C贸digo fuente para qiskit.algorithms.minimum_eigen_solvers.numpy_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 Numpy Minimum Eigensolver algorithm."""
from __future__ import annotations

import logging
import warnings
from collections.abc import Callable

import numpy as np

from qiskit.opflow import OperatorBase
from qiskit.utils.deprecation import deprecate_func
from ..eigen_solvers.numpy_eigen_solver import NumPyEigensolver
from .minimum_eigen_solver import MinimumEigensolver, MinimumEigensolverResult
from ..list_or_dict import ListOrDict

logger = logging.getLogger(__name__)

[documentos]class NumPyMinimumEigensolver(MinimumEigensolver): """ Deprecated: Numpy Minimum Eigensolver algorithm. The NumPyMinimumEigensolver class has been superseded by the :class:`qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver` class. This class will be deprecated in a future release and subsequently removed after that. """ @deprecate_func( additional_msg=( "Instead, use the class " "``qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver``. " "See https://qisk.it/algo_migration for a migration guide." ), since="0.24.0", ) def __init__( self, filter_criterion: Callable[ [list | np.ndarray, float, ListOrDict[float] | None], bool ] = None, ) -> None: """ Args: filter_criterion: callable that allows to filter eigenvalues/eigenstates. The minimum eigensolver is only searching over feasible states and returns an eigenstate that has the smallest eigenvalue among feasible states. The callable has the signature `filter(eigenstate, eigenvalue, aux_values)` and must return a boolean to indicate whether to consider this value or not. If there is no feasible element, the result can even be empty. """ with warnings.catch_warnings(): warnings.simplefilter("ignore") super().__init__() self._ces = NumPyEigensolver(filter_criterion=filter_criterion) self._ret = MinimumEigensolverResult() @property def filter_criterion( self, ) -> Callable[[list | np.ndarray, float, ListOrDict[float] | None], bool] | None: """returns the filter criterion if set""" return self._ces.filter_criterion @filter_criterion.setter def filter_criterion( self, filter_criterion: Callable[[list | np.ndarray, float, ListOrDict[float] | None], bool] | None, ) -> None: """set the filter criterion""" self._ces.filter_criterion = filter_criterion
[documentos] @classmethod def supports_aux_operators(cls) -> bool: return NumPyEigensolver.supports_aux_operators()
[documentos] def compute_minimum_eigenvalue( self, operator: OperatorBase, aux_operators: ListOrDict[OperatorBase] | None = None ) -> MinimumEigensolverResult: super().compute_minimum_eigenvalue(operator, aux_operators) result_ces = self._ces.compute_eigenvalues(operator, aux_operators) self._ret = MinimumEigensolverResult() if result_ces.eigenvalues is not None and len(result_ces.eigenvalues) > 0: self._ret.eigenvalue = result_ces.eigenvalues[0] self._ret.eigenstate = result_ces.eigenstates[0] if result_ces.aux_operator_eigenvalues: self._ret.aux_operator_eigenvalues = result_ces.aux_operator_eigenvalues[0] logger.debug("MinimumEigensolver:\n%s", self._ret) return self._ret