# NumPyEigensolver¶

class NumPyEigensolver(k=1, filter_criterion=None)[source]

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 SciPy’s spmatrix 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
• k (int) – How many eigenvalues are to be computed, has a min. value of 1.

• filter_criterion (Optional[Callable[[Union[List, ndarray], float, Union[List[Optional[float]], Dict[str, float], None]], 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 Computes eigenvalues. supports_aux_operators Whether computing the expectation value of auxiliary operators is supported.

Attributes

filter_criterion

returns the filter criterion if set

Return type

Optional[Callable[[Union[List, ndarray], float, Union[List[Optional[float]], Dict[str, float], None]], bool]]

k

returns k (number of eigenvalues requested)

Return type

int