QSVR¶

class QSVR(*, quantum_kernel=None, **kwargs)[source]¶

Bases: SVR, SerializableModelMixin

Quantum Support Vector Regressor that extends the scikit-learn sklearn.svm.SVR regressor and introduces an additional quantum_kernel parameter.

This class shows how to use a quantum kernel for regression. The class inherits its methods like fit and predict from scikit-learn, see the example below. Read more in the scikit-learn user guide.

Example

qsvr = QSVR(quantum_kernel=qkernel)
qsvr.fit(sample_train,label_train)
qsvr.predict(sample_test)

Parameters:

quantum_kernel (BaseKernel | None) -- Quantum kernel to be used for regression.
*args -- Variable length argument list to pass to SVR constructor.
**kwargs -- Arbitrary keyword arguments to pass to SVR constructor.

Attributes

`class_weight_`
`coef_`	Weights assigned to the features when kernel="linear".
`n_support_`	Number of support vectors for each class.
`quantum_kernel`	Returns quantum kernel
`unused_param`

Methods

`fit`(X, y[, sample_weight])	Fit the SVM model according to the given training data.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`load`(file_name)	Loads a model from the file.
`predict`(X)	Perform regression on samples in X.
`save`(file_name)	Saves this model to the specified file.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.