QSVC

class QSVC(*, quantum_kernel=None, **kwargs)

기반 클래스: SVC, SerializableModelMixin

Quantum Support Vector Classifier that extends the scikit-learn sklearn.svm.SVC classifier and introduces an additional quantum_kernel parameter.

This class shows how to use a quantum kernel for classification. 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

qsvc = QSVC(quantum_kernel=qkernel)
qsvc.fit(sample_train,label_train)
qsvc.predict(sample_test)

매개변수:

• quantum_kernel (BaseKernel | None) – Quantum kernel to be used for classification.

• *args – Variable length argument list to pass to SVC constructor.

• **kwargs – Arbitrary keyword arguments to pass to SVC constructor.

Attributes

coef_	Weights assigned to the features when kernel="linear".
n_support_	Number of support vectors for each class.
probA_	Parameter learned in Platt scaling when probability=True.
probB_	Parameter learned in Platt scaling when probability=True.
quantum_kernel	Returns quantum kernel
unused_param

Methods

decision_function(X)	Evaluate the decision function for the samples in X.
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 classification on samples in X.
predict_log_proba(X)	Compute log probabilities of possible outcomes for samples in X.
predict_proba(X)	Compute probabilities of possible outcomes for samples in X.
save(file_name)	Saves this model to the specified file.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
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.