Python: Select all
model=BoseHubbarModel(model_params)
init_state = ['1'] * model.lat.N_sites
psi = MPS.from_product_state(model.lat.mps_sites(), init_state, bc=model.lat.bc_MPS)
dmrg_params = {"trunc_params": {"chi_max": 150, "svd_min": 1.e-7}, 'max_sweeps': 10, "mixer": True}
info = dmrg.run(psi, model, dmrg_params)
#print("E =", info['E'])
ops = ['Bd'] * model.lat.N_sites
print(ops)
exp_values = psi.expectation_value(ops)
print("Expectation values:", exp_values)
opss=['B']*model.lat.N_sites
print(opss)
expp_values=psi.expectation_value(opss)
print(expp_values)
correlations = []
i = 4
for j in range(4, N+1):
corr_val = psi.correlation_function("Bd", "B", sites1=[i], sites2=[j])[0, 0]
expected_prod = exp_values[i] * expp_values[j]
corrected_corr = np.absolute(corr_val - expected_prod)
correlations.append(corrected_corr)