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)
var_form = TwoLocal(int(np.sum(num_qubits)), 'ry', 'cz', entanglement=entangler_map,
reps=1, initial_state=init_distribution)
# Set generator's initial parameters
init_params = aqua_globals.random.rand(
var_form.num_parameters_settable) * 2 * np.pi
# Set generator circuit
g_circuit = UnivariateVariationalDistribution(int(sum(num_qubits)), var_form, init_params,
low=bounds[0], high=bounds[1])
print("g_circuit set")
# Set quantum generator
qgan.set_generator(generator_circuit=g_circuit)
# Set classical discriminator neural network
discriminator = NumPyDiscriminator(len(num_qubits))
qgan.set_discriminator(discriminator)
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