QForge is an innovative framework designed to facilitate the development and optimization of quantum circuits using reinforcement learning techniques. This project aims to bridge the gap between quantum computing and machine learning by providing tools to automatically design and test quantum circuits under various configurations and constraints.
Note: this project is still in its early stages and is not yet practical.
- Modular Quantum Environment: Simulate and interact with quantum circuits as part of a reinforcement learning setup.
- Reinforcement Learning Integration: Optimizes quantum circuits using advanced RL techniques for better performance and efficiency.
- Extensible and Scalable: Built to be flexible, allowing for easy integration with other quantum computing frameworks and machine learning libraries.
QForge is committed to pushing the boundaries of quantum computing research by making it accessible to machine learning practitioners and researchers, aiming to uncover new possibilities in both fields through collaborative experimentation and innovation.
- Clone the repository to get started with QForge.
- Detailed documentation and tutorials will be available in the
docs/andnotebooks/directories to help you set up and explore the capabilities of QForge.
We welcome contributions and suggestions to make QForge a robust tool for the quantum computing and machine learning communities. If you are willing to contribute to QForge, you can either:
- Open an Issue with your proposal.
- Contact the primary repository maintainer here.