def set_discriminator(self, discriminator=None):
"""
Initialize discriminator.
Args:
discriminator (Discriminator): discriminator
"""
if discriminator is None:
self._discriminator = NumpyDiscriminator(len(self._num_qubits))
else:
self._discriminator = discriminator
self._discriminator.set_seed(self._random_seed)
def set_discriminator(self, discriminator=None):
"""
Initialize discriminator.
Args:
discriminator:
Returns:
"""
if discriminator is None:
from qiskit.aqua.components.neural_networks.pytorch_discriminator import ClassicalDiscriminator
self._discriminator = NumpyDiscriminator(len(self._num_qubits))
else:
self._discriminator = discriminator
self._discriminator.set_seed(self._random_seed)
return
print(init_dist.probabilities)
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=1, 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)
# -
# Run qGAN
qgan.run(quantum_instance)
# +
# Plot progress w.r.t the generator's and the discriminator's loss function
t_steps = np.arange(num_epochs)
plt.figure(figsize=(6,5))
plt.title("Progress in the loss function")
plt.plot(t_steps, qgan.g_loss, label = "Generator loss function", color = 'mediumvioletred', linewidth = 2)
plt.plot(t_steps, qgan.d_loss, label = "Discriminator loss function", color = 'rebeccapurple', linewidth = 2)
plt.grid()
plt.legend(loc = 'best')