qbiocode.learning.compute_qsvc module#

Summary#

Functions:

compute_qsvc

This function computes a quantum support vector classifier (QSVC) using the Qiskit Machine Learning library.

Reference#

compute_qsvc(X_train, X_test, y_train, y_test, args, model='QSVC', data_key='', C=1, gamma='scale', pegasos=False, encoding='ZZ', entanglement='linear', primitive='sampler', reps=2, verbose=False)[source]#

This function computes a quantum support vector classifier (QSVC) using the Qiskit Machine Learning library. It takes training and testing datasets, along with various parameters to configure the QSVC model. It initializes the quantum feature map, sets up the backend and session, and fits the QSVC model to the training data. It then predicts the labels for the test data and evaluates the model’s performance. The function returns the performance results, including accuracy, F1-score, AUC, runtime, as well as model parameters, and other relevant metrics.

Parameters:

  • X_train (np.ndarray) – Training feature set.

  • X_test (np.ndarray) – Testing feature set.

  • y_train (np.ndarray) – Training labels.

  • y_test (np.ndarray) – Testing labels.

  • args (dict) – Dictionary containing arguments for the quantum backend and other settings.

  • model (str) – Model type, default is ‘QSVC’.

  • data_key (str) – Key for the dataset, default is an empty string.

  • C (float) – Regularization parameter for the SVM, default is 1.

  • gamma (str or float) – Kernel coefficient, default is ‘scale’.

  • pegasos (bool) – Whether to use Pegasos QSVC, default is False.

  • encoding (str) – Feature map encoding type, options are ‘ZZ’, ‘Z’, or ‘P’, default is ‘ZZ’.

  • entanglement (str) – Entanglement strategy for the feature map, default is ‘linear’.

  • primitive (str) – Primitive type to use, default is ‘sampler’.

  • reps (int) – Number of repetitions for the feature map, default is 2.

  • verbose (bool) – Whether to print additional information, default is False.

Returns:

A dictionary containing the evaluation results, including accuracy, runtime, model parameters, and other relevant metrics.

Return type:

modeleval (dict)