qbiocode.learning.compute_pqk module#

Summary#

Functions:

compute_pqk

This function generates quantum circuits, computes projections of the data onto these circuits, and evaluates the performance of a Support Vector Classifier (SVC) on the projected data.

Reference#

compute_pqk(X_train, X_test, y_train, y_test, args, model='PQK', data_key='', verbose=False, encoding='Z', primitive='estimator', entanglement='linear', reps=2)[source]#

This function generates quantum circuits, computes projections of the data onto these circuits, and evaluates the performance of a Support Vector Classifier (SVC) on the projected data. It uses a feature map to encode the data into quantum states and then measures the expectation values of Pauli operators to obtain the features. The SVC is trained on the projected training data and evaluated on the projected test data. The function returns evaluation metrics and model parameters. This function requires a quantum backend (simulator or real quantum hardware) for execution. It supports various configurations such as encoding methods, entanglement strategies, and repetitions of the feature map. The results are saved to files for training and test projections, which are reused if they already exist to avoid redundant computations. This function is part of the main quantum machine learning pipeline (QProfiler.py) and is intended for use in supervised learning tasks. It leverages quantum computing to enhance feature extraction and classification performance on complex datasets. 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 data features.

  • X_test (np.ndarray) – Test data features.

  • y_train (np.ndarray) – Training data labels.

  • y_test (np.ndarray) – Test data labels.

  • args (dict) – Arguments containing backend and other configurations.

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

  • data_key (str) – Key for the dataset, default is ‘’.

  • verbose (bool) – If True, print additional information, default is False.

  • encoding (str) – Encoding method for the quantum circuit, default is ‘Z’.

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

  • entanglement (str) – Entanglement strategy, default is ‘linear’.

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

Returns:

A dictionary containing evaluation metrics and model parameters.

Return type:

modeleval (dict)