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Changelog

All notable changes to the Quantum Data Embedding Suite will be documented in this file.

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

[Unreleased]

Added

  • Initial release of Quantum Data Embedding Suite
  • Support for multiple quantum embedding techniques
  • Quantum kernel implementations
  • Comprehensive metrics for embedding evaluation
  • Multi-backend support (Qiskit, PennyLane)
  • Command-line interface (CLI)
  • Visualization tools for quantum embeddings
  • Documentation and examples

[0.1.0] - 2025-01-15

Added

  • Core Embeddings
  • Angle embedding for classical data encoding
  • Amplitude embedding for direct state preparation
  • IQP (Instantaneous Quantum Polynomial) embedding
  • Data re-uploading embedding with variational circuits

  • Hamiltonian embedding for physics-inspired encoding

  • Quantum Kernels

  • Fidelity quantum kernel implementation
  • Projected quantum kernel for feature space mapping

  • Trainable quantum kernel with optimization support

  • Metrics and Analysis

  • Expressibility measurement for embedding quality
  • Trainability analysis for barren plateau detection
  • Gradient variance computation

  • Effective dimension calculation for kernel matrices

  • Backend Support

  • Qiskit backend with IBM Quantum integration
  • PennyLane backend with multi-device support
  • Automatic device selection and optimization

  • Custom backend extension framework

  • Command-Line Interface

  • qdes-cli benchmark for performance evaluation
  • qdes-cli compare for embedding comparison
  • qdes-cli visualize for data visualization

  • qdes-cli experiment for custom experiments

  • Visualization Tools

  • Embedding visualization in reduced dimensions
  • Kernel matrix heatmaps
  • Metrics dashboard

  • Interactive plots with Plotly

  • Examples and Tutorials

  • Basic workflow examples
  • Embedding comparison studies
  • Real quantum hardware usage
  • Custom embedding development

Infrastructure

  • Comprehensive test suite with pytest
  • Documentation with MkDocs Material
  • Continuous integration with GitHub Actions
  • PyPI package distribution
  • Docker containerization support

Performance

  • Optimized circuit compilation
  • Parallel execution support
  • Memory-efficient implementations
  • Caching for repeated computations

[0.0.1] - 2024-12-01

Added

  • Initial project structure
  • Basic embedding framework
  • Prototype implementations

Release Notes

Version 0.1.0 Highlights

This initial release provides a comprehensive suite for quantum data embedding research and applications. Key features include:

🚀 Ready-to-use Implementations: Five different embedding techniques with proven quantum advantage potential.

⚡ Performance Optimized: Efficient implementations with multi-backend support for various quantum devices.

📊 Comprehensive Analysis: Built-in metrics for evaluating embedding quality and detecting common issues like barren plateaus.

🔧 Easy Integration: Simple API design with extensive documentation and examples.

🌐 Multi-Platform: Support for IBM Quantum, AWS Braket, and local simulators.

Future Roadmap

  • v0.2.0: Advanced optimization algorithms, noise-aware embeddings
  • v0.3.0: Quantum neural network integration, AutoML features
  • v0.4.0: Distributed computing support, cloud integration
  • v1.0.0: Production-ready release with enterprise features

Contributing

We welcome contributions! See our Contributing Guide for details on how to get involved.

Support