def init_args(self, oracle, incremental=False, num_iterations=1):
if QuantumAlgorithm.is_statevector_backend(self.backend):
raise ValueError(
'Selected backend "{}" does not support measurements.'.format(
QuantumAlgorithm.backend_name(self.backend)))
self._oracle = oracle
self._max_num_iterations = 2**(len(self._oracle.variable_register()) /
2)
self._incremental = incremental
self._num_iterations = num_iterations
if incremental:
logger.debug(
'Incremental mode specified, ignoring "num_iterations".')
else:
if num_iterations > self._max_num_iterations:
logger.warning(
'The specified value {} for "num_iterations" might be too high.'
.format(num_iterations))
def init_args(self, training_dataset, test_dataset, datapoints, optimizer,
feature_map, var_form):
"""Initialize the object
Args:
training_dataset (dict): {'A': numpy.ndarray, 'B': numpy.ndarray, ...}
test_dataset (dict): the same format as `training_dataset`
datapoints (numpy.ndarray): NxD array, N is the number of data and D is data dimension
optimizer (Optimizer): Optimizer instance
feature_map (FeatureMap): FeatureMap instance
var_form (VariationalForm): VariationalForm instance
Notes:
We used `label` denotes numeric results and `class` means the name of that class (str).
"""
if QuantumAlgorithm.is_statevector_backend(self.backend):
raise ValueError('Selected backend "{}" is not supported.'.format(
QuantumAlgorithm.backend_name(self.backend)))
if training_dataset is None:
raise AlgorithmError('Training dataset must be provided')
self._training_dataset, self._class_to_label = split_dataset_to_data_and_labels(
training_dataset)
if test_dataset is not None:
self._test_dataset = split_dataset_to_data_and_labels(
test_dataset, self._class_to_label)
self._label_to_class = {
label: class_name
for class_name, label in self._class_to_label.items()
}
self._num_classes = len(list(self._class_to_label.keys()))
self._datapoints = datapoints
self._optimizer = optimizer
self._feature_map = feature_map
self._var_form = var_form
self._num_qubits = self._feature_map.num_qubits