qiskit.aqua.algorithms.NumPyEigensolver
class NumPyEigensolver(operator=None, k=1, aux_operators=None, filter_criterion=None)
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.
__init__
__init__(operator=None, k=1, aux_operators=None, filter_criterion=None)
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
__init__([operator, k, aux_operators, …]) | type operatorUnion[OperatorBase, LegacyBaseOperator, None] |
compute_eigenvalues([operator, aux_operators]) | Computes eigenvalues. |
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 |
k | returns k (number of eigenvalues requested) |
operator | Return the operator. |
random | Return a numpy random. |
aux_operators
Returns the auxiliary operators.
Return type
Optional[List[Optional[OperatorBase]]]
compute_eigenvalues
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
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 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.
Return type
bool
Returns
True if aux_operator expectations can be evaluated, False otherwise