Tutorials

Welcome to the QBioCode tutorials! These Jupyter notebooks provide hands-on examples demonstrating how to use various features and applications of QBioCode for quantum healthcare and life sciences applications.

Getting Started

Before running these tutorials, make sure you have:

• Installed QBioCode following the Installation Guide

• Set up your Python environment with all required dependencies

• Access to quantum computing resources (if running quantum algorithms)

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Tutorial Gallery

1. Artificial Data Generation

Learn how to generate synthetic datasets for testing and benchmarking quantum machine learning algorithms.

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2. QProfiler - Automated ML Model Benchmarking

Learn how to use QProfiler to systematically benchmark and compare quantum and classical machine learning models on artificial datasets. This tutorial demonstrates:

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What You’ll Learn:

• Generate artificial datasets with specific characteristics

• Configure and run QProfiler experiments via YAML configuration

• Evaluate multiple ML models (quantum and classical) automatically

• Analyze performance metrics (accuracy, F1-score, AUC)

• Visualize model comparisons and correlations

• Interpret results for model selection

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3. QSage - Quantum-Inspired Feature Importance

Explore QSage, an intelligent meta-learning system that predicts which machine learning models will perform best on your dataset before you run them. By learning from data complexity patterns across multiple datasets, QSage provides data-driven model recommendations. This tutorial shows how to:

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What You’ll Learn:

• Load pre-trained QSage models

• Analyze dataset characteristics (intrinsic dimension, Fisher discriminant ratio, etc.) from compiled ML benchmark results

• Apply QSAGE to predict the model

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4. Quantum Projection Learning (QPL)

Learn about Quantum Projection Learning (QPL), a technique that combines quantum feature maps with multiple classical machine learning algorithms. This comprehensive tutorial demonstrates how to systematically evaluate quantum-enhanced features across different learners.

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What You’ll Learn:

• Generate synthetic datasets with controlled complexity

• Apply quantum feature maps to create quantum projections

• Train multiple classical models (SVC, RF, XGBoost, MLP, LR) on quantum features

• Compare quantum-enhanced vs. classical baseline performance

• Visualize and analyze comprehensive performance metrics

• Use QProfiler for automated QPL experiments

Key Concepts:

• Quantum projection methods and expectation value measurements

• Ensemble learning with quantum features

• Data complexity analysis for quantum advantage prediction

• Systematic model comparison and evaluation

• Integration with classical ML pipelines

Workflow:

1. Generate or load classification datasets

2. Configure QPL experiments via YAML files

3. Apply quantum feature maps (ZZ, Pauli, etc.)

4. Extract quantum projections from circuits

5. Train 5+ classical models on quantum features

6. Compare with classical baselines

7. Analyze results and identify quantum advantages

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5. Projected Quantum Kernel (PQK) - Ovarian Cancer Survival Prediction

Learn how to apply Projected Quantum Kernels (PQK) to real-world cancer genomics data for survival prediction. This advanced tutorial demonstrates quantum-enhanced machine learning on multi-omics ovarian cancer data from the Multi-Omics Cancer Benchmark (TCGA preprocessed data).

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What You’ll Learn:

• Automatically download and process multi-omics cancer data

• Create 3-year survival labels from clinical data

• Apply quantum feature maps to high-dimensional genomics data

• Use PQK to create quantum feature representations

• Compare quantum-enhanced vs. classical SVM performance

• Work with multi-omics data (miRNA, methylation, gene expression)

• Perform comprehensive hyperparameter tuning for quantum kernels

• Evaluate quantum performance on real biomedical datasets

Dataset:

• Ovarian cancer (OV) multi-omics data from Multi-Omics Cancer Benchmark

• TCGA preprocessed data with automatic download

• 3-year survival prediction task

• Four data modalities: miRNA, DNA methylation, gene expression, and integrated

Key Techniques:

• Automated data download and preprocessing pipeline

• Patient ID standardization across multi-omics datasets

• Survival label creation from clinical data

• Quantum kernel methods with ZZ feature maps

• Pairwise qubit entanglement strategies

• PCA dimensionality reduction for quantum encoding

• Stratified cross-validation for robust evaluation

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Additional Resources

• API Documentation - Detailed API reference

• QProfiler App - Standalone profiling application

• QSage App - Feature selection application

• GitHub Repository - Source code and examples

Support

If you encounter any issues or have questions about the tutorials:

• Check the GitHub Issues

• Review the Contributing Guide

• Consult the API documentation for detailed function references