def test_cvqnnlayers_uniform_dimensions(self, n_subsystems, n_layers):
"""Confirm that pennylane.init.cvqnn_layers_uniform()
returns an array with the right dimensions."""
a = (n_layers, n_subsystems)
b = (n_layers, n_subsystems * (n_subsystems - 1) // 2)
p = cvqnn_layers_uniform(n_wires=n_subsystems,
n_layers=n_layers,
seed=0)
dims = [p_.shape for p_ in p]
assert dims == [b, b, a, a, a, b, b, a, a, a, a]
def test_cvqnnlayers_uniform_edgecase(self, seed, tol):
"""Test sampling edge case of pennylane.init.cvqnn_layers_uniform()."""
p = cvqnn_layers_uniform(n_layers=2,
n_wires=10,
low=1,
high=1,
mean_active=1,
std_active=0,
seed=seed)
p_mean = np.mean(np.array([np.mean(pp) for p_ in p for pp in p_]))
assert np.allclose(p_mean, 1, atol=tol, rtol=0.)
def test_cvqnnlayers_uniform_interval(self, seed):
"""Confirm that no uniform sample in pennylane.init.cvqnn_layers_uniform() lies outside of interval."""
low = -2
high = 1
p = cvqnn_layers_uniform(n_layers=2,
n_wires=10,
low=low,
high=high,
seed=seed)
p_uni = [p[i] for i in [0, 1, 2, 4, 5, 6, 7, 9]]
assert all([(p_ <= high).all() and (p_ >= low).all() for p_ in p_uni])
def test_cvqnnlayers_uniform_seed(self, seed, tol):
"""Confirm that pennylane.init.cvqnn_layers_uniform() invokes the correct np.random sampling function
for a given seed."""
low = -2
high = 1
mean_a = 0.5
std_a = 2
n_wires = 3
n_layers = 2
n_if = n_wires * (n_wires - 1) // 2
p = cvqnn_layers_uniform(n_layers=n_layers,
n_wires=n_wires,
low=low,
high=high,
mean_active=mean_a,
std_active=std_a,
seed=seed)
np.random.seed(seed)
theta_1 = np.random.uniform(low=low, high=high, size=(n_layers, n_if))
phi_1 = np.random.uniform(low=low, high=high, size=(n_layers, n_if))
varphi_1 = np.random.uniform(low=low,
high=high,
size=(n_layers, n_wires))
r = np.random.normal(loc=mean_a, scale=std_a, size=(n_layers, n_wires))
phi_r = np.random.uniform(low=low, high=high, size=(n_layers, n_wires))
theta_2 = np.random.uniform(low=low, high=high, size=(n_layers, n_if))
phi_2 = np.random.uniform(low=low, high=high, size=(n_layers, n_if))
varphi_2 = np.random.uniform(low=low,
high=high,
size=(n_layers, n_wires))
a = np.random.normal(loc=mean_a, scale=std_a, size=(n_layers, n_wires))
phi_a = np.random.uniform(low=low, high=high, size=(n_layers, n_wires))
k = np.random.normal(loc=mean_a, scale=std_a, size=(n_layers, n_wires))
p_target = [
theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a,
phi_a, k
]
assert np.allclose(p, p_target, atol=tol, rtol=0.)