def setUp(self):
super().setUp()
self.seed = 7
aqua_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 = aqua_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]]
# Set an initial state for the generator circuit
init_dist = UniformDistribution(sum(num_qubits), low=self._bounds[0], high=self._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)
# Set generator's initial parameters
init_params = aqua_globals.random.random(2 * sum(num_qubits)) * 2 * 1e-2
# Set variational form
var_form = RealAmplitudes(sum(num_qubits), reps=1, initial_state=init_distribution,
entanglement=entangler_map)
self.generator_circuit = UnivariateVariationalDistribution(sum(num_qubits), var_form,
init_params,
low=self._bounds[0],
high=self._bounds[1])
def setUp(self):
super().setUp()
# Number training data samples
N = 5000
# Load data samples from log-normal distribution with mean=1 and standard deviation=1
mu = 1
sigma = 1
self._real_data = np.random.lognormal(mean=mu, sigma=sigma, size=N)
# 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]]
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
self._params_torch = {
'algorithm': {
'name': 'QGAN',
'num_qubits': num_qubits,
'batch_size': batch_size,
'num_epochs': num_epochs
},
'problem': {
'name': 'distribution_learning_loading',
'random_seed': 7
},
'generative_network': {
'name': 'QuantumGenerator',
'bounds': self._bounds,
'num_qubits': num_qubits,
'init_params': None,
'snapshot_dir': None
},
'discriminative_network': {
'name': 'PytorchDiscriminator',
'n_features': len(num_qubits)
}
}
self._params_numpy = {
'algorithm': {
'name': 'QGAN',
'num_qubits': num_qubits,
'batch_size': batch_size,
'num_epochs': num_epochs
},
'problem': {
'name': 'distribution_learning_loading',
'random_seed': 7
},
'generative_network': {
'name': 'QuantumGenerator',
'bounds': self._bounds,
'num_qubits': num_qubits,
'init_params': None,
'snapshot_dir': None
},
'discriminative_network': {
'name': 'NumpyDiscriminator',
'n_features': len(num_qubits)
}
}
# 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.quantum_instance_statevector = QuantumInstance(
backend=BasicAer.get_backend('statevector_simulator'),
circuit_caching=False,
seed_simulator=2,
seed_transpiler=2)
self.quantum_instance_qasm = QuantumInstance(
backend=BasicAer.get_backend('qasm_simulator'),
shots=1000,
circuit_caching=False,
seed_simulator=2,
seed_transpiler=2)
# Set entangler map
entangler_map = [[0, 1]]
# Set an initial state for the generator circuit
init_dist = UniformDistribution(sum(num_qubits),
low=self._bounds[0],
high=self._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)
# Set variational form
var_form = RY(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 * 1e-2
# Set generator circuit
g_circuit = UnivariateVariationalDistribution(sum(num_qubits),
var_form,
init_params,
low=self._bounds[0],
high=self._bounds[1])
# initial_distribution=init_distribution,
# Set quantum generator
self.qgan.set_generator(generator_circuit=g_circuit)
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)
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
for i in sortedkeys:
for j in counts:
if (i == j):
sortedcounts.append(counts.get(j))
plt.suptitle('Normal Distribution')
plt.plot(sortedcounts)
plt.show()
print("\n Uniform Distribution")
print("-----------------")
circuit = QuantumCircuit(q, c)
uniform = UniformDistribution(num_target_qubits=5, low=-0, high=1)
uniform.build(circuit, q)
circuit.measure(q, c)
circuit.draw(output='mpl', filename='uniform.png')
job = execute(circuit, backend, shots=8192)
job_monitor(job)
counts = job.result().get_counts()
print(counts)
sortedcounts = []
sortedkeys = sorted(counts)
for i in sortedkeys:
for j in counts:
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