Enterprise-grade framework for quantum-enhanced AI and AGI, integrating next-generation quantum computing with advanced machine intelligence.

QuantumAI aims to create a seamless fusion between quantum computing and deep learning to unlock AI capabilities beyond classical limitations, while maintaining industrial-grade security and ethical considerations.

• Quantum-Classical Hybrid Neural Networks
• Multi-Modal Learning with Quantum Enhancement
• Post-Quantum Cryptography Security
• AGI Development Framework
• Hardware Abstraction Layer for Multiple Quantum Backends

poetry install

from quantumai.q_fabric import QuantumCircuit
from quantumai.ai_engine import HybridNetwork

# Initialize quantum circuit
qc = QuantumCircuit()

# Create hybrid network
model = HybridNetwork()

See the docs/ directory for detailed documentation.

All AGI components are sandboxed and require cryptographic signatures for execution.

Proprietary - All Rights Reserved

The future of AI is Quantum - Core framework combining Quantum Computing and AI

app/
    api/        # FastAPI endpoints
    core/       # Core quantum computing logic
    dao/        # Data access layer
    models/     # Data models
    services/   # Business logic
    utils/      # Helpers and utilities
contracts/      # Smart contracts for licensing
docs/          # Documentation
frontend/      # React-based UI
notebooks/     # Jupyter notebooks
scripts/       # Utility scripts  
src/           # Core quantum-AI implementation
test/          # Test suite

• API Documentation
• Development Guide
• Chat Interface
• License System

QuantumAI is a proprietary AI-Quantum computing framework that enhances machine learning algorithms with quantum-powered optimizations. This project is designed for serious researchers, AI engineers, and enterprises seeking to leverage quantum-enhanced AI models.

🔒 Commercial usage requires a paid license. See LICENSE.md for terms.

✅ Quantum-enhanced neural networks – Unlock AI capabilities beyond classical computing.
✅ Hybrid Classical-Quantum Optimization – Combines classical deep learning with quantum optimization.
✅ Quantum Feature Mapping – Transform classical data into quantum states for superior efficiency.
✅ Multi-Quantum Hardware Support – Compatible with IBM Q, Rigetti, Google Quantum AI, IonQ, and more.
✅ FastAPI-Powered API – Expose quantum models via RESTful API & WebSockets.
✅ Built-in Quantum ML Benchmarking – Evaluate classical vs. quantum performance.

To run QuantumAI, ensure you have the following:

🔹 Python 3.9+
🔹 Poetry (Dependency manager)
🔹 gcc/g++ (For compiling core components)

🔹 NVIDIA CUDA – For faster deep learning computations
🔹 cuQuantum SDK – Optimized quantum circuit simulations

• numpy == 1.23.5
• pennylane == 0.31.0

sudo apt-get update
sudo apt-get install python3-dev build-essential gcc g++

poetry config virtualenvs.in-project true
poetry install --no-cache

poetry run pip install --no-cache-dir numpy==1.23.5
poetry install

poetry run uvicorn quantum_ai.api.main:app --reload

from quantum_ai.circuits import QuantumCircuit
qc = QuantumCircuit()
qc.run()

Run the test suite:

poetry run pytest

QuantumAI follows a modular architecture, ensuring extensibility and seamless integration of quantum and classical AI models.

📂 quantum_ai/circuits/

• Gate-based quantum circuits
• Variational quantum algorithms

📂 quantum_ai/api/

• FastAPI-based REST API
• WebSocket support for real-time quantum inference

📂 quantum_ai/embeddings/

• Quantum Feature Mapping
• Hybrid classical-quantum embeddings

📂 quantum_ai/training/

• Quantum-enhanced neural networks
• Hybrid QML optimizers

🚀 Q1 2025: Quantum GANs – Generative adversarial networks powered by quantum sampling.
🚀 Q2 2025: Quantum NLP – Explore quantum-enhanced natural language processing.
🚀 Q3 2025: Federated Quantum Learning – Secure, decentralized AI training.

📜 Full Roadmap

🔹 Fork the Repository
🔹 Create a Feature Branch
🔹 Run Tests Before Submitting PRs
🔹 Submit a Pull Request with Detailed Notes

📘 API Docs: http://localhost:8000/docs
📘 Architecture Overview
📘 Development Guide

QuantumAI is licensed under the QuantumAI Proprietary License (QPL v1.1).

⚠️ This software is NOT open-source. Commercial use requires a paid license.

📜 Read Full Terms: LICENSE.md

📧 Email: [email protected]
🌎 Website: quantum.api

A next-generation chat interface with quantum computing capabilities.

• 🚀 Real-time quantum-enhanced chat responses
• ✨ Animated message transitions
• 📝 Markdown support in messages
• 🎵 Sound effects for interactions
• 👍 Message reactions
• ⌨️ Typing indicators
• 📱 Responsive design
• 🎨 Dark mode interface

1. Install dependencies:

npm install
# or
yarn install

2. Install required packages:

npm install framer-motion react-markdown react-icons use-sound axios

3. Add sound effects:

• Create a public folder in your project root
• Add message-sound.mp3 to the public folder

4. Start the development server:

npm run dev
# or
yarn dev

Create a .env file in the root directory:

REACT_APP_API_URL=your_api_url

• React with TypeScript
• Framer Motion for animations
• React Markdown for message formatting
• Use-Sound for audio effects
• Axios for API calls

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

A blockchain-based licensing system for AI model access control and monetization.

The QuantumAI License Management System provides:

• Time-based access control for AI models
• Automated license validation and enforcement
• Usage-based billing and royalty collection
• Programmatic access revocation
• Transparent transaction history

1. License Token (ERC-1155)

   • Represents active license ownership
   • Includes metadata about license terms
   • Non-transferable implementation

2. Revenue Sharing (ERC-2981)

   • Automated royalty distribution
   • Configurable revenue split
   • Per-transaction enforcement

3. Access Control

   • Time-based validation
   • Grace period handling
   • Blacklist functionality

const contract = await QuantumAILicense.deploy(
  licenseFee,    // Base fee in wei
  royaltyRate    // Percentage (1-100)
);

// Purchase license
await contract.purchaseLicense(duration, { value: fee });

// Validate license
const isValid = await contract.hasValidLicense(address);

// Revoke access
await contract.revokeLicense(address);

from web3 import Web3
from quantum_ai.licensing import LicenseValidator

def verify_access(user_address: str) -> bool:
    return await LicenseValidator.check_license(user_address)

• Immutable license records
• Cryptographic access verification
• Automated compliance enforcement
• Transparent audit trail

• Smart Contract Reference
• API Integration Guide
• Security Model

Commercial use requires a valid on-chain license. See LICENSE.md.

A cutting-edge framework integrating Quantum Computing with Artificial Intelligence and AGI systems.

📂 q_fabric/      - Quantum computation modules and simulators
📂 ai_engine/     - AI models and quantum-enhanced layers
📂 security/      - Cryptographic and quantum-secure authentication
📂 docs/          - Documentation and API references
📂 tests/         - Unit tests for all components

• Universal quantum backend wrapper (Qiskit, PennyLane, Cirq, Braket)
• Quantum-enhanced neural networks
• Post-quantum cryptography
• AGI governance system
• Real-time quantum hardware execution
• Quantum-safe model protection

pip install quantum-ai

from quantum_ai import QuantumNeuralNetwork
from quantum_ai.security import QUANTUM_SHIELDWALL

# Initialize a quantum-enhanced neural network
qnn = QuantumNeuralNetwork()

Visit docs/ for complete documentation.

All AI models are protected by QUANTUM_SHIELDWALL™ technology.

MIT License

• Use type hints for all Python code
• Follow PEP 8 guidelines
• Document all public functions and classes
• Use meaningful variable names

• Write unit tests for all new features
• Maintain minimum 80% code coverage
• Include integration tests for API endpoints
• Test quantum circuits with simulation backends

• Profile quantum circuits before deployment
• Optimize classical-quantum interfaces
• Minimize quantum gate depth where possible
• Cache intermediate results when appropriate

• All PRs must pass security scan
• Implement access controls for quantum resources
• Follow quantum-safe cryptography practices
• Regular security audits required

5c96586 (refactoring)