class QGAN(QuantumAlgorithm):
"""
The Quantum Generative Adversarial Network algorithm.
`qGAN <https://arxiv.org/abs/1904.00043>`__ is a hybrid quantum-classical algorithm used
for generative modeling tasks.
This adaptive algorithm uses the interplay of a generative
:class:`~qiskit.aqua.components.neural_networks.GenerativeNetwork` and a
discriminative :class:`~qiskit.aqua.components.neural_networks.DiscriminativeNetwork`
network to learn the probability distribution underlying given training data.
These networks are trained in alternating optimization steps, where the discriminator tries to
differentiate between training data samples and data samples from the generator and the
generator aims at generating samples which the discriminator classifies as training data
samples. Eventually, the quantum generator learns the training data's underlying probability
distribution. The trained quantum generator loads a quantum state which is a model of the
target distribution.
"""
def __init__(
self,
data: np.ndarray,
bounds: Optional[np.ndarray] = None,
num_qubits: Optional[np.ndarray] = None,
batch_size: int = 500,
num_epochs: int = 3000,
seed: int = 7,
discriminator: Optional[DiscriminativeNetwork] = None,
generator: Optional[GenerativeNetwork] = None,
tol_rel_ent: Optional[float] = None,
snapshot_dir: Optional[str] = None,
quantum_instance: Optional[Union[QuantumInstance, BaseBackend]] = None
) -> None:
"""
Args:
data: Training data of dimension k
bounds: k min/max data values [[min_0,max_0],...,[min_k-1,max_k-1]]
if univariate data: [min_0,max_0]
num_qubits: k numbers of qubits to determine representation resolution,
i.e. n qubits enable the representation of 2**n values
[num_qubits_0,..., num_qubits_k-1]
batch_size: Batch size, has a min. value of 1.
num_epochs: Number of training epochs
seed: Random number seed
discriminator: Discriminates between real and fake data samples
generator: Generates 'fake' data samples
tol_rel_ent: Set tolerance level for relative entropy.
If the training achieves relative entropy equal or lower than tolerance it finishes.
snapshot_dir: Directory in to which to store cvs file with parameters,
if None (default) then no cvs file is created.
quantum_instance: Quantum Instance or Backend
Raises:
AquaError: invalid input
"""
validate_min('batch_size', batch_size, 1)
super().__init__(quantum_instance)
if data is None:
raise AquaError('Training data not given.')
self._data = np.array(data)
if bounds is None:
bounds_min = np.percentile(self._data, 5, axis=0)
bounds_max = np.percentile(self._data, 95, axis=0)
bounds = []
for i, _ in enumerate(bounds_min):
bounds.append([bounds_min[i], bounds_max[i]])
if np.ndim(data) > 1:
if len(bounds) != (len(num_qubits) or len(data[0])):
raise AquaError(
'Dimensions of the data, the length of the data bounds '
'and the numbers of qubits per '
'dimension are incompatible.')
else:
if (np.ndim(bounds) or len(num_qubits)) != 1:
raise AquaError(
'Dimensions of the data, the length of the data bounds '
'and the numbers of qubits per '
'dimension are incompatible.')
self._bounds = np.array(bounds)
self._num_qubits = num_qubits
# pylint: disable=unsubscriptable-object
if np.ndim(data) > 1:
if self._num_qubits is None:
self._num_qubits = np.ones[len(data[0])] * 3
else:
if self._num_qubits is None:
self._num_qubits = np.array([3])
self._data, self._data_grid, self._grid_elements, self._prob_data = \
discretize_and_truncate(self._data, self._bounds, self._num_qubits,
return_data_grid_elements=True,
return_prob=True, prob_non_zero=True)
self._batch_size = batch_size
self._num_epochs = num_epochs
self._snapshot_dir = snapshot_dir
self._g_loss = []
self._d_loss = []
self._rel_entr = []
self._tol_rel_ent = tol_rel_ent
self._random_seed = seed
if generator is None:
self.set_generator()
else:
self._generator = generator
if discriminator is None:
self.set_discriminator()
else:
self._discriminator = discriminator
self.seed = self._random_seed
self._ret = {}
@property
def seed(self):
""" Returns random seed """
return self._random_seed
@seed.setter
def seed(self, s):
"""
Sets the random seed for QGAN and updates the aqua_globals seed
at the same time
Args:
s (int): random seed
"""
self._random_seed = s
aqua_globals.random_seed = self._random_seed
self._discriminator.set_seed(self._random_seed)
@property
def tol_rel_ent(self):
""" Returns tolerance for relative entropy """
return self._tol_rel_ent
@tol_rel_ent.setter
def tol_rel_ent(self, t):
"""
Set tolerance for relative entropy
Args:
t (float): or None, Set tolerance level for relative entropy.
If the training achieves relative
entropy equal or lower than tolerance it finishes.
"""
self._tol_rel_ent = t
@property
def generator(self):
""" Returns generator """
return self._generator
# pylint: disable=unused-argument
def set_generator(self,
generator_circuit=None,
generator_init_params=None,
generator_optimizer=None):
"""
Initialize generator.
Args:
generator_circuit (VariationalForm): parameterized quantum circuit which sets
the structure of the quantum generator
generator_init_params(numpy.ndarray): initial parameters for the generator circuit
generator_optimizer (Optimizer): optimizer to be used for the training of the generator
"""
self._generator = QuantumGenerator(self._bounds, self._num_qubits,
generator_circuit,
generator_init_params,
self._snapshot_dir)
@property
def discriminator(self):
""" Returns discriminator """
return self._discriminator
def set_discriminator(self, discriminator=None):
"""
Initialize discriminator.
Args:
discriminator (Discriminator): discriminator
"""
if discriminator is None:
self._discriminator = NumPyDiscriminator(len(self._num_qubits))
else:
self._discriminator = discriminator
self._discriminator.set_seed(self._random_seed)
@property
def g_loss(self):
""" Returns generator loss """
return self._g_loss
@property
def d_loss(self):
""" Returns discriminator loss """
return self._d_loss
@property
def rel_entr(self):
""" Returns relative entropy between target and trained distribution """
return self._rel_entr
def get_rel_entr(self):
""" Get relative entropy between target and trained distribution """
samples_gen, prob_gen = self._generator.get_output(
self._quantum_instance)
temp = np.zeros(len(self._grid_elements))
for j, sample in enumerate(samples_gen):
for i, element in enumerate(self._grid_elements):
if sample == element:
temp[i] += prob_gen[j]
prob_gen = temp
prob_gen = [1e-8 if x == 0 else x for x in prob_gen]
rel_entr = entropy(prob_gen, self._prob_data)
return rel_entr
def _store_params(self, e, d_loss, g_loss, rel_entr):
with open(os.path.join(self._snapshot_dir, 'output.csv'),
mode='a') as csv_file:
fieldnames = [
'epoch', 'loss_discriminator', 'loss_generator',
'params_generator', 'rel_entropy'
]
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writerow({
'epoch':
e,
'loss_discriminator':
np.average(d_loss),
'loss_generator':
np.average(g_loss),
'params_generator':
self._generator.generator_circuit.params,
'rel_entropy':
rel_entr
})
self._discriminator.save_model(
self._snapshot_dir) # Store discriminator model
def train(self):
"""
Train the qGAN
"""
if self._snapshot_dir is not None:
with open(os.path.join(self._snapshot_dir, 'output.csv'),
mode='w') as csv_file:
fieldnames = [
'epoch', 'loss_discriminator', 'loss_generator',
'params_generator', 'rel_entropy'
]
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writeheader()
for e in range(self._num_epochs):
aqua_globals.random.shuffle(self._data)
index = 0
while (index + self._batch_size) <= len(self._data):
real_batch = self._data[index:index + self._batch_size]
index += self._batch_size
generated_batch, generated_prob = self._generator.get_output(
self._quantum_instance, shots=self._batch_size)
# 1. Train Discriminator
ret_d = self._discriminator.train(
[real_batch, generated_batch], [
np.ones(len(real_batch)) / len(real_batch),
generated_prob
])
d_loss_min = ret_d['loss']
# 2. Train Generator
self._generator.set_discriminator(self._discriminator)
ret_g = self._generator.train(self._quantum_instance,
shots=self._batch_size)
g_loss_min = ret_g['loss']
self._d_loss.append(np.around(float(d_loss_min), 4))
self._g_loss.append(np.around(g_loss_min, 4))
rel_entr = self.get_rel_entr()
self._rel_entr.append(np.around(rel_entr, 4))
self._ret['params_d'] = ret_d['params']
self._ret['params_g'] = ret_g['params']
self._ret['loss_d'] = np.around(float(d_loss_min), 4)
self._ret['loss_g'] = np.around(g_loss_min, 4)
self._ret['rel_entr'] = np.around(rel_entr, 4)
if self._snapshot_dir is not None:
self._store_params(e, np.around(d_loss_min, 4),
np.around(g_loss_min, 4),
np.around(rel_entr, 4))
logger.debug('Epoch %s/%s...', e + 1, self._num_epochs)
logger.debug('Loss Discriminator: %s',
np.around(float(d_loss_min), 4))
logger.debug('Loss Generator: %s', np.around(g_loss_min, 4))
logger.debug('Relative Entropy: %s', np.around(rel_entr, 4))
if self._tol_rel_ent is not None:
if rel_entr <= self._tol_rel_ent:
break
def _run(self):
"""
Run qGAN training
Returns:
dict: with generator(discriminator) parameters & loss, relative entropy
Raises:
AquaError: invalid backend
"""
if self._quantum_instance.backend_name == ('unitary_simulator'
or 'clifford_simulator'):
raise AquaError(
'Chosen backend not supported - '
'Set backend either to statevector_simulator, qasm_simulator'
' or actual quantum hardware')
self.train()
return self._ret
class IQGAN(QuantumAlgorithm):
"""Incremental Quantum Generative Adversarial Network.
The code is forked from Zoufal et al.,
`Quantum Generative Adversarial Networks for learning and loading random distributions
<https://www.nature.com/articles/s41534-019-0223-2>`_
"""
def __init__(
self,
initial_data: np.ndarray,
target_rel_ent: float,
num_qubits: np.ndarray,
bounds: np.ndarray,
batch_size: int = 500,
seed: int = 7,
freq_storage: bool = False,
max_data_length: Optional[int] = None,
discriminator: Optional[DiscriminativeNetwork] = None,
generator: Optional[GenerativeNetwork] = None,
verbose: bool = False,
prob_data_real: Optional[np.ndarray] = None,
snapshot_dir: Optional[str] = None,
quantum_instance: Optional[Union[QuantumInstance, BaseBackend,
Backend]] = None
) -> None:
"""
Args:
initial_data: Initial training data of dimension k
target_rel_ent: Set target level for relative entropy.
If the training achieves relative entropy equal or lower than tolerance it finishes.
num_qubits: k numbers of qubits to determine representation resolution,
i.e. n qubits enable the representation of 2**n values
[num_qubits_0,..., num_qubits_k-1]
bounds: k min/max data values [[min_0,max_0],...,[min_k-1,max_k-1]]
if univariate data: [min_0,max_0]
batch_size: Batch size, has a min. value of 1.
seed: Random number seed
freq_storage: Flag indicating if storing only data probabilities is enabled.
Probability distribution will store all passed data. No concept shift can be learned in this case.
max_data_length: Maximum length of data array.
When this length is exceeded, the oldest samples will be removed.
discriminator: Discriminates between real and fake data samples
generator: Generates 'fake' data samples
verbose: Additional output on training process.
prob_data_real: Unknown target data probabilities.
Used only for real relative entropy output.
snapshot_dir: Directory in to which to store cvs file with parameters,
if None (default) then no cvs file is created.
quantum_instance: Quantum Instance or Backend
Raises:
AquaError: invalid input
"""
validate_min('batch_size', batch_size, 1)
super().__init__(quantum_instance)
if initial_data is None:
raise AquaError('Initial training data not given.')
self._stop_training = False
self._training_thread = threading.Thread(target=self._train,
daemon=True)
self._epoch = 0
self._bounds = np.array(bounds)
self._num_qubits = num_qubits
self._batch_size = batch_size
self._freq_storage = freq_storage
self._verbose = verbose
self._snapshot_dir = snapshot_dir
self._g_loss = [] # type: List[float]
self._d_loss = [] # type: List[float]
self._rel_entr = [] # type: List[float]
self._target_rel_ent = target_rel_ent
self._max_data_length = max_data_length
self._random_seed = seed
self._training_handler = None
if prob_data_real is not None:
self._prob_data_real = prob_data_real
self._rel_entr_real = []
self._rel_entr_data = []
else:
self._prob_data_real = None
self._rel_entr_real = None
self._rel_entr_data = None
if self._freq_storage:
self._data = []
self._grid_elements = []
self._prob_data = []
self._prob_data_length = 0
self._update(np.array(initial_data))
else:
self._data = np.array(initial_data)
self._update([])
if generator is None:
self.set_generator()
else:
self._generator = generator
if discriminator is None:
self.set_discriminator()
else:
self._discriminator = discriminator
self.seed = self._random_seed
self._ret = {} # type: Dict[str, Any]
@property
def epoch(self):
""" Return current epoch number """
return self._epoch
def set_training_handler(self, func):
""" Setup function for handling every training epoch with `epoch: int` input argument """
self._training_handler = func
@property
def seed(self):
""" Returns random seed """
return self._random_seed
@seed.setter
def seed(self, s):
"""
Sets the random seed for QGAN and updates the aqua_globals seed
at the same time
Args:
s (int): random seed
"""
self._random_seed = s
aqua_globals.random_seed = self._random_seed
self._discriminator.set_seed(self._random_seed)
@property
def target_rel_ent(self):
""" Returns tolerance for relative entropy """
return self._target_rel_ent
@target_rel_ent.setter
def target_rel_ent(self, t):
"""
Set target for relative entropy
Args:
t (float): or None, Set tolerance level for relative entropy.
If the training achieves relative
entropy equal or lower than tolerance it finishes.
"""
self._target_rel_ent = t
@property
def generator(self):
""" Returns generator """
return self._generator
# pylint: disable=unused-argument
def set_generator(self,
generator_circuit: Optional[Union[
QuantumCircuit, UnivariateVariationalDistribution,
MultivariateVariationalDistribution]] = None,
generator_init_params: Optional[np.ndarray] = None,
generator_optimizer: Optional[Optimizer] = None):
"""Initialize generator.
Args:
generator_circuit: parameterized quantum circuit which sets
the structure of the quantum generator
generator_init_params: initial parameters for the generator circuit
generator_optimizer: optimizer to be used for the training of the generator
"""
self._generator = QuantumGenerator(self._bounds, self._num_qubits,
generator_circuit,
generator_init_params,
generator_optimizer,
self._snapshot_dir)
@property
def discriminator(self):
""" Returns discriminator """
return self._discriminator
def set_discriminator(self, discriminator=None):
"""
Initialize discriminator.
Args:
discriminator (Discriminator): discriminator
"""
if discriminator is None:
self._discriminator = NumPyDiscriminator(len(self._num_qubits))
else:
self._discriminator = discriminator
self._discriminator.set_seed(self._random_seed)
@property
def g_loss(self) -> List[float]:
""" Returns generator loss """
return self._g_loss
@property
def d_loss(self) -> List[float]:
""" Returns discriminator loss """
return self._d_loss
@property
def rel_entr(self) -> List[float]:
""" Returns relative entropy between target and trained distribution """
return self._rel_entr
def get_rel_entr(self) -> float:
""" Get relative entropy between target and trained distribution """
samples_gen, prob_gen = self._generator.get_output(
self._quantum_instance)
temp = np.zeros(len(self._grid_elements))
for j, sample in enumerate(samples_gen):
for i, element in enumerate(self._grid_elements):
if sample == element:
temp[i] += prob_gen[j]
prob_gen = temp
print('Generator parameters: ', self._generator._bound_parameters)
print('Generated probabilities: ', prob_gen)
prob_gen = [1e-8 if x == 0 else x for x in prob_gen]
rel_entr = entropy(prob_gen, self._prob_data)
print('')
return rel_entr
@property
def rel_entr_real(self) -> List[float]:
""" Returns relative entropy between unknown target and trained distribution """
return self._rel_entr_real
def get_rel_entr_real(self) -> float:
""" Get relative entropy between unknown target and trained distribution """
samples_gen, prob_gen = self._generator.get_output(
self._quantum_instance)
temp = np.zeros(len(self._grid_elements))
for j, sample in enumerate(samples_gen):
for i, element in enumerate(self._grid_elements):
if sample == element:
temp[i] += prob_gen[j]
prob_gen = temp
prob_gen = [1e-8 if x == 0 else x for x in prob_gen]
rel_entr = entropy(prob_gen, self._prob_data_real)
return rel_entr
@property
def rel_entr_data(self) -> List[float]:
""" Returns relative entropy between unknown target and known data """
return self._rel_entr_data
def get_rel_entr_data(self) -> float:
""" Get relative entropy between unknown target and known data """
rel_entr = entropy(self._prob_data, self._prob_data_real)
return rel_entr
def _update(self, data: np.ndarray):
print('Updating data...')
if self._freq_storage:
print('Old grid elements: ', self._grid_elements)
print('Old data probabilities: ', self._prob_data)
print('Old data count: ', self._prob_data_length)
print('New data count: ', len(data))
print('New data: ', data)
new_data_length = len(data)
_, _, new_grid_elements, new_prob_data = \
discretize_and_truncate(data, self._bounds, self._num_qubits,
return_data_grid_elements=True,
return_prob=True, prob_non_zero=True)
# Common merged grid elements
temp_grid_elements = np.unique(
np.concatenate((self._grid_elements, new_grid_elements), 0))
temp_prob_data = np.zeros(len(temp_grid_elements))
for j, sample in enumerate(temp_grid_elements):
for i, element in enumerate(self._grid_elements):
if sample == element:
temp_prob_data[
j] += self._prob_data[i] * self._prob_data_length
break
for i, element in enumerate(new_grid_elements):
if sample == element:
temp_prob_data[j] += new_prob_data[i] * new_data_length
break
# Normalize data
temp_prob_data[j] /= (self._prob_data_length + new_data_length)
self._prob_data_length += new_data_length
self._grid_elements = temp_grid_elements
self._prob_data = temp_prob_data
print('Processed data count: ', self._prob_data_length)
print('Processed grid elements: ', self._grid_elements)
print('Processed data probabilities: ', self._prob_data)
if self._prob_data_real is not None:
print('Unknown real data probabilities: ',
self._prob_data_real)
print('')
else:
print('Old data count: ', len(self._data))
print('New data count: ', len(data))
print('New data: ', data)
if self._max_data_length is None:
self._data = np.append(self._data, np.array(data))
else:
elements_left = self._max_data_length - len(data)
if elements_left > 0:
self._data = np.append(self._data[-elements_left:],
np.array(data))
else:
self._data = np.array(data[-self._max_data_length:])
self._data, _, self._grid_elements, self._prob_data = \
discretize_and_truncate(self._data, self._bounds, self._num_qubits,
return_data_grid_elements=True,
return_prob=True, prob_non_zero=True)
print('Processed data count: ', len(self._data))
print('Processed grid elements: ', self._grid_elements)
print('Processed data probabilities: ', self._prob_data)
if self._prob_data_real is not None:
print('Unknown real data probabilities: ',
self._prob_data_real)
print('')
def update(self, data: np.ndarray, target_rel_ent: Optional[float] = None):
self.stop_training()
self._update(data)
if target_rel_ent is not None:
self._target_rel_ent = target_rel_ent
current_rel_entr = self.get_rel_entr()
print('Current relative entropy: ', current_rel_entr)
print('Target relative entropy: ', self._target_rel_ent)
print('')
if current_rel_entr > self._target_rel_ent:
self.train()
def stop_training(self):
print('Stopping training...')
self._stop_training = True
self._training_thread.join()
self._stop_training = False
print('Training stopped')
print('')
def _store_params(self, e, d_loss, g_loss, rel_entr):
with open(os.path.join(self._snapshot_dir, 'output.csv'),
mode='a') as csv_file:
fieldnames = [
'epoch', 'loss_discriminator', 'loss_generator',
'params_generator', 'rel_entropy'
]
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writerow({
'epoch':
e,
'loss_discriminator':
np.average(d_loss),
'loss_generator':
np.average(g_loss),
'params_generator':
self._generator.generator_circuit.params,
'rel_entropy':
rel_entr
})
self._discriminator.save_model(
self._snapshot_dir) # Store discriminator model
def train(self):
"""
Train the model
Raises:
AquaError: Batch size bigger than the number of items in the truncated data set
"""
print('Training...')
print('')
if self._training_thread.is_alive():
print('Training is already in process')
print('')
else:
self._training_thread = threading.Thread(target=self._train,
daemon=True)
self._training_thread.start()
def _train(self):
if self._snapshot_dir is not None:
with open(os.path.join(self._snapshot_dir, 'output.csv'),
mode='w') as csv_file:
fieldnames = [
'epoch', 'loss_discriminator', 'loss_generator',
'params_generator', 'rel_entropy'
]
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writeheader()
if len(self._data) < self._batch_size and not self._freq_storage:
raise AquaError('The batch size needs to be less than the '
'truncated data size of {}'.format(len(
self._data)))
if self._epoch == 0:
# First training launch
# Add initial relative entropy
self._rel_entr.append(np.around(self.get_rel_entr(), 4))
if self._rel_entr_real is not None:
self._rel_entr_real.append(
np.around(self.get_rel_entr_real(), 4))
if self._rel_entr_data is not None:
self._rel_entr_data.append(
np.around(self.get_rel_entr_data(), 4))
while True:
training_data = np.array(self._data)
aqua_globals.random.shuffle(training_data)
index = 0
while (not self._freq_storage and (index + self._batch_size) <= len(training_data)) \
or (self._freq_storage and index <= self._prob_data_length):
if self._freq_storage:
real_batch = np.random.choice(self._grid_elements,
self._batch_size,
p=self._prob_data)
else:
real_batch = training_data[index:index + self._batch_size]
index += self._batch_size
generated_batch, generated_prob = self._generator.get_output(
self._quantum_instance, shots=self._batch_size)
if self._verbose:
#print('Real batch: ', real_batch)
print('Generated batch: ', generated_batch)
print('Generated probabilities: ', generated_prob)
print('Discriminator training...')
# 1. Train Discriminator
ret_d = self._discriminator.train(
[real_batch, generated_batch], [
np.ones(len(real_batch)) / len(real_batch),
generated_prob
])
d_loss_min = ret_d['loss']
if self._verbose:
print('Discriminator loss: ', d_loss_min)
print('')
print('Generator training...')
# 2. Train Generator
self._generator.set_discriminator(self._discriminator)
ret_g = self._generator.train(self._quantum_instance,
shots=self._batch_size)
g_loss_min = ret_g['loss']
if self._verbose:
print('Generator loss: ', g_loss_min)
print('')
self._d_loss.append(np.around(float(d_loss_min), 4))
self._g_loss.append(np.around(g_loss_min, 4))
rel_entr = self.get_rel_entr()
rel_entr_real = None
self._rel_entr.append(np.around(rel_entr, 4))
if self._rel_entr_real is not None:
rel_entr_real = self.get_rel_entr_real()
self._rel_entr_real.append(np.around(rel_entr_real, 4))
if self._rel_entr_data is not None:
self._rel_entr_data.append(
np.around(self.get_rel_entr_data(), 4))
self._ret['params_d'] = ret_d['params']
self._ret['params_g'] = ret_g['params']
self._ret['loss_d'] = np.around(float(d_loss_min), 4)
self._ret['loss_g'] = np.around(g_loss_min, 4)
self._ret['rel_entr'] = np.around(rel_entr, 4)
self._epoch += 1
if self._snapshot_dir is not None:
self._store_params(self._epoch, np.around(d_loss_min, 4),
np.around(g_loss_min, 4),
np.around(rel_entr, 4))
print('Epoch ', self._epoch)
print('Loss Discriminator: ', np.around(float(d_loss_min), 4))
print('Loss Generator: ', np.around(g_loss_min, 4))
print('Relative Entropy: ', np.around(rel_entr, 4))
if rel_entr_real is not None:
print('Real Relative Entropy: ', np.around(rel_entr_real, 4))
print('----------------------')
print('')
if self._training_handler is not None:
thread = threading.Thread(target=self._training_handler,
args=([self._epoch, rel_entr]))
thread.daemon = True
thread.start()
thread.join(timeout=0.2)
if rel_entr <= self._target_rel_ent:
break
if self._stop_training:
self._stop_training = False
break
def _run(self):
"""
Run qGAN training
Returns:
dict: with generator(discriminator) parameters & loss, relative entropy
Raises:
AquaError: invalid backend
"""
if self._quantum_instance.backend_name == ('unitary_simulator'
or 'clifford_simulator'):
raise AquaError(
'Chosen backend not supported - '
'Set backend either to statevector_simulator, qasm_simulator'
' or actual quantum hardware')
self.train()
self._training_thread.join()
return self._ret
@property
def training_data(self):
return self._data