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Código fonte de qiskit.algorithms.minimum_eigen_solvers.minimum_eigen_solver

# This code is part of Qiskit.
#
# (C) Copyright IBM 2020, 2022.
#
# 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.

"""The Minimum Eigensolver interface"""

from abc import ABC, abstractmethod
from typing import Optional, Tuple

import numpy as np

from qiskit.opflow import OperatorBase
from qiskit.utils.deprecation import deprecate_function
from ..algorithm_result import AlgorithmResult
from ..list_or_dict import ListOrDict


[documentos]class MinimumEigensolver(ABC): """Pending deprecation: Minimum Eigensolver Interface. The Minimum Eigensolver interface has been superseded by the :class:`qiskit.algorithms.minimum_eigensolvers.MinimumEigensolver` interface. This interface will be deprecated in a future release and subsequently removed after that. Algorithms that can compute a minimum eigenvalue for an operator may implement this interface to allow different algorithms to be used interchangeably. """ @deprecate_function( "The Minimum Eigensolver interface has been superseded by the " "qiskit.algorithms.minimum_eigensolvers.MinimumEigensolver interface. " "This interface will be deprecated in a future release and subsequently " "removed after that.", category=PendingDeprecationWarning, ) def __init__(self) -> None: pass
[documentos] @abstractmethod def compute_minimum_eigenvalue( self, operator: OperatorBase, aux_operators: Optional[ListOrDict[OperatorBase]] = None ) -> "MinimumEigensolverResult": """ Computes minimum eigenvalue. Operator and aux_operators can be supplied here and if not None will override any already set into algorithm so it can be reused with different operators. While an operator is required by algorithms, aux_operators are optional. To 'remove' a previous aux_operators array use an empty list here. Args: operator: Qubit operator of the Observable aux_operators: Optional list of auxiliary operators to be evaluated with the eigenstate of the minimum eigenvalue main result and their expectation values returned. For instance in chemistry these can be dipole operators, total particle count operators so we can get values for these at the ground state. Returns: MinimumEigensolverResult """ return MinimumEigensolverResult()
[documentos] @classmethod def supports_aux_operators(cls) -> bool: """Whether computing the expectation value of auxiliary operators is supported. If the minimum eigensolver computes an eigenstate of the main operator then it can compute the expectation value of the aux_operators for that state. Otherwise they will be ignored. Returns: True if aux_operator expectations can be evaluated, False otherwise """ return False
[documentos]class MinimumEigensolverResult(AlgorithmResult): """Pending deprecation: Minimum Eigensolver Result. The MinimumEigensolverResult class has been superseded by the :class:`qiskit.algorithms.minimum_eigensolvers.MinimumEigensolverResult` class. This class will be deprecated in a future release and subsequently removed after that. """ @deprecate_function( "The MinimumEigensolverResult class has been superseded by the " "qiskit.algorithms.minimum_eigensolvers.MinimumEigensolverResult class. " "This class will be deprecated in a future release and subsequently " "removed after that.", category=PendingDeprecationWarning, ) def __init__(self) -> None: super().__init__() self._eigenvalue = None self._eigenstate = None self._aux_operator_eigenvalues = None @property def eigenvalue(self) -> Optional[complex]: """returns eigen value""" return self._eigenvalue @eigenvalue.setter def eigenvalue(self, value: complex) -> None: """set eigen value""" self._eigenvalue = value @property def eigenstate(self) -> Optional[np.ndarray]: """return eigen state""" return self._eigenstate @eigenstate.setter def eigenstate(self, value: np.ndarray) -> None: """set eigen state""" self._eigenstate = value @property def aux_operator_eigenvalues(self) -> Optional[ListOrDict[Tuple[complex, complex]]]: """Return aux operator expectation values. These values are in fact tuples formatted as (mean, standard deviation). """ return self._aux_operator_eigenvalues @aux_operator_eigenvalues.setter def aux_operator_eigenvalues(self, value: ListOrDict[Tuple[complex, complex]]) -> None: """set aux operator eigen values""" self._aux_operator_eigenvalues = value