# num_samples = 100
# h1 = np.random.rand(num_samples, 1) * delta
# h2 = np.random.rand(num_samples, 1) * delta + (0.82 - delta)
# h = np.vstack((h1, h2))
gap = [0.5]
for delta in gap:
num_samples = 100
h1 = np.random.rand(num_samples, 1) * delta
h2 = np.random.rand(num_samples, 1) * delta + (1 - delta)
h = np.vstack((h1, h2))
j = [1]
tol = 1e-4 # to judge if the state has two-fold degeneracy
lattice = 'chain'
para = pm.generate_parameters_dmrg(lattice)
para['spin'] = 'one'
para['bound_cond'] = 'periodic'
para['chi'] = 128
para['l'] = 12
para['jxy'] = 1
para['hx'] = 0
model = 'Spin_' + para['spin'] + '_' + lattice
# 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'])
fidelities_to_original_state, act_umpo_on_mps from library.TensorBasicModule import open_mps_product_state_spin_up import copy, sys length = np.arange(4, 156, 4, dtype=int) pre_fix = path.basename(__file__)[:-3] + '_' num_len = length.size 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
def dmrg_finite_size(para=None):
from library.MPSClass import MpsOpenBoundaryClass as Mob
t_start = time.time()
info = dict()
print('Preparing the parameters and MPS')
if para is None:
para = pm.generate_parameters_dmrg()
# Initialize MPS
is_parallel = para['isParallel']
if is_parallel or para['isParallelEnvLMR']:
par_pool = dict()
par_pool['n'] = n_nodes
par_pool['pool'] = ThreadPool(n_nodes)
else:
par_pool = None
A = Mob(length=para['l'],
d=para['d'],
chi=para['chi'],
way='qr',
ini_way='r',
operators=para['op'],
debug=is_debug,
is_parallel=para['isParallel'],
par_pool=par_pool,
is_save_op=para['is_save_op'],
eig_way=para['eigWay'],
is_env_parallel_lmr=para['isParallelEnvLMR'])
A.correct_orthogonal_center(para['ob_position'])
print('Starting to sweep ...')
e0_per_site = 0
info['convergence'] = 1
ob = dict()
for t in range(0, para['sweep_time']):
if_ob = ((t + 1) % para['dt_ob'] == 0) or t == (para['sweep_time'] - 1)
if if_ob:
print('In the %d-th round of sweep ...' % (t + 1))
for n in range(para['ob_position'] + 1, para['l']):
if para['if_print_detail']:
print('update the %d-th tensor from left to right...' % n)
A.update_tensor_eigs(n,
para['index1'],
para['index2'],
para['coeff1'],
para['coeff2'],
para['tau'],
para['is_real'],
tol=para['eigs_tol'])
for n in range(para['l'] - 2, -1, -1):
if para['if_print_detail']:
print('update the %d-th tensor from right to left...' % n)
A.update_tensor_eigs(n,
para['index1'],
para['index2'],
para['coeff1'],
para['coeff2'],
para['tau'],
para['is_real'],
tol=para['eigs_tol'])
for n in range(1, para['ob_position']):
if para['if_print_detail']:
print('update the %d-th tensor from left to right...' % n)
A.update_tensor_eigs(n,
para['index1'],
para['index2'],
para['coeff1'],
para['coeff2'],
para['tau'],
para['is_real'],
tol=para['eigs_tol'])
if if_ob:
ob['eb_full'] = A.observe_bond_energy(para['index2'],
para['coeff2'])
ob['mx'] = A.observe_magnetization(1)
ob['mz'] = A.observe_magnetization(3)
ob['e_per_site'] = (sum(ob['eb_full']) - para['hx'] * sum(ob['mx'])
- para['hz'] * sum(ob['mz'])) / A.length
# if para['lattice'] in ('square', 'chain'):
# ob['e_per_site'] = (sum(ob['eb_full']) - para['hx']*sum(ob['mx']) - para['hz'] *
# sum(ob['mz']))/A.length
# else:
# ob['e_per_site'] = sum(ob['eb_full'])
# for n in range(0, para['l']):
# ob['e_per_site'] += para['hx'][n] * ob['mx'][n]
# ob['e_per_site'] += para['hz'][n] * ob['mz'][n]
# ob['e_per_site'] /= A.length
info['convergence'] = abs(ob['e_per_site'] - e0_per_site)
if info['convergence'] < para['break_tol']:
print(
'Converged at the %d-th sweep with error = %g of energy per site.'
% (t + 1, info['convergence']))
break
else:
print('Convergence error of energy per site = %g' %
info['convergence'])
e0_per_site = ob['e_per_site']
if t == para['sweep_time'] - 1 and info['convergence'] > para[
'break_tol']:
print('Not converged with error = %g of eb per bond' %
info['convergence'])
print('Consider to increase para[\'sweep_time\']')
ob['eb'] = get_bond_energies(ob['eb_full'], para['positions_h2'],
para['index2'])
A.calculate_entanglement_spectrum()
A.calculate_entanglement_entropy()
ob['corr_x'] = A.observe_correlators_from_middle(1, 1)
ob['corr_z'] = A.observe_correlators_from_middle(3, 3)
info['t_cost'] = time.time() - t_start
print('Simulation finished in %g seconds' % info['t_cost'])
A.clean_to_save()
if A._is_parallel:
par_pool['pool'].close()
return ob, A, info, para
from algorithms.DMRG_anyH import dmrg_finite_size
from library import Parameters as Pm
para = Pm.generate_parameters_dmrg('husimi')
para['spin'] = 'half'
para['depth'] = 2
# The interactions are assumed to be uniform; if not, use parameter_dmrg_arbitrary instead
para['jxy'] = 1
para['jz'] = 1
para['hx'] = 0
para['hz'] = 0
para['chi'] = 32 # Virtual bond dimension cut-off
para['eigWay'] = 1
para = Pm.make_consistent_parameter_dmrg(para)
ob, A, info1, para1 = dmrg_finite_size(para)
print(ob['e_per_site'])
from algorithms.DMRG_anyH import dmrg_finite_size
from library import Parameters as Pm
import numpy as np
from os import path
from library.BasicFunctions import load_pr, save_pr, plot, output_txt, get_size
from algorithms.DeepMPSfinite import parameters_dmps, deep_mps_qubit, \
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'],
