NumPyEigensolver¶

class NumPyEigensolver(operator=None, k=1, aux_operators=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 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

Attributes

`NumPyEigensolver.aux_operators`	returns aux operators
`NumPyEigensolver.k`	returns k (number of eigenvalues requested)
`NumPyEigensolver.operator`	returns operator
`NumPyEigensolver.random`	Return a numpy random.

Methods

`NumPyEigensolver.run`()	Execute the classical algorithm.
`NumPyEigensolver.supports_aux_operators`()	If will process auxiliary operators or not