qbiocode.learning.compute_qnn module#

Summary#

Functions:

compute_qnn

This function computes a Quantum Neural Network (QNN) model on the provided training data and evaluates it on the test data.

Reference#

compute_qnn(X_train, X_test, y_train, y_test, args, model='QNN', data_key='', primitive='sampler', verbose=False, local_optimizer='COBYLA', maxiter=100, encoding='Z', entanglement='linear', reps=2, ansatz_type='amp')[source]#

This function computes a Quantum Neural Network (QNN) model on the provided training data and evaluates it on the test data. It constructs a QNN circuit with a specified feature map and ansatz, optimizes it using a chosen optimizer, and fits the 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 (array-like) – Training feature set.

  • X_test (array-like) – Test feature set.

  • y_train (array-like) – Training labels.

  • y_test (array-like) – Test labels.

  • args (dict) – Dictionary containing configuration parameters for the QNN.

  • model (str, optional) – Model type. Defaults to ‘QNN’.

  • data_key (str, optional) – Key for the dataset. Defaults to ‘’.

  • primitive (Literal['estimator', 'sampler'], optional) – Type of primitive to use. Defaults to ‘sampler’.

  • verbose (bool, optional) – If True, prints additional information. Defaults to False.

  • local_optimizer (Literal['COBYLA', 'L_BFGS_B', 'GradientDescent'], optional) – Optimizer to use. Defaults to ‘COBYLA’.

  • maxiter (int, optional) – Maximum number of iterations for the optimizer. Defaults to 100.

  • encoding (str, optional) – Feature encoding method. Defaults to ‘Z’.

  • entanglement (str, optional) – Entanglement strategy for the circuit. Defaults to ‘linear’.

  • reps (int, optional) – Number of repetitions for the feature map and ansatz. Defaults to 2.

  • ansatz_type (str, optional) – Type of ansatz to use. Defaults to ‘amp’.

Returns:

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

Return type:

modeleval (dict)