def test_qgan_training_run_algo_numpy(self):
""" qgan training run algo numpy test """
# 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=NumPyDiscriminator(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)
def test_qgan_training_run_algo_numpy_multivariate(self):
"""Test QGAN training using a NumPy discriminator, for multivariate distributions."""
# 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=NumPyDiscriminator(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)
def test_qgan_save_model(self):
"""Test the QGAN functionality to store the current model."""
# 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
with tempfile.TemporaryDirectory() as tmpdirname:
_qgan = QGAN(self._real_data,
self._bounds,
num_qubits,
batch_size,
num_epochs,
discriminator=NumPyDiscriminator(n_features=len(num_qubits)),
snapshot_dir=tmpdirname)
_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)
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')
print(type(reshaped_data))
#reshaped_norm_data = [t+1 for t in reshaped_data]
num_qubits = [4]
k = len(num_qubits)
# Set number of training epochs
# Note: The algorithm's runtime can be shortened by reducing the number of training epochs.
num_epochs = 10
# Batch size
# Initialize qGAN
qgan = QGAN(reshaped_data, bounds=bounds, num_qubits=num_qubits,
batch_size=1, num_epochs=num_epochs, snapshot_dir="data")
print("QGAN set")
qgan.seed = 1
# Set quantum instance to run the quantum generator
quantum_instance = QuantumInstance(
backend=BasicAer.get_backend('statevector_simulator'))
print("quantum_instance set")
# Set entangler map
entangler_map = [[0, 1]]
# Set an initial state for the generator circuit
init_dist = UniformDistribution(sum(num_qubits), low=bounds[0], high=bounds[1])
q = QuantumRegister(sum(num_qubits), name='q')
qc = QuantumCircuit(q)
init_dist.build(qc, q)
init_distribution = Custom(num_qubits=sum(num_qubits), circuit=qc)
def QGAN_method(kk, num_qubit, epochs, batch, bound, snap, data):
start = time.time()
real_data = data
# In[41]:
# Number training data samples
N = 1000
# Load data samples from log-normal distribution with mean=1 and standard deviation=1
mu = 1
sigma = 1
# real_data = np.random.lognormal(mean = mu, sigma=sigma, size=N)
# print(real_data)
# Set the data resolution
# Set upper and lower data values as list of k min/max data values [[min_0,max_0],...,[min_k-1,max_k-1]]
bounds = np.array([0, bound])
# Set number of qubits per data dimension as list of k qubit values[#q_0,...,#q_k-1]
num_qubits = [num_qubit]
k = kk
# In[52]:
# Set number of training epochs
# Note: The algorithm's runtime can be shortened by reducing the number of training epochs.
num_epochs = epochs
# Batch size
batch_size = batch
# Initialize qGAN
qgan = QGAN(real_data,
bounds,
num_qubits,
batch_size,
num_epochs,
snapshot_dir=snap)
qgan.seed = 1
# Set quantum instance to run the quantum generator
quantum_instance = QuantumInstance(
backend=BasicAer.get_backend('statevector_simulator'))
# Set entangler map
entangler_map = [[0, 1]]
# Set an initial state for the generator circuit
init_dist = UniformDistribution(sum(num_qubits),
low=bounds[0],
high=bounds[1])
q = QuantumRegister(sum(num_qubits), name='q')
qc = QuantumCircuit(q)
init_dist.build(qc, q)
init_distribution = Custom(num_qubits=sum(num_qubits), circuit=qc)
var_form = RY(int(np.sum(num_qubits)),
depth=k,
initial_state=init_distribution,
entangler_map=entangler_map,
entanglement_gate='cz')
# Set generator's initial parameters
init_params = aqua_globals.random.rand(
var_form._num_parameters) * 2 * np.pi
# Set generator circuit
g_circuit = UnivariateVariationalDistribution(int(sum(num_qubits)),
var_form,
init_params,
low=bounds[0],
high=bounds[1])
# Set quantum generator
qgan.set_generator(generator_circuit=g_circuit)
# Set classical discriminator neural network
discriminator = NumPyDiscriminator(len(num_qubits))
qgan.set_discriminator(discriminator)
# In[53]:
# Run qGAN
qgan.run(quantum_instance)
# Runtime
end = time.time()
print('qGAN training runtime: ', (end - start) / 60., ' min')
return qgan