# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""The Eigensolver algorithm."""
from typing import List, Optional, Union
import logging
import pprint
import warnings
import numpy as np
from scipy import sparse as scisparse
from qiskit.aqua import AquaError
from qiskit.aqua.algorithms import ClassicalAlgorithm
from qiskit.aqua.operators import OperatorBase, LegacyBaseOperator, I, StateFn, ListOp
from qiskit.aqua.utils.validation import validate_min
from .eigen_solver_result import EigensolverResult
logger = logging.getLogger(__name__)
# pylint: disable=invalid-name
[docs]class NumPyEigensolver(ClassicalAlgorithm):
r"""
The NumPy Eigensolver algorithm.
NumPy Eigensolver computes up to the first :math:`k` eigenvalues of a complex-valued square
matrix of dimension :math:`n \times n`, with :math:`k \leq n`.
Note:
Operators are automatically converted to :class:`~qiskit.aqua.operators.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.
"""
def __init__(self,
operator: Optional[Union[OperatorBase, LegacyBaseOperator]] = None,
k: int = 1,
aux_operators: Optional[List[Optional[Union[OperatorBase,
LegacyBaseOperator]]]] = None
) -> None:
"""
Args:
operator: 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: How many eigenvalues are to be computed, has a min. value of 1.
aux_operators: Auxiliary operators to be evaluated at each eigenvalue
"""
validate_min('k', k, 1)
super().__init__()
self._operator = None
self._aux_operators = None
self._in_k = k
self._k = k
self.operator = operator
self.aux_operators = aux_operators
self._ret = {}
@property
def operator(self) -> Optional[OperatorBase]:
""" returns operator """
return self._operator
@operator.setter
def operator(self, operator: Union[OperatorBase, LegacyBaseOperator]) -> None:
""" set operator """
if isinstance(operator, LegacyBaseOperator):
operator = operator.to_opflow()
if operator is None:
self._operator = None
else:
self._operator = operator
self._check_set_k()
@property
def aux_operators(self) -> Optional[List[Optional[OperatorBase]]]:
""" returns aux operators """
return self._aux_operators
@aux_operators.setter
def aux_operators(self,
aux_operators: Optional[List[Optional[Union[OperatorBase,
LegacyBaseOperator]]]]) -> None:
""" set aux operators """
if aux_operators is None:
self._aux_operators = []
else:
aux_operators = \
[aux_operators] if not isinstance(aux_operators, list) else aux_operators
converted = [op.to_opflow() if op is not None else None for op in aux_operators]
# Chemistry passes aux_ops with 0 qubits and paulis sometimes
zero_op = I.tensorpower(self.operator.num_qubits) * 0.0
converted = [zero_op if op == 0 else op for op in converted]
self._aux_operators = converted
@property
def k(self) -> int:
""" returns k (number of eigenvalues requested) """
return self._in_k
@k.setter
def k(self, k: int) -> int:
""" set k (number of eigenvalues requested) """
validate_min('k', k, 1)
self._in_k = k
self._check_set_k()
[docs] def supports_aux_operators(self) -> bool:
""" If will process auxiliary operators or not """
return True
def _check_set_k(self):
if self._operator is not None:
if self._in_k > 2**(self._operator.num_qubits):
self._k = 2**(self._operator.num_qubits)
logger.debug("WARNING: Asked for %s eigenvalues but max possible is %s.",
self._in_k, self._k)
else:
self._k = self._in_k
def _solve(self):
sp_mat = self._operator.to_spmatrix()
# If matrix is diagonal, the elements on the diagonal are the eigenvalues. Solve by sorting.
if scisparse.csr_matrix(sp_mat.diagonal()).nnz == sp_mat.nnz:
diag = sp_mat.diagonal()
eigval = np.sort(diag)[:self._k]
temp = np.argsort(diag)[:self._k]
eigvec = np.zeros((sp_mat.shape[0], self._k))
for i, idx in enumerate(temp):
eigvec[idx, i] = 1.0
else:
if self._k >= 2**(self._operator.num_qubits) - 1:
logger.debug("SciPy doesn't support to get all eigenvalues, using NumPy instead.")
eigval, eigvec = np.linalg.eig(self._operator.to_matrix())
else:
eigval, eigvec = scisparse.linalg.eigs(self._operator.to_spmatrix(),
k=self._k, which='SR')
if self._k > 1:
idx = eigval.argsort()
eigval = eigval[idx]
eigvec = eigvec[:, idx]
self._ret['eigvals'] = eigval
self._ret['eigvecs'] = eigvec.T
def _get_ground_state_energy(self):
if 'eigvals' not in self._ret or 'eigvecs' not in self._ret:
self._solve()
self._ret['energy'] = self._ret['eigvals'][0].real
self._ret['wavefunction'] = self._ret['eigvecs']
def _get_energies(self):
if 'eigvals' not in self._ret or 'eigvecs' not in self._ret:
self._solve()
energies = np.empty(self._k)
for i in range(self._k):
energies[i] = self._ret['eigvals'][i].real
self._ret['energies'] = energies
if self._aux_operators:
aux_op_vals = []
for i in range(self._k):
aux_op_vals.append(self._eval_aux_operators(self._ret['eigvecs'][i]))
self._ret['aux_ops'] = aux_op_vals
def _eval_aux_operators(self, wavefn, threshold=1e-12):
values = []
for operator in self._aux_operators:
if operator is None:
values.append(None)
continue
value = 0.0
if operator.coeff != 0:
mat = operator.to_spmatrix()
# Terra doesn't support sparse yet, so do the matmul directly if so
# This is necessary for the particle_hole and other chemistry tests because the
# pauli conversions are 2^12th large and will OOM error if not sparse.
if isinstance(mat, scisparse.spmatrix):
value = mat.dot(wavefn).dot(np.conj(wavefn))
else:
value = StateFn(operator, is_measurement=True).eval(wavefn)
value = value.real if abs(value.real) > threshold else 0.0
values.append((value, 0))
return np.asarray(values)
def _run(self):
"""
Run the algorithm to compute up to the requested k number of eigenvalues.
Returns:
dict: Dictionary of results
Raises:
AquaError: if no operator has been provided
"""
if self._operator is None:
raise AquaError("Operator was never provided")
self._ret = {}
self._solve()
self._get_ground_state_energy()
self._get_energies()
logger.debug('NumPyEigensolver _run result:\n%s',
pprint.pformat(self._ret, indent=4))
result = EigensolverResult()
if 'eigvals' in self._ret:
result.eigenvalues = self._ret['eigvals']
if 'eigvecs' in self._ret:
result.eigenstates = ListOp([StateFn(vec) for vec in self._ret['eigvecs']])
if 'aux_ops' in self._ret:
result.aux_operator_eigenvalues = self._ret['aux_ops']
logger.debug('EigensolverResult dict:\n%s',
pprint.pformat(result.data, indent=4))
return result
class ExactEigensolver(NumPyEigensolver):
"""
The deprecated Eigensolver algorithm.
"""
def __init__(self, operator: LegacyBaseOperator, k: int = 1,
aux_operators: Optional[List[LegacyBaseOperator]] = None) -> None:
warnings.warn('Deprecated class {}, use {}.'.format('ExactEigensolver',
'NumPyEigensolver'),
DeprecationWarning)
super().__init__(operator, k, aux_operators)