def test_ecev(self):
""" European Call Expected Value test """
bounds = np.array([0., 7.])
num_qubits = [3]
entangler_map = []
for i in range(sum(num_qubits)):
entangler_map.append([i, int(np.mod(i + 1, sum(num_qubits)))])
g_params = [
0.29399714, 0.38853322, 0.9557694, 0.07245791, 6.02626428,
0.13537225
]
# Set an initial state for the generator circuit
init_dist = NormalDistribution(int(sum(num_qubits)),
mu=1.,
sigma=1.,
low=bounds[0],
high=bounds[1])
init_distribution = np.sqrt(init_dist.probabilities)
init_distribution = Custom(num_qubits=sum(num_qubits),
state_vector=init_distribution)
var_form = TwoLocal(int(np.sum(num_qubits)),
'ry',
'cz',
reps=1,
initial_state=init_distribution,
entanglement=entangler_map)
uncertainty_model = UnivariateVariationalDistribution(int(
sum(num_qubits)),
var_form,
g_params,
low=bounds[0],
high=bounds[1])
strike_price = 2
c_approx = 0.25
european_call = EuropeanCallExpectedValue(uncertainty_model,
strike_price=strike_price,
c_approx=c_approx)
uncertainty_model.set_probabilities(
QuantumInstance(BasicAer.get_backend('statevector_simulator')))
algo = AmplitudeEstimation(5, european_call)
result = algo.run(
quantum_instance=BasicAer.get_backend('statevector_simulator'))
self.assertAlmostEqual(result['estimation'], 1.2580, places=4)
self.assertAlmostEqual(result['max_probability'], 0.8785, places=4)
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 __init__(self,
bounds: np.ndarray,
num_qubits: List[int],
generator_circuit: Optional[
Union[UnivariateVariationalDistribution,
MultivariateVariationalDistribution,
QuantumCircuit]] = None,
init_params: Optional[Union[List[float], np.ndarray]] = None,
snapshot_dir: Optional[str] = None) -> None:
"""
Args:
bounds: k min/max data values [[min_1,max_1],...,[min_k,max_k]],
given input data dim k
num_qubits: k numbers of qubits to determine representation resolution,
i.e. n qubits enable the representation of 2**n values [n_1,..., n_k]
generator_circuit: a UnivariateVariationalDistribution for univariate data,
a MultivariateVariationalDistribution for multivariate data,
or a QuantumCircuit implementing the generator.
init_params: 1D numpy array or list, Initialization for
the generator's parameters.
snapshot_dir: str or None, if not None save the optimizer's parameter after every
update step to the given directory
Raises:
AquaError: Set multivariate variational distribution to represent multivariate data
"""
super().__init__()
self._bounds = bounds
self._num_qubits = num_qubits
self.generator_circuit = generator_circuit
if self.generator_circuit is None:
entangler_map = []
if np.sum(num_qubits) > 2:
for i in range(int(np.sum(num_qubits))):
entangler_map.append(
[i, int(np.mod(i + 1, np.sum(num_qubits)))])
else:
if np.sum(num_qubits) > 1:
entangler_map.append([0, 1])
if len(num_qubits) > 1:
num_qubits = list(map(int, num_qubits))
low = bounds[:, 0].tolist()
high = bounds[:, 1].tolist()
init_dist = MultivariateUniformDistribution(num_qubits,
low=low,
high=high)
q = QuantumRegister(sum(num_qubits))
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')
if init_params is None:
init_params = aqua_globals.random.rand(
var_form.num_parameters) * 2 * 1e-2
# Set generator circuit
self.generator_circuit = MultivariateVariationalDistribution(
num_qubits, var_form, init_params, low=low, high=high)
else:
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(sum(num_qubits),
depth=1,
initial_state=init_distribution,
entangler_map=entangler_map,
entanglement_gate='cz')
if init_params is None:
init_params = aqua_globals.random.rand(
var_form.num_parameters) * 2 * 1e-2
# Set generator circuit
self.generator_circuit = UnivariateVariationalDistribution(
int(np.sum(num_qubits)),
var_form,
init_params,
low=bounds[0],
high=bounds[1])
if len(num_qubits) > 1:
if isinstance(self.generator_circuit,
MultivariateVariationalDistribution):
pass
else:
raise AquaError('Set multivariate variational distribution '
'to represent multivariate data')
else:
if isinstance(self.generator_circuit,
UnivariateVariationalDistribution):
pass
else:
raise AquaError('Set univariate variational distribution '
'to represent univariate data')
# Set optimizer for updating the generator network
self._optimizer = ADAM(maxiter=1,
tol=1e-6,
lr=1e-3,
beta_1=0.7,
beta_2=0.99,
noise_factor=1e-6,
eps=1e-6,
amsgrad=True,
snapshot_dir=snapshot_dir)
if np.ndim(self._bounds) == 1:
bounds = np.reshape(self._bounds, (1, len(self._bounds)))
else:
bounds = self._bounds
for j, prec in enumerate(self._num_qubits):
# prepare data grid for dim j
grid = np.linspace(bounds[j, 0], bounds[j, 1], (2**prec))
if j == 0:
if len(self._num_qubits) > 1:
self._data_grid = [grid]
else:
self._data_grid = grid
self._grid_elements = grid
elif j == 1:
self._data_grid.append(grid)
temp = []
for g_e in self._grid_elements:
for g in grid:
temp0 = [g_e]
temp0.append(g)
temp.append(temp0)
self._grid_elements = temp
else:
self._data_grid.append(grid)
temp = []
for g_e in self._grid_elements:
for g in grid:
temp0 = deepcopy(g_e)
temp0.append(g)
temp.append(temp0)
self._grid_elements = deepcopy(temp)
self._data_grid = np.array(self._data_grid)
self._shots = None
self._discriminator = None
self._ret = {}
class QuantumGenerator(GenerativeNetwork):
"""
Quantum Generator.
The quantum generator is a parametrized quantum circuit which can be trained with the
:class:`~qiskit.aqua.algorithms.QGAN` algorithm
to generate a quantum state which approximates the probability
distribution of given training data. At the beginning of the training the parameters will
be set randomly, thus, the output will is random. Throughout the training the quantum
generator learns to represent the target distribution.
Eventually, the trained generator can be used for state preparation e.g. in QAE.
"""
def __init__(self,
bounds: np.ndarray,
num_qubits: List[int],
generator_circuit: Optional[
Union[UnivariateVariationalDistribution,
MultivariateVariationalDistribution,
QuantumCircuit]] = None,
init_params: Optional[Union[List[float], np.ndarray]] = None,
snapshot_dir: Optional[str] = None) -> None:
"""
Args:
bounds: k min/max data values [[min_1,max_1],...,[min_k,max_k]],
given input data dim k
num_qubits: k numbers of qubits to determine representation resolution,
i.e. n qubits enable the representation of 2**n values [n_1,..., n_k]
generator_circuit: a UnivariateVariationalDistribution for univariate data,
a MultivariateVariationalDistribution for multivariate data,
or a QuantumCircuit implementing the generator.
init_params: 1D numpy array or list, Initialization for
the generator's parameters.
snapshot_dir: str or None, if not None save the optimizer's parameter after every
update step to the given directory
Raises:
AquaError: Set multivariate variational distribution to represent multivariate data
"""
super().__init__()
self._bounds = bounds
self._num_qubits = num_qubits
self.generator_circuit = generator_circuit
if self.generator_circuit is None:
entangler_map = []
if np.sum(num_qubits) > 2:
for i in range(int(np.sum(num_qubits))):
entangler_map.append(
[i, int(np.mod(i + 1, np.sum(num_qubits)))])
else:
if np.sum(num_qubits) > 1:
entangler_map.append([0, 1])
if len(num_qubits) > 1:
num_qubits = list(map(int, num_qubits))
low = bounds[:, 0].tolist()
high = bounds[:, 1].tolist()
init_dist = MultivariateUniformDistribution(num_qubits,
low=low,
high=high)
q = QuantumRegister(sum(num_qubits))
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')
if init_params is None:
init_params = aqua_globals.random.rand(
var_form.num_parameters) * 2 * 1e-2
# Set generator circuit
self.generator_circuit = MultivariateVariationalDistribution(
num_qubits, var_form, init_params, low=low, high=high)
else:
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(sum(num_qubits),
depth=1,
initial_state=init_distribution,
entangler_map=entangler_map,
entanglement_gate='cz')
if init_params is None:
init_params = aqua_globals.random.rand(
var_form.num_parameters) * 2 * 1e-2
# Set generator circuit
self.generator_circuit = UnivariateVariationalDistribution(
int(np.sum(num_qubits)),
var_form,
init_params,
low=bounds[0],
high=bounds[1])
if len(num_qubits) > 1:
if isinstance(self.generator_circuit,
MultivariateVariationalDistribution):
pass
else:
raise AquaError('Set multivariate variational distribution '
'to represent multivariate data')
else:
if isinstance(self.generator_circuit,
UnivariateVariationalDistribution):
pass
else:
raise AquaError('Set univariate variational distribution '
'to represent univariate data')
# Set optimizer for updating the generator network
self._optimizer = ADAM(maxiter=1,
tol=1e-6,
lr=1e-3,
beta_1=0.7,
beta_2=0.99,
noise_factor=1e-6,
eps=1e-6,
amsgrad=True,
snapshot_dir=snapshot_dir)
if np.ndim(self._bounds) == 1:
bounds = np.reshape(self._bounds, (1, len(self._bounds)))
else:
bounds = self._bounds
for j, prec in enumerate(self._num_qubits):
# prepare data grid for dim j
grid = np.linspace(bounds[j, 0], bounds[j, 1], (2**prec))
if j == 0:
if len(self._num_qubits) > 1:
self._data_grid = [grid]
else:
self._data_grid = grid
self._grid_elements = grid
elif j == 1:
self._data_grid.append(grid)
temp = []
for g_e in self._grid_elements:
for g in grid:
temp0 = [g_e]
temp0.append(g)
temp.append(temp0)
self._grid_elements = temp
else:
self._data_grid.append(grid)
temp = []
for g_e in self._grid_elements:
for g in grid:
temp0 = deepcopy(g_e)
temp0.append(g)
temp.append(temp0)
self._grid_elements = deepcopy(temp)
self._data_grid = np.array(self._data_grid)
self._shots = None
self._discriminator = None
self._ret = {}
def set_seed(self, seed):
"""
Set seed.
Args:
seed (int): seed
"""
aqua_globals.random_seed = seed
def set_discriminator(self, discriminator):
"""
Set discriminator network.
Args:
discriminator (Discriminator): Discriminator used to compute the loss function.
"""
self._discriminator = discriminator
def construct_circuit(self, params=None):
"""
Construct generator circuit.
Args:
params (numpy.ndarray): parameters which should be used to run the generator,
if None use self._params
Returns:
Instruction: construct the quantum circuit and return as gate
"""
q = QuantumRegister(sum(self._num_qubits), name='q')
qc = QuantumCircuit(q)
if params is None:
self.generator_circuit.build(qc=qc, q=q)
else:
generator_circuit_copy = deepcopy(self.generator_circuit)
generator_circuit_copy.params = params
generator_circuit_copy.build(qc=qc, q=q)
# return qc.copy(name='qc')
return qc.to_instruction()
def get_output(self,
quantum_instance,
qc_state_in=None,
params=None,
shots=None):
"""
Get classical data samples from the generator.
Running the quantum generator circuit results in a quantum state.
To train this generator with a classical discriminator, we need to sample classical outputs
by measuring the quantum state and mapping them to feature space defined by the training
data.
Args:
quantum_instance (QuantumInstance): Quantum Instance, used to run the generator
circuit.
qc_state_in (QuantumCircuit): deprecated
params (numpy.ndarray): array or None, parameters which should
be used to run the generator, if None use self._params
shots (int): if not None use a number of shots that is different from the
number set in quantum_instance
Returns:
list: generated samples, array: sample occurrence in percentage
"""
instance_shots = quantum_instance.run_config.shots
q = QuantumRegister(sum(self._num_qubits), name='q')
qc = QuantumCircuit(q)
qc.append(self.construct_circuit(params), q)
if quantum_instance.is_statevector:
pass
else:
c = ClassicalRegister(sum(self._num_qubits), name='c')
qc.add_register(c)
qc.measure(q, c)
if shots is not None:
quantum_instance.set_config(shots=shots)
result = quantum_instance.execute(qc)
generated_samples = []
if quantum_instance.is_statevector:
result = result.get_statevector(qc)
values = np.multiply(result, np.conj(result))
values = list(values.real)
keys = []
for j in range(len(values)):
keys.append(np.binary_repr(j, int(sum(self._num_qubits))))
else:
result = result.get_counts(qc)
keys = list(result)
values = list(result.values())
values = [float(v) / np.sum(values) for v in values]
generated_samples_weights = values
for i, _ in enumerate(keys):
index = 0
temp = []
for k, p in enumerate(self._num_qubits):
bin_rep = 0
j = 0
while j < p:
bin_rep += int(keys[i][index]) * 2**(int(p) - j - 1)
j += 1
index += 1
if len(self._num_qubits) > 1:
temp.append(self._data_grid[k][int(bin_rep)])
else:
temp.append(self._data_grid[int(bin_rep)])
generated_samples.append(temp)
self.generator_circuit._probabilities = generated_samples_weights
if shots is not None:
# Restore the initial quantum_instance configuration
quantum_instance.set_config(shots=instance_shots)
return generated_samples, generated_samples_weights
def loss(self, x, weights): # pylint: disable=arguments-differ
"""
Loss function for training the generator's parameters.
Args:
x (numpy.ndarray): sample label (equivalent to discriminator output)
weights (numpy.ndarray): probability for measuring the sample
Returns:
float: loss function
"""
try:
# pylint: disable=no-member
loss = (-1) * np.dot(np.log(x).transpose(), weights)
except Exception: # pylint: disable=broad-except
loss = (-1) * np.dot(np.log(x), weights)
return loss.flatten()
def _get_objective_function(self, quantum_instance, discriminator):
"""
Get objective function
Args:
quantum_instance (QuantumInstance): used to run the quantum circuit.
discriminator (torch.nn.Module): discriminator network to compute the sample labels.
Returns:
objective_function: objective function for quantum generator optimization
"""
def objective_function(params):
"""
Objective function
Args:
params (numpy.ndarray): generator parameters
Returns:
self.loss: loss function
"""
generated_data, generated_prob = self.get_output(quantum_instance,
params=params,
shots=self._shots)
prediction_generated = discriminator.get_label(generated_data,
detach=True)
return self.loss(prediction_generated, generated_prob)
return objective_function
def train(self, quantum_instance=None, shots=None):
"""
Perform one training step w.r.t to the generator's parameters
Args:
quantum_instance (QuantumInstance): used to run the generator circuit.
shots (int): Number of shots for hardware or qasm execution.
Returns:
dict: generator loss(float) and updated parameters (array).
"""
self._shots = shots
# Force single optimization iteration
self._optimizer._maxiter = 1
self._optimizer._t = 0
objective = self._get_objective_function(quantum_instance,
self._discriminator)
self.generator_circuit.params, loss, _ = \
self._optimizer.optimize(num_vars=len(self.generator_circuit.params),
objective_function=objective,
initial_point=self.generator_circuit.params)
self._ret['loss'] = loss
self._ret['params'] = self.generator_circuit.params
return self._ret
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)
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)
class QuantumGenerator(GenerativeNetwork):
"""
Generator
"""
CONFIGURATION = {
'name': 'QuantumGenerator',
'description': 'qGAN Generator Network',
'input_schema': {
'$schema': 'http://json-schema.org/draft-07/schema#',
'id': 'generator_schema',
'type': 'object',
'properties': {
'bounds': {
'type': 'array'
},
'num_qubits': {
'type': 'array'
},
'init_params': {
'type': ['array', 'null'],
'default': None
},
'snapshot_dir': {
'type': ['string', 'null'],
'default': None
}
},
'additionalProperties': False
}
}
def __init__(self,
bounds,
num_qubits,
generator_circuit=None,
init_params=None,
snapshot_dir=None):
"""
Initialize the generator network.
Args:
bounds (numpy.ndarray): k min/max data values [[min_1,max_1],...,[min_k,max_k]],
given input data dim k
num_qubits (list): k numbers of qubits to determine representation resolution,
i.e. n qubits enable the representation of 2**n values [n_1,..., n_k]
generator_circuit (Union): generator circuit
UnivariateVariationalDistribution for univariate data/
MultivariateVariationalDistribution for multivariate data, Quantum circuit
to implement the generator.
init_params (Union(list, numpy.ndarray)): 1D numpy array or list, Initialization for
the generator's parameters.
snapshot_dir (str): str or None, if not None save the optimizer's parameter after every
update step to the given directory
Raises:
AquaError: Set multivariate variational distribution to represent multivariate data
"""
super().__init__()
self._bounds = bounds
self._num_qubits = num_qubits
self.generator_circuit = generator_circuit
if self.generator_circuit is None:
entangler_map = []
if np.sum(num_qubits) > 2:
for i in range(int(np.sum(num_qubits))):
entangler_map.append(
[i, int(np.mod(i + 1, np.sum(num_qubits)))])
else:
if np.sum(num_qubits) > 1:
entangler_map.append([0, 1])
if len(num_qubits) > 1:
num_qubits = list(map(int, num_qubits))
low = bounds[:, 0].tolist()
high = bounds[:, 1].tolist()
init_dist = MultivariateUniformDistribution(num_qubits,
low=low,
high=high)
q = QuantumRegister(sum(num_qubits))
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')
if init_params is None:
init_params = aqua_globals.random.rand(
var_form.num_parameters) * 2 * 1e-2
# Set generator circuit
self.generator_circuit = MultivariateVariationalDistribution(
num_qubits, var_form, init_params, low=low, high=high)
else:
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(sum(num_qubits),
depth=1,
initial_state=init_distribution,
entangler_map=entangler_map,
entanglement_gate='cz')
if init_params is None:
init_params = aqua_globals.random.rand(
var_form.num_parameters) * 2 * 1e-2
# Set generator circuit
self.generator_circuit = UnivariateVariationalDistribution(
int(np.sum(num_qubits)),
var_form,
init_params,
low=bounds[0],
high=bounds[1])
if len(num_qubits) > 1:
if isinstance(self.generator_circuit,
MultivariateVariationalDistribution):
pass
else:
raise AquaError('Set multivariate variational distribution '
'to represent multivariate data')
else:
if isinstance(self.generator_circuit,
UnivariateVariationalDistribution):
pass
else:
raise AquaError('Set univariate variational distribution '
'to represent univariate data')
# Set optimizer for updating the generator network
self._optimizer = ADAM(maxiter=1,
tol=1e-6,
lr=1e-3,
beta_1=0.7,
beta_2=0.99,
noise_factor=1e-6,
eps=1e-6,
amsgrad=True,
snapshot_dir=snapshot_dir)
if np.ndim(self._bounds) == 1:
bounds = np.reshape(self._bounds, (1, len(self._bounds)))
else:
bounds = self._bounds
for j, prec in enumerate(self._num_qubits):
# prepare data grid for dim j
grid = np.linspace(bounds[j, 0], bounds[j, 1], (2**prec))
if j == 0:
if len(self._num_qubits) > 1:
self._data_grid = [grid]
else:
self._data_grid = grid
self._grid_elements = grid
elif j == 1:
self._data_grid.append(grid)
temp = []
for g_e in self._grid_elements:
for g in grid:
temp0 = [g_e]
temp0.append(g)
temp.append(temp0)
self._grid_elements = temp
else:
self._data_grid.append(grid)
temp = []
for g_e in self._grid_elements:
for g in grid:
temp0 = deepcopy(g_e)
temp0.append(g)
temp.append(temp0)
self._grid_elements = deepcopy(temp)
self._data_grid = np.array(self._data_grid)
self._shots = None
self._discriminator = None
self._ret = {}
@classmethod
def init_params(cls, params):
"""
Initialize via parameters dictionary and algorithm input instance.
Args:
params (dict): parameters dictionary
Returns:
QuantumGenerator: vqe object
Raises:
AquaError: invalid input
"""
generator_params = params.get(Pluggable.SECTION_KEY_GENERATIVE_NETWORK)
bounds = generator_params.get('bounds')
if bounds is None:
raise AquaError("Data value bounds are required.")
num_qubits = generator_params.get('num_qubits')
if num_qubits is None:
raise AquaError("Numbers of qubits per dimension required.")
init_params = generator_params.get('init_params')
snapshot_dir = generator_params.get('snapshot_dir')
return cls(bounds,
num_qubits,
generator_circuit=None,
init_params=init_params,
snapshot_dir=snapshot_dir)
@classmethod
def get_section_key_name(cls):
return Pluggable.SECTION_KEY_GENERATIVE_NETWORK
def set_seed(self, seed):
"""
Set seed.
Args:
seed (int): seed
"""
aqua_globals.random_seed = seed
def set_discriminator(self, discriminator):
"""
Set discriminator
Args:
discriminator (Discriminator): Discriminator used to compute the loss function.
"""
self._discriminator = discriminator
def construct_circuit(self, params=None):
"""
Construct generator circuit.
Args:
params (numpy.ndarray): parameters which should be used to run the generator,
if None use self._params
Returns:
Instruction: construct the quantum circuit and return as gate
"""
q = QuantumRegister(sum(self._num_qubits), name='q')
qc = QuantumCircuit(q)
if params is None:
self.generator_circuit.build(qc=qc, q=q)
else:
generator_circuit_copy = deepcopy(self.generator_circuit)
generator_circuit_copy.params = params
generator_circuit_copy.build(qc=qc, q=q)
# return qc.copy(name='qc')
return qc.to_instruction()
def get_output(self,
quantum_instance,
qc_state_in=None,
params=None,
shots=None):
"""
Get data samples from the generator.
Args:
quantum_instance (QuantumInstance): Quantum Instance, used to run the generator
circuit.
qc_state_in (QuantumCircuit): depreciated
params (numpy.ndarray): array or None, parameters which should
be used to run the generator,
if None use self._params
shots (int): if not None use a number of shots that is different from the
number set in quantum_instance
Returns:
list: generated samples, array: sample occurrence in percentage
"""
instance_shots = quantum_instance.run_config.shots
q = QuantumRegister(sum(self._num_qubits), name='q')
qc = QuantumCircuit(q)
qc.append(self.construct_circuit(params), q)
if quantum_instance.is_statevector:
pass
else:
c = ClassicalRegister(sum(self._num_qubits), name='c')
qc.add_register(c)
qc.measure(q, c)
if shots is not None:
quantum_instance.set_config(shots=shots)
result = quantum_instance.execute(qc)
generated_samples = []
if quantum_instance.is_statevector:
result = result.get_statevector(qc)
values = np.multiply(result, np.conj(result))
values = list(values.real)
keys = []
for j in range(len(values)):
keys.append(np.binary_repr(j, int(sum(self._num_qubits))))
else:
result = result.get_counts(qc)
keys = list(result)
values = list(result.values())
values = [float(v) / np.sum(values) for v in values]
generated_samples_weights = values
for i, _ in enumerate(keys):
index = 0
temp = []
for k, p in enumerate(self._num_qubits):
bin_rep = 0
j = 0
while j < p:
bin_rep += int(keys[i][index]) * 2**(int(p) - j - 1)
j += 1
index += 1
if len(self._num_qubits) > 1:
temp.append(self._data_grid[k][int(bin_rep)])
else:
temp.append(self._data_grid[int(bin_rep)])
generated_samples.append(temp)
self.generator_circuit._probabilities = generated_samples_weights
if shots is not None:
# Restore the initial quantum_instance configuration
quantum_instance.set_config(shots=instance_shots)
return generated_samples, generated_samples_weights
def loss(self, x, weights): # pylint: disable=arguments-differ
"""
Loss function
Args:
x (numpy.ndarray): sample label (equivalent to discriminator output)
weights (numpy.ndarray): probability for measuring the sample
Returns:
float: loss function
"""
try:
# pylint: disable=no-member
loss = (-1) * np.dot(np.log(x).transpose(), weights)
except Exception: # pylint: disable=broad-except
loss = (-1) * np.dot(np.log(x), weights)
return loss.flatten()
def _get_objective_function(self, quantum_instance, discriminator):
"""
Get objective function
Args:
quantum_instance (QuantumInstance): used to run the quantum circuit.
discriminator (torch.nn.Module): discriminator network to compute the sample labels.
Returns:
objective_function: objective function for quantum generator optimization
"""
def objective_function(params):
"""
Objective function
Args:
params (numpy.ndarray): generator parameters
Returns:
self.loss: loss function
"""
generated_data, generated_prob = self.get_output(quantum_instance,
params=params,
shots=self._shots)
prediction_generated = discriminator.get_label(generated_data,
detach=True)
return self.loss(prediction_generated, generated_prob)
return objective_function
def train(self, quantum_instance=None, shots=None):
"""
Perform one training step w.r.t to the generator's parameters
Args:
quantum_instance (QuantumInstance): used to run the generator circuit.
shots (int): Number of shots for hardware or qasm execution.
Returns:
dict: generator loss(float) and updated parameters (array).
"""
self._shots = shots
# Force single optimization iteration
self._optimizer._maxiter = 1
self._optimizer._t = 0
objective = self._get_objective_function(quantum_instance,
self._discriminator)
self.generator_circuit.params, loss, _ = \
self._optimizer.optimize(num_vars=len(self.generator_circuit.params),
objective_function=objective,
initial_point=self.generator_circuit.params)
self._ret['loss'] = loss
self._ret['params'] = self.generator_circuit.params
return self._ret
def test_ecev(self, use_circuits):
""" European Call Expected Value test """
if not use_circuits:
# ignore deprecation warnings from the deprecation of VariationalForm as input for
# the univariate variational distribution
warnings.filterwarnings("ignore", category=DeprecationWarning)
bounds = np.array([0., 7.])
num_qubits = [3]
entangler_map = []
for i in range(sum(num_qubits)):
entangler_map.append([i, int(np.mod(i + 1, sum(num_qubits)))])
g_params = [
0.29399714, 0.38853322, 0.9557694, 0.07245791, 6.02626428,
0.13537225
]
# Set an initial state for the generator circuit
init_dist = NormalDistribution(int(sum(num_qubits)),
mu=1.,
sigma=1.,
low=bounds[0],
high=bounds[1])
init_distribution = np.sqrt(init_dist.probabilities)
init_distribution = Custom(num_qubits=sum(num_qubits),
state_vector=init_distribution)
var_form = RY(int(np.sum(num_qubits)),
depth=1,
initial_state=init_distribution,
entangler_map=entangler_map,
entanglement_gate='cz')
if use_circuits:
theta = ParameterVector('θ', var_form.num_parameters)
var_form = var_form.construct_circuit(theta)
uncertainty_model = UnivariateVariationalDistribution(int(
sum(num_qubits)),
var_form,
g_params,
low=bounds[0],
high=bounds[1])
if use_circuits:
uncertainty_model._var_form_params = theta
strike_price = 2
c_approx = 0.25
european_call = EuropeanCallExpectedValue(uncertainty_model,
strike_price=strike_price,
c_approx=c_approx)
uncertainty_model.set_probabilities(
QuantumInstance(BasicAer.get_backend('statevector_simulator')))
algo = AmplitudeEstimation(5, european_call)
result = algo.run(
quantum_instance=BasicAer.get_backend('statevector_simulator'))
self.assertAlmostEqual(result['estimation'], 1.2580, places=4)
self.assertAlmostEqual(result['max_probability'], 0.8785, places=4)
if not use_circuits:
warnings.filterwarnings(action="always",
category=DeprecationWarning)
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
# Load the trained circuit parameters
g_params = [0.29399714, 0.38853322, 0.9557694, 0.07245791, 6.02626428, 0.13537225]
# Set an initial state for the generator circuit
init_dist = NormalDistribution(sum(num_qubits), mu=1., sigma=1., low=bounds[0], high=bounds[1])
init_distribution = np.sqrt(init_dist.probabilities)
init_distribution = Custom(num_qubits=sum(num_qubits), state_vector=init_distribution)
# construct the variational form
var_form = RealAmplitudes(sum(num_qubits), entanglement=entangler_map, reps=1, initial_state=init_distribution)
var_form.entanglement_blocks = 'cz'
theta = ParameterVector('θ', var_form.num_parameters)
var_form = var_form.assign_parameters(theta)
# Set generator circuit
g_circuit = UnivariateVariationalDistribution(sum(num_qubits), var_form, g_params,
low=bounds[0], high=bounds[1])
g_circuit._var_form_params = theta
# construct circuit factory for uncertainty model
uncertainty_model = g_circuit
# set the strike price (should be within the low and the high value of the uncertainty)
strike_price = 2
# set the approximation scaling for the payoff function
c_approx = 0.25
# construct circuit factory for payoff function
european_call = EuropeanCallExpectedValue(
uncertainty_model,