def test_qgan_errors(backend_name):
if not K.check_availability("tensorflow"): # pragma: no cover
pytest.skip("Skipping StyleQGAN test because tensorflow backend is not available.")
original_backend = qibo.get_backend()
if backend_name != "tensorflow":
qibo.set_backend(backend_name)
with pytest.raises(RuntimeError):
qgan = models.StyleQGAN(latent_dim=2)
qibo.set_backend("tensorflow")
with pytest.raises(ValueError):
qgan = models.StyleQGAN(latent_dim=2)
circuit = models.Circuit(2)
with pytest.raises(ValueError):
qgan = models.StyleQGAN(latent_dim=2, layers=2, circuit=circuit)
with pytest.raises(ValueError):
qgan = models.StyleQGAN(latent_dim=2, circuit=circuit)
with pytest.raises(ValueError):
qgan = models.StyleQGAN(latent_dim=2, layers=2, set_parameters=lambda x: x)
reference_distribution = generate_distribution(10)
qgan = models.StyleQGAN(latent_dim=2, circuit=circuit, set_parameters=lambda x: x)
with pytest.raises(ValueError):
qgan.fit(reference_distribution, save=False)
initial_params = np.random.uniform(-0.15, 0.15, 18)
qgan = models.StyleQGAN(latent_dim=2, layers=2)
with pytest.raises(ValueError):
qgan.fit(reference_distribution, initial_params=initial_params, save=False)
qibo.set_backend(original_backend)
def test_qgan_circuit_error():
if not K.check_availability("tensorflow"): # pragma: no cover
pytest.skip("Skipping StyleQGAN test because tensorflow backend is not available.")
original_backend = qibo.get_backend()
qibo.set_backend("tensorflow")
reference_distribution = generate_distribution(10)
# use wrong number of qubits so that we capture the error
nqubits = reference_distribution.shape[1] + 1
nlayers = 1
circuit = models.Circuit(nqubits)
for l in range(nlayers):
for q in range(nqubits):
circuit.add(gates.RY(q, 0))
circuit.add(gates.RZ(q, 0))
for i in range(0, nqubits - 1):
circuit.add(gates.CZ(i, i + 1))
circuit.add(gates.CZ(nqubits - 1, 0))
for q in range(nqubits):
circuit.add(gates.RY(q, 0))
initial_params = np.random.uniform(-0.15, 0.15, 18)
qgan = models.StyleQGAN(
latent_dim=2,
circuit=circuit,
set_parameters=lambda x: x
)
with pytest.raises(ValueError):
qgan.fit(reference_distribution, initial_params=initial_params, n_epochs=1, save=False)
qibo.set_backend(original_backend)
def test_custom_qgan():
if not K.check_availability("tensorflow"): # pragma: no cover
pytest.skip("Skipping StyleQGAN test because tensorflow backend is not available.")
original_backend = qibo.get_backend()
qibo.set_backend("tensorflow")
def set_params(circuit, params, x_input, i):
"""Set the parameters for the quantum generator circuit."""
p = []
index = 0
noise = 0
for l in range(1):
for q in range(3):
p.append(params[index]*x_input[noise][i] + params[index+1])
index+=2
noise=(noise+1)%2
p.append(params[index]*x_input[noise][i] + params[index+1])
index+=2
noise=(noise+1)%2
for q in range(3):
p.append(params[index]*x_input[noise][i] + params[index+1])
index+=2
noise=(noise+1)%2
circuit.set_parameters(p)
nqubits = 3
nlayers = 1
reference_distribution = generate_distribution(10)
circuit = models.Circuit(nqubits)
for l in range(nlayers):
for q in range(nqubits):
circuit.add(gates.RY(q, 0))
circuit.add(gates.RZ(q, 0))
for i in range(0, nqubits - 1):
circuit.add(gates.CZ(i, i + 1))
circuit.add(gates.CZ(nqubits - 1, 0))
for q in range(nqubits):
circuit.add(gates.RY(q, 0))
initial_params = np.random.uniform(-0.15, 0.15, 18)
qgan = models.StyleQGAN(
latent_dim=2,
circuit=circuit,
set_parameters=set_params
)
qgan.fit(reference_distribution, initial_params=initial_params, n_epochs=1, save=False)
assert qgan.latent_dim == 2
assert qgan.batch_samples == 128
assert qgan.n_epochs == 1
assert qgan.lr == 0.5
qibo.set_backend(original_backend)
def test_default_qgan():
if not K.check_availability("tensorflow"): # pragma: no cover
pytest.skip("Skipping StyleQGAN test because tensorflow backend is not available.")
original_backend = qibo.get_backend()
qibo.set_backend("tensorflow")
reference_distribution = generate_distribution(10)
qgan = models.StyleQGAN(latent_dim=2, layers=1)
qgan.fit(reference_distribution, n_epochs=1, save=False)
assert qgan.layers == 1
assert qgan.latent_dim == 2
assert qgan.batch_samples == 128
assert qgan.n_epochs == 1
assert qgan.lr == 0.5
qibo.set_backend(original_backend)
def test_qgan_custom_discriminator():
if not K.check_availability("tensorflow"): # pragma: no cover
pytest.skip("Skipping StyleQGAN test because tensorflow backend is not available.")
from tensorflow.keras.models import Sequential # pylint: disable=E0611,E0401
from tensorflow.keras.layers import Dense # pylint: disable=E0611,E0401
original_backend = qibo.get_backend()
qibo.set_backend("tensorflow")
reference_distribution = generate_distribution(10)
# use wrong number of qubits so that we capture the error
nqubits = reference_distribution.shape[1] + 1
discriminator = Sequential()
discriminator.add(Dense(200, use_bias=False, input_dim=nqubits))
discriminator.add(Dense(1, activation='sigmoid'))
qgan = models.StyleQGAN(latent_dim=2, layers=1, discriminator=discriminator)
with pytest.raises(ValueError):
qgan.fit(reference_distribution, n_epochs=1, save=False)
qibo.set_backend(original_backend)
