def run_ES(parameters, model:MODEL, utils):
n_step = parameters['n_step']
n_protocol = 2**n_step
exact_data = np.zeros((n_protocol,2), dtype=np.float64) # 15 digits precision
b2_array = lambda n10 : np.array(list(np.binary_repr(n10, width=n_step)), dtype=np.int)
st=time.time()
# ---> measuring estimated time <---
model.update_protocol(b2_array(0))
psi = model.compute_evolved_state()
model.compute_fidelity(psi_evolve=psi)
model.compute_energy(psi_evolve=psi)
print("Est. run time : \t %.3f s"%(0.5*n_protocol*(time.time()-st)))
# ---> Starting real calculation <---
st=time.time()
for p in range(n_protocol):
model.update_protocol(b2_array(p))
psi = model.compute_evolved_state()
exact_data[p] = (model.compute_fidelity(psi_evolve=psi), model.compute_energy(psi_evolve=psi))
outfile = utils.make_file_name(parameters,root=parameters['root'])
with open(outfile,'wb') as f:
pickle.dump(exact_data, f, protocol=4)
print("Total run time : \t %.3f s"%(time.time()-st))
print("\n Thank you and goodbye !")
f.close()
def run_SA(parameters, model:MODEL, utils, save = True):
if parameters['verbose'] == 0:
blockPrint()
outfile = utils.make_file_name(parameters,root=parameters['root'])
n_exist_sample, all_result = utils.read_current_results(outfile)
n_sample = parameters['n_sample']
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 ... <-----------")
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_random = False)
n_fid_eval += n_fid_eval_SD
energy = model.compute_energy(protocol = best_protocol)
result = [n_fid_eval, best_fid, energy, 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)
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:
print("Saved results in %s"%outfile)
pickle.dump([parameters, all_result],f)
f.close()
return all_result
def Gibbs_Sampling(param, model:MODEL):
# should also measure acceptance rate
Ti = param['Ti']
beta = 1./Ti
n_step = param['n_step']
n_equilibrate = 10000
n_auto_correlate = n_step*10 # should look at auto-correlation time !
# initial random protocol
model.update_protocol( np.random.randint(0, model.n_h_field, size=n_step) )
old_fid = model.compute_fidelity()
best_fid = old_fid
for i in range(n_equilibrate):
random_time = np.random.randint(0,n_step)
current_hx = model.protocol_hx(random_time)
model.update_hx(random_time, model.random_flip(random_time))
new_fid = model.compute_fidelity()
d_fid = new_fid - old_fid
if d_fid > 0. : # accept move
old_fid = new_fid
elif np.exp(beta*d_fid) > np.random.uniform() : # accept move
old_fid = new_fid
else: # reject move
model.update_hx(random_time, current_hx)
samples = []
fid_samples = []
energy_samples = []
for i in range(n_sample):
for j in range(n_auto_correlate):
random_time = np.random.randint(0,n_step)
current_hx = model.protocol_hx(random_time)
model.update_hx(random_time, model.random_flip(random_time))
new_fid = model.compute_fidelity()
d_fid = new_fid - old_fid
if d_fid > 0. : # accept move
old_fid = new_fid
elif np.exp(beta*d_fid) > np.random.uniform() : # accept move
old_fid = new_fid
else: # reject move
model.update_hx(random_time, current_hx)
samples.append(np.copy(model.protocol()))
fid_samples.append(model.compute_fidelity())
energy_samples.append(model.compute_energy())
return samples, fid_samples, energy_samples
def main():
# Reading parameters from para.dat file
parameters = utils.read_parameter_file()
# Printing parameters for user
utils.print_parameters(parameters)
# Defining Hamiltonian
H = HAMILTONIAN(**parameters)
# Defines the model, and precomputes evolution matrices given set of states
model = MODEL(H, parameters)
L = 6
T = 0.1
n_step = 28
param = {'L' : L, 'T': T, 'n_step': n_step}
file_name = make_file_name(param, root= "/projectnb/fheating/SGD/ES/dynamicQL/SA/ES/data/")
with open(file_name, 'rb') as f:
fidelities=pickle.load(f)
nfid=fidelities.shape[0]
fid_and_energy=np.empty((nfid,2),dtype=np.float)
for i,f in zip(range(nfid),fidelity):
if i%10000 == 0: print(i)
model.update_protocol(b2_array(i, w = 28))
psi = model.compute_evolved_state()
fid_and_energy[i][0]=model.compute_fidelity(psi_evolve = psi)
fid_and_energy[i][1]=model.compute_energy(psi_evolve = psi)
print(fid_and_energy[0],'\t',f)
break
with open("ES_L-06_T-0.500_n_step-28-test.pkl", ‘wb’) as f:
fidelities=pickle.dump(fid_and_energy,f, protocol=4)
def run_SD(parameters, model:MODEL, utils, save = True):
if parameters['verbose'] == 0:
blockPrint()
outfile = utils.make_file_name(parameters,root=parameters['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, n_visit = SD(parameters, model, init_random=True) # -- --> performing stochastic descent here <-- --
elif parameters['task'] == 'SD2':
best_fid, best_protocol, n_fid_eval, n_visit = SD_2SF(parameters, model, init_random=True) # -- --> performing 2 spin flip stochastic descent here <-- --
elif parameters['task'] == 'SD2M0':
best_fid, best_protocol, n_fid_eval, n_visit = SD_2SF_M0(parameters, model, init_random=True) # -- --> performing 2 spin flip stochastic descent here <-- --
#elif parameters['task'] == 'SD4':
#best_fid, best_protocol, n_fid_eval, n_enc_state = SD_4SF(parameters, model, init_random=True) # -- --> performing 2 spin flip stochastic descent here <-- --
else:
assert False, 'Error in task specification'
energy = model.compute_energy(protocol = best_protocol)
result = [n_fid_eval, best_fid, energy, n_visit, best_protocol]
print("\n----------> RESULT FOR STOCHASTIC DESCENT NO %i <-------------"%(it+1))
print("Number of fidelity eval \t%i"%n_fid_eval)
print("Number of states visited \t%i"%n_visit)
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 : # final saving !
with open(outfile,'wb') as f:
pickle.dump([parameters, all_result],f)
print("Saved results in %s"%outfile)
f.close()
return all_result
