Building autonomous quantum-enhanced AI systems
A cutting-edge quantum computing research platform that provides a unified API for quantum agentic AI systems.
SuperQuantX bridges the gap between quantum computing and artificial intelligence, enabling researchers and developers to build sophisticated quantum-enhanced AI agents with a single, unified API.
- 🔗 Unified API: Single interface for multiple quantum computing backends
- 🎯 Agentic AI Focus: Specialized tools for quantum agent development
- 🚀 Multi-Backend Support: PennyLane, Qiskit, Cirq, Amazon Braket, TKET, D-Wave Ocean
- 📊 Advanced Algorithms: Pre-built quantum machine learning and optimization algorithms
- 🛠️ Developer Friendly: Comprehensive documentation and examples
- ⚡ High Performance: Optimized for research and production workloads
| Backend | Provider | Features |
|---|---|---|
| PennyLane | Multi-vendor | Differentiable programming, ML integration |
| Qiskit | IBM | IBM hardware, advanced transpilation |
| Cirq | Google hardware, NISQ algorithms | |
| Amazon Braket | AWS | AWS cloud quantum computing |
| TKET | Cambridge Quantum Computing | Advanced optimization |
| D-Wave Ocean | D-Wave | Quantum annealing |
Get started with SuperQuantX in minutes:
import superquantx as sqx
# Choose your backend
backend = sqx.get_backend('simulator')
# Create a quantum circuit
circuit = backend.create_circuit(n_qubits=2)
circuit = backend.add_gate(circuit, 'H', 0) # Hadamard gate
circuit = backend.add_gate(circuit, 'CNOT', [0, 1]) # CNOT gate
circuit = backend.add_measurement(circuit)
# Execute the circuit
result = backend.execute_circuit(circuit, shots=1024)
print(f"Results: {result['counts']}")
### 📚 Getting Started
Learn the basics and get up and running quickly.
### 👨💻 User Guide
Comprehensive guides for using SuperQuantX.
### 🧪 Tutorials
Hands-on tutorials and examples.
### 📖 API Reference
Complete API documentation.
SuperQuantX accelerates research in:
- Quantum Machine Learning: QSVM, QNN, quantum feature maps
- Quantum Optimization: QAOA, VQE, quantum annealing
- Quantum Agents: Decision-making quantum systems
- Hybrid Algorithms: Classical-quantum hybrid approaches
- NISQ Applications: Near-term quantum device algorithms
- 📧 Email: research@super-agentic.ai
- 🐛 Issues: GitHub Issues
- 💬 Discussions: GitHub Discussions
- 📖 Source Code: GitHub Repository
Built with ❤️ for the Quantum AI research community
Developed by Superagentic AI