def _compute_gradients(self, excitation_pool, theta, delta, var_form,
operator, optimizer):
"""
Computes the gradients for all available excitation operators.
Args:
excitation_pool (list): pool of excitation operators
theta (list): list of (up to now) optimal parameters
delta (float): finite difference step size (for gradient computation)
var_form (VariationalForm): current variational form
operator (LegacyBaseOperator): system Hamiltonian
optimizer (Optimizer): classical optimizer algorithm
Returns:
list: List of pairs consisting of gradient and excitation operator.
"""
res = []
# compute gradients for all excitation in operator pool
for exc in excitation_pool:
# push next excitation to variational form
var_form.push_hopping_operator(exc)
# construct auxiliary VQE instance
vqe = VQE(operator, var_form, optimizer)
vqe.quantum_instance = self.quantum_instance
# evaluate energies
parameter_sets = theta + [-delta] + theta + [delta]
energy_results = vqe._energy_evaluation(np.asarray(parameter_sets))
# compute gradient
gradient = (energy_results[0] - energy_results[1]) / (2 * delta)
res.append((np.abs(gradient), exc))
# pop excitation from variational form
var_form.pop_hopping_operator()
return res
def _compute_gradients(
self,
excitation_pool: List[WeightedPauliOperator],
theta: List[float],
vqe: VQE,
) -> List[Tuple[float, WeightedPauliOperator]]:
"""
Computes the gradients for all available excitation operators.
Args:
excitation_pool: pool of excitation operators
theta: list of (up to now) optimal parameters
vqe: the variational quantum eigensolver instance used for solving
Returns:
List of pairs consisting of gradient and excitation operator.
"""
res = []
# compute gradients for all excitation in operator pool
for exc in excitation_pool:
# push next excitation to variational form
vqe.var_form.push_hopping_operator(exc)
# NOTE: because we overwrite the var_form inside of the VQE, we need to update the VQE's
# internal _var_form_params, too. We can do this by triggering the var_form setter. Once
# the VQE does not store this pure var_form property any longer this can be removed.
vqe.var_form = vqe.var_form
# We also need to invalidate the internally stored expectation operator because it needs
# to be updated for the new var_form.
vqe._expect_op = None
# evaluate energies
parameter_sets = theta + [-self._delta] + theta + [self._delta]
energy_results = vqe._energy_evaluation(np.asarray(parameter_sets))
# compute gradient
gradient = (energy_results[0] - energy_results[1]) / (2 *
self._delta)
res.append((np.abs(gradient), exc))
# pop excitation from variational form
vqe.var_form.pop_hopping_operator()
return res
