def test_evolve_trotter_euclidean(num_sites, phys_dim, graph):
psi = tf.complex(
tf.random_normal([phys_dim] * num_sites, dtype=tf.float64),
tf.random_normal([phys_dim] * num_sites, dtype=tf.float64))
h = tf.complex(
tf.random_normal((phys_dim**2, phys_dim**2), dtype=tf.float64),
tf.random_normal((phys_dim**2, phys_dim**2), dtype=tf.float64))
h = 0.5 * (h + tf.linalg.adjoint(h))
h = tf.reshape(h, (phys_dim, phys_dim, phys_dim, phys_dim))
H = [h] * (num_sites - 1)
norm1 = wavefunctions.inner(psi, psi)
en1 = sum(wavefunctions.expval(psi, H[i], i) for i in range(num_sites - 1))
if graph:
psi, t = wavefunctions.evolve_trotter_defun(psi,
H,
0.1,
10,
euclidean=True)
else:
psi, t = wavefunctions.evolve_trotter(psi, H, 0.1, 10, euclidean=True)
norm2 = wavefunctions.inner(psi, psi)
en2 = sum(wavefunctions.expval(psi, H[i], i) for i in range(num_sites - 1))
np.testing.assert_allclose(t, 1.0)
np.testing.assert_almost_equal(norm2, 1.0)
assert en2.numpy() / norm2.numpy() < en1.numpy() / norm1.numpy()
def test_evolve_trotter(num_sites, phys_dim, graph):
psi = tf.complex(
tf.random.normal([phys_dim] * num_sites, dtype=tf.float64),
tf.random.normal([phys_dim] * num_sites, dtype=tf.float64))
h = tf.complex(
tf.random.normal((phys_dim**2, phys_dim**2), dtype=tf.float64),
tf.random.normal((phys_dim**2, phys_dim**2), dtype=tf.float64))
h = 0.5 * (h + tf.linalg.adjoint(h))
h = tf.reshape(h, (phys_dim, phys_dim, phys_dim, phys_dim))
H = [h] * (num_sites - 1)
norm1 = wavefunctions.inner(psi, psi)
en1 = sum(wavefunctions.expval(psi, H[i], i) for i in range(num_sites - 1))
if graph:
psi, t = wavefunctions.evolve_trotter_defun(psi, H, 0.001, 10)
else:
psi, t = wavefunctions.evolve_trotter(psi, H, 0.001, 10)
norm2 = wavefunctions.inner(psi, psi)
en2 = sum(wavefunctions.expval(psi, H[i], i) for i in range(num_sites - 1))
np.testing.assert_allclose(t, 0.01)
np.testing.assert_almost_equal(norm1 / norm2, 1.0)
np.testing.assert_almost_equal(en1 / en2, 1.0, decimal=2)