The NumPy eigensolver algorithm.
The NumPy Eigensolver computes up to the first eigenvalues of a complex-valued square matrix of dimension , with .
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.
- k (int (opens in a new tab)) – Number of eigenvalues are to be computed, with a minimum value of 1.
- filter_criterion (FilterType | None) – 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 will have fewer elements and can even be empty.
Return the filter criterion if set.
Return k (number of eigenvalues requested).
Computes the minimum eigenvalue. The
aux_operators are supplied here. While an
operator is required by algorithms,
aux_operators are optional.
- operator (BaseOperator | PauliSumOp) – Qubit operator of the observable.
- aux_operators (ListOrDict[BaseOperator | PauliSumOp] | None) – Optional list of auxiliary operators to be evaluated with the eigenstate of the minimum eigenvalue main result and their expectation values returned. For instance, in chemistry, these can be dipole operators and total particle count operators, so we can get values for these at the ground state.
An eigensolver result.
Whether computing the expectation value of auxiliary operators is supported.
If the eigensolver computes the eigenvalues of the main operator, then it can compute the expectation value of the
aux_operators for that state. Otherwise they will be ignored.
aux_operator expectations can be evaluated,