- class NumPyEigensolver(k=1, filter_criterion=None)[ソース]¶
Deprecated: NumPy Eigensolver algorithm.
The NumPyEigensolver class has been superseded by the
qiskit.algorithms.eigensolvers.NumPyEigensolverclass. This class will be deprecated in a future release and subsequently removed after that.
NumPy Eigensolver computes up to the first \(k\) eigenvalues of a complex-valued square matrix of dimension \(n \times n\), with \(k \leq n\).
Operators are automatically converted to SciPy’s
spmatrixas 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.
バージョン 0.24.0 で非推奨: The class
qiskit.algorithms.eigen_solvers.numpy_eigen_solver.NumPyEigensolveris deprecated as of qiskit-terra 0.24.0. It will be removed no earlier than 3 months after the release date. Instead, use the class
qiskit.algorithms.eigensolvers.NumPyEigensolver. See https://qisk.it/algo_migration for a migration guide.
k (int) – How many eigenvalues are to be computed, has a min. value of 1.
filter_criterion (Callable[[list | np.ndarray, float, ListOrDict[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.
Whether computing the expectation value of auxiliary operators is supported.
returns the filter criterion if set
returns k (number of eigenvalues requested)