def exec_sampling():
for _ in range(num_reads):
_exec_time, state = measure_time(hubo_simulated_annealing)(
bhom, init_state, schedule, var_type=var_type)
execution_time.append(_exec_time)
response.states.append(state)
response.energies.append(bhom.calc_energy(state))
def exec_sampling():
previous_state = _generate_init_state()
for _ in range(self.iteration):
if reinitilize_state:
sa_system.reset_spins(_generate_init_state())
else:
sa_system.reset_spins(previous_state)
_exec_time = measure_time(simulated_annealing)(sa_system)
execution_time.append(_exec_time)
previous_state = cxxjij.result.get_solution(sa_system)
states.append(previous_state)
energies.append(model.calc_energy(
previous_state,
need_to_convert_from_spin=True))
def exec_sampling():
# import pdb
# pdb.set_trace()
previous_state = _generate_init_state()
for _ in range(self.iteration):
if reinitilize_state:
sqa_system.reset_spins(_generate_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))