qiskit.aqua.algorithms.NumPyMinimumEigensolver
class NumPyMinimumEigensolver(operator=None, aux_operators=None, filter_criterion=None)
The Numpy Minimum Eigensolver algorithm.
Parameters
- operator (
Union[OperatorBase,LegacyBaseOperator,None]) – Operator instance - aux_operators (
Optional[List[Union[OperatorBase,LegacyBaseOperator,None]]]) – Auxiliary operators to be evaluated at minimum eigenvalue - filter_criterion (
Optional[Callable[[Union[List,ndarray],float,Optional[List[float]]],bool]]) – 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.
__init__
__init__(operator=None, aux_operators=None, filter_criterion=None)
Parameters
- operator (
Union[OperatorBase,LegacyBaseOperator,None]) – Operator instance - aux_operators (
Optional[List[Union[OperatorBase,LegacyBaseOperator,None]]]) – Auxiliary operators to be evaluated at minimum eigenvalue - filter_criterion (
Optional[Callable[[Union[List,ndarray],float,Optional[List[float]]],bool]]) – 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.
Methods
__init__([operator, aux_operators, …]) | type operatorUnion[OperatorBase, LegacyBaseOperator, None] |
compute_minimum_eigenvalue([operator, …]) | Computes minimum eigenvalue. |
run() | Execute the classical algorithm. |
supports_aux_operators() | Whether computing the expectation value of auxiliary operators is supported. |
Attributes
aux_operators | Returns the auxiliary operators. |
filter_criterion | returns the filter criterion if set |
operator | Return the operator. |
random | Return a numpy random. |
aux_operators
Returns the auxiliary operators.
Return type
Optional[List[Optional[OperatorBase]]]
compute_minimum_eigenvalue
compute_minimum_eigenvalue(operator=None, aux_operators=None)
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.
Parameters
- operator (
Union[OperatorBase,LegacyBaseOperator,None]) – If not None replaces operator in algorithm - aux_operators (
Optional[List[Union[OperatorBase,LegacyBaseOperator,None]]]) – If not None replaces aux_operators in algorithm
Return type
MinimumEigensolverResult
Returns
MinimumEigensolverResult
filter_criterion
returns the filter criterion if set
Return type
Optional[Callable[[Union[List, ndarray], float, Optional[List[float]]], bool]]
operator
Return the operator.
Return type
Optional[OperatorBase]
random
Return a numpy random.
run
run()
Execute the classical algorithm.
Returns
results of an algorithm.
Return type
dict
supports_aux_operators
classmethod supports_aux_operators()
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.
Return type
bool
Returns
True if aux_operator expectations can be evaluated, False otherwise