qbiocode.learning package

Machine Learning Module for QBioCode

This module provides implementations of classical and quantum machine learning algorithms for classification tasks. Each algorithm includes both standard and optimized versions (where applicable) with hyperparameter tuning.

Classical Algorithms

• Decision Tree (DT)

• Logistic Regression (LR)

• Multi-Layer Perceptron (MLP)

• Naive Bayes (NB)

• Random Forest (RF)

• Support Vector Classifier (SVC)

• XGBoost (XGB)

Quantum Algorithms

• Quantum Neural Network (QNN)

• Quantum Support Vector Classifier (QSVC)

• Variational Quantum Classifier (VQC)

• Projected Quantum Kernel (PQK)

• Quantum Ensemble (QEnsemble) - supports both fixed swap and random unitary methods

Usage

>>> from qbiocode.learning import compute_rf, compute_qsvc, compute_qensemble
>>> # Train classical model
>>> results = compute_rf(X_train, y_train, X_test, y_test)
>>> # Train quantum model
>>> qresults = compute_qsvc(X_train, y_train, X_test, y_test)
>>> # Train quantum ensemble with fixed swaps (default)
>>> qens_results = compute_qensemble(X_train, X_test, y_train, y_test, args)
>>> # Train quantum ensemble with random unitaries
>>> qens_random = compute_qensemble(X_train, X_test, y_train, y_test, args,
...                                 ensemble_method="random_unitary")

Submodules:

• qbiocode.learning.compute_dt module
• qbiocode.learning.compute_lr module
• qbiocode.learning.compute_mlp module
• qbiocode.learning.compute_nb module
• qbiocode.learning.compute_pqk module
• qbiocode.learning.compute_qensemble module
• qbiocode.learning.compute_qnn module
• qbiocode.learning.compute_qpl module
• qbiocode.learning.compute_qsvc module
• qbiocode.learning.compute_rf module
• qbiocode.learning.compute_svc module
• qbiocode.learning.compute_vqc module
• qbiocode.learning.compute_xgb module

Summary

__all__ Functions:

compute_dt	This function generates a model using a Decision Tree (DT) Classifier method as implemented in scikit-learn.
compute_dt_opt	This function also generates a model using a Decision Tree (DT) Classifier method as implemented in scikit-learn.
compute_lr	This function generates a model using a Logistic Regression (LR) method as implemented in scikit-learn.
compute_lr_opt	This function also generates a model using a Logistic Regression (LR) method as implemented in scikit-learn.
compute_mlp	This function generates a model using a Multi-layer Perceptron (mlp), a neural network, method as implemented in scikit-learn.
compute_mlp_opt	This function also generates a model using a Multi-layer Perceptron (mlp), a neural network, as implemented in scikit-learn (https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html).
compute_nb	This function generates a model using a Gaussian Naive Bayes (NB) Classifier method as implemented in scikit-learn.
compute_nb_opt	This function generates a model using a Gaussian Naive Bayes (NB) Classifier method as implemented in scikit-learn.
compute_pqk	This function generates quantum circuits, computes projections of the data onto these circuits, and evaluates the performance of classical machine learning models on the projected data.
compute_qensemble	Compute quantum ensemble classifier predictions.
compute_qnn	This function computes a Quantum Neural Network (QNN) model on the provided training data and evaluates it on the test data.
compute_qpl	This function generates quantum circuits, computes projections of the data onto these circuits, and evaluates the performance of classical machine learning models on the projected data.
compute_qsvc	This function computes a quantum support vector classifier (QSVC) using the Qiskit Machine Learning library.
compute_rf	This function generates a model using a Random Forest (RF) Classifier method as implemented in scikit-learn.
compute_rf_opt	This function also generates a model using a Random Forest (RF) Classifier method as implemented in scikit-learn.
compute_svc	This function generates a model using a Support Vector Classifier (SVC) method as implemented in scikit-learn.
compute_svc_opt	This function generates a model using a Support Vector Classifier (SVC) method as implemented in scikit-learn.
compute_vqc	This function computes a Variational Quantum Classifier (VQC) using the Qiskit Machine Learning library.
compute_xgb	This function generates a model using an Extreme Gradient Boositing (xgb) Classifier method as implemented in xgboost.
compute_xgb_opt	This function generates a model using an Extreme Gradient Boositing (xgb) Classifier method as implemented in xgboost.