fid = np.zeros((num_len, ))
fid_prod0 = np.zeros((num_len, ))
ent0_mid = np.zeros((num_len, ))
fid_ini = np.zeros((num_len,))
lattice = 'chain'
para_dmrg = Pm.generate_parameters_dmrg(lattice)
para_dmrg['spin'] = 'half'
para_dmrg['bound_cond'] = 'open'
para_dmrg['chi'] = 100
para_dmrg['l'] = 12
para_dmrg['jxy'] = 0
para_dmrg['jz'] = 1
para_dmrg['hx'] = 0.5
para_dmrg['hz'] = 0
para_dmrg = Pm.make_consistent_parameter_dmrg(para_dmrg)
para_dmps = parameters_dmps()
para_dmps['num_layers'] = 1
para_dmps['chi_overlap'] = 256
para_dmps['theta'] = 0
para_dmps['num_theta'] = 1
for n in range(num_len):
print('var_para = ' + str(length[n]))
para_dmrg['l'] = int(length[n])
para_dmrg = Pm.make_consistent_parameter_dmrg(para_dmrg)
if path.isfile(path.join(para_dmrg['data_path'], para_dmrg['data_exp'] + '.pr')):
print('Load existing MPS data ...')
a, ob = load_pr(path.join(para_dmrg['data_path'], para_dmrg['data_exp'] + '.pr'), ['a', 'ob'])
# para['data_path'] = '..\\dataQubism\\states_' + model + '\\'
para['data_path'] = 'E:\\tmpData\\states_' + model + '\\'
para[
'image_path'] = '..\\dataQubism\\images_' + model + '\\train 0-' + str(
delta)
mkdir(para['data_path'])
mkdir(para['image_path'])
n_mid = int(para['l'] / 2)
nj = len(j)
nh = len(h)
for n1 in range(0, nj):
for n2 in range(0, nh):
para['jz'] = j[n1]
para['hz'] = h[n2]
para = pm.make_consistent_parameter_dmrg(para)
# Run DMRG
if path.isfile(
path.join(para['data_path'], para['data_exp'] + '.pr')):
print('Load existing data ...')
a = load_pr(
path.join(para['data_path'], para['data_exp'] + '.pr'),
'a')
else:
print('Start DMRG calculation ...')
ob, a, info, para = dmrg.dmrg_finite_size(para)
save_pr(para['data_path'], para['data_exp'] + '.pr',
(ob, a, info, para), ('ob', 'a', 'info', 'para'))
print('The entanglement gap is ' +
str((a.lm[n_mid][0] - a.lm[n_mid][1]) / a.lm[n_mid][0]))
if (a.lm[n_mid][0] - a.lm[n_mid][1]) / a.lm[n_mid][0] < tol:
fidelities_to_original_state, act_umpo_on_mps
from library.TensorBasicModule import open_mps_product_state_spin_up
"""
NOTE: for Linux, add the fold "T-Nalg" in your system path
"""
para_dmrg = Pm.generate_parameters_dmrg(
'chain') # set default parameters of DMRG
para_dmrg['spin'] = 'half' # spin-half model
para_dmrg['bound_cond'] = 'open' # open boundary condition
para_dmrg['chi'] = 48 # dimension cut-off of DMRG
para_dmrg['l'] = 48 # system size
para_dmrg['jxy'] = 0 # coupling constant - jx and jy in the Heisenberg model
para_dmrg['jz'] = 1 # coupling constant - jz in the Heisenberg model
para_dmrg['hx'] = 0.3 # magnetic field in the x direction
para_dmrg['hz'] = 0 # magnetic field in the z direction
para_dmrg = Pm.make_consistent_parameter_dmrg(
para_dmrg) # check consistency of the parameters
para_dmps = parameters_dmps() # set default parameters of MPS encoding
para_dmps['num_layers'] = 9 # number of the MPU layers in the circuit
para_dmps['chi_overlap'] = 256 # dimension cut-off of the disentangled MPS
# calculate the ground state by DMRG
pre_fix = path.basename(__file__)[:-3] + '_'
if path.isfile(path.join(para_dmrg['data_path'],
para_dmrg['data_exp'] + '.pr')):
# check if data exist in para_dmrg['data_path']; load if exist.
# Note: you may need to modify para_dmrg['data_path'] to a local folder when
# running this code with different computers or OS
print('Load existing MPS data ...')
a, ob = load_pr(
path.join(para_dmrg['data_path'], para_dmrg['data_exp'] + '.pr'),
