🧠 Quantum Self-Learning Matrix v1.0

AI-powered adaptive learning system combining quantum computing simulation, EEG brainwave analysis, and generative AI to personalize education in real time.

🚀 What It Does

Quantum Self-Learning Matrix encodes educational concepts as quantum circuits, simulates them on a local quantum backend (IBM Qiskit Aer), reads optional real-time EEG brainwave data (BrainFlow / OpenBCI), and uses a Llama 4 language model to generate deeply personalized explanations — all without sending sensitive data to the cloud.

Feature	Technology
Quantum circuit encoding	Qiskit + Aer Simulator
Brainwave feedback (optional)	BrainFlow / OpenBCI
AI content generation	Llama 4 via Groq / HeckAI API
REST API backend	Flask (Python)
Interactive UI	Vanilla JS + HTML5 Canvas

📸 Demo

Input  →  "Machine Learning"
Output →  Mastery Score: 92.3%  |  Entanglement Map  |  AI Explanation

🗂️ Project Structure

quantum-learning-matrix/
├── backend/
│   ├── server.py            # Flask REST API + Qiskit integration
│   ├── quantum_engine.py    # Advanced quantum learning engine + EEG
│   └── requirements.txt     # Python dependencies
├── frontend/
│   ├── index.html           # Main UI
│   ├── app.js               # Async JS logic + EEG mock
│   └── style.css            # Dark-mode responsive styles
├── .github/
│   └── FUNDING.yml          # Sponsor button config
├── CHANGELOG.md
├── CONTRIBUTING.md
├── LICENSE                  # © swordenkisk — All Rights Reserved
└── README.md

⚡ Quick Start

1. Clone the repo

git clone https://github.com/swordenkisk/quantum-learning-matrix.git
cd quantum-learning-matrix

2. Install Python dependencies

pip install -r backend/requirements.txt

3. Set your API key

# Linux / macOS
export LLAMA_API_KEY="your-key-here"

# Windows PowerShell
$env:LLAMA_API_KEY="your-key-here"

Get a free key at console.groq.com (Llama 4 Scout is free-tier).

4. Run the backend

python backend/server.py
# → Running on http://localhost:5000

5. Open the frontend

Open frontend/index.html in any modern browser. No build step required.

🧬 How the Quantum Engine Works

Concept → Embedding: The LLM generates a semantic vector for the input concept.
Quantum Encoding: The embedding is mapped onto 8 qubits using RY rotations.
Entanglement Layer: A Hadamard + CNOT chain creates long-range qubit correlations simulating memory consolidation.
Measurement & Scoring: 1024-shot simulation returns a probability distribution; entropy of the distribution becomes the mastery score.
EEG Feedback (optional): Alpha/Theta wave ratios modulate circuit depth for optimal cognitive state alignment.

🛣️ Roadmap

Quantum circuit encoding + local simulator
Llama 4 AI explanation generation
Mock EEG feedback loop
Real OpenBCI hardware integration
AWS / GCP one-click deployment
React Native mobile app
Multi-user dashboard with progress analytics
Subscription billing via Stripe

🤝 Contributing

See CONTRIBUTING.md. Feature requests and bug reports via Issues are welcome.

⚠️ Disclaimer

EEG-based "learning acceleration" is a conceptual prototype. The quantum simulation runs on classical hardware (Aer). Claims about learning speed are illustrative, not clinically validated.

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