def test_uniform(self):
"""Test the circuit of the uniform distribution is a simple layer of Hadamards."""
circuit = UniformDistribution(3)
expected = QuantumCircuit(3)
expected.h([0, 1, 2])
self.assertEqual(circuit.decompose(), expected)
def setUp(self):
super().setUp()
self.seed = 7
algorithm_globals.random_seed = self.seed
# Number training data samples
n_v = 5000
# Load data samples from log-normal distribution with mean=1 and standard deviation=1
m_u = 1
sigma = 1
self._real_data = algorithm_globals.random.lognormal(mean=m_u,
sigma=sigma,
size=n_v)
# Set upper and lower data values as list of k
# min/max data values [[min_0,max_0],...,[min_k-1,max_k-1]]
self._bounds = [0., 3.]
# 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 = 10
num_epochs = 5
# Initialize qGAN
self.qgan = QGAN(self._real_data,
self._bounds,
num_qubits,
batch_size,
num_epochs,
snapshot_dir=None)
self.qgan.seed = 7
# Set quantum instance to run the quantum generator
self.qi_statevector = QuantumInstance(
backend=BasicAer.get_backend('statevector_simulator'),
seed_simulator=2,
seed_transpiler=2)
self.qi_qasm = QuantumInstance(
backend=BasicAer.get_backend('qasm_simulator'),
shots=1000,
seed_simulator=2,
seed_transpiler=2)
# Set entangler map
entangler_map = [[0, 1]]
qc = UniformDistribution(sum(num_qubits))
ansatz = RealAmplitudes(sum(num_qubits),
reps=1,
entanglement=entangler_map)
qc.compose(ansatz, inplace=True)
self.generator_circuit = qc
# Initialize qGAN
iqgan = IQGAN(
real_data,
target_rel_ent,
num_qubits,
bounds,
batch_size,
freq_storage=freq_storage,
verbose=False,
max_data_length=max_data_length,
prob_data_real=unknown_target_data_prob,
quantum_instance=quantum_instance
)
# Set an initial state for the generator circuit
init_dist = UniformDistribution(sum(num_qubits))
# Generator's depth
k = 2
# Set the ansatz circuit
var_form = TwoLocal(int(np.sum(num_qubits)), 'ry', 'cz', reps=k)
# Generator's initial parameters
#init_params = [0.02789928, 0.02950349, 0.35598046, 0.24428748, 0.02875957, 0.02955343] #lognormal 1 1 (ent=0.001)
#init_params = [0.01436921, 0.02010115, 0.45493457, 0.32251614, 0.0178407, 0.02214568] #lognormal 1 1 (ent=0.1)
init_params = np.zeros(np.sum(num_qubits)*(k+1))
# Set generator circuit by adding the initial distribution infront of the ansatz
g_circuit = var_form.compose(init_dist, front=True)
print('Generator circuit:')
print(g_circuit)
print('')