# Quellcode fÃ¼r qiskit.algorithms.eigensolvers.eigensolver

```# This code is part of Qiskit.
#
#
# obtain a copy of this license in the LICENSE.txt file in the root directory
#
# 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 eigensolver interface and result."""

from __future__ import annotations

from abc import ABC, abstractmethod
from typing import Any
import numpy as np

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

[Doku]class Eigensolver(ABC):
"""The eigensolver interface.

Algorithms that can compute eigenvalues for an operator
may implement this interface to allow different algorithms to be
used interchangeably.
"""

[Doku]    @abstractmethod
def compute_eigenvalues(
self,
operator: BaseOperator | PauliSumOp,
aux_operators: ListOrDict[BaseOperator | PauliSumOp] | None = None,
) -> "EigensolverResult":
"""
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
eigenstate 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:
An eigensolver result.
"""
return EigensolverResult()

[Doku]    @classmethod
def supports_aux_operators(cls) -> bool:
"""Whether computing the expectation value of auxiliary operators is supported.

If the eigensolver computes the eigenvalues 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

[Doku]class EigensolverResult(AlgorithmResult):
"""Eigensolver result."""

def __init__(self) -> None:
super().__init__()
self._eigenvalues: np.ndarray | None = None
self._aux_operators_evaluated: list[
ListOrDict[tuple[complex, dict[str, Any]]]
] | None = None

@property
def eigenvalues(self) -> np.ndarray | None:
"""Return the eigenvalues."""
return self._eigenvalues

@eigenvalues.setter
def eigenvalues(self, value: np.ndarray) -> None:
"""Set the eigenvalues."""
self._eigenvalues = value

@property
def aux_operators_evaluated(
self,
) -> list[ListOrDict[tuple[complex, dict[str, Any]]]] | None:
"""Return the aux operator expectation values.

These values are in fact tuples formatted as (mean, metadata).
"""
return self._aux_operators_evaluated

@aux_operators_evaluated.setter
def aux_operators_evaluated(
self, value: list[ListOrDict[tuple[complex, dict[str, Any]]]]
) -> None:
"""Set the aux operator eigenvalues."""
self._aux_operators_evaluated = value
```