def test_ascending_descending(chi, dtype):
"""
test if ascending and descending operations are doing the right thing
"""
wC, uC, rho_0 = bml.initialize_binary_MERA_random(phys_dim=2,
chi=chi,
dtype=dtype)
wC, uC = bml.unlock_layer(wC, uC) #add a transitional layer
wC, uC = bml.unlock_layer(wC, uC) #add a transitional layer
ham_0 = bml.initialize_TFI_hams(dtype)
rho = [0 for n in range(len(wC) + 1)]
ham = [0 for n in range(len(wC) + 1)]
rho[-1] = bml.steady_state_density_matrix(10, rho_0, wC[-1], uC[-1])
ham[0] = ham_0
for p in range(len(rho) - 2, -1, -1):
rho[p] = bml.descending_super_operator(rho[p + 1], wC[p], uC[p])
for p in range(len(wC)):
ham[p + 1] = bml.ascending_super_operator(ham[p], wC[p], uC[p])
energies = [
tn.ncon([rho[p], ham[p]], [[1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6]])
for p in range(len(rho))
]
np.testing.assert_allclose(
np.array(
[energies[p] / energies[p + 1] for p in range(len(energies) - 1)]),
0.5)
def test_padding(chi, dtype):
wC, uC, rho_0 = bml.initialize_binary_MERA_random(phys_dim=2,
chi=chi,
dtype=dtype)
wC, uC = bml.unlock_layer(wC, uC) #add a transitional layer
wC, uC = bml.unlock_layer(wC, uC) #add a transitional layer
ham_0 = bml.initialize_TFI_hams(dtype)
def get_energies(wC, uC, rho_0, ham_0):
rho = [0 for n in range(len(wC) + 1)]
ham = [0 for n in range(len(wC) + 1)]
rho[-1] = bml.steady_state_density_matrix(10, rho_0, wC[-1], uC[-1])
ham[0] = ham_0
for p in range(len(rho) - 2, -1, -1):
rho[p] = bml.descending_super_operator(rho[p + 1], wC[p], uC[p])
for p in range(len(wC)):
ham[p + 1] = bml.ascending_super_operator(ham[p], wC[p], uC[p])
energies = [
tn.ncon([rho[p], ham[p]], [[1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6]])
for p in range(len(rho))
]
return energies
energies_1 = get_energies(wC, uC, rho_0, ham_0)
chi_new = chi + 1
wC, uC = bml.pad_mera_tensors(chi_new, wC, uC)
rho_0 = misc_mera.pad_tensor(rho_0, [chi_new] * 6)
energies_2 = get_energies(wC, uC, rho_0, ham_0)
np.testing.assert_allclose(energies_1, energies_2)
