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 \(k\) eigenvalues of a complex-valued square matrix of dimension \(n \times n\), with \(k \leq n\).

Note

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__(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 operator Union[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.

property aux_operators

Returns the auxiliary operators.

Return type

Optional[List[Optional[OperatorBase]]]

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

property filter_criterion

returns the filter criterion if set

Return type

Optional[Callable[[Union[List, ndarray], float, Optional[List[float]]], bool]]

property k

returns k (number of eigenvalues requested)

Return type

int

property operator

Return the operator.

Return type

Optional[OperatorBase]

property random

Return a numpy random.

run()

Execute the classical algorithm.

Returns

results of an algorithm.

Return type

dict

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