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Changelog

All notable changes to QBioCode will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.1.0 - 2026-04-06

⚠️ Breaking Changes

  • Minimum Python version increased to 3.10 (was 3.9)
    • Required for compatibility with latest Qiskit ecosystem (qiskit-ibm-runtime 0.44.0+)
    • Python 3.9 reaches end-of-life in October 2025

Added

Core Features

  • QProfiler: Automated machine learning benchmarking with data complexity analysis

    • Support for multiple ML models (RF, SVM, LR, DT, NB, MLP, XGBoost)
    • Support for quantum ML models (QSVC, PQK, VQC, QNN)
    • Comprehensive data profiling and complexity metrics
    • Batch processing mode for large-scale experiments
    • CLI interface for easy usage

  • QSage: Meta-learning framework for intelligent model selection

    • Model recommendation based on dataset characteristics
    • Pre-trained models for quick predictions
    • Integration with QProfiler results

  • Data Generation: Artificial dataset generation with controlled complexity

    • Multiple dataset types (circles, moons, spirals, S-curve, Swiss roll, spheres)
    • Configurable noise levels and complexity parameters
    • Support for multi-class classification problems

  • Embeddings: Dimensionality reduction and feature extraction

    • Autoencoder implementations
    • Integration with classical embedding methods

  • Evaluation: Comprehensive model and dataset evaluation

    • Multiple metrics (accuracy, F1-score, AUC, training time)
    • Cross-validation support
    • Statistical analysis tools

  • Visualization: Publication-quality plotting functions

    • Correlation analysis plots with scientific styling
    • Heatmaps with customizable colormaps
    • High-resolution output (600 DPI) for publications

Documentation

  • Complete API documentation with Sphinx
  • Tutorials for QProfiler, QSage, and data generation
  • Installation guides for multiple platforms
  • Galaxy integration documentation
  • Example notebooks and workflows

Project Infrastructure

  • GitHub issue templates (bug report, feature request, documentation, question)
  • Pull request template with comprehensive checklists
  • Security policy (SECURITY.md)
  • Support documentation (SUPPORT.md)
  • Contributing guidelines (CONTRIBUTING.md)
  • Code of conduct (CODE_OF_CONDUCT.md)
  • Citation file (CITATION.cff)
  • Zenodo metadata for DOI generation (.zenodo.json)

CI/CD

  • GitHub Actions workflow for continuous integration
    • Multi-OS testing (Ubuntu, macOS, Windows)
    • Multi-Python version testing (3.10, 3.11)
    • Code quality checks (flake8, black, isort, mypy)
    • Test coverage reporting
    • Documentation building
  • GitHub Actions workflow for automated releases
    • PyPI publishing
    • Zenodo archiving
    • Release asset management

Command-Line Tools

  • qprofiler: Run QProfiler experiments
  • qprofiler-batch: Batch processing mode
  • qsage: Model recommendation tool

Changed

  • Updated visualization functions for publication quality
    • Enhanced scatter plots with better colormaps
    • Improved heatmaps with professional styling
    • Better legend positioning and labeling
    • Increased DPI for high-quality output

Fixed

  • NaN handling in correlation visualization
  • Windows path compatibility issues
  • Automated QSage model download

Dependencies

  • Python >= 3.10, < 3.13
  • Qiskit for quantum computing functionality
  • scikit-learn for classical ML algorithms
  • pandas, numpy for data manipulation
  • matplotlib, seaborn for visualization
  • XGBoost for gradient boosting
  • hydra-core for configuration management

[Unreleased]

Added

  • Testing Infrastructure: Comprehensive test suite for core functionality

    • tests/test_data_generation.py: Tests for data generation utilities
    • tests/test_file_utilities.py: Tests for file operations and utilities
    • tests/test_generator_dispatch.py: Tests for generator dispatch logic
    • tests/conftest.py: Pytest configuration and fixtures
    • Test coverage for utility modules and data generation helpers

  • Code Quality Tools: Enhanced development tooling

    • isort integration for consistent import ordering
    • Configuration in pyproject.toml for isort settings
    • Added to dev and all dependency groups

  • Documentation: Testing instructions in README

    • Added "Running Tests" section with pytest usage
    • Instructions for installing development dependencies

Changed

  • Code Formatting: Applied consistent code style across entire codebase

    • Ran black formatter on all Python files
    • Ran isort for standardized import ordering
    • Fixed invalid escape sequences in visualization module
    • Improved code readability and maintainability

  • CI/CD Improvements: Stabilized continuous integration pipeline

    • Updated GitHub Actions workflows to Node.js 24
    • Fixed CI code quality checks for import ordering
    • Fixed CI type-check issues
    • Fixed documentation build process
    • Fixed Pandoc compatibility issues
    • Enhanced workflow reliability across all platforms

  • Testing: Improved test reliability

    • Fixed path-order assumptions in duplicate-file tests
    • Tests now work consistently across different file systems

Fixed

  • Invalid escape sequence in qbiocode/visualization/visualize_correlation.py
  • Import ordering issues throughout codebase
  • Type-check errors in CI pipeline
  • Documentation build failures
  • Path handling in cross-platform tests

Planned Features

  • Additional quantum ML algorithms
  • Enhanced meta-learning capabilities
  • More dataset complexity metrics
  • Performance optimizations
  • Extended Galaxy tool integration

Release Notes

Version 0.1.0 - Initial Public Release

This is the first public release of QBioCode, a comprehensive framework for quantum machine learning applications in healthcare and life sciences. The release includes:

  • Complete implementation of QProfiler and QSage applications
  • Support for both quantum and classical machine learning models
  • Extensive documentation and tutorials
  • Professional project infrastructure for open-source collaboration
  • Automated CI/CD pipelines
  • Ready for PyPI distribution and Zenodo archiving

Note: This is an alpha release. APIs may change in future versions. Please report any issues on our GitHub issue tracker.