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legacy_tutorials/aqua/Aqua_how_to_build_a_pluggable_algorithm_components.ipynb

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"0.7182824381334073\n"
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"0.39490096089606685\n"
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}
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{
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"<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.11.0.dev0+4164568</td></tr><tr><td>Aer</td><td>0.4.0</td></tr><tr><td>Ignis</td><td>0.3.0.dev0+6145a4e</td></tr><tr><td>Aqua</td><td>0.7.0.dev0+2d7a26f</td></tr><tr><td>IBM Q Provider</td><td>0.4.0rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
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"[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>8.0</td></tr><tr><td colspan='2'>Mon Nov 11 17:29:56 2019 EST</td></tr></table>"
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"<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.14.1</td></tr><tr><td>Aer</td><td>0.5.1</td></tr><tr><td>Ignis</td><td>0.3.0</td></tr><tr><td>Aqua</td><td>0.7.0</td></tr><tr><td>IBM Q Provider</td><td>0.7.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
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"[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>8.0</td></tr><tr><td colspan='2'>Mon May 04 09:17:45 2020 EDT</td></tr></table>"
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"<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
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"<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2020.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
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legacy_tutorials/aqua/amplitude_estimation.ipynb

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legacy_tutorials/aqua/chemistry/dissociation_profile_of_molecule.ipynb

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legacy_tutorials/aqua/chemistry/programmatic_approach.ipynb

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legacy_tutorials/aqua/evolutionfidelity/evolutionfidelity/__init__.py

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# -*- coding: utf-8 -*-
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# Copyright 2018 IBM.
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# This code is part of Qiskit.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# (C) Copyright IBM 2018, 2020.
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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# This code is licensed under the Apache License, Version 2.0. You may
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# obtain a copy of this license in the LICENSE.txt file in the root directory
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# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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# Any modifications or derivative works of this code must retain this
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# copyright notice, and modified files need to carry a notice indicating
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# that they have been altered from the originals.
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from .evolutionfidelity import EvolutionFidelity
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legacy_tutorials/aqua/evolutionfidelity/evolutionfidelity/evolutionfidelity.py

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# -*- coding: utf-8 -*-
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# Copyright 2018 IBM.
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# This code is part of Qiskit.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# (C) Copyright IBM 2018, 2020.
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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# This code is licensed under the Apache License, Version 2.0. You may
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# obtain a copy of this license in the LICENSE.txt file in the root directory
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# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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# Any modifications or derivative works of this code must retain this
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# copyright notice, and modified files need to carry a notice indicating
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# that they have been altered from the originals.
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"""
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The Fidelity of Quantum Evolution.
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This is a simple tutorial example to show how to build an algorithm to extend
2017
Qiskit Aqua library.
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"""
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from typing import Optional, Union
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import logging
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import numpy as np
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from qiskit import QuantumRegister
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from qiskit.quantum_info import state_fidelity
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from qiskit.providers import BaseBackend
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from qiskit.aqua import QuantumInstance
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from qiskit.aqua.algorithms import QuantumAlgorithm
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from qiskit.aqua.operators import op_converter
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from qiskit.aqua.components.initial_states import InitialState
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from qiskit.aqua.operators.legacy import LegacyBaseOperator
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logger = logging.getLogger(__name__)
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class EvolutionFidelity(QuantumAlgorithm):
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CONFIGURATION = {
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}
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def __init__(self, operator, initial_state, expansion_order=1):
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self.validate(locals())
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super().__init__()
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def __init__(self,
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operator: LegacyBaseOperator,
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initial_state: InitialState,
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expansion_order: int = 1,
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quantum_instance: Optional[Union[QuantumInstance, BaseBackend]] = None) -> None:
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super().__init__(quantum_instance)
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self._operator = operator
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self._initial_state = initial_state
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self._expansion_order = expansion_order

legacy_tutorials/aqua/finance/data_providers/time_series.ipynb

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legacy_tutorials/aqua/finance/machine_learning/qgan_option_pricing.ipynb

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legacy_tutorials/aqua/finance/optimization/portfolio_diversification.ipynb

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legacy_tutorials/aqua/finance/optimization/portfolio_optimization.ipynb

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"source": [
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"# _*Qiskit Finance: Portfolio Optimization*_ \n",
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"\n",
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"The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
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"The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorials.\n",
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"\n",
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"***\n",
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"### Contributors\n",
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"outputs": [],
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"source": [
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"from qiskit import BasicAer\n",
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"from qiskit.circuit.library import TwoLocal\n",
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"from qiskit.aqua import QuantumInstance\n",
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"from qiskit.finance.ising import portfolio\n",
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"from qiskit.optimization.ising.common import sample_most_likely\n",
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"from qiskit.finance.applications.ising import portfolio\n",
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"from qiskit.optimization.applications.ising.common import sample_most_likely\n",
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"from qiskit.finance.data_providers import RandomDataProvider\n",
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"from qiskit.aqua.algorithms import VQE, QAOA, ExactEigensolver\n",
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"from qiskit.aqua.algorithms import VQE, QAOA, NumPyMinimumEigensolver\n",
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"from qiskit.aqua.components.optimizers import COBYLA\n",
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"from qiskit.aqua.components.variational_forms import RY\n",
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"import numpy as np\n",
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"import datetime"
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]
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" return x\n",
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"\n",
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"def print_result(result):\n",
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" selection = sample_most_likely(result['eigvecs'][0])\n",
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" selection = sample_most_likely(result.eigenstate)\n",
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" value = portfolio.portfolio_value(selection, mu, sigma, q, budget, penalty)\n",
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" print('Optimal: selection {}, value {:.4f}'.format(selection, value))\n",
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"\n",
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" probabilities = np.abs(result['eigvecs'][0])**2\n",
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" \n",
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" eigenvector = result.eigenstate if isinstance(result.eigenstate, np.ndarray) else result.eigenstate.to_matrix()\n",
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" probabilities = np.abs(eigenvector)**2\n",
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" i_sorted = reversed(np.argsort(probabilities))\n",
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" print('\\n----------------- Full result ---------------------')\n",
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" print('selection\\tvalue\\t\\tprobability')\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ExactEigensolver (as a classical reference)\n",
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"### NumPyMinimumEigensolver (as a classical reference)\n",
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"Lets solve the problem. First classically...\n",
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"\n",
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"We can now use the Operator we built above without regard to the specifics of how it was created. We set the algorithm for the ExactEigensolver so we can have a classical reference. The problem is set for 'ising'. Backend is not required since this is computed classically not using quantum computation. The result is returned as a dictionary."
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"We can now use the Operator we built above without regard to the specifics of how it was created. We set the algorithm for the NumPyMinimumEigensolver so we can have a classical reference. The problem is set for 'ising'. Backend is not required since this is computed classically not using quantum computation. The result is returned as a dictionary."
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Optimal: selection [0. 0. 1. 1.], value -0.0196\n",
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"Optimal: selection [1 0 0 1], value 0.0041\n",
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"\n",
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"----------------- Full result ---------------------\n",
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"selection\tvalue\t\tprobability\n",
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"---------------------------------------------------\n",
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" [0 0 1 1]\t-0.0196\t\t1.0000\n",
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" [1 1 1 1]\t15.9850\t\t0.0000\n",
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" [0 1 1 1]\t3.9823\t\t0.0000\n",
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" [1 0 1 1]\t3.9831\t\t0.0000\n",
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" [1 1 0 1]\t4.0007\t\t0.0000\n",
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" [0 1 0 1]\t-0.0018\t\t0.0000\n",
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" [1 0 0 1]\t-0.0014\t\t0.0000\n",
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" [0 0 0 1]\t3.9961\t\t0.0000\n",
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" [1 1 1 0]\t3.9887\t\t0.0000\n",
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" [0 1 1 0]\t-0.0139\t\t0.0000\n",
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" [1 0 1 0]\t-0.0131\t\t0.0000\n",
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" [0 0 1 0]\t3.9843\t\t0.0000\n",
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" [1 1 0 0]\t0.0044\t\t0.0000\n",
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" [0 1 0 0]\t4.0021\t\t0.0000\n",
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" [1 0 0 0]\t4.0023\t\t0.0000\n",
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" [1 0 0 1]\t0.0041\t\t1.0000\n",
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" [1 1 1 1]\t16.0109\t\t0.0000\n",
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" [0 1 1 1]\t4.0083\t\t0.0000\n",
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" [1 0 1 1]\t4.0079\t\t0.0000\n",
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" [0 0 1 1]\t0.0050\t\t0.0000\n",
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" [1 1 0 1]\t4.0071\t\t0.0000\n",
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" [0 1 0 1]\t0.0044\t\t0.0000\n",
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" [0 0 0 1]\t4.0012\t\t0.0000\n",
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" [1 1 1 0]\t4.0095\t\t0.0000\n",
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" [0 1 1 0]\t0.0069\t\t0.0000\n",
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" [1 0 1 0]\t0.0066\t\t0.0000\n",
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" [0 0 1 0]\t4.0038\t\t0.0000\n",
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" [1 1 0 0]\t0.0057\t\t0.0000\n",
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" [0 1 0 0]\t4.0031\t\t0.0000\n",
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" [1 0 0 0]\t4.0029\t\t0.0000\n",
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" [0 0 0 0]\t16.0000\t\t0.0000\n"
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]
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}
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],
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"source": [
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"exact_eigensolver = ExactEigensolver(qubitOp, k=1)\n",
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"exact_eigensolver = NumPyMinimumEigensolver(qubitOp)\n",
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"result = exact_eigensolver.run()\n",
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"\n",
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"print_result(result)"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Optimal: selection [1. 0. 1. 0.], value -0.0131\n",
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"Optimal: selection [0. 0. 1. 1.], value 0.0050\n",
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"\n",
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"----------------- Full result ---------------------\n",
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"selection\tvalue\t\tprobability\n",
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"---------------------------------------------------\n",
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" [1 0 1 0]\t-0.0131\t\t0.6997\n",
252-
" [0 1 0 1]\t-0.0018\t\t0.1837\n",
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" [0 1 1 0]\t-0.0139\t\t0.0634\n",
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" [1 1 0 0]\t0.0044\t\t0.0296\n",
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" [1 0 0 1]\t-0.0014\t\t0.0218\n",
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" [0 0 1 1]\t-0.0196\t\t0.0018\n",
257-
" [1 1 0 1]\t4.0007\t\t0.0000\n",
258-
" [0 1 1 1]\t3.9823\t\t0.0000\n",
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" [0 0 1 0]\t3.9843\t\t0.0000\n",
260-
" [0 1 0 0]\t4.0021\t\t0.0000\n",
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" [0 0 0 0]\t16.0000\t\t0.0000\n",
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" [1 0 0 0]\t4.0023\t\t0.0000\n",
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" [0 0 0 1]\t3.9961\t\t0.0000\n",
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" [1 1 1 0]\t3.9887\t\t0.0000\n",
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" [1 1 1 1]\t15.9850\t\t0.0000\n",
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" [1 0 1 1]\t3.9831\t\t0.0000\n"
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" [0 0 1 1]\t0.0050\t\t0.6656\n",
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" [0 1 0 1]\t0.0044\t\t0.3151\n",
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" [0 1 1 0]\t0.0069\t\t0.0153\n",
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" [1 0 0 1]\t0.0041\t\t0.0029\n",
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" [1 1 0 0]\t0.0057\t\t0.0005\n",
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" [1 0 1 0]\t0.0066\t\t0.0003\n",
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" [1 0 0 0]\t4.0029\t\t0.0001\n",
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" [1 1 1 0]\t4.0095\t\t0.0001\n",
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" [1 1 0 1]\t4.0071\t\t0.0000\n",
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" [1 0 1 1]\t4.0079\t\t0.0000\n",
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" [0 1 0 0]\t4.0031\t\t0.0000\n",
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" [0 1 1 1]\t4.0083\t\t0.0000\n",
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" [0 0 0 1]\t4.0012\t\t0.0000\n",
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" [0 0 1 0]\t4.0038\t\t0.0000\n",
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" [1 1 1 1]\t16.0109\t\t0.0000\n",
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" [0 0 0 0]\t16.0000\t\t0.0000\n"
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]
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}
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],
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"\n",
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"cobyla = COBYLA()\n",
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"cobyla.set_options(maxiter=500)\n",
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"ry = RY(qubitOp.num_qubits, depth=3, entanglement='full')\n",
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"ry = TwoLocal(qubitOp.num_qubits, 'ry', 'cz', reps=3, entanglement='full')\n",
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"vqe = VQE(qubitOp, ry, cobyla)\n",
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"vqe.random_seed = seed\n",
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"\n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Optimal: selection [0. 0. 1. 1.], value -0.0196\n",
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"Optimal: selection [0. 1. 1. 0.], value 0.0069\n",
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"\n",
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"----------------- Full result ---------------------\n",
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"selection\tvalue\t\tprobability\n",
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"---------------------------------------------------\n",
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" [0 0 1 1]\t-0.0196\t\t0.1668\n",
311-
" [0 1 1 0]\t-0.0139\t\t0.1667\n",
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" [1 0 1 0]\t-0.0131\t\t0.1667\n",
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" [0 1 0 1]\t-0.0018\t\t0.1666\n",
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" [1 0 0 1]\t-0.0014\t\t0.1666\n",
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" [1 1 0 0]\t0.0044\t\t0.1665\n",
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" [0 1 1 1]\t3.9823\t\t0.0000\n",
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" [1 0 1 1]\t3.9831\t\t0.0000\n",
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" [1 1 1 0]\t3.9887\t\t0.0000\n",
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" [0 1 0 0]\t4.0021\t\t0.0000\n",
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" [1 0 0 0]\t4.0023\t\t0.0000\n",
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" [0 0 1 0]\t3.9843\t\t0.0000\n",
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" [1 1 1 1]\t15.9850\t\t0.0000\n",
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" [0 0 0 1]\t3.9961\t\t0.0000\n",
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" [1 1 0 1]\t4.0007\t\t0.0000\n",
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" [0 0 0 0]\t16.0000\t\t0.0000\n"
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" [0 1 1 0]\t0.0069\t\t0.1670\n",
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" [1 0 1 0]\t0.0066\t\t0.1669\n",
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" [1 1 0 0]\t0.0057\t\t0.1667\n",
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" [0 0 1 1]\t0.0050\t\t0.1664\n",
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" [0 1 0 1]\t0.0044\t\t0.1662\n",
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" [1 0 0 1]\t0.0041\t\t0.1661\n",
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" [0 0 0 1]\t4.0012\t\t0.0001\n",
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" [1 0 0 0]\t4.0029\t\t0.0001\n",
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" [0 1 0 0]\t4.0031\t\t0.0001\n",
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" [0 0 1 0]\t4.0038\t\t0.0001\n",
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" [1 1 0 1]\t4.0071\t\t0.0001\n",
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" [1 0 1 1]\t4.0079\t\t0.0001\n",
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" [0 1 1 1]\t4.0083\t\t0.0001\n",
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" [1 1 1 0]\t4.0095\t\t0.0001\n",
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" [0 0 0 0]\t16.0000\t\t0.0000\n",
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" [1 1 1 1]\t16.0109\t\t0.0000\n"
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]
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}
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],
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{
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"data": {
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"text/html": [
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"<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.11.0.dev0+4164568</td></tr><tr><td>Aer</td><td>0.4.0</td></tr><tr><td>Ignis</td><td>0.3.0.dev0+6145a4e</td></tr><tr><td>Aqua</td><td>0.7.0.dev0+2d7a26f</td></tr><tr><td>IBM Q Provider</td><td>0.4.0rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
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"[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>8.0</td></tr><tr><td colspan='2'>Mon Nov 11 14:14:33 2019 EST</td></tr></table>"
359+
"<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.14.1</td></tr><tr><td>Aer</td><td>0.5.1</td></tr><tr><td>Ignis</td><td>0.3.0</td></tr><tr><td>Aqua</td><td>0.7.0</td></tr><tr><td>IBM Q Provider</td><td>0.7.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
360+
"[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>8.0</td></tr><tr><td colspan='2'>Wed May 13 21:06:53 2020 EDT</td></tr></table>"
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{
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"<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
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"<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2020.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
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"version": "3.7.3"
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"version": "3.7.4"
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