- class VQC(num_qubits=None, feature_map=None, ansatz=None, loss='cross_entropy', optimizer=None, warm_start=False, quantum_instance=None, initial_point=None, callback=None, *, sampler=None)[fuente]¶
A convenient Variational Quantum Classifier implementation.
The variational quantum classifier (VQC) is a variational algorithm where the measured bitstrings are interpreted as the output of a classifier.
Constructs a quantum circuit and corresponding neural network, then uses it to instantiate a neural network classifier.
Labels can be passed in various formats, they can be plain labels, a one dimensional numpy array that contains integer labels like [0, 1, 2, …], or a numpy array with categorical string labels. One hot encoded labels are also supported. Internally, labels are transformed to one hot encoding and the classifier is always trained on one hot labels.
Multi-label classification is not supported. E.g., \([[1, 1, 0], [0, 1, 1], [1, 0, 1]]\).
num_qubits (int | None) – The number of qubits for the underlying QNN. If
Noneis given, the number of qubits is derived from the feature map or ansatz. If neither of those is given, raises an exception. The number of qubits in the feature map and ansatz are adjusted to this number if required.
feature_map (QuantumCircuit | None) – The (parametrized) circuit to be used as a feature map for the underlying QNN. If
Noneis given, the
ZZFeatureMapis used if the number of qubits is larger than 1. For a single qubit classification problem the
ZFeatureMapis used by default.
ansatz (QuantumCircuit | None) – The (parametrized) circuit to be used as an ansatz for the underlying QNN. If
Noneis given then the
RealAmplitudescircuit is used.
loss (str | Loss) – A target loss function to be used in training. Default value is
optimizer (Optimizer | Minimizer | None) – An instance of an optimizer or a callable to be used in training. Refer to
Minimizerfor more information on the callable protocol. When None defaults to
warm_start (bool) – Use weights from previous fit to start next fit.
quantum_instance (QuantumInstance | Backend | None) – Deprecated: If a quantum instance is sent and
None, the underlying QNN will be of type
CircuitQNN, and the quantum instance will be used to compute the neural network’s results. If a sampler instance is also set, it will override the quantum_instance parameter and a
SamplerQNNwill be used instead.
initial_point (np.ndarray | None) – Initial point for the optimizer to start from.
callback (Callable[[np.ndarray, float], None] | None) – a reference to a user’s callback function that has two parameters and returns
None. The callback can access intermediate data during training. On each iteration an optimizer invokes the callback and passes current weights as an array and a computed value as a float of the objective function being optimized. This allows to track how well optimization / training process is going on.
sampler (BaseSampler | None) – If a sampler instance is sent, the underlying QNN will be of type
SamplerQNN, and the sampler primitive will be used to compute the neural network’s results.
QiskitMachineLearningError – Needs at least one out of
ansatzto be given. Or the number of qubits in the feature map and/or ansatz can’t be adjusted to
Returns the used ansatz.
Return the callback.
Returns the underlying quantum circuit.
Returns the used feature map.
Returns a resulting object from the optimization procedure.
Returns current initial point
Returns the underlying neural network.
Returns the underlying neural network.
The number of classes found in the most recent fit.
Returns the number of qubits used by ansatz and feature map.
Returns an optimizer to be used in training.
Returns the warm start flag.
Returns trained weights as a numpy array.
Fit the model to data matrix X and target(s) y.
Loads a model from the file.
Predict using the network specified to the model.
Saves this model to the specified file.
score(X, y[, sample_weight])
Returns a score of this model given samples and true values for the samples.