Quantum Machine Learning and Optimization Examples

Welcome to the code repository for "A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms". This repository provides hands-on examples from the book, with detailed code implementations of various quantum algorithms used in machine learning and optimization tasks.

Overview

This project explores cutting-edge concepts in Quantum Machine Learning (QML) and Quantum Optimization (QO), giving readers a practical understanding of modern quantum algorithms. The repository contains code implementations of the algorithms discussed in the book

Project Structure

.
├── Part 1: I, for One, Welcome our New Quantum Overlords
│   ├── Chapter 1: Foundations of Quantum Computing
│   ├── Chapter 2: The Tools of the Trade in Quantum Computing
├── Part 2: When Time is Gold: Tools for Quantum Optimization
│   ├── Chapter 3: Working with Quadratic Unconstrained Binary Optimization Problems
│   ├── Chapter 4: Adiabatic Quantum Computing and Quantum Annealing
│   ├── Chapter 5: QAOA: Quantum Approximate Optimization Algorithm
│   ├── Chapter 6: GAS: Grover Adaptive Search
│   └── Chapter 7: VQE: Variational Quantum Eigensolver
├── Part 3: A Match Made in Heaven: Quantum Machine Learning
│   ├── Chapter 8: What Is Quantum Machine Learning?
│   ├── Chapter 9: Quantum Support Vector Machines
│   ├── Chapter 10: Quantum Neural Networks
│   ├── Chapter 11: The Best of Both Worlds: Hybrid Architectures
│   └── Chapter 12: Quantum Generative Adversarial Networks
├── Dockerfile
├── requeriments.txt
└── README.md

• Dockerfile: To set up a consistent development environment using Docker.
• requeriments: Requirements need to run the project.
• README.md: This document providing an overview of the project.

Prerequisites

You can install the required Python libraries using the requirements.txt file:

pip install -r requirements.txt

Running the Code

Option 1: Local Environment

1. Clone the repository:

   git clone https://github.com/matheus-araujo/quantum-ml-optimization-examples.git
   cd quantum-ml-optimization-examples

2. Install dependencies:

   pip install -r requirements.txt

Option 2: Using Docker

Alternatively, you can run the examples using Docker for a consistent development environment.

1. Build the Docker image:

   docker build -t quantum-ml-optimization .

2. Run the container:

   docker run -it --rm -v $(pwd):/app quantum-ml-optimization

Project Goals

• Educational Resource: To provide hands-on quantum computing examples in quantum computing, machine learning and optimization.
• Experimentation Platform: To enable users to explore and modify algorithms as they learn about quantum computing.
• Community Contributions: To allow contributions from developers and researchers interested in advancing quantum technologies.

Contributing

Contributions are welcome! If you’d like to contribute:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/new-feature).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature/new-feature).
5. Open a pull request.

License

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

Acknowledgments

Special thanks to the authors of the book "A Practical Guide to Quantum Machine Learning and Quantum Optimization" for providing the theoretical background and inspiration for these examples.

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This README file provides a concise project summary, how to get started, and how to contribute. Feel free to adjust the content to fit your project's details!