def test_randomlayers_normal_edgecase(self, seed, tol):
"""Test sampling edge case of pennylane.init.random_layers_normal()."""
n_layers = 3
p = random_layers_normal(n_layers=n_layers,
n_wires=10,
mean=1,
std=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_randomlayers_normal_dimensions(self, n_subsystems, n_layers,
n_rots):
"""Confirm that the pennylane.init.random_layers_normal()
returns an array with the right dimensions."""
if n_rots is None:
n_rots = n_subsystems
a = (n_layers, n_rots)
p = random_layers_normal(n_layers=n_layers,
n_wires=n_subsystems,
n_rots=n_rots,
seed=0)
dims = [p_.shape for p_ in p]
assert dims == [a]
def test_randomlayers_normal_seed(self, seed, tol):
"""Confirm that pennylane.init.random_layers_normal() invokes the correct np.random sampling function
for a given seed."""
mean = -2
std = 1
n_wires = 3
n_rots = 5
n_layers = 3
p = random_layers_normal(n_layers=n_layers,
n_wires=n_wires,
n_rots=n_rots,
mean=mean,
std=std,
seed=seed)
np.random.seed(seed)
p_target = np.random.normal(loc=mean,
scale=std,
size=(n_layers, n_rots))
assert np.allclose(p[0], p_target, atol=tol, rtol=0.)
class TestInitializationIntegration:
"""Tests integration with the parameter initialization functions from pennylane.init"""
# TODO: Combine CV and Qubit tests, since the only difference is the device
def make_n_features(self, n):
"""Helper to prepare dummy feature inputs for templates that have
as many features as number of wires."""
return [i for i in range(n)]
QUBIT_INIT = [(StronglyEntanglingLayers,
{'weights': strong_ent_layers_uniform(n_layers=3, n_wires=2), 'wires': range(2)}),
(StronglyEntanglingLayers,
{'weights': strong_ent_layers_uniform(n_layers=2, n_wires=3), 'wires': range(3)}),
(StronglyEntanglingLayers,
{'weights': strong_ent_layers_normal(n_layers=3, n_wires=2), 'wires': range(2)}),
(StronglyEntanglingLayers,
{'weights': strong_ent_layers_normal(n_layers=2, n_wires=3), 'wires': range(3)}),
(RandomLayers,
{'weights': random_layers_uniform(n_layers=3, n_rots=2, n_wires=2), 'wires': range(2)}),
(RandomLayers,
{'weights': random_layers_uniform(n_layers=3, n_rots=2, n_wires=2), 'wires': range(2)}),
(RandomLayers,
{'weights': random_layers_normal(n_layers=2, n_rots=2, n_wires=3), 'wires': range(3)}),
(RandomLayers,
{'weights': random_layers_normal(n_layers=2, n_rots=2, n_wires=3), 'wires': range(3)}),
(QAOAEmbedding,
{'features': [1., 2.], 'weights': qaoa_embedding_uniform(n_layers=3, n_wires=2), 'wires': range(2)}),
(QAOAEmbedding,
{'features': [1., 2.], 'weights': qaoa_embedding_uniform(n_layers=3, n_wires=2), 'wires': range(2)}),
(QAOAEmbedding,
{'features': [1., 2.], 'weights': qaoa_embedding_normal(n_layers=2, n_wires=3), 'wires': range(3)}),
(QAOAEmbedding,
{'features': [1., 2.], 'weights': qaoa_embedding_normal(n_layers=2, n_wires=3), 'wires': range(3)}),
(QAOAEmbedding,
{'features': [1., 2.], 'weights': qaoa_embedding_normal(n_layers=2, n_wires=1), 'wires': range(1)}),
(QAOAEmbedding,
{'features': [1., 2.], 'weights': qaoa_embedding_uniform(n_layers=2, n_wires=1), 'wires': range(1)})
]
CV_INIT = [(CVNeuralNetLayers,
{'theta_1': cvqnn_layers_theta_uniform(n_layers=3, n_wires=2),
'phi_1': cvqnn_layers_phi_uniform(n_layers=3, n_wires=2),
'varphi_1': cvqnn_layers_varphi_uniform(n_layers=3, n_wires=2),
'r': cvqnn_layers_r_uniform(n_layers=3, n_wires=2),
'phi_r': cvqnn_layers_phi_r_uniform(n_layers=3, n_wires=2),
'theta_2': cvqnn_layers_theta_uniform(n_layers=3, n_wires=2),
'phi_2': cvqnn_layers_phi_uniform(n_layers=3, n_wires=2),
'varphi_2': cvqnn_layers_varphi_uniform(n_layers=3, n_wires=2),
'a': cvqnn_layers_a_uniform(n_layers=3, n_wires=2),
'phi_a': cvqnn_layers_phi_a_uniform(n_layers=3, n_wires=2),
'k': cvqnn_layers_kappa_uniform(n_layers=3, n_wires=2),
'wires': range(2)}),
(CVNeuralNetLayers,
{'theta_1': cvqnn_layers_theta_normal(n_layers=3, n_wires=2),
'phi_1': cvqnn_layers_phi_normal(n_layers=3, n_wires=2),
'varphi_1': cvqnn_layers_varphi_normal(n_layers=3, n_wires=2),
'r': cvqnn_layers_r_normal(n_layers=3, n_wires=2),
'phi_r': cvqnn_layers_phi_r_normal(n_layers=3, n_wires=2),
'theta_2': cvqnn_layers_theta_normal(n_layers=3, n_wires=2),
'phi_2': cvqnn_layers_phi_normal(n_layers=3, n_wires=2),
'varphi_2': cvqnn_layers_varphi_normal(n_layers=3, n_wires=2),
'a': cvqnn_layers_a_normal(n_layers=3, n_wires=2),
'phi_a': cvqnn_layers_phi_a_normal(n_layers=3, n_wires=2),
'k': cvqnn_layers_kappa_normal(n_layers=3, n_wires=2),
'wires': range(2)}),
(Interferometer,
{'phi': interferometer_phi_uniform(n_wires=2), 'varphi': interferometer_varphi_uniform(n_wires=2),
'theta': interferometer_theta_uniform(n_wires=2), 'wires': range(2)}),
(Interferometer,
{'phi': interferometer_phi_normal(n_wires=2), 'varphi': interferometer_varphi_normal(n_wires=2),
'theta': interferometer_theta_normal(n_wires=2), 'wires': range(2)}),
(Interferometer,
{'phi': interferometer_phi_uniform(n_wires=3), 'varphi': interferometer_varphi_uniform(n_wires=3),
'theta': interferometer_theta_uniform(n_wires=3), 'wires': range(3)}),
(Interferometer,
{'phi': interferometer_phi_normal(n_wires=3), 'varphi': interferometer_varphi_normal(n_wires=3),
'theta': interferometer_theta_normal(n_wires=3), 'wires': range(3)})
]
@pytest.mark.parametrize("template, dict", QUBIT_INIT)
def test_integration_qubit_init(self, template, dict):
"""Checks parameter initialization compatible with qubit templates."""
n_wires = len(dict['wires'])
dev = qml.device('default.qubit', wires=n_wires)
@qml.qnode(dev)
def circuit():
template(**dict)
return qml.expval(qml.Identity(0))
# Check that execution does not throw error
circuit()
@pytest.mark.parametrize("template, dict", CV_INIT)
def test_integration_qubit_init(self, template, dict, gaussian_dummy):
"""Checks parameter initialization compatible with qubit templates."""
n_wires = len(dict['wires'])
dev = gaussian_dummy(n_wires)
@qml.qnode(dev)
def circuit():
template(**dict)
return qml.expval(qml.Identity(0))
# Check that execution does not throw error
circuit()