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qiskit.quantum_info.analysis.distance의 소스 코드

# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""A collection of discrete probability metrics."""
import numpy as np


[문서]def hellinger_fidelity(dist_p, dist_q): """Computes the Hellinger fidelity between two counts distributions. The fidelity is defined as 1-H where H is the Hellinger distance. This value is bounded in the range [0, 1]. Parameters: dist_p (dict): First dict of counts. dist_q (dict): Second dict of counts. Returns: float: Fidelity Example: .. jupyter-execute:: from qiskit import QuantumCircuit, execute, BasicAer from qiskit.quantum_info.analysis import hellinger_fidelity qc = QuantumCircuit(5, 5) qc.h(2) qc.cx(2, 1) qc.cx(2, 3) qc.cx(3, 4) qc.cx(1, 0) qc.measure(range(5), range(5)) sim = BasicAer.get_backend('qasm_simulator') res1 = execute(qc, sim).result() res2 = execute(qc, sim).result() hellinger_fidelity(res1.get_counts(), res2.get_counts()) """ p_sum = sum(dist_p.values()) q_sum = sum(dist_q.values()) p_normed = {} for key, val in dist_p.items(): p_normed[key] = val/p_sum q_normed = {} for key, val in dist_q.items(): q_normed[key] = val/q_sum total = 0 for key, val in p_normed.items(): if key in q_normed.keys(): total += (np.sqrt(val) - np.sqrt(q_normed[key]))**2 del q_normed[key] else: total += val total += sum(q_normed.values()) dist = np.sqrt(total)/np.sqrt(2) return 1-dist

© Copyright 2020, Qiskit Development Team. 최종 업데이트: 2021/01/22

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