def main():
# Utility object for reading, writing parameters, etc.
utils = UTILS()
# Reading parameters from para.dat file
parameters = utils.read_parameter_file(file="../para.dat")
parameters['root'] = "../data/"
utils.read_command_line_arg(parameters,sys.argv)
# Printing parameters for user
utils.print_parameters(parameters)
# Defining Hamiltonian
H = HAMILTONIAN(**parameters)
# Defines the model, and precomputes
model = MODEL(H, parameters)
# save interacting states
if abs(parameters['J'] - 1.0) < 0.001 :
with open('psi_L=2_J=1.pkl','wb') as f :
pickle.dump([model.psi_i, model.psi_target], f)
# load interacting states
if abs(parameters['J']) < 0.0001:
with open('psi_L=2_J=1.pkl','rb') as f :
model.psi_i, model.psi_target = pickle.load(f)
print(model.psi_i)
def main():
# Utility object for reading, writing parameters, etc.
utils = UTILS()
# Reading parameters from para.dat file
parameters = utils.read_parameter_file()
# Command line specified parameters overide parameter file values
utils.read_command_line_arg(parameters, sys.argv)
parameters_nonint = parameters.copy()
parameters_nonint['J'] = 0.0
# Printing parameters for user
utils.print_parameters(parameters)
# Defining Hamiltonian
H = HAMILTONIAN(**parameters)
H_nonint = HAMILTONIAN(**parameters_nonint)
# Defines the model, and precomputes evolution matrices given set of states
model = MODEL(H, parameters)
model_nonint = MODEL(H_nonint, parameters_nonint)
# evolve with non-int H but starting and targeting interacting states
model_nonint.psi_i = model.psi_i.copy()
model_nonint.psi_target = model.psi_target.copy()
model_nonint.H_target = model.H_target.copy()
# model_nonint.param=model.param.copy()
model = model_nonint
parameters = parameters_nonint
#print( abs(model.psi_i.T.dot(model.psi_target))**2 )
#exit()
root = 'data/non-int-evo/'
#root='data/data_non-int/'
# Run simulated annealing
if parameters['task'] == 'SA':
print("Simulated annealing")
run_SA(parameters, model, utils, root)
elif parameters['task'] == 'GB':
print("Gibbs sampling")
run_GS(parameters, model)
elif parameters['task'] == 'SD' or parameters['task'] == 'SD2':
print("Stochastic descent")
run_SD(parameters, model, utils, root)
elif parameters['task'] == 'ES':
print("Exact spectrum")
run_ES(parameters, model, utils, root)
elif parameters['task'] == 'SASD':
print("Simulating annealing followed by stochastic descent")
run_SA(parameters, model, utils, root)
exit()
def main():
# Utility object for reading, writing parameters, etc.
utils = UTILS()
# Reading parameters from para.dat file
parameters = utils.read_parameter_file()
# Command line specified parameters overide parameter file values
utils.read_command_line_arg(parameters, sys.argv)
# 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)
#root='data/data_ES/'
#root='data/data_SD/'
root = 'data/data_GRAPE/'
# Run simulated annealing
if parameters['task'] == 'SA':
print("Simulated annealing")
run_SA(parameters, model, utils, root)
elif parameters['task'] == 'GB':
print("Gibbs sampling")
run_GS(parameters, model)
elif parameters['task'] == 'SD' or parameters['task'] == 'SD2':
print("Stochastic descent")
run_SD(parameters, model, utils, root)
elif parameters['task'] == 'GRAPE':
print("GRAPE")
run_GRAPE(parameters, model, utils, root)
elif parameters['task'] == 'ES':
print("Exact spectrum")
run_ES(parameters, model, utils, root)
elif parameters['task'] == 'SASD':
print("Simulating annealing followed by stochastic descent")
run_SA(parameters, model, utils, root)
exit()
def main():
# Utility object for reading, writing parameters, etc.
utils = UTILS()
# Reading parameters from para.dat file
parameters = utils.read_parameter_file()
# Command line specified parameters overide parameter file values
utils.read_command_line_arg(parameters, sys.argv)
# 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)
#n_step = parameters['n_step']
#X,y=sample_m0(10000,n_step,model)
#print(y[0:10])
#plt.hist(y,bins=20)
#plt.show()
rob_vs_T = {}
n_eval = {}
fid = {}
res = {}
visit = {}
T_list = np.arange(0.1, 4.01, 0.1)
n_step = 100
fid_list = []
for T in T_list:
parameters['T'] = T
parameters['n_step'] = n_step
parameters['dt'] = T / n_step
file = utils.make_file_name(parameters, root='data/')
res = parse_data(file, v=3)
fid_list.append(np.mean(res['F']))
#n_eval[(n_step,hash(T))]=res['n_fid']
#fid[(n_step,hash(T))]=res['F']
#visit[(n_step,hash(T))] = res['n_visit']
plt.plot(T_list, fid_list)
plt.xlabel('T')
plt.ylabel('Fidelity')
plt.show()
exit()
n_step_list = [40, 50, 60, 70, 80, 90, 100, 110]
for T in T_list:
for n_step in n_step_list: #[40,50,60,70,80,90,100,110,120]:
##for T in np.arange(0.025,10.001,0.025):
# for n_step in [100,200,400] :
parameters['T'] = T
parameters['n_step'] = n_step
parameters['dt'] = T / n_step
file = utils.make_file_name(parameters, root='data/')
res = parse_data(file)
n_eval[(n_step, hash(T))] = res['n_fid']
fid[(n_step, hash(T))] = res['F']
visit[(n_step, hash(T))] = res['n_visit']
''' with open(file,'rb') as f:
_, data = pickle.load(f)
n_elem = len(data)
n_eval[(n_step,hash(T))]=[]
n_fid[(n_step,hash(T))]=[]
for elem in data:
n_eval[(n_step,hash(T))].append(elem[0])
n_fid[(n_step,hash(T))].append(elem[1])'''
#print(n_eval)
#exit()
n_eval_mean = {}
fid_mean = {}
visit_mean = {}
#print(visit[(40,115292150460684704)])
#exit()
for n_step in n_step_list:
n_eval_mean[n_step] = []
fid_mean[n_step] = []
visit_mean[n_step] = []
for T in T_list:
hT = hash(T)
n_eval_mean[n_step].append(
[T, np.mean(n_eval[(n_step, hT)]) / (n_step * n_step)])
fid_mean[n_step].append([T, np.mean(fid[(n_step, hT)])])
visit_mean[n_step].append(
[T, np.mean(visit[(n_step, hT)]) / (n_step)])
c_list = [
'#d53e4f', '#f46d43', '#fdae61', '#fee08b', '#e6f598', '#abdda4',
'#66c2a5', '#3288bd'
]
for i, n_step in enumerate(n_step_list):
x = np.array(n_eval_mean[n_step])
plt.plot(x[:, 0], x[:, 1], c='black', zorder=0)
plt.scatter(x[:, 0],
x[:, 1],
c=c_list[i],
marker='o',
s=5,
label='$N=%i$' % n_step,
zorder=1)
plt.title('Number of fidelity evaluations vs. ramp time \n for 2 flip')
plt.ylabel('$N_{eval}/N^2$')
plt.xlabel('$T$')
plt.legend(loc='best')
plt.tight_layout()
plt.show()
for i, n_step in enumerate(n_step_list):
x = np.array(visit_mean[n_step])
plt.plot(x[:, 0], x[:, 1], c='black', zorder=0)
plt.scatter(x[:, 0],
x[:, 1],
c=c_list[i],
marker='o',
s=5,
label='$N=%i$' % n_step,
zorder=1)
plt.title('Number of visited states vs. ramp time \n for 2 flip')
plt.ylabel('$N_{visit}/N$')
plt.xlabel('$T$')
plt.legend(loc='best')
plt.tight_layout()
plt.show()
'''
