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PyPI - Version Python Version License Tests Documentation Code style: black Ruff

SuperQuantX

The foundation for the future of Agentic and Quantum AI

SuperQuantX unified API for the next wave of Quantum AI. It's a foundation to build powerful Quantum Agentic AI systems with a single interface to Qiskit, Cirq, PennyLane, and more. SuperQuantX is your launchpad into the world of Quantum + Agentic AI.

Unified Quantum Computing Platform - Building autonomous quantum-enhanced AI systems

📖 Read the Full Documentation →

Research by Superagentic AI - Quantum AI Research

🚀 What is SuperQuantX?

SuperQuantX is a unified quantum computing platform that makes quantum algorithms and quantum machine learning accessible through a single, consistent API. Whether you're a researcher, developer, or quantum enthusiast, SuperQuantX provides:

  • 🎯 Single API - Works across all major quantum backends (IBM, Google, AWS, Quantinuum, D-Wave)
  • 🤖 Quantum Agents - Pre-built autonomous agents for trading, research, and optimization
  • 🧠 Quantum ML - Advanced quantum machine learning algorithms and neural networks
  • ⚡ Easy Setup - Get started in minutes with comprehensive documentation

✨ Key Features

🔗 Universal Quantum Backend Support

# Same code works on ANY quantum platform
qsvm = sqx.QuantumSVM(backend='pennylane')  # PennyLane
qsvm = sqx.QuantumSVM(backend='qiskit')     # IBM Qiskit
qsvm = sqx.QuantumSVM(backend='cirq')       # Google Cirq
qsvm = sqx.QuantumSVM(backend='braket')     # AWS Braket
qsvm = sqx.QuantumSVM(backend='quantinuum') # Quantinuum H-Series

🤖 Autonomous Quantum Agents

Ready-to-deploy intelligent agents powered by quantum algorithms:

  • QuantumTradingAgent - Portfolio optimization and risk analysis
  • QuantumResearchAgent - Scientific hypothesis generation and testing
  • QuantumOptimizationAgent - Complex combinatorial and continuous optimization
  • QuantumClassificationAgent - Advanced ML with quantum advantage

🧠 Quantum Machine Learning

State-of-the-art quantum ML algorithms:

  • Quantum Support Vector Machines - Enhanced pattern recognition
  • Quantum Neural Networks - Hybrid quantum-classical architectures
  • QAOA & VQE - Optimization and molecular simulation
  • Quantum Clustering - Advanced data analysis techniques

🚀 Quick Start

Installation

# Install with uv (recommended)
curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/SuperagenticAI/superquantx.git
cd superquantx
uv sync --extra all

# Or with pip
pip install superquantx

Deploy Your First Quantum Agent

import superquantx as sqx

# Deploy quantum trading agent
agent = sqx.QuantumTradingAgent(
    strategy="quantum_portfolio",
    risk_tolerance=0.3
)
results = agent.deploy()
print(f"Performance: {results.result['performance']}")

Quantum Machine Learning

# Quantum SVM with automatic backend selection
import numpy as np
qsvm = sqx.QuantumSVM(backend='auto')

# Mock training data for demonstration
X_train = np.random.rand(20, 4)
y_train = np.random.choice([0, 1], 20)
X_test = np.random.rand(10, 4)
y_test = np.random.choice([0, 1], 10)

qsvm.fit(X_train, y_train)
accuracy = qsvm.score(X_test, y_test)
print(f"Quantum SVM accuracy: {accuracy}")

Advanced Quantum Algorithms

# Molecular simulation with VQE
import numpy as np
from sklearn.datasets import make_classification

# Create sample Hamiltonian for VQE
hamiltonian = np.array([[1, 0], [0, -1]])  # Simple Pauli-Z
vqe = sqx.VQE(hamiltonian=hamiltonian, backend="pennylane")
ground_state = vqe.find_ground_state()
print(f"Ground state energy: {ground_state}")

# Optimization with QAOA
X, y = make_classification(n_samples=10, n_features=4, n_classes=2, random_state=42)
qaoa = sqx.QAOA(backend="pennylane")
qaoa.fit(X, y)
print("✅ QAOA successfully fitted for optimization tasks")

📖 Documentation

Complete documentation is available at superagenticai.github.io/superquantx

The documentation includes comprehensive guides for getting started, detailed API references, tutorials, and examples for all supported quantum backends. Visit the documentation site for:

  • Getting Started - Installation, configuration, and your first quantum program
  • User Guides - Platform overview, backends, and algorithms
  • Tutorials - Hands-on quantum computing and machine learning examples
  • API Reference - Complete API documentation with examples
  • Development - Contributing guidelines, architecture, and testing

🎯 Supported Platforms

SuperQuantX provides unified access to all major quantum computing platforms:

Backend Provider Hardware Simulator
PennyLane Multi-vendor ✅ Various ✅
Qiskit IBM ✅ IBM Quantum ✅
Cirq Google ✅ Google Quantum AI ✅
AWS Braket Amazon ✅ IonQ, Rigetti ✅
TKET Quantinuum ✅ H-Series ✅
Ocean D-Wave ✅ Advantage ✅

🤖 Quantum Agents

Pre-built autonomous agents for complex problem solving:

  • 🏦 QuantumTradingAgent - Portfolio optimization and risk analysis
  • 🔬 QuantumResearchAgent - Scientific hypothesis generation and testing
  • ⚡ QuantumOptimizationAgent - Combinatorial and continuous optimization
  • 🧠 QuantumClassificationAgent - Advanced ML with quantum advantage

🧮 Quantum Algorithms

Comprehensive library of quantum algorithms and techniques:

🔍 Quantum Machine Learning

  • Quantum Support Vector Machines (QSVM) - Enhanced pattern recognition with quantum kernels
  • Quantum Neural Networks (QNN) - Hybrid quantum-classical neural architectures
  • Quantum Principal Component Analysis (QPCA) - Quantum dimensionality reduction
  • Quantum K-Means - Clustering with quantum distance calculations

⚡ Optimization Algorithms

  • Quantum Approximate Optimization Algorithm (QAOA) - Combinatorial optimization
  • Variational Quantum Eigensolver (VQE) - Molecular simulation and optimization
  • Quantum Annealing - Large-scale optimization with D-Wave systems

🧠 Advanced Quantum AI

  • Quantum Reinforcement Learning - RL with quantum advantage
  • Quantum Natural Language Processing - Quantum-enhanced text analysis
  • Quantum Computer Vision - Image processing with quantum circuits

💡 Why SuperQuantX?

Traditional Approach SuperQuantX Advantage
❌ Multiple complex SDKs ✅ Single unified API
❌ Months to learn quantum ✅ Minutes to first algorithm
❌ Backend-specific code ✅ Write once, run anywhere
❌ Manual optimization ✅ Automatic backend selection
❌ Limited algorithms ✅ Comprehensive algorithm library

🤝 Contributing

We welcome contributions to SuperQuantX! Here's how to get involved:

🔧 Development Setup

# Fork and clone the repository
git clone https://github.com/your-username/superquantx.git
cd superquantx

# Install development dependencies
uv sync --extra dev

# Run tests to verify setup
uv run pytest

🐛 Bug Reports & Feature Requests

📝 Documentation

Help improve our documentation:

  • Fix typos and clarify explanations
  • Add examples and tutorials
  • Improve API documentation
  • Translate documentation

🔗 Resources & Community

📚 Learn More

📄 License

SuperQuantX is released under the Apache License 2.0. Feel free to use it in your projects, research, and commercial applications.

🚀 Get Started Now

# Install SuperQuantX
pip install superquantx

# Deploy your first quantum agent
python -c "
import superquantx as sqx
agent = sqx.QuantumOptimizationAgent()
print('✅ SuperQuantX is ready!')
"

Ready to explore quantum computing?

👉 Start with the Quick Start Guide →

SuperQuantX: Making Quantum Computing Accessible to all

Built with ❤️ by Superagentic AI

⭐ Star this repo if SuperQuantX helps your quantum journey!