Quantum Machine Learning and Quantum AI Implementations

This repository contains implementations of various quantum machine learning (QML) and quantum AI models using PennyLane, Qiskit, TensorFlow Quantum, and other popular libraries.

Project Structure

• basic_quantum_circuits/: Basic quantum circuits and gates implementations
• variational_circuits/: Variational quantum circuits (VQC) implementations
• qnn_models/: Quantum Neural Network models
• hybrid_models/: Hybrid quantum-classical models
• quantum_kernels/: Quantum kernel methods
• quantum_optimization/: Quantum optimization algorithms
• quantum_generative/: Quantum generative models
• quantum_reinforcement/: Quantum reinforcement learning
• quantum_transfer/: Quantum transfer learning
• quantum_feature/: Quantum feature maps and embeddings
• quantum_ensemble/: Quantum ensemble methods
• utils/: Utility functions and helpers
• examples/: Example applications and use cases
• notebooks/: Jupyter notebooks with tutorials

Requirements

• Python 3.8+
• PennyLane
• Qiskit
• TensorFlow Quantum
• PyTorch
• NumPy
• SciPy
• Matplotlib

Installation

pip install -r requirements.txt

Usage

Each module contains detailed documentation and examples. See the examples/ directory for complete use cases.

Models Implemented

1. Variational Quantum Classifiers (VQC)
2. Quantum Convolutional Neural Networks (QCNN)
3. Quantum Generative Adversarial Networks (QGAN)
4. Quantum Boltzmann Machines (QBM)
5. Quantum Approximate Optimization Algorithm (QAOA)
6. Variational Quantum Eigensolver (VQE)
7. Quantum Kernel Methods
8. Quantum Support Vector Machines (QSVM)
9. Quantum K-means
10. Quantum Principal Component Analysis (QPCA)
11. Quantum Reinforcement Learning
12. Quantum Transfer Learning
13. Quantum Ensemble Methods
14. Quantum Feature Maps and Embeddings
15. Quantum Natural Gradient
16. Quantum Circuit Born Machines
17. Quantum Recurrent Neural Networks
18. Quantum Attention Models
19. Quantum Transformers
20. Quantum Federated Learning

License

MIT