Status: Preprint on arXiv (December 2025)
Overview
QubitPulseOpt is a Python framework for quantum gate optimization using GRAPE (Gradient Ascent Pulse Engineering) with realistic noise modeling. The project demonstrates that high-fidelity quantum gate operations can be achieved through systematic pulse optimization, reaching 99.14% X-gate fidelity in 20ns simulations—a 77% error reduction compared to Gaussian baseline pulses.
Key Results
| Metric | Value |
|---|---|
| X-gate Fidelity | 99.14% |
| Error Reduction | 77% vs. Gaussian baseline |
| Simulation Time | 20ns |
| Unit Tests | 864 |
| Code Coverage | 74% |
Technical Approach
Noise Modeling
- Lindblad master equation for realistic T1/T2 decoherence effects
- Hardware-representative workflow via API connectivity to IQM Garnet 20-qubit processor for calibration retrieval
- Microwave control pulse optimization for transmon qubits
Optimization
- GRAPE algorithm with gradient-based pulse refinement
- Numerical quantum state evolution using matrix exponentials
- Multiple optimization backends (gradient descent, L-BFGS)
Software Engineering
- Engineered following NASA JPL Power-of-10 coding standards
- CI/CD pipeline with GitHub Actions
- 864 unit tests, 74% code coverage
- Modular architecture for extensibility
Technology Stack
- Quantum Simulation: QuTiP, NumPy, SciPy
- Optimization: GRAPE, L-BFGS, gradient descent
- Testing: pytest, GitHub Actions CI/CD
- Hardware Integration: IQM Garnet API
Future Integration
This project is designed to integrate with the Quantum Circuit Optimizer for end-to-end quantum compilation: from high-level circuits through gate optimization and qubit routing, down to noise-robust control pulses.
Links
Citation
Malarchick, R. (2025). “GRAPE Pulse Optimization for Quantum Gates with Hardware-Representative Noise.” arXiv:2511.12799.