🧪 Quantum Random Number Generator (QRNG) with Django & IBM Qiskit

This project implements a Quantum Random Number Generator (QRNG) using IBM Quantum's real quantum backends through Qiskit Runtime, wrapped in a Django web framework for API access and frontend interaction. It generates high-entropy quantum random numbers by leveraging quantum superposition and measurement, with support for entropy analysis and range-based random number scaling.

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🌐 Live Preview

Run locally on: http://127.0.0.1:8000/
Access the random number API at: POST http://127.0.0.1:8000/random

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📌 Key Features

• ✅ Quantum randomness using real IBM Q backends (or simulator fallback)
• ✅ Secure IBM Quantum token handling using .env file
• ✅ Shannon Entropy analysis for randomness strength
• ✅ User-defined range scaling of quantum-generated output
• ✅ Web frontend + JSON API output
• ✅ Django-powered backend structure
• ✅ Modular and clean code organization

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🧰 Tech Stack

Component	Technology
Quantum Backend	IBM Quantum (Qiskit Runtime)
Language	Python 3.11
Web Framework	Django
Frontend	HTML, CSS (Basic Styling)
API Format	JSON
Hosting	Localhost (127.0.0.1)
Security	.env + .gitignore to hide secrets

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⚙️ Setup Instructions

1. 🧬 Clone the Repository

git clone https://github.com/radhikajamwal/Quantum-Random-Number-Generator.git
cd Quantum-Random-Number-Generator

2.🔐 Create .env File

In the root folder, create a .env file and add your IBM Quantum API token:

TOKEN=your_ibmq_token_here

3. 📦 Install Dependencies

Make sure Python 3.11+ is installed and IBM account is active

4. 🚀 Run the Django Server

python manage.py runserver

Open in browser: http://127.0.0.1:8000

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📡 API Usage

🔸 Endpoint: /random Method: POST

Content-Type: application/json

🔸 Sample Request:

{
  "device": "ibmq_qasm_simulator",
  "min": 0,
  "max": 100
}

🔸 Sample Response:

{
  "result": 47,
  "entropy": 4.91,
  "bitstrings": {
    "01001": 57,
    "10011": 43
  }
}

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🔢 Entropy Calculation

Shannon entropy is used to measure unpredictability of the bitstring distribution:

H(X) = -Σ p(x) * log₂(p(x))

🔸 Ideal entropy for 5 qubits = 5.0

🔸 Higher entropy = better randomness

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📁 Project Structure

qsite/
├── qrng/              # Django app (views, URLs)
│   ├── views.py
│   └── urls.py
├── templates/         # Frontend templates
│   └── index.html
├── static/            # CSS/JS assets (optional)
├── qsite/             # Django config
├── .env               # Your IBM Quantum token (ignored in git)
├── .gitignore
├── manage.py
└── README.md

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✅ .gitignore

.env
__pycache__/
*.pyc
*.log
db.sqlite3

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🛡️ Security Notes

🔸 Use os.getenv("TOKEN") in your code to fetch the API key

🔸 Never hardcode secrets in Python files

🔸 Revoke/reset your IBM Q token if accidentally exposed

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📖 References

Qiskit Documentation

IBM Quantum Platform

Django Docs

Shannon Entropy (Wikipedia)

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👩‍💻 Author

Radhika Jamwal
🔗 LinkedIn
🌐 GitHub