def run_SD(parameters, model: MODEL, utils, root, save=True):
if parameters['verbose'] == 0:
blockPrint()
outfile = utils.make_file_name(parameters, root=root)
n_exist_sample, all_result = utils.read_current_results(outfile)
n_sample = parameters['n_sample']
if n_exist_sample >= n_sample:
print(
"\n\n-----------> Samples already computed in file -- terminating ... <-----------"
)
return all_result
print("\n\n-----------> Starting stochastic descent <-----------")
n_iteration_left = n_sample - n_exist_sample # data should be saved 10 times --> no more (otherwise things are way too slow !)
n_mod = max([1, n_iteration_left // 10])
for it in range(n_iteration_left):
start_time = time.time()
if parameters['task'] == 'SD':
best_fid, best_protocol, n_fid_eval = SD(
parameters, model,
init=True) # -- --> performing stochastic descent here <-- --
elif parameters['task'] == 'SD2':
best_fid, best_protocol, n_fid_eval = SD_2SF(
parameters, model, init=True
) # -- --> performing 2 spin flip stochastic descent here <-- --
else:
assert False, 'Error in task specification'
energy, delta_energy, Sent = model.compute_observables(
protocol=best_protocol)
result = [
n_fid_eval, best_fid, energy, delta_energy, Sent, best_protocol
]
print(
"\n----------> RESULT FOR STOCHASTIC DESCENT NO %i <-------------"
% (it + 1))
print("Number of fidelity eval \t%i" % n_fid_eval)
print("Best fidelity \t\t\t%.16f" % best_fid)
print("Best hx_protocol\t\t", list(best_protocol))
all_result.append(result)
if save and it % n_mod == 0:
with open(outfile, 'wb') as f:
pickle.dump([parameters, all_result], f)
f.close()
print("Saved iteration --> %i to %s" %
(it + n_exist_sample, outfile))
print("Iteration run time --> %.4f s" % (time.time() - start_time))
print("\n Thank you and goodbye !")
enablePrint()
if save:
with open(outfile, 'wb') as f:
pickle.dump([parameters, all_result], f)
f.close()
return all_result
def run_SA(parameters, model: MODEL, utils, root, save=True):
if parameters['verbose'] == 0:
blockPrint()
outfile = utils.make_file_name(parameters, root=root)
n_exist_sample, optimal_index, all_result = utils.read_current_results(
outfile)
n_sample = parameters['n_sample']
if optimal_index is not None:
best_seen_fid = all_result[optimal_index][1]
else:
best_seen_fid = 0.0
if parameters['Ti'] < 0.:
parameters['Ti'] = compute_initial_Ti(parameters, model, n_sample=1000)
print("Initial temperature Ti=%.3f" % parameters['Ti'])
if n_exist_sample >= n_sample:
print(
"\n\n-----------> Samples already computed in file -- terminating ... <-----------"
)
print(
"\n\n-------> Best encountered fidelity over all samples is %0.8f <-------"
% (best_seen_fid))
print("\n\n-------> Best encountered hx_protocol over all samples:")
print(list(all_result[optimal_index][5]))
print("<-------")
return all_result
print("\n\n-----------> Starting simulated annealing <-----------")
n_iteration_left = n_sample - n_exist_sample # data should be saved 10 times --> no more (otherwise things are way too slow !)
n_mod = max([1, n_iteration_left // 10])
for it in range(n_iteration_left):
start_time = time.time()
best_fid, best_protocol, n_fid_eval = SA(
parameters, model) # -- --> performing annealing here <-- --
if parameters['task'] == 'SASD':
print(' -> Stochastic descent ... ')
model.update_protocol(best_protocol)
best_fid, best_protocol, n_fid_eval_SD = SD(parameters,
model,
init=False)
n_fid_eval += n_fid_eval_SD
energy, delta_energy, Sent = model.compute_observables(
protocol=best_protocol)
result = [
n_fid_eval, best_fid, energy, delta_energy, Sent, best_protocol
]
print("\n----------> RESULT FOR ANNEALING NO %i <-------------" %
(it + 1))
print("Number of fidelity eval \t%i" % n_fid_eval)
print("Best fidelity \t\t\t%.4f" % best_fid)
print("Best hx_protocol\t\t", list(best_protocol))
all_result.append(result)
# check if a better fidelity has been seen in previous samples
if best_fid > best_seen_fid:
best_seen_fid = best_fid
optimal_index = len(all_result) - 1
if save and it % n_mod == 0:
with open(outfile, 'wb') as f:
pickle.dump([parameters, all_result], f)
f.close()
print("Saved iteration --> %i to %s" %
(it + n_exist_sample, outfile))
print("Iteration run time --> %.4f s" % (time.time() - start_time))
# print best seen fidelity and protocol over all times
print(
"\n\n-------> Best encountered fidelity over all samples is %0.8f <-------"
% (best_seen_fid))
print("\n\n-------> Best encountered hx_protocol over all samples:")
print(list(all_result[optimal_index][5]))
print("<-------")
print("\n Thank you and goodbye !")
enablePrint()
if save:
with open(outfile, 'wb') as f:
pickle.dump([parameters, all_result], f)
f.close()
return all_result
