원 단계\\n\",\n \"\\n\",\n \"전자 배치 복원 루프의 첫 번째 단계는 전자 배치를 복원하는 것입니다. 즉, 이 단계에서는 오류완화(error mitigation)가 수행됩니다.\\n\",\n \"\\n\",\n \"위에서 구성된 LUCJ 가설해에 해당하는 양자 회로를 실제 양자 컴퓨터에서 실행하면, 왼쪽 하단 그림과 같은 비트열과 그 출현 횟수 형태의 결과를 얻을 수 있습니다. 그러나 현재의 양자 컴퓨터는 노이즈로 인해 결과에 오류가 포함될 수밖에 없습니다. 문제로 주어진 분자는 \\\"스핀-$\\\\alpha$\\\" 와 \\\"스핀-$\\\\beta$\\\" 각각의 입자 수가 고정되어 있으므로, 입자 수가 정확히 표현되도록 결과로 얻은 비트열의 일부 비트를 뒤집어 오류를 교정할 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 9. LUCJ 가설해 회로로부터 초기 생성된 비트열 (왼쪽) 과 오류 교정을 통해 복원된 비트열 (오른쪽)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"857741ce-d6d9-490a-9c64-44ce60217815\",\n \"metadata\": {},\n \"source\": [\n \"예를 들어, 4개의 스핀 오비탈을 고려하는 문제에서 \\\"스핀-$\\\\alpha$\\\" 전자 2개와 \\\"스핀-$\\\\beta$\\\" 전자 2개를 가진 분자를 생각해 보겠습니다. 만약 오비탈이 낮은 에너지 준위부터 차례대로 채워진다면, 양자 상태는 아래 왼쪽 그림과 같이 |0011 0011>로 표현될 것입니다.\\n\",\n \"이때 양자 컴퓨터 실행 결과가 |1110 0011>로 나타났다고 한다면, \\\"스핀-$\\\\beta$\\\" 전자가 3개로 너무 많으므로, 이중 하나의 비트를 1에서 0으로 뒤집어 전자의 수가 정확히 2개가 되도록 교정할 수 있습니다.\\n\",\n \"또한 |0011 0001> 이 관측된 경우를 생각해 보면, \\\"스핀-$\\\\alpha$\\\" 전자가 1개로 부족한 상태이므로, 0으로 표현된 비트 중 하나를 1로 뒤집어서 정확히 두 개의 \\\"스핀-$\\\\alpha$\\\" 전자가 존재하도록 비트열을 교정할 수 있습니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 10. 전자의 수와 각 오비탈의 점유 기댓값을 바탕으로 최대 가중 확률 방법을 사용하여 비트열을 교정하는 과정의 도식\\n\",\n \"\\n\",\n \"이때, 어떤 전자에 해당하는 비트를 뒤집을지 결정할 때는 각 오비탈의 평균 점유율을 사용하여, 비트를 뒤집을 확률이 최대인 전자를 선택합니다.\\n\",\n \"평균 오비탈 점유율은 전자 배치 복원 루프의 마지막 단계에서 계산됩니다. 따라서 첫 번째 루프에서는 아직 계산된 평균 점유율이 존재하지 않으므로 이 방법을 사용하지 않으며, 잘못된 입자 수를 가진 샘플들은 모두 버리게 됩니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"aa2b0d64-63b6-455e-999e-f35c7d52b9e1\",\n \"metadata\": {},\n \"source\": [\n \"### 전자 배치 복원 루프 2: 부분 샘플링\\n\",\n \"\\n\",\n \"다음 단계는 앞 단계에서 오류가 교정된 샘플들 중에서 일부를 선택하는 것입니다.\\n\",\n \"예를 들어, 이번 경우에는 100,000회의 측정이 이루어졌으므로, 여기서 무작위 50개의 샘플을 추출하는 과정을 5회 반복하여, 총 5개의 배치(batch)를 얻을 수 있습니다. 큰 분자 시스템을 다룰 경우, 배치 단위로 처리하면서 슈퍼컴퓨터에서 병렬 계산이 가능합니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 11. 오류가 정정된 샘플들로부터의 부분 샘플링 과정\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"fe817330-971e-4b02-b1a5-eb9c89a27a9e\",\n \"metadata\": {},\n \"source\": [\n \"### 전자 배치 복원 루프 3: 부분공간에서의 대각화 \\n\",\n \"\\n\",\n \"다음 단계는 부분공간에서의 해밀토니안 대각화입니다. 앞서 예시로 든 문제에서 원래 해밀토니안 $H$ 은 $2^{32}\\\\times2^{32}$ 크기의 행렬입니다. 이 행렬을 50개의 비트열, 즉 $|x_i\\\\rangle$ 로 생성된 부분공간으로 투영하여 크기를 축소한 후 이를 대각화합니다.\\n\",\n \"이 투영 행렬 $P_S$는 이론적으로 0...0 부터 1...1 까지 총 $2^{32}$ 개의 가능한 모든 비트열을 가질 수 있으나, 실제로 측정된 최대 50개의 비트열만을 포함하게 됩니다. $P_S$는 대각에 50개의 원소만이 1을 가지고 나머지 원소는 모두 0으로 구성된 행렬입니다.\\n\",\n \"따라서, 원래 해밀토니안 $H$ 와 투영 행렬 $P_S$ 를 곱하여 얻은 축소된 해밀토니안은 오직 $50\\\\times50$개 이고, 나머지 원소들은 전부 0입니다. 결국, 원래 크기인 $2^{32}\\\\times2^{32}$ 의 거대한 행렬 대신 $50\\\\times50$ 크기의 작은 행렬만을 대각화하면 됩니다.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Fig 12. 부분공간에서의 행렬 대각화 과정을 나타낸 도식\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"52d3b688-89d0-4451-a03b-db232442a1a3\",\n \"metadata\": {},\n \"source\": [\n \"### 전자 배치 복원 루프 4: 최저 에너지 계산\\n\",\n \"\\n\",\n \"마지막으로, 앞서 수행한 모든 배치의 결과로부터 평균 오비탈 점유율에 대해 추정값과 바닥상태에 해당하는 최저 에너지를 얻습니다. 이 정보를 다음 반복을 위한 데이터로 업데이트합니다.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"2566ce1e-cf95-4915-b986-0c1a68662afa\",\n \"metadata\": {},\n \"source\": [\n \"## Qiskit pattern\\n\",\n \"\\n\",\n \"SQD 알고리즘을 다음과 같이 Qiskit pattern을 활용하여 구현할 수 있습니다:\\n\",\n \"\\n\",\n \"1. **Step 1: 양자 문제로 정의**\\n\",\n \" - 바닥상태를 추정하기 위해 가설해를 생성합니다.\\n\",\n \"2. **Step 2: 문제 최적화**\\n\",\n \" - 사용하고자 하는 양자 하드웨어(backend)에 맞게 가설해 회로를 트랜스파일합니다.\\n\",\n \"3. **Step 3: 실험 실행**\\n\",\n \" - Qiskit의 ``Sampler`` primitive을 이용하여 가설해 회로로부터 비트열 샘플을 수집합니다.\\n\",\n \"4. **Step 4: 결과 후처리**\\n\",\n \" - 자기 일관적 전자 배치 복원 루프를 수행합니다.\\n\",\n \" - 입자 수에 대한 사전 정보와 이전 반복에서 계산된 평균 오비탈 점유율을 사용하여 전체 비트열 샘플을 후처리 합니다.\\n\",\n \" - 복원된 비트열로부터 부분 샘플을 확률적으로 생성하여 배치를 구성합니다.\\n\",\n \" - 각 부분공간에 대해 분자 해밀토니안을 투영하고, 축소된 행렬을 고전적으로 대각화합니다.\\n\",\n \" - 모든 배치에서 얻어진 바닥상태 에너지 추정값 중 최솟값을 선택하고, 이를 바탕으로 평균 오비탈 점유율을 업데이트합니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"157e65a0-06a9-4ac9-bafa-702824a84eb2\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Map classical inputs to a quantum problem\\n\",\n \"\\n\",\n \"We will find an approximation to the ground state of the molecule at equilibrium in the 6-31G basis set. First, we specify the molecule and its properties.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c5c49c74-d658-42f6-a729-ec1c7687e9aa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"\\n\",\n \"# 분자의 성질 특정\\n\",\n \"open_shell = False\\n\",\n \"spin_sq = 0\\n\",\n \"\\n\",\n \"# N2 분자 구축\\n\",\n \"mol = pyscf.gto.Mole()\\n\",\n \"mol.build(\\n\",\n \" atom=[[\\\"N\\\", (0, 0, 0)], [\\\"N\\\", (1.0, 0, 0)]],\\n\",\n \" basis=\\\"6-31g\\\",\\n\",\n \" symmetry=\\\"Dooh\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# 활성 공간 정의\\n\",\n \"n_frozen = 2\\n\",\n \"active_space = range(n_frozen, mol.nao_nr())\\n\",\n \"\\n\",\n \"# 분자 적분 계산\\n\",\n \"scf = pyscf.scf.RHF(mol).run()\\n\",\n \"num_orbitals = len(active_space)\\n\",\n \"n_electrons = int(sum(scf.mo_occ[active_space]))\\n\",\n \"num_elec_a = (n_electrons + mol.spin) // 2\\n\",\n \"num_elec_b = (n_electrons - mol.spin) // 2\\n\",\n \"cas = pyscf.mcscf.CASCI(scf, num_orbitals, (num_elec_a, num_elec_b))\\n\",\n \"mo = cas.sort_mo(active_space, base=0)\\n\",\n \"hcore, nuclear_repulsion_energy = cas.get_h1cas(mo)\\n\",\n \"eri = pyscf.ao2mo.restore(1, cas.get_h2cas(mo), num_orbitals)\\n\",\n \"\\n\",\n \"# 분자의 정확한 에너지 계산\\n\",\n \"exact_energy = cas.run().e_tot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91fa0157-366c-4c0e-b040-6a18f5546416\",\n \"metadata\": {},\n \"source\": [\n \"`LUCJ` 가설해 회로를 구성하기 전에, 다음 코드 셀에서 CCSD 계산을 먼저 수행합니다. 여기서 얻어진 [$t_1$ 및 $t_2$ 진폭](https://en.wikipedia.org/wiki/Coupled_cluster#Cluster_operator)은 이후 가설해의 매개변수 초기화에 사용됩니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f72b68e1-065f-49fc-a7cb-7afe3d1c3f65\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 가설해의 초기 매개변수로 사용할 CCSD의 t1, t2 진폭을 계산\\n\",\n \"ccsd = pyscf.cc.CCSD(scf, frozen=[i for i in range(mol.nao_nr()) if i not in active_space]).run()\\n\",\n \"t1 = ccsd.t1\\n\",\n \"t2 = ccsd.t2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"adb2fd92-294b-4f87-a438-f42d6589cd73\",\n \"metadata\": {},\n \"source\": [\n \"이제, [ffsim](https://github.com/qiskit-community/ffsim)을 이용하여 가설해 회로를 구성합니다. 고려하는 분자는 닫힌 껍질(closed-shell)의 하트리-폭 상태를 가지므로, 스핀 균형을 이루는 UCJ 가설해 변형인 [UCJOpSpinBalanced](https://qiskit-community.github.io/ffsim/api/ffsim.html#ffsim.UCJOpSpinBalanced)를 사용합니다. 이때 상호작용 쌍은 중 육각 격자 큐비트 위상구조에 적합하도록 설정하여 전달합니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"527f5873-f5be-4890-960e-4ad24c1e423d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"n_reps = 1\\n\",\n \"alpha_alpha_indices = [(p, p + 1) for p in range(num_orbitals - 1)]\\n\",\n \"alpha_beta_indices = [(p, p) for p in range(0, num_orbitals, 4)]\\n\",\n \"\\n\",\n \"ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\\n\",\n \" t2=t2,\\n\",\n \" t1=t1,\\n\",\n \" n_reps=n_reps,\\n\",\n \" interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\\n\",\n \")\\n\",\n \"\\n\",\n \"nelec = (num_elec_a, num_elec_b)\\n\",\n \"\\n\",\n \"# 빈 양자회로 생성\\n\",\n \"qubits = QuantumRegister(2 * num_orbitals, name=\\\"q\\\")\\n\",\n \"circuit = QuantumCircuit(qubits)\\n\",\n \"\\n\",\n \"# 하트리-폭 상태 상태를 준비하여 양자회로에 추가\\n\",\n \"circuit.append(ffsim.qiskit.PrepareHartreeFockJW(num_orbitals, nelec), qubits)\\n\",\n \"\\n\",\n \"# UCJ 연산자 적용\\n\",\n \"circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\\n\",\n \"circuit.measure_all()\\n\",\n \"\\n\",\n \"circuit.decompose().decompose().draw(\\\"mpl\\\", fold =-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5a385896-0c00-425a-b06f-dc929371678a\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: 양자 실행을 위한 문제 최적화\\n\",\n \"다음 단계는 양자 하드웨어에서 실행할 수 있도록 양자 회로를 최적화하는 것입니다. 여기서는 127-큐비트 QPU인 `ibm_brisbane`을 사용합니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ff55ea1b-22ef-4082-af12-d663ba3b7d6c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"service = QiskitRuntimeService(name=\\\"qgss-2025\\\")\\n\",\n \"backend = service.backend(\\\"ibm_brisbane\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"11c41725-68be-4289-95ee-562763547ca6\",\n \"metadata\": {},\n \"source\": [\n \"가설해를 최적화하고 양자 하드웨어와의 호환성을 높이기 위해 다음과 같은 절차를 권장합니다.\\n\",\n \"\\n\",\n \"* 목표 하드웨어로부터 위에서 설명한 지그재그 패턴에 적합한 물리적 큐비트 배치(`initial_layout`)를 선택합니다. 이 방식으로 큐비트를 배치하면 하드웨어와 호환되는 효율적인 양자 회로를 구성할 수 있으며, 회로의 전체 게이트 수를 최소화할 수 있습니다.\\n\",\n \"* 선택한 `backend`와 위에서 결정한 `initial_layout`을 사용하여 Qiskit의 [generate\\\\_preset\\\\_pass\\\\_manager](https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.transpiler.generate_preset_pass_manager)함수를 통해 단계화된 패스 매니저(pass manager)를 생성합니다.\\n\",\n \"* 생성된 단계화된 패스 매니저의 `pre_init`단계를 `ffsim.qiskit.PRE_INIT`으로 설정합니다. `ffsim.qiskit.PRE_INIT`은 게이트를 오비탈 회전으로 분해한 뒤, 병합하는 Qsikit 트랜스파일러 패스를 포함하며, 그 결과 최종 양자 회로의 게이트 수가 감소하게 됩니다.\\n\",\n \"* 마지막으로 위에서 구성된 패스 매니저를 양자 회로에 실행합니다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b77ff882-7da3-40be-bbef-cadab8f9c841\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spin_a_layout = [0, 14, 18, 19, 20, 33, 39, 40, 41, 53, 60, 61, 62, 72, 81, 82]\\n\",\n \"spin_b_layout = [2, 3, 4, 15, 22, 23, 24, 34, 43, 44, 45, 54, 64, 65, 66, 73]\\n\",\n \"initial_layout = spin_a_layout + spin_b_layout\\n\",\n \"\\n\",\n \"pass_manager = generate_preset_pass_manager(\\n\",\n \" optimization_level=3, backend=backend, initial_layout=initial_layout\\n\",\n \")\\n\",\n \"\\n\",\n \"# 하드웨어 실행을 위해 이 패스 매니저에 의해 생성된 회로를 사용\\n\",\n \"pass_manager.pre_init = ffsim.qiskit.PRE_INIT\\n\",\n \"isa_circuit = pass_manager.run(circuit)\\n\",\n \"print(f\\\"Gate counts (w/ pre-init passes): {isa_circuit.count_ops()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"04a7a60b-e87a-4b13-9f35-573db129526c\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Qiskit Primitive를 이용한 양자 회로 실행\\n\",\n \"\\n\",\n \"하드웨어 실행을 위해 최적화된 양자 회로가 준비되었으므로, 이제 이 회로를 실제 양자 하드웨어에서 실행하여 바닥상태 에너지 추정을 위한 샘플을 수집할 수 있습니다.\\n\",\n \"\\n\",\n \"![](\\\"data:image/png;base64,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\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0598739c\",\n \"metadata\": {},\n \"source\": [\n \"(https://arxiv.org/pdf/2308.07915 에서 가져온 이미지: Gross 코드의 Tanner 그래프 표현. 여기서 toroidal 격자에서 정의된 정보 큐비트는 동그라미, 스태빌라이저/검사 큐비트는 사각형으로 그려졌습니다.)\\n\",\n \"\\n\",\n \"## 3.2 연습에서 사용할 큐비트 레이아웃과 표기 원칙\\n\",\n \"\\n\",\n \"단순화된 모델을 이용하여 서로 다른 코드 구조가 성능에 어떤 영향을 끼치는지 비교하기 위해서, 우리는 위의 이미지를 이용해 똑같은 물리적 큐비트의 배치 상에서 코드를 구성할 것입니다. Patrick Rall이 \\\"Toward Fault-tolerant Quantum Computing with IBM qLDPC codes\\\" 강의에서 다룬 개념을 토대로 진행해보도록 하겠습니다.\\n\",\n \"\\n\",\n \"우리의 목표는 주기적인 경계 조건을 갖는 2차원 격자 (torus) 에서 정의된 두 가지 코드의 패리티 검사 행렬을 구성하는 데에 있습니다. 이는 제공된 Tanner 그래프에서 시각적으로 확인할 수 있습니다:\\n\",\n \"1. 국소적 연결성을 갖는 **Toric 코드**를 나타내는 그래프\\n\",\n \"2. 추가적인 장거리 연결성을 갖는 **Gross 코드**를 나타내는 그래프\\n\",\n \"\\n\",\n \"**우리의 모델에 있어서 중요한 원소들:**\\n\",\n \"\\n\",\n \"* **정보 큐비트 (원):** 양자 정보를 저장하는 역할을 합니다. 주어진 도표에서 이들은 파란색과 주황색 원으로 나타납니다. 여기에는 $12 \\\\times 6 = 72$ 개의 파란색 큐비트와 $12 \\\\times 6 = 72$ 개의 주황색 큐비트가 있어, 전체 정보 큐비트 숫자는 $n = 144$ 개 입니다.\\n\",\n \"\\n\",\n \"* **스태빌라이저/검사 큐비트 (사각):** 코드의 스태빌라이저 연산자를 정의합니다.\\n\",\n \" * **X 스태빌라이저** (파울리 X의 곱)은 **빨간색** 사각형으로 표기됩니다.\\n\",\n \" * **Y 스태빌라이저** (파울리 Z의 곱)은 **초록색** 사각형으로 표기됩니다.\\n\",\n \"* **CSS 코드 구조:** 여기서도 CSS 코드의 프레임워크를 그대로 차용합니다.\\n\",\n \"\\n\",\n \"명료한 **큐비트 표기** 원칙은 Tanner 그래프의 연결성을 패리티 검사 행렬로 바꾸는데에 있어 매우 핵심적입니다. 이 행렬의 행은 스태빌라이저에 해당할 것이고, 열은 정보 큐비트에 해당할 것입니다.\\n\",\n \"\\n\",\n \"**큐비트 표기 원칙 (총 144개의 정보 큐비트):**\\n\",\n \"\\n\",\n \"* **파란색** 데이터 큐비트 (인덱스 0-71): 왼쪽 열에서 오른쪽 열로 가며 표기되고, 그 뒤에 각 열 내에서 아래에서 위로 표기됩니다.\\n\",\n \" * 좌측 하단 파란색 큐비트는 `0`입니다; 그 뒤에 있는 큐비트는 `1`부터 시작하여 `5`까지 갑니다.\\n\",\n \" * 그 다음 행의 파란색 큐비트는 `6`부터 `11`까지 있고, 이러한 패턴으로 이어집니다.\\n\",\n \"* **주황색** 데이터 큐비트 (인덱스 72-143): 똑같은 표기를 이어가고, 좌측 하단 주황색 큐비트 `72`부터 시작합니다.\\n\",\n \"\\n\",\n \"**(이번 랩을 위한) 패리티 검사 행렬 표기 원칙:**\\n\",\n \"\\n\",\n \"우리는 이제 두 개의 이진 패리티 검사 행렬을 정의할 것입니다:\\n\",\n \"\\n\",\n \"* **X 스태빌라이저 $H_X$:**\\n\",\n \" * 각 행은 **빨간색 사각형**으로부터 유도된 X 스태빌라이저를 의미합니다.\\n\",\n \" * 이 행렬 $H_X$ 는 **Z 오류**를 탐지하는데 쓰입니다. (징후: $s_x$)\\n\",\n \"* **Z 스태빌라이저 $H_Z$:**\\n\",\n \" * 각 행은 **초록색 사각형**으로부터 유도된 Z 스태빌라이저를 의미합니다.\\n\",\n \" * 이 행렬 $H_Z$ 는 **X 오류**를 탐지하는데 쓰입니다. (징후: $s_z$)\\n\",\n \"\\n\",\n \"위와 같은 레이아웃과 표기 원칙을 사용하여, 우리는 이제 연습을 위해 *같은* 물리 큐비트 레이아웃에서 정의된 두 개의 서로 다른 코드 Tanner 그래프의 연결성에 따라 스태빌라이저를 정의하겠습니다.\\n\",\n \"\\n\",\n \"1. **Toric 코드:** 이 버전은 주기적인 경계 조건을 갖는 격자에서의 표준적인 toric 코드 모델을 따라 스태빌라이저 (사각형) 과 이들이 작용하는 정보 큐비트 (원형) 사이의 국소적이고 가장 가까운 이웃과의 연결성만을 이용해 구성됩니다. 참고: Toric 코드와 surface 코드는 매우 비슷하지만, toric 코드는 $x$ 와 $y$ 방향에서의 주기적 경계 조건이 있는 torus에서 정의가 됩니다. 이는 이러한 경계 조건이 없는 surface 코드에 비해 하나의 추가적인 논리 큐비트를 더 인코딩할 수 있게 합니다.\\n\",\n \"\\n\",\n \"2. **Gross 코드:** 이 버전은 toric 코드의 국소적 연결에 더해 \\\"장거리\\\" 연결을 이용할 것입니다. 이러한 장거리 연결은 위의 그래프에서 나뭇잎처럼 생긴 모양의 연결입니다. 이러한 추가적인 연결은 스태빌라이저 낳음 연산자의 정의를 바꿀 것이고, 이는 같은 큐비트 레이아웃에서 정의된 표준적인 toric 코드와는 다른 파라미터와 성질을 갖는 코드를 만들어 냅니다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c827399a\",\n \"metadata\": {},\n \"source\": [\n 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\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"610ae37c\",\n \"metadata\": {},\n \"source\": [\n \"## 3.3 Toric 코드 연습\\n\",\n \"\\n\",\n \"### HX\\n\",\n \"\\n\",\n \"우리는 이제 torus 상에서 가장 가까운 이웃끼리의 연결만을 고려하는 toric 코드의 (빨간색 사각형들과 연관된) **X 스태빌라이저**를 이진수로 표현할 것입니다. 이는 여러분이 나중에 구성하게 될 (연습 문제의 출력 변수 $H_X$ 로 표기된) 행렬의 행이 될 것입니다.\\n\",\n \"\\n\",\n \"도표의 좌측 하단의 빨간색 사각형을 고려해봅시다. 이 X 스태빌라이저는 이웃한 네 개의 정보 큐비트에 작용합니다:\\n\",\n \"1. 아래에 있는 파란색 정보 큐비트: 이 파란색 큐비트는 0번째 열과 0번째 행에 있으므로 `0`입니다.\\n\",\n \"2. 위에 있는 파란색 정보 큐비트: 이 파란색 큐비트는 0번째 열과 1번째 행에 있으므로 `1`입니다.\\n\",\n \"3. 오른쪽에 있는 주황색 정보 큐비트: 이는 좌측 하단의 주황색 큐비트이므로 `72`입니다.\\n\",\n \"4. 주기적 경계를 건너 왼쪽에 있는 주황색 정보 큐비트: 이는 우측 하단 주황색 큐비트입니다. 주황색 큐비트들은 72-143까지 표기되고, 주황색 큐비트는 12개의 열이 있고, 각 열에는 6개의 큐비트가 있습니다. 이 마지막 주황색 열 (11번째 열)은 큐비트 $72 + 11 \\\\times 6 + $ row_idx 에 해당합니다. $72 + 11 \\\\times 6 + 0 = 72 + 66 = 138$ 이므로, 우측 하단에 있는 큐비트는 주황색 큐비트 `138`입니다.\\n\",\n \"\\n\",\n \"따라서 이 첫 번째 X 스태빌라이저는 정보 큐비트 `0`, `1`, `72`와 `138`에 작용합니다. (이 연습에 있어서 $H_X$ 의 행이 될 것이고 표준 표기법에서는 $H_Z$ 가 되는) 이진 행렬의 행은 열 0, 1, 72와 138에 1이 있고, 나머지는 모두 0으로 채워집니다. 이러한 144개의 원소가 있는 행 벡터는 다음과 같습니다:\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"문제에서 \\\"패리티 검사 행렬 $H_X$\\\"의 첫 6개의 행을 찾는 (첫 6개의 빨간색 사각형에 해당하는) 예시는 같은 논리가 적용됩니다:\\n\",\n \"\\n\",\n \"```\\n\",\n \"[[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0],\\n\",\n \"\\n\",\n \" [0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0],\\n\",\n \"\\n\",\n \" [0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0],\\n\",\n \"\\n\",\n \" [0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0],\\n\",\n \"\\n\",\n \" [0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0],\\n\",\n \"\\n\",\n \" [1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]]\\n\",\n \"```\\n\",\n \"\\n\",\n \"### HZ\\n\",\n \"\\n\",\n \"HZ는 HX와 같은 기본 규칙에 따라 가장 가까운 이웃을 찾습니다. 차이점은 HZ가 초록색 사각형으로 표기되며, 전체 격자의 왼쪽에서 두 번째 열의 최하단에서 시작한다는 것입니다.\\n\",\n \"\\n\",\n \"도표에서 좌측 하단의 초록색 사각형을 고려해봅시다. 이 Z 스태빌라이저는 네 개의 이웃한 정보 큐비트에 작용합니다:\\n\",\n \"1. 왼쪽에 있는 파란색 정보 큐비트: 이는 파란색 큐비트 `0`입니다. (파란색 0 행, 0 열).\\n\",\n \"2. 오른쪽에 있는 파란색 정보 큐비트: 이는 파란색 큐비트 `6`입니다. (파란색 0 행, 1 열).\\n\",\n \"3. 위에 있는 주황색 정보 큐비트: 이는 좌측 하단 주황색 큐비트이고, 이는 주황색 큐비트 `72`입니다. (주황색 0 행, 0 열).\\n\",\n \"4. 경계 조건 넘어 아래에 있는 주황색 정보 큐비트: 이는 좌측 상단 주황색 큐비트이므로 이는 주황색 큐비트 `77`입니다. 이 큐비트는 주황색 큐비트 `72`보다 5행 아래에 있습니다. (주황색 5 행, 0 열)\\n\",\n \"\\n\",\n \"따라서 첫 Z 스태빌라이저는 정보 큐비트 `0`, `6`, `72`와 `77`에 작용합니다. (이 연습에 있어서 $H_Z$ 의 행이 될 것이고 표준 표기법에서는 $H_X$ 가 되는) 이진 행렬의 행은 열 0, 6, 72와 77에 1이 있고, 나머지는 모두 0으로 채워집니다. 이러한 144개의 원소가 있는 행 벡터는 다음과 같습니다:\\n\",\n \"```\\n\",\n \" [1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n 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\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5dde8af2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 연습문제 4를 시작하기 위한 도움말\\n\",\n \"\\n\",\n \"\\n\",\n \"# Toric 코드의 패리티 검사 행렬을 정의합니다\\n\",\n \"HXtc = np.zeros((72, 144),dtype=int) # 행렬을 먼저 초기화합니다\\n\",\n \"HZtc = np.zeros((72, 144),dtype=int)\\n\",\n \"\\n\",\n \"# 우리는 여러분에게 알맞은 곳에 1을 더함으로써 행렬을 수정하도록 요청할 것입니다\\n\",\n \"# 예시를 위해 같이 toric 코드의 첫 몇 개의 행을 채워보도록 하겠습니다\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"# HXtc와 HZtc를 계산하기 위한 코드를 작성하세요\\n\",\n \"\\n\",\n \"# 0번째 스태빌라이저\\n\",\n \"HXtc[0][0] = 1\\n\",\n \"HXtc[0][1] = 1\\n\",\n \"HXtc[0][72] = 1\\n\",\n \"HXtc[0][138] = 1\\n\",\n \"\\n\",\n \"# 1번째 스태빌라이저\\n\",\n \"HXtc[1][1] = 1\\n\",\n \"HXtc[1][2] = 1\\n\",\n \"HXtc[1][73] = 1\\n\",\n \"HXtc[1][139] = 1\\n\",\n \"\\n\",\n \"# 2번째 스태빌라이저\\n\",\n \"HXtc[2][2] = 1\\n\",\n \"HXtc[2][3] = 1\\n\",\n \"HXtc[2][74] = 1\\n\",\n \"HXtc[2][140] = 1\\n\",\n \"\\n\",\n \"# 3번째 스태빌라이저\\n\",\n \"HXtc[3][3] = 1\\n\",\n \"HXtc[3][4] = 1\\n\",\n \"HXtc[3][75] = 1\\n\",\n \"HXtc[3][141] = 1\\n\",\n \"\\n\",\n \"# 4번째 스태빌라이저\\n\",\n \"HXtc[4][4] = 1\\n\",\n \"HXtc[4][5] = 1\\n\",\n \"HXtc[4][76] = 1\\n\",\n \"HXtc[4][142] = 1\\n\",\n \"\\n\",\n \"# 5번째 스태빌라이저\\n\",\n \"HXtc[5][5] = 1\\n\",\n \"HXtc[5][np.mod(6,6)] = 1\\n\",\n \"HXtc[5][72 + 5] = 1\\n\",\n \"HXtc[5][72 + 6*np.mod(-1,12) + 5] = 1\\n\",\n \"\\n\",\n \"# 마지막 정의는 어느 위치에 1을 두어야 하는지에 대한 일반적인 규칙을 제시합니다\\n\",\n \"# HXtc[j][j] = 1\\n\",\n \"# HXtc[j][6*np.floor(j/6) + np.mod(j+1,6)]\\n\",\n \"# HXtc[j][j+72] = 1\\n\",\n \"# HXtc[j][72 + 6*np.mod(np.floor(j/6)-1,12) + np.mod(j,6)]\\n\",\n \"\\n\",\n \"# 0부터 143까지의 j에 대한 반복문을 작성하여 모든 열을 채워넣어 보세요\\n\",\n \"\\n\",\n \"# 위의 예시를 참고하여, Z 패리티 검사 행렬을 반복문을 사용하여 작성하고 HXtc와 HZtc를 완성하세요\\n\",\n \"\\n\",\n \"HZtc[0][0] = 1\\n\",\n \"HZtc[0][6] = 1\\n\",\n \"HZtc[0][72] = 1\\n\",\n \"HZtc[0][77] = 1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e7bd2d52-72a8-4b5b-99c9-b0e9a75b7dc3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# HXtc와 HZtc의 연결성을 확인해보세요\\n\",\n \"\\n\",\n \"from lab4_util import generate_stabilizer_plots\\n\",\n \"generate_stabilizer_plots(HXtc, HZtc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f81fb0f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab4_ex4(HXtc, HZtc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fc9be9fa\",\n \"metadata\": {},\n \"source\": [\n \"## 3.4 Gross 코드 연습\\n\",\n \"\\n\",\n \"이제 우리는 Gross 코드의 패리티 검사 행렬을 탐구해볼 것입니다. 이 코드는 도표에서 추상적으로 표현된 장거리 연결을 포함하여 만들어집니다. 이러한 장거리 연결은 나뭇잎처럼 표현되어 있지만, 정확한 규칙은 코드의 정의에 따르게 됩니다. 근본적인 큐비트 레이아웃과 표기 원칙은 toric 코드 때와 동일합니다.\\n\",\n \"\\n\",\n \"Gross 코드와 toric 코드의 중요한 차이점은 각각의 스태빌라이저 (사각형 검사) 에 네 개의 가장 가까운 이웃을 연결하는 연결에 더해 두 개의 장거리 연결이 추가된다는 것입니다. 이는 Gross 코드의 스태빌라이저가 총 $4+2=6$ 개의 정보 큐비트에 작용한다는 것을 의미합니다. 따라서 Gross 코드의 패리티 검사 행렬 ($H_X$ 와 $H_Z$) 의 각 행에는 이제 여섯 개의 '1'이 있게 됩니다.\\n\",\n \"\\n\",\n \"이러한 행렬을 구축하는 효과적인 방법은 toric 코드의 연결성 (가장 가까운 이웃하고의 연결) 에서 시작하여 각 스태빌라이저에 두 개의 장거리 연결을 더하는 것입니다.\\n\",\n \"\\n\",\n \"$H_X$ 의 첫 행을 채우면서 이를 적용해봅시다. 이 첫 행은 좌측 하단의 빨간색 사각형 ($c_s=0, r_s=0$)에 있는 X 스태빌라이저에 대응될 것입니다. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f7b4b32d\",\n \"metadata\": {},\n \"source\": [\n 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\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2dffcaea\",\n \"metadata\": {},\n \"source\": [\n \"#### HX\\n\",\n \"\\n\",\n \"1. Toric 코드에서와 같은 가장 가까운 이웃과의 연결:\\n\",\n \"3.3 장에서 살펴보았듯이 좌측 하단의 빨간색 사각형은 다음 큐비트들과 가장 가까운 이웃으로 연결되어 있습니다:\\n\",\n \"* 인덱스 $\\\\mathbf{0}$에 있는 파란색 정보 큐비트 (아래에 존재하는, 파란색 0 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{1}$에 있는 파란색 정보 큐비트 (위에 존재하는, 파란색 1 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{72}$에 있는 주황색 정보 큐비트 (오른쪽에 존재하는, 주황색 0 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{138}$에 있는 주황색 정보 큐비트 (주기적 경계를 건너 왼쪽에 존재하는, 주황색 0 행, 11 열)\\n\",\n \"\\n\",\n \"2. Gross 코드만의 추가적인 장거리 연결:\\n\",\n \"현재 고려하고 있는 Gross 코드 구성에서는 좌측 하단 빨간색 사각형 ($c_s=0, r_s=0$)이 두 개의 추가적인 장거리 연결을 가질 것입니다:\\n\",\n \"\\n\",\n \"* 첫 번째 장거리 연결: 파란색 정보 큐비트 20 과의 연결.\\n\",\n \" * 이 연결은 세 행 위에 있고 여섯 열 오른쪽에 있는 파란색 큐비트와의 연결입니다. Gross 코드의 규칙은 이러한 상대적인 위치가 torus에서의 큐비트 인덱스와 어떻게 연결되는지 결정합니다. 이 스태빌라이저에서는 해당 규칙이 전역 인덱스 $\\\\mathbf{20}$ 을 갖는 파란색 큐비트와의 연결을 지정합니다.\\n\",\n \"\\n\",\n \"* 두 번째 장거리 연결: 주황색 정보 큐비트 135 와의 연결.\\n\",\n \" * 이 연결은 여섯 행 아래에 있고 세 열 왼쪽에 있는 주황색 큐비트와의 연결입니다. Gross 코드의 규칙은 이러한 상대적인 위치가 torus에서의 큐비트 인덱스와 어떻게 연결되는지 결정합니다. 이 스태빌라이저에서는 해당 규칙이 전역 인덱스 $\\\\mathbf{135}$ 를 갖는 주황색 큐비트와의 연결을 지정합니다.\\n\",\n \"\\n\",\n \"3. Grosss 코드 $H_X$ 의 첫 번째 행:\\n\",\n \"가장 가까운 이웃과의 연결과 방금 설명한 장거리 연결을 합쳐서 생각하면 이제 좌측 하단의 X 스태빌라이저는 다음 인덱스의 정보 큐비트에 작용합니다: `0, 1, 20, 72, 135, 138`.\\n\",\n \"\\n\",\n \"따라서 이 Gross 코드 $H_X$ 행렬의 첫 번째 행 (144개의 원소를 갖는 이진 벡터) 은 6개의 위치에 '1'을 갖고, 나머지 위치에는 '0'을 갖게 될 것입니다. 이를 다음 코드 조각에서 확인해봅시다:\\n\",\n \"\\n\",\n \"**코드 조각:**\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"이는 Gross 코드 $H_X$ 행렬에서 발췌한 첫 번째 행에, 앞에서 찾은 가장 가까운 이웃 및 장거리 연결을 조합한 전역 인덱스 0, 1, 20, 72, 135, 138에 정확히 6개의 1이 있음을 보여줍니다.\\n\",\n \"\\n\",\n \"#### HZ\\n\",\n \"\\n\",\n \"$H_Z$ 를 찾기 위해서는 Tanner 그래프로 돌아가 장거리 연결의 `leaf` 모양을 유심히 살펴봐야합니다.\\n\",\n \"\\n\",\n \"1. Toric 코드에서와 같은 가장 가까운 이웃과의 연결:\\n\",\n \"3.3 장에서 살펴보았듯이 좌측 하단의 초록색 사각형은 다음 큐비트들과 가장 가까운 이웃으로 연결되어 있습니다:\\n\",\n \"* 인덱스 $\\\\mathbf{77}$에 있는 주황색 정보 큐비트 (아래에 존재하는, 주황색 5 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{72}$에 있는 주황색 정보 큐비트 (위에 존재하는, 주황색 0 행, 0 열)\\n\",\n \"* 인덱스 $\\\\mathbf{6}$에 있는 파랑색 정보 큐비트 (오른쪽에 존재하는, 파랑색 0 행, 1 열)\\n\",\n \"* 인덱스 $\\\\mathbf{0}$에 있는 파랑색 정보 큐비트 (왼쪽에 존재하는, 파랑색 0 행, 0 열)\\n\",\n \"\\n\",\n \"2. Gross 코드만의 추가적인 장거리 연결:\\n\",\n \"* 첫 번째 장거리 연결: 파란색 정보 큐비트 15 와의 연결.\\n\",\n \" * 이 연결은 여섯 행 위에 있고 세 열 오른쪽에 있는 파란색 큐비트와의 연결입니다. Gross 코드의 규칙은 이러한 상대적인 위치가 torus에서의 큐비트 인덱스와 어떻게 연결되는지 결정합니다. 이 스태빌라이저에서는 해당 규칙이 전역 인덱스 $\\\\mathbf{15}$ 를 갖는 파란색 큐비트와의 연결을 지정합니다.\\n\",\n \"\\n\",\n \"* 두 번째 장거리 연결: 주황색 정보 큐비트 130 과의 연결.\\n\",\n \" * 이 연결은 세 행 아래에 있고 여섯 열 왼쪽에 있는 주황색 큐비트와의 연결입니다. Gross 코드의 규칙은 이러한 상대적인 위치가 torus에서의 큐비트 인덱스와 어떻게 연결되는지 결정합니다. 이 스태빌라이저에서는 해당 규칙이 전역 인덱스 $\\\\mathbf{130}$ 을 갖는 주황색 큐비트와의 연결을 지정합니다.\\n\",\n \"\\n\",\n \"3. Grosss 코드 $H_Z$ 의 첫 번째 행:\\n\",\n \"가장 가까운 이웃과의 연결과 방금 설명한 장거리 연결을 합쳐서 생각하면 이제 좌측 하단의 Z 스태빌라이저는 다음 인덱스의 정보 큐비트에 작용합니다: `0, 6, 15, 72, 77, 130`.\\n\",\n \"\\n\",\n \"따라서 이 Gross 코드 $H_Z$ 행렬의 첫 번째 행 (144개의 원소를 갖는 이진 벡터) 은 6개의 위치에 '1'을 갖고, 나머지 위치에는 '0'을 갖게 될 것입니다. 이를 다음 코드 조각에서 확인해봅시다:\\n\",\n \"\\n\",\n \"\\n\",\n \"**코드 조각:**\\n\",\n \"\\n\",\n \"```\\n\",\n \"[1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n \"```\\n\",\n \"\\n\",\n \"이는 Gross 코드 $H_Z$ 행렬에서 발췌한 첫 번째 행에, 앞에서 찾은 가장 가까운 이웃 및 장거리 연결을 조합한 전역 인덱스 0, 6, 15, 72, 77, 130에 정확히 6개의 1이 있음을 보여줍니다.\\n\",\n \"\\n\",\n \"두 장거리 연결성을 더하는 이 방식이 *모든* 스태빌라이저 (빨간색 사각형의 X 스태빌라이저와 초록색 사각형의 Z 스태빌라이저 모두!) 에 대해 반복되면 toric 코드 행렬을 Gross 코드 행렬로 바꿀 수 있습니다. 이 격자에서 Gross 코드를 구성하며 정의한 패턴은 다른 스태빌라이저를 정의할 때도 똑같이 쓰입니다.\\n\",\n \"\\n\",\n \"![](\\\"data:image/png;base64,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\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e46c47ef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Gross 코드의 패리티 검사 행렬을 정의합니다\\n\",\n \"HXgc = np.zeros((72,144),dtype=int) # 행렬을 먼저 초기화합니다\\n\",\n \"HZgc = np.zeros((72,144),dtype=int)\\n\",\n \"# --- 아래에 코드를 작성해주세요 ---\\n\",\n \"# HXtc와 HZtc를 계산하기 위한 코드를 작성하세요\\n\",\n \"\\n\",\n \"# 0번째 X 스태빌라이저\\n\",\n \"HXgc[0][0] = 1\\n\",\n \"HXgc[0][1] = 1\\n\",\n \"HXgc[0][20] = 1\\n\",\n \"HXgc[0][72] = 1\\n\",\n \"HXgc[0][135] = 1\\n\",\n \"HXgc[0][138] = 1\\n\",\n \"\\n\",\n \"\\n\",\n \"# 0번째 Z 스태빌라이저\\n\",\n \"HZgc[0][0] = 1\\n\",\n \"HZgc[0][6] = 1\\n\",\n \"HZgc[0][15] = 1\\n\",\n \"HZgc[0][72] = 1\\n\",\n \"HZgc[0][77] = 1\\n\",\n \"HZgc[0][130] = 1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# --- 코드 작성이 완료되었습니다 ---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ac83de2d-bdb3-4bd4-bf20-a1cef7e6640a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 스태빌라이저를 확인해보세요\\n\",\n \"\\n\",\n \"generate_stabilizer_plots(HXgc, HZgc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a180a7cd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 아래의 함수를 실행해 답안을 제출해주세요\\n\",\n \"grade_lab4_ex5(HXgc, HZgc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0f37a761\",\n \"metadata\": {},\n \"source\": [\n \"## 3.5 논리 큐비트의 개수 찾기\\n\",\n \"\\n\",\n \"$H_X$ 와 $H_Z$ 로 표기되는 패리티 검사 행렬은 코드의 스태빌라이저로부터 구성되었습니다. 이 행렬들의 각 행은 스태빌라이저 군의 낳음 연산자를 나타냅니다. 하지만 이렇게 고른 낳음 연산자들은 독립적이지 않을 수도 있습니다; 하나의 스태빌라이저 낳음 연산자가 다른 것들의 곱일 수 있습니다. 이런 반복적인 낳음 연산자는 코드 공간에 새로운 제약을 걸지 못합니다.\\n\",\n \"\\n\",\n \"독립적인 스태빌라이저 낳음 연산자의 실제 개수를 찾기 위해서는, 우리는 패리티 검사 행렬의 **rank**를 계산해야 합니다. 우리는 큐비트와 파울리 연산자를 다루고 있기에 모든 rank 계산에 있어서 행렬 연산은 modulo 2로 계산되어야 합니다. Rank는 스태빌라이저에 의해 강제된 고유한 조건의 실제 개수를 알려줍니다.\\n\",\n \"\\n\",\n \"보통 양자 스태빌라이저 코드에서는:\\n\",\n \"코드에서 쓰이는 물리 정보 큐비트의 숫자가 $n$ 이고 독립적인 스태빌라이저 낳음 연산자의 총 숫자가 $r$ 이면 코드에 인코딩되는 논리 큐비트의 숫자 ($k$)는 다음과 같이 주어집니다:\\n\",\n \"$$k = n - r$$\\n\",\n \"각 독립적인 스태빌라이저 낳음 연산자는 힐베르트 공간을 양분하므로, 하나의 자유도를 고정합니다. 따라서 $r$ 개의 독립적인 스태빌라이저는 $n$ 물리 큐비트의 $2^n$ 차원 공간을 $2^{n-r}$ 차원 코드 공간으로 줄입니다. 이러한 코드 공간은 $k = n-r$ 개의 논리 큐비트를 인코딩할 수 있습니다.\\n\",\n \"\\n\",\n \"Toric과 Gross 코드와 같은 CSS 코드에서는 X 스태빌라이저와 Z 스태빌라이저는 두 개의 서로 다른 교환 가능한 군을 이룹니다. 이는 그들의 독립적인 낳음 연산자를 개별적으로 셀 수 있게 해줍니다.\\n\",\n \"만약 $r_X$ 가 개별적인 X 스태빌라이저의 숫자이고 $r_Z$ 가 개별적인 Z 스태빌라이저의 숫자이면 CSS 코드에 인코딩된 논리 큐비트의 숫자는 다음과 같습니다:\\n\",\n \"$$k = n - r_X - r_Z$$\\n\",\n \"$r_X$ ($r_Z$) 는 각 행이 X (Z) 스태빌라이저 낳음 연산자를 이루는 빨간색 (초록색) 사각형의 $H_X$ ($H_Z$) 행렬의 rank를 구함으로써 얻어집니다.\\n\",\n \"이 공식은 X-스태빌라이저와 Z-스태빌라이저가 서로에 대해 모두 교환 가능하도록 골라졌고, 코드 공간에 강제되는 독립적인 조건의 총 숫자가 독립적인 X와 Z 조건의 숫자들의 합과 같기 때문에 성립합니다.\\n\",\n \"\\n\",\n \"