def __init__(self, dims, state_type='pure', ini='random', operators=None):
self.dims = dims
self.dim_tot = int(np.prod(self.dims))
self.l = len(self.dims)
self.is_vec = True # If self.v is saved as a vector or tensor
self.state_type = state_type
if type(ini) is np.ndarray:
self.v = ini
elif ini is 'random':
if state_type is 'pure':
self.v = np.random.randn(self.dim_tot, )
self.v = tm.normalize_tensor(self.v)[0]
else:
self.v = np.random.randn(self.dim_tot, self.dim_tot)
self.v = self.v + self.v.transpose((1, 0))
else:
if state_type is 'pure':
self.v = np.ones((self.dim_tot, 1)) / (self.dim_tot**0.5)
else:
self.v = np.eye(self.dim_tot)
if operators is None:
op = spin_operators('half')
self.operators = [
op['id'], op['sx'], op['sy'], op['sz'], op['su'], op['sd']
]
else:
self.operators = operators
def update_tensor_gradient(self, nt, step):
# for n in self.mps.mps:
# print(n)
# input()
self.mps.correct_orthogonal_center(nt)
env = self.env_tensor((nt, 'all', 'gradient'))
env = tm.normalize_tensor(env)[0]
# env /= np.linalg.norm(env.reshape(-1, ))
self.mps.mps[nt] = self.mps.mps[nt] * (1 - step) + step * env.reshape(
self.mps.mps[nt].shape)
self.mps.mps[nt] /= np.linalg.norm(self.mps.mps[nt].reshape(-1, ))
def bond_env_matrix_simple(self, nt, vb, is_symme=True, is_normalize=True):
# the nb-th bond matrix of the nt-th tensor (including the phys bond)
bonds = list(range(0, self.nVirtual))
bonds.remove(vb)
tmp = self.absorb_lm(nt, False, bonds)
bonds = list(range(0, self.nVirtual + 1))
bonds.remove(vb + 1)
tmp = np.tensordot(tmp, tmp, (bonds, bonds))
if is_symme:
tmp = (tmp + tmp.conj().T) / 2
if is_normalize:
tmp = tm.normalize_tensor(tmp)[0]
return tmp
