SuperQuantX Documentation

SuperQuantX

Unified Quantum Computing Platform

Building autonomous quantum-enhanced AI systems

A cutting-edge quantum computing research platform that provides a unified API for quantum agentic AI systems.

Welcome to SuperQuantX

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.

🚀 Key Features

• 🔗 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

🎯 Supported Backends

Backend	Provider	Features
PennyLane	Multi-vendor	Differentiable programming, ML integration
Qiskit	IBM	IBM hardware, advanced transpilation
Cirq	Google	Google hardware, NISQ algorithms
Amazon Braket	AWS	AWS cloud quantum computing
TKET	Cambridge Quantum Computing	Advanced optimization
D-Wave Ocean	D-Wave	Quantum annealing

Quick Start

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']}")

Navigation

📚 Getting Started

Learn the basics and get up and running quickly.

• Installation Guide
• Quick Start
• First Program
• Configuration

👨‍💻 User Guide

Comprehensive guides for using SuperQuantX.

• Platform Overview
• Quantum Backends
• Algorithm Guide

🧪 Tutorials

Hands-on tutorials and examples.

• Basic Quantum Computing
• Quantum Machine Learning

📖 API Reference

Complete API documentation.

• Core API
• Backends
• Algorithms
• Circuits
• Agents

Research Areas

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

Community & Support

• 📧 Email: research@super-agentic.ai
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📖 Source Code: GitHub Repository

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Built with ❤️ for the Quantum AI research community

Developed by Superagentic AI