C贸digo fuente para qiskit.algorithms.minimum_eigensolvers.minimum_eigensolver

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"""The minimum eigensolver interface and result."""

from __future__ import annotations

from abc import ABC, abstractmethod
from typing import Any

from qiskit.opflow import PauliSumOp
from qiskit.quantum_info.operators.base_operator import BaseOperator

from ..algorithm_result import AlgorithmResult
from ..list_or_dict import ListOrDict

[documentos]class MinimumEigensolver(ABC): """The minimum eigensolver interface. Algorithms that can compute a minimum eigenvalue for an operator may implement this interface to allow different algorithms to be used interchangeably. """
[documentos] @abstractmethod def compute_minimum_eigenvalue( self, operator: BaseOperator | PauliSumOp, aux_operators: ListOrDict[BaseOperator | PauliSumOp] | None = None, ) -> "MinimumEigensolverResult": """ Computes the minimum eigenvalue. The ``operator`` and ``aux_operators`` are supplied here. While an ``operator`` is required by algorithms, ``aux_operators`` are optional. Args: operator: Qubit operator of the observable. aux_operators: Optional list of auxiliary operators to be evaluated with the parameters of the minimum eigenvalue main result and their expectation values returned. For instance in chemistry these can be dipole operators and total particle count operators, so we can get values for these at the ground state. Returns: A minimum eigensolver result. """ 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 eigenvalue 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): """Minimum eigensolver result.""" def __init__(self) -> None: super().__init__() self._eigenvalue: complex | None = None self._aux_operators_evaluated: ListOrDict[tuple[complex, dict[str, Any]]] | None = None @property def eigenvalue(self) -> complex | None: """The computed minimum eigenvalue.""" return self._eigenvalue @eigenvalue.setter def eigenvalue(self, value: complex) -> None: self._eigenvalue = value @property def aux_operators_evaluated(self) -> ListOrDict[tuple[complex, dict[str, Any]]] | None: """The aux operator expectation values. These values are in fact tuples formatted as (mean, (variance, shots)). """ return self._aux_operators_evaluated @aux_operators_evaluated.setter def aux_operators_evaluated(self, value: ListOrDict[tuple[complex, dict[str, Any]]]) -> None: self._aux_operators_evaluated = value