# Source code for qiskit.algorithms.minimum_eigen_solvers.minimum_eigen_solver

```
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# 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 Dict, Optional, List, Union, Tuple, TypeVar
import numpy as np
from qiskit.opflow import OperatorBase
from ..algorithm_result import AlgorithmResult
# Introduced new type to maintain readability.
_T = TypeVar("_T") # Pylint does not allow single character class names.
ListOrDict = Union[List[Optional[_T]], Dict[str, _T]]
[docs]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.
"""
[docs] @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()
[docs] @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
[docs]class MinimumEigensolverResult(AlgorithmResult):
"""Minimum Eigensolver Result."""
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
```