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