[](https://pypi.org/project/superquantx/)
[](https://pypi.org/project/superquantx/)
[](https://opensource.org/licenses/Apache-2.0)
[](https://github.com/SuperagenticAI/superquantx/actions)
[](https://superagenticai.github.io/superquantx)
[](https://github.com/psf/black)
[](https://github.com/astral-sh/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 →](https://superagenticai.github.io/superquantx/)**
**Research by [Superagentic AI](https://super-agentic.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
<div align="center">
<a href="https://super-agentic.ai" target="_blank">
<img src="resources/logo.png" alt="SuperQuantX Logo" width="500">
</a>
</div>
## ✨ Key Features
### **🔗 Universal Quantum Backend Support**
```python
# 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
```bash
# 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
```python
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
```python
# 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
```python
# 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](https://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**
```bash
# 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**
- **[Open an issue](https://github.com/SuperagenticAI/superquantx/issues)** - Report bugs or request features
- **[Read contributing guide](docs/development/contributing.md)** - Detailed contribution guidelines
### **📝 Documentation**
Help improve our documentation:
- Fix typos and clarify explanations
- Add examples and tutorials
- Improve API documentation
- Translate documentation
## 🔗 Resources & Community
### **📚 Learn More**
- **[Official Documentation](docs/)** - Complete guides and API reference
- **[Tutorial Notebooks](examples/)** - Jupyter notebooks with examples
## 📄 License
SuperQuantX is released under the [Apache License 2.0](LICENSE). Feel free to use it in your projects, research, and commercial applications.
---
## 🚀 Get Started Now
```bash
# 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 →](https://superagenticai.github.io/superquantx/)**
---
<div align="center">
**SuperQuantX: Making Quantum Computing Accessible to all**
*Built with ❤️ by [Superagentic AI](https://super-agentic.ai)*
⭐ **Star this repo** if SuperQuantX helps your quantum journey!
</div>
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"description": "[](https://pypi.org/project/superquantx/)\n[](https://pypi.org/project/superquantx/)\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://github.com/SuperagenticAI/superquantx/actions)\n[](https://superagenticai.github.io/superquantx)\n[](https://github.com/psf/black)\n[](https://github.com/astral-sh/ruff)\n\n# SuperQuantX\n### The foundation for the future of Agentic and Quantum AI\nSuperQuantX 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.\n\n**Unified Quantum Computing Platform - Building autonomous quantum-enhanced AI systems**\n\n> \ud83d\udcd6 **[Read the Full Documentation \u2192](https://superagenticai.github.io/superquantx/)**\n\n**Research by [Superagentic AI](https://super-agentic.ai) - Quantum AI Research**\n\n## \ud83d\ude80 What is SuperQuantX?\n\nSuperQuantX 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:\n\n- **\ud83c\udfaf Single API** - Works across all major quantum backends (IBM, Google, AWS, Quantinuum, D-Wave)\n- **\ud83e\udd16 Quantum Agents** - Pre-built autonomous agents for trading, research, and optimization\n- **\ud83e\udde0 Quantum ML** - Advanced quantum machine learning algorithms and neural networks\n- **\u26a1 Easy Setup** - Get started in minutes with comprehensive documentation\n\n<div align=\"center\">\n <a href=\"https://super-agentic.ai\" target=\"_blank\">\n <img src=\"resources/logo.png\" alt=\"SuperQuantX Logo\" width=\"500\">\n </a>\n</div>\n\n## \u2728 Key Features\n\n### **\ud83d\udd17 Universal Quantum Backend Support**\n```python\n# Same code works on ANY quantum platform\nqsvm = sqx.QuantumSVM(backend='pennylane') # PennyLane\nqsvm = sqx.QuantumSVM(backend='qiskit') # IBM Qiskit\nqsvm = sqx.QuantumSVM(backend='cirq') # Google Cirq\nqsvm = sqx.QuantumSVM(backend='braket') # AWS Braket\nqsvm = sqx.QuantumSVM(backend='quantinuum') # Quantinuum H-Series\n```\n\n### **\ud83e\udd16 Autonomous Quantum Agents**\nReady-to-deploy intelligent agents powered by quantum algorithms:\n- **QuantumTradingAgent** - Portfolio optimization and risk analysis\n- **QuantumResearchAgent** - Scientific hypothesis generation and testing\n- **QuantumOptimizationAgent** - Complex combinatorial and continuous optimization\n- **QuantumClassificationAgent** - Advanced ML with quantum advantage\n\n### **\ud83e\udde0 Quantum Machine Learning**\nState-of-the-art quantum ML algorithms:\n- **Quantum Support Vector Machines** - Enhanced pattern recognition\n- **Quantum Neural Networks** - Hybrid quantum-classical architectures\n- **QAOA & VQE** - Optimization and molecular simulation\n- **Quantum Clustering** - Advanced data analysis techniques\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n```bash\n# Install with uv (recommended)\ncurl -LsSf https://astral.sh/uv/install.sh | sh\ngit clone https://github.com/SuperagenticAI/superquantx.git\ncd superquantx\nuv sync --extra all\n\n# Or with pip\npip install superquantx\n```\n\n### Deploy Your First Quantum Agent\n```python\nimport superquantx as sqx\n\n# Deploy quantum trading agent\nagent = sqx.QuantumTradingAgent(\n strategy=\"quantum_portfolio\",\n risk_tolerance=0.3\n)\nresults = agent.deploy()\nprint(f\"Performance: {results.result['performance']}\")\n```\n\n### Quantum Machine Learning\n```python\n# Quantum SVM with automatic backend selection\nimport numpy as np\nqsvm = sqx.QuantumSVM(backend='auto')\n\n# Mock training data for demonstration\nX_train = np.random.rand(20, 4)\ny_train = np.random.choice([0, 1], 20)\nX_test = np.random.rand(10, 4)\ny_test = np.random.choice([0, 1], 10)\n\nqsvm.fit(X_train, y_train)\naccuracy = qsvm.score(X_test, y_test)\nprint(f\"Quantum SVM accuracy: {accuracy}\")\n```\n\n### Advanced Quantum Algorithms\n```python\n# Molecular simulation with VQE\nimport numpy as np\nfrom sklearn.datasets import make_classification\n\n# Create sample Hamiltonian for VQE\nhamiltonian = np.array([[1, 0], [0, -1]]) # Simple Pauli-Z\nvqe = sqx.VQE(hamiltonian=hamiltonian, backend=\"pennylane\")\nground_state = vqe.find_ground_state()\nprint(f\"Ground state energy: {ground_state}\")\n\n# Optimization with QAOA\nX, y = make_classification(n_samples=10, n_features=4, n_classes=2, random_state=42)\nqaoa = sqx.QAOA(backend=\"pennylane\")\nqaoa.fit(X, y)\nprint(\"\u2705 QAOA successfully fitted for optimization tasks\")\n```\n\n## \ud83d\udcd6 Documentation\n\n**Complete documentation is available at [superagenticai.github.io/superquantx](https://superagenticai.github.io/superquantx/)**\n\nThe documentation includes comprehensive guides for getting started, detailed API references, tutorials, and examples for all supported quantum backends. Visit the documentation site for:\n\n- **Getting Started** - Installation, configuration, and your first quantum program\n- **User Guides** - Platform overview, backends, and algorithms\n- **Tutorials** - Hands-on quantum computing and machine learning examples\n- **API Reference** - Complete API documentation with examples\n- **Development** - Contributing guidelines, architecture, and testing\n\n## \ud83c\udfaf Supported Platforms\n\nSuperQuantX provides unified access to **all major quantum computing platforms**:\n\n| Backend | Provider | Hardware | Simulator |\n|---------|----------|----------|-----------|\n| **PennyLane** | Multi-vendor | \u2705 Various | \u2705 |\n| **Qiskit** | IBM | \u2705 IBM Quantum | \u2705 |\n| **Cirq** | Google | \u2705 Google Quantum AI | \u2705 |\n| **AWS Braket** | Amazon | \u2705 IonQ, Rigetti | \u2705 |\n| **TKET** | Quantinuum | \u2705 H-Series | \u2705 |\n| **Ocean** | D-Wave | \u2705 Advantage | \u2705 |\n\n## \ud83e\udd16 Quantum Agents\n\nPre-built autonomous agents for complex problem solving:\n\n- **\ud83c\udfe6 QuantumTradingAgent** - Portfolio optimization and risk analysis\n- **\ud83d\udd2c QuantumResearchAgent** - Scientific hypothesis generation and testing\n- **\u26a1 QuantumOptimizationAgent** - Combinatorial and continuous optimization\n- **\ud83e\udde0 QuantumClassificationAgent** - Advanced ML with quantum advantage\n\n## \ud83e\uddee Quantum Algorithms\n\nComprehensive library of quantum algorithms and techniques:\n\n### **\ud83d\udd0d Quantum Machine Learning**\n- **Quantum Support Vector Machines (QSVM)** - Enhanced pattern recognition with quantum kernels\n- **Quantum Neural Networks (QNN)** - Hybrid quantum-classical neural architectures\n- **Quantum Principal Component Analysis (QPCA)** - Quantum dimensionality reduction\n- **Quantum K-Means** - Clustering with quantum distance calculations\n\n### **\u26a1 Optimization Algorithms**\n- **Quantum Approximate Optimization Algorithm (QAOA)** - Combinatorial optimization\n- **Variational Quantum Eigensolver (VQE)** - Molecular simulation and optimization\n- **Quantum Annealing** - Large-scale optimization with D-Wave systems\n\n### **\ud83e\udde0 Advanced Quantum AI**\n- **Quantum Reinforcement Learning** - RL with quantum advantage\n- **Quantum Natural Language Processing** - Quantum-enhanced text analysis\n- **Quantum Computer Vision** - Image processing with quantum circuits\n\n## \ud83d\udca1 Why SuperQuantX?\n\n| Traditional Approach | SuperQuantX Advantage |\n|--------------------|---------------------|\n| \u274c Multiple complex SDKs | \u2705 Single unified API |\n| \u274c Months to learn quantum | \u2705 Minutes to first algorithm |\n| \u274c Backend-specific code | \u2705 Write once, run anywhere |\n| \u274c Manual optimization | \u2705 Automatic backend selection |\n| \u274c Limited algorithms | \u2705 Comprehensive algorithm library |\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions to SuperQuantX! Here's how to get involved:\n\n### **\ud83d\udd27 Development Setup**\n```bash\n# Fork and clone the repository\ngit clone https://github.com/your-username/superquantx.git\ncd superquantx\n\n# Install development dependencies\nuv sync --extra dev\n\n# Run tests to verify setup\nuv run pytest\n```\n\n### **\ud83d\udc1b Bug Reports & Feature Requests**\n- **[Open an issue](https://github.com/SuperagenticAI/superquantx/issues)** - Report bugs or request features\n- **[Read contributing guide](docs/development/contributing.md)** - Detailed contribution guidelines\n\n### **\ud83d\udcdd Documentation**\nHelp improve our documentation:\n- Fix typos and clarify explanations\n- Add examples and tutorials\n- Improve API documentation\n- Translate documentation\n\n## \ud83d\udd17 Resources & Community\n\n### **\ud83d\udcda Learn More**\n- **[Official Documentation](docs/)** - Complete guides and API reference\n- **[Tutorial Notebooks](examples/)** - Jupyter notebooks with examples\n\n\n## \ud83d\udcc4 License\n\nSuperQuantX is released under the [Apache License 2.0](LICENSE). Feel free to use it in your projects, research, and commercial applications.\n\n---\n\n## \ud83d\ude80 Get Started Now\n\n```bash\n# Install SuperQuantX\npip install superquantx\n\n# Deploy your first quantum agent\npython -c \"\nimport superquantx as sqx\nagent = sqx.QuantumOptimizationAgent()\nprint('\u2705 SuperQuantX is ready!')\n\"\n```\n\n**Ready to explore quantum computing?**\n\n\ud83d\udc49 **[Start with the Quick Start Guide \u2192](https://superagenticai.github.io/superquantx/)**\n\n---\n\n<div align=\"center\">\n\n**SuperQuantX: Making Quantum Computing Accessible to all**\n\n*Built with \u2764\ufe0f by [Superagentic AI](https://super-agentic.ai)*\n\n\u2b50 **Star this repo** if SuperQuantX helps your quantum journey!\n\n</div>\n",
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