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)
Tutorial Gallery#
1. Artificial Data Generation#
Learn how to generate synthetic datasets for testing and benchmarking quantum machine learning algorithms.
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:
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
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:
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
4. Quantum Ensemble Learning#
Learn how to use quantum ensemble methods to improve classification performance by leveraging quantum superposition to evaluate multiple training set configurations simultaneously. This tutorial demonstrates two quantum ensemble approaches.
What You’ll Learn:
Generate blob datasets for binary classification
Implement fixed swap-based quantum ensemble method
Implement random unitary-based quantum ensemble method
Use quantum SWAP test for cosine similarity measurement
Compare quantum ensemble with classical baselines (Random Forest, XGBoost)
Evaluate performance using accuracy and Brier score metrics
Understand quantum superposition for ensemble learning
Key Concepts:
Quantum ensemble learning via superposition
SWAP test for quantum state comparison
Controlled-SWAP operations for deterministic data rearrangement
Haar-random unitaries for general mixing
One-hot encoding for quantum state preparation
Quantum advantage in ensemble methods
Methods:
Swap Method: Uses fixed controlled-SWAP operations to create deterministic permutations of training data
Random Unitary Method: Applies Haar-random unitary transformations for more general data mixing
References:
Macaluso et al. (2023) - “A variational algorithm for quantum neural networks”
Rhrissorrakrai et al. (2025) - “Quantum Ensemble Learning” (arXiv:2506.02213)
5. 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.
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:
Generate or load classification datasets
Configure QPL experiments via YAML files
Apply quantum feature maps (ZZ, Pauli, etc.)
Extract quantum projections from circuits
Train 5+ classical models on quantum features
Compare with classical baselines
Analyze results and identify quantum advantages
6. 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).
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
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