Entanglement Classifier

A full-stack application for classifying quantum states as entangled or separable using machine learning, inspired by the paper:

Transforming Bell's inequalities into state classifiers with machine learning
Yue-Chi Ma and Man-Hong Yung, npj Quantum Information (2018)

Features

• Quantum State Generation: Generate random two-qubit quantum states.
• Feature Extraction: Compute CHSH/Bell-like features for each state.
• Entanglement Labeling: Label states using the PPT criterion.
• Machine Learning Classifier: Neural network trained to classify states as entangled or separable.
• REST API: FastAPI backend for state generation and classification.
• Frontend UI: React app for user interaction and visualization.

---

Project Structure

entanglementClassifier/
├── backend/           # Python FastAPI backend, ML, quantum logic
│   ├── data/
│   ├── models/
│   ├── utils/
│   ├── main.py
│   ├── requirements.txt
│   └── ...
├── frontend/          # React frontend
│   ├── src/
│   ├── public/
│   └── ...
├── README.md
└── ...

---

Setup Instructions

1. Backend (FastAPI + ML)

a. Install Python dependencies

pip install -r backend/requirements.txt

b. Train the ML model

python -m backend.models.train_model

This will generate and save a neural network model at backend/models/entanglement_classifier.h5.

c. Start the FastAPI server

uvicorn backend.main:app --reload

The API will be available at http://localhost:8000.

2. Frontend (React)

a. Install Node dependencies

cd frontend
npm install

b. Start the React development server

npm start

The app will be available at http://localhost:3000.

---

Usage

• Generate State: Click "Generate State" to get a random quantum state, its features, and entanglement label.
• Classify Custom State: Enter four CHSH features and click "Classify" to get a prediction from the ML model.

---

Deployment

• Frontend: Can be deployed to Vercel, Netlify, or any static hosting.
• Backend: Can be deployed to platforms like Render, Railway, or as a serverless function (with adaptation).
• Update API URLs in the frontend as needed for production.

---

References

• Transforming Bell's inequalities into state classifiers with machine learning (npj Quantum Information, 2018)
• QuTiP: Quantum Toolbox in Python
• FastAPI
• React

---

License

This project is for academic and research purposes. See the original paper for scientific credit.