Quantum-Enhanced-ML-Applications

⚠️ This repository is a work in progress and currently under development. Contributions and feedback are welcome!

Overview

This repository explores the implementation of hybrid quantum-classical machine learning algorithms, inspired by cutting-edge research in quantum-enhanced machine learning. The focus is on leveraging quantum computing platforms like Qiskit, Cirq, and others to solve problems in computer vision and beyond.

Key highlights include:

• Hybrid Models: Combining quantum and classical computing approaches.
• Quantum Algorithms: Exploring data re-uploading schemes, patch-based GANs, and quantum-enhanced optimization.
• Applications: Practical implementations for tasks such as image classification, feature extraction, and generative modeling.

Project Goals

1. Demonstrate the potential of quantum computing in enhancing traditional ML tasks.
2. Implement algorithms discussed in quantum ML research papers.
3. Provide clear and reproducible code for hybrid quantum-classical models.
4. Serve as a learning resource for quantum ML enthusiasts.

Features

• Quantum Data Re-Uploading: Implementing quantum circuits for efficient data encoding and iterative processing.
• Patch-Based Generative Adversarial Networks (GANs): Enhancing image generation with quantum elements.
• Quantum Optimization: Solving ML optimization problems with quantum algorithms.
• Visualization: Using tools to visualize quantum circuits and results.

Technologies Used

• Quantum Computing Frameworks:

  • Qiskit (IBM)
  • Cirq (Google)
  • TensorFlow Quantum

• Programming Language: Python

• Libraries:

  • numpy, scikit-learn, matplotlib for classical ML.
  • Quantum SDK libraries (Qiskit, Cirq) for quantum implementations.

• Platforms:

  • IBM Quantum for Qiskit-based experiments.
  • Google Quantum AI for Cirq-based models.

Repository Structure

Quantum-Enhanced-ML-Applications/  
│  
├── data/  
│   ├── raw/                  # Raw datasets used in experiments  
│   └── processed/            # Preprocessed datasets  
│  
├── models/  
│   ├── quantum_data_reuploading/   # Quantum data re-uploading implementation  
│   └── patch_gan/                  # Patch GAN with quantum circuits  
│  
├── notebooks/  
│   ├── experiments.ipynb      # Jupyter notebooks for model experiments  
│   └── visualization.ipynb    # Quantum circuit and result visualization  
│  
├── src/  
│   ├── utils.py               # Utility scripts for preprocessing and evaluation  
│   └── quantum_models.py      # Implementation of hybrid quantum-classical models  
│  
├── results/                   # Results and logs from experiments  
│  
└── README.md                  # Repository documentation  

Getting Started

Prerequisites

1. Python 3.8+
2. Install the required libraries using the following command:

   pip install -r requirements.txt

3. Set up access to a quantum computing platform:
   • For Qiskit: IBM Quantum
   • For Cirq: Google Quantum AI

Installation

1. Clone this repository:

   git clone https://github.com/iamvisheshsrivastava/Quantum-Enhanced-ML-Applications.git
   cd Quantum-Enhanced-ML-Applications

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Usage

1. Explore the Jupyter notebooks in the notebooks/ folder to understand individual experiments.
2. Modify the configuration in src/quantum_models.py to run different models.
3. Run experiments directly from the terminal:

   python src/quantum_models.py

Roadmap

• Initial setup of the repository
• Implementation of quantum data re-uploading scheme
• Implementation of patch-based GAN with quantum elements
• Optimization experiments using quantum annealing
• Adding more datasets for testing and validation

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or fix:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add new feature"  

4. Push to the branch:

   git push origin feature-name

5. Submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

References

1. Exploring Quantum-Enhanced Machine Learning
2. Qiskit Documentation
3. Cirq Documentation

Contact

For any questions or suggestions, feel free to reach out:

• Email: [email protected]
• LinkedIn: Vishesh Srivastava