def update_virtual_vecs_train(self, which_t, which_side):
if (which_side is 'left') or (which_side is 'both'):
tmp = tm.khatri(self.vecsLeft[which_t], self.vecsImages[:, which_t, :].squeeze())
self.vecsLeft[which_t + 1] = np.tensordot(
self.mps.mps[which_t], tmp, ([0, 1], [0, 1]))
if (which_side is 'right') or (which_side is 'both'):
tmp = tm.khatri(self.vecsRight[which_t], self.vecsImages[:, which_t, :].squeeze())
self.vecsRight[which_t - 1] = np.tensordot(
self.mps.mps[which_t], tmp, ([2, 1], [0, 1]))
def env_tensor(self, nt, way):
s = self.mps.mps[nt].shape
env = tm.khatri(tm.khatri(
self.vecsLeft[nt], self.vecsImages[:, nt, :].squeeze()).reshape(
self.mps.virtual_dim[nt] * self.d, self.numVecSample), self.vecsRight[nt])
if way is 'mera':
env = env.dot(np.ones((self.numVecSample, )))
elif way is 'gradient':
weight = self.mps.mps[nt].reshape(1, -1).dot(env)
env = env.dot(1 / weight)
return env.reshape(s)
def update_virtual_vecs_train(self, which_t, which_side):
if (which_side is 'left') or (which_side is 'both'):
tmp = tm.khatri(self.vecsLeft[which_t],
self.vecsImages[:, which_t, :])
self.vecsLeft[which_t + 1] = np.tensordot(self.mps.mps[which_t],
tmp, ([0, 1], [0, 1]))
norm = np.linalg.norm(self.vecsLeft[which_t + 1], axis=0)
self.norms[which_t, :] = norm
self.vecsLeft[which_t + 1] /= norm.repeat(
self.mps.virtual_dim[which_t + 1]).reshape(
self.numVecSample, self.mps.virtual_dim[which_t + 1]).T
if (which_side is 'right') or (which_side is 'both'):
tmp = tm.khatri(self.vecsRight[which_t],
self.vecsImages[:, which_t, :])
self.vecsRight[which_t - 1] = np.tensordot(self.mps.mps[which_t],
tmp, ([2, 1], [0, 1]))
norm = np.linalg.norm(self.vecsRight[which_t - 1], axis=0)
self.norms[which_t, :] = norm
self.vecsRight[which_t - 1] /= norm.repeat(
self.mps.virtual_dim[which_t]).reshape(
self.numVecSample, self.mps.virtual_dim[which_t]).T
def compute_fidelities(self, mps):
fid = np.zeros((self.numVecSample, 1))
vecs = np.ones((1, self.numVecSample))
for nt in range(self.length):
s = mps[nt].shape
vecs = np.tensordot(mps[nt],
tm.khatri(vecs, self.vecsImages[:, nt, :]),
([0, 1], [0, 1]))
norm = np.linalg.norm(vecs, axis=0)
vecs /= norm.repeat(s[2]).reshape(self.numVecSample, s[2]).T
fid -= np.log(norm.reshape(self.numVecSample, 1))
return fid / self.length
def compute_bond_vectors(self, nb=0):
# Contract the samples to the MPS but left the nb-th physical bond empty
# The bond vectors are normalized
vecsL = np.ones((1, self.numVecSample))
for nt in range(0, nb):
s = self.mps.mps[nt].shape
tmp = tm.khatri(vecsL, self.vecsImages[:, nt, :].squeeze())
vecsL = np.tensordot(self.mps.mps[nt], tmp, ([0, 1], [0, 1]))
norm = np.linalg.norm(vecsL, axis=0)
vecsL /= norm.repeat(s[2]).reshape(self.numVecSample, s[2]).T
vecsR = np.ones((1, self.numVecSample))
for nt in range(self.length - 1, nb, -1):
s = self.mps.mps[nt].shape
tmp = tm.khatri(vecsR, self.vecsImages[:, nt, :].squeeze())
vecsR = np.tensordot(self.mps.mps[nt], tmp, ([2, 1], [0, 1]))
norm = np.linalg.norm(vecsR, axis=0)
vecsR /= norm.repeat(s[2]).reshape(self.numVecSample, s[2]).T
s = self.mps.mps[nb].shape
tmp = np.tensordot(self.mps.mps[nb], tm.khatri(vecsL, vecsR),
([0, 2], [0, 1]))
norm = np.linalg.norm(tmp, axis=0)
tmp /= norm.repeat(s[2]).reshape(self.numVecSample, s[2]).T
return tmp
