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_steady_state(chi, dtype):
isometry = misc_mera.w_update_svd_numpy(
np.random.rand(chi, chi, chi).astype(dtype.as_numpy_dtype))
unitary = misc_mera.u_update_svd_numpy(
np.random.rand(chi, chi, chi, chi).astype(dtype.as_numpy_dtype))
rho = tf.reshape(
tf.eye(chi * chi * chi, dtype=dtype), (chi, chi, chi, chi, chi, chi))
rho_ss = bml.steady_state_density_matrix(
nsteps=60, rho=rho, isometry=isometry, unitary=unitary)
rho_test = bml.descending_super_operator(rho_ss, isometry, unitary)
np.testing.assert_array_less(rho_ss - rho_test, 1E-6)
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
def benchmark_descending_operator(rho, w, u, num_layers):
"""
run benchmark for descending super operator
Args:
rhoab (tf.Tensor): reduced densit matrix on a-b lattice
rhoba (tf.Tensor): reduced densit matrix on b-a lattice
w (tf.Tensor): isometry
v (tf.Tensor): isometry
u (tf.Tensor): disentangler
num_layers(int): number of layers over which to descend the hamiltonian
Returns:
runtime (float): the runtime
"""
t1 = time.time()
for p in range(num_layers):
rho = bml.descending_super_operator(rho, w, u)
return time.time() - t1