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def exec_sampling():
previous_state = _init_state()
for _ in range(self.iteration):
if reinitilize_state:
sqa_system.reset_spins(_init_state())
else:
sqa_system.reset_spins(previous_state)
_exec_time = measure_time(sqa_algorithm)(sqa_system)
execution_time.append(_exec_time)
# trotter_spins is transposed because it is stored as [Spin space][Trotter].
# [-1] is excluded because it is a tentative spin of s = 1 for convenience in SQA.
q_state = self._post_process4state(
sqa_system.trotter_spins[:-1].T)
q_energies.append(
[model.calc_energy(state,
need_to_convert_from_spin=True)
for state in q_state])
q_states.append(q_state.astype(np.int))
@measure_time
def exec_sampling():
previous_state = _init_state()
for _ in range(self.iteration):
if reinitilize_state:
sqa_system.reset_spins(_init_state())
else:
sqa_system.reset_spins(previous_state)
_exec_time = measure_time(sqa_algorithm)(sqa_system)
execution_time.append(_exec_time)
# trotter_spins is transposed because it is stored as [Spin space][Trotter].
# [-1] is excluded because it is a tentative spin of s = 1 for convenience in SQA.
q_state = self._post_process4state(
sqa_system.trotter_spins[:-1].T)
q_energies.append(
[model.calc_energy(state,
need_to_convert_from_spin=True)