def test_qgan_training_run_algo_torch(self):
"""Test QGAN training using a PyTorch discriminator."""
try:
# Set number of qubits per data dimension as list of k qubit values[#q_0,...,#q_k-1]
num_qubits = [2]
# Batch size
batch_size = 100
# Set number of training epochs
num_epochs = 5
_qgan = QGAN(self._real_data,
self._bounds,
num_qubits,
batch_size,
num_epochs,
discriminator=PyTorchDiscriminator(n_features=len(num_qubits)),
snapshot_dir=None)
_qgan.seed = self.seed
_qgan.set_generator()
trained_statevector = _qgan.run(QuantumInstance(
BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
trained_qasm = _qgan.run(QuantumInstance(BasicAer.get_backend('qasm_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
self.assertAlmostEqual(trained_qasm['rel_entr'],
trained_statevector['rel_entr'], delta=0.1)
except MissingOptionalLibraryError:
self.skipTest('pytorch not installed, skipping test')
def test_qgan_training_run_algo_torch(self):
""" qgan training run algo torch test """
try:
# Set number of qubits per data dimension as list of k qubit values[#q_0,...,#q_k-1]
num_qubits = [2]
# Batch size
batch_size = 100
# Set number of training epochs
num_epochs = 5
_qgan = QGAN(
self._real_data,
self._bounds,
num_qubits,
batch_size,
num_epochs,
discriminator=PyTorchDiscriminator(n_features=len(num_qubits)),
snapshot_dir=None)
_qgan.seed = self.seed
_qgan.set_generator()
trained_statevector = _qgan.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
trained_qasm = _qgan.run(
QuantumInstance(BasicAer.get_backend('qasm_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
self.assertAlmostEqual(trained_qasm['rel_entr'],
trained_statevector['rel_entr'],
delta=0.1)
except Exception as ex: # pylint: disable=broad-except
self.skipTest(str(ex))
def test_qgan_training_run_algo_torch_multivariate(self):
"""Test QGAN training using a PyTorch discriminator, for multivariate distributions."""
try:
# Set number of qubits per data dimension as list of k qubit values[#q_0,...,#q_k-1]
num_qubits = [1, 2]
# Batch size
batch_size = 100
# Set number of training epochs
num_epochs = 5
# Reshape data in a multi-variate fashion
# (two independent identically distributed variables,
# each represented by half of the generated samples)
real_data = self._real_data.reshape((-1, 2))
bounds = [self._bounds, self._bounds]
_qgan = QGAN(
real_data,
bounds,
num_qubits,
batch_size,
num_epochs,
discriminator=PyTorchDiscriminator(n_features=len(num_qubits)),
snapshot_dir=None)
_qgan.seed = self.seed
_qgan.set_generator()
trained_statevector = _qgan.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
trained_qasm = _qgan.run(
QuantumInstance(BasicAer.get_backend('qasm_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
self.assertAlmostEqual(trained_qasm['rel_entr'],
trained_statevector['rel_entr'],
delta=0.1)
except MissingOptionalLibraryError:
self.skipTest('pytorch not installed, skipping test')