An open-source educational utility and platform for evaluating hardware noise in Quantum circuits and visualizing Zero-Noise Extrapolation (ZNE) mitigation.
| 🧠 Quantum AI Circuits | 📡 Hardware Profiling | 🛡️ ZNE Mitigation | 🗺️ Topology Optimizer |
|---|---|---|---|
| Complete Algorithm Suite Benchmarks QNNs, VQE, Bernstein-Vazirani, Simon's, Grover's, and QFT. |
Advanced Noise Models Tests Depolarizing, Amplitude Damping, Phase Damping, and live IBM T1/T2 times. |
ZNE Implementation Custom circuit folding and Richardson extrapolation for error mitigation. |
Smart Routing Analyzes physical backend layout to minimize SWAP gate errors. |
- 🎯 Key Features at a Glance
- 🛠️ Installation
- 🚀 How to Run (3 Ways)
- 🏗️ Architecture
- 🌍 Applications & Use Cases
- 🔮 Future Scope
- 🛡️ Security Note
- 🤝 Contributing & Feedback
- ⭐ Show Your Support
- 👤 Author & Contact
# Clone the repository
git clone https://github.com/shlok926/q-refine.git
cd q-refine
# Install dependencies
pip install qiskit>=1.0.0 qiskit-aer>=0.14.0 matplotlib numpy streamlit>=1.30.0 jupyterQ-Refine is designed for different types of users, from enterprise managers to core quantum researchers.
You can test the platform instantly without installing anything via our cloud deployment: 👉 Live Cloud Dashboard: q-refine.streamlit.app
Or run it locally on your own machine:
streamlit run app.pyThis will open a browser window at http://localhost:8501.
Run the automated pipeline to execute the entire benchmarking process in one shot. It will print the analysis and generate a q_refine_dashboard.png image.
python q_refine_pipeline.pyIf you want to play with the Q-Sanitizer step-by-step or modify the circuits mathematically, use the interactive notebook.
jupyter notebook demo.ipynbq_refine/
├── circuits/ # Quantum Algorithms (QNN, VQE, BV, Simon's, Grover, QFT)
├── benchmark_engine/ # Hardware Profilers & IBM Digital Twins
├── mitigation_engine/ # Custom ZNE Engine & Topology Optimizers
└── core/ # Dashboards & Utilities
Q-Refine is built to accelerate research and production in the most critical areas of quantum computing:
- Quantum Machine Learning (QML) & Quantum AI: Evaluate how hardware noise degrades the accuracy of Parameterized Quantum Circuits (PQCs) and Quantum Neural Networks, and use ZNE to restore predictive power.
- Quantum Cryptography & Security: Benchmark cryptographic cracking algorithms (like Grover's) against real-world decoherence to understand the true timeline and threat level of quantum attacks.
- Quantum Hardware Development: Hardware engineers can use Q-Refine as a diagnostic tool to test the efficacy of their physical qubits against standard algorithmic workloads.
While Q-Refine is actively being developed, the roadmap for future expansion includes:
- Machine Learning Predictor: Utilizing classical ML models to predict the robustness of arbitrary quantum circuits before execution.
- Live QPU Execution: Transitioning from Digital Twins (FakeBackends) to live, queued execution on IBM's physical Quantum Processing Units (QPUs) using premium cloud accounts.
- Hybrid Algorithm Support: Adding support for QAOA (Quantum Approximate Optimization Algorithm) for solving logistics and financial modeling problems under noisy conditions.
This tool evaluates algorithms locally. If you switch use_real_hardware=True in the Profiler, ensure your IBM Quantum API Token is saved using QiskitRuntimeService.save_account() and never hardcoded into the scripts.
Contributions, suggestions, and feedback are highly welcome!
- Got suggestions or feature requests? Feel free to open a new Issue or share your ideas.
- Want to contribute? Feel free to fork this repository, make your changes, and submit a Pull Request.
✨ Star the repository (GitHub Star Button)
🐛 Report bugs (GitHub Issues)
💡 Suggest features (GitHub Discussions)
📣 Share with others (LinkedIn/Twitter)
🤝 Contribute code (Pull Requests)
Let's connect on LinkedIn, collaborate, and build amazing things together!
Made with Shlok!for Quantum Computing Innovation • Back to Top