NumPyEigensolver
class NumPyEigensolver(operator=None, k=1, aux_operators=None, filter_criterion=None)
Bases: qiskit.aqua.algorithms.classical_algorithm.ClassicalAlgorithm, qiskit.aqua.algorithms.eigen_solvers.eigen_solver.Eigensolver
The NumPy Eigensolver algorithm.
NumPy Eigensolver computes up to the first eigenvalues of a complex-valued square matrix of dimension , with .
Operators are automatically converted to MatrixOperator as needed and this conversion can be costly in terms of memory and performance as the operator size, mostly in terms of number of qubits it represents, gets larger.
Parameters
- operator (
Union[OperatorBase,LegacyBaseOperator,None]) – Operator instance. If None is supplied it must be provided later before run() is called. Allowing None here permits the algorithm to be configured and used later when operator is available, say creating an instance an letting application stack use this algorithm with an operator it creates. - k (
int) – How many eigenvalues are to be computed, has a min. value of 1. - aux_operators (
Optional[List[Union[OperatorBase,LegacyBaseOperator,None]]]) – Auxiliary operators to be evaluated at each eigenvalue - filter_criterion (
Optional[Callable[[Union[List,ndarray],float,Optional[List[float]]],bool]]) – callable that allows to filter eigenvalues/eigenstates, only feasible eigenstates are returned in the results. The callable has the signature filter(eigenstate, eigenvalue, aux_values) and must return a boolean to indicate whether to keep this value in the final returned result or not. If the number of elements that satisfies the criterion is smaller than k then the returned list has fewer elements and can even be empty.
Methods
compute_eigenvalues
NumPyEigensolver.compute_eigenvalues(operator=None, aux_operators=None)
Computes eigenvalues. 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
EigensolverResult
Returns
EigensolverResult
run
NumPyEigensolver.run()
Execute the classical algorithm.
Returns
results of an algorithm.
Return type
dict
supports_aux_operators
classmethod NumPyEigensolver.supports_aux_operators()
Whether computing the expectation value of auxiliary operators is supported.
Return type
bool
Returns
True if aux_operator expectations can be evaluated, False otherwise
Attributes
aux_operators
Return type
Optional[List[Optional[OperatorBase]]]
filter_criterion
returns the filter criterion if set
Return type
Optional[Callable[[Union[List, ndarray], float, Optional[List[float]]], bool]]
k
returns k (number of eigenvalues requested)
Return type
int
operator
Return type
Optional[OperatorBase]
random
Return a numpy random.