Overview
QubitPulseOpt is a Python framework for quantum gate optimization using GRAPE (Gradient Ascent Pulse Engineering) with realistic noise modeling. The project shows that high-fidelity quantum gate operations can be achieved through systematic pulse optimization, reaching 99.14% X-gate fidelity in closed-system simulations. Such yields a 77× error reduction over Gaussian baseline pulses.
Key Results
| Metric | Value |
|---|---|
| X-gate Fidelity (closed system) | 99.14% |
| Gaussian Baseline Fidelity | 33.40% |
| Error Reduction Factor | 77.17× |
| Optimization Time | 12.64s |
| Unit Tests | 822 |
| Code Coverage | 74% |
Project structure
The project has three component groups: a reinforcement-learning controller, a physics simulation core, and an analysis layer for characterization.
Reinforcement learning
- Gymnasium environment for quantum feedback control
- PPO agent via stable-baselines3
- Autonomous quantum control exploration
Physics core
- GRAPE and Krotov optimization algorithms
- Lindblad master equation for T1/T2 decoherence
- Stochastic master equation for trajectory simulation
Analysis
- Criticality detection and phase slip analysis
- Filter functions for noise characterization
- Robustness testing and benchmarking
Technical Features
Core Physics Engine
| Module | Description |
|---|---|
| GRAPE Optimizer | Gradient ascent with line search, momentum, amplitude constraints |
| Krotov's Method | Monotonically convergent alternative to GRAPE |
| Lindblad Master Equation | T1/T2 decoherence, Ramsey experiments, thermal states |
| Stochastic Master Equation | Quantum trajectory simulation for feedback control |
Pulse Library
- Gaussian, DRAG, Blackman, Cosine pulses
- Pulse scaling and area normalization for π-pulses
- Smooth pulse shaping for leakage suppression
Hardware Integration
| Feature | Status |
|---|---|
| IQM Translator | Qiskit-to-IQM circuit conversion |
| Async Job Manager | Asynchronous job submission |
| Hardware Characterization | Parameter extraction |
| Hardware Connectivity | IQM Garnet 20-qubit superconducting, API |
Analysis & Visualization
- Bloch sphere animation
- Fidelity dashboards
- Robustness testing
- Filter functions
- Benchmarking suite
- Report generation
Verified Results
Results verified and documented in verified_results/PROVENANCE.md:
| Metric | Value |
|---|---|
| GRAPE X-gate Fidelity | 99.14% |
| Gaussian Baseline | 33.40% |
| Error Reduction | 77.17× |
| Optimization Time | 12.64s |
Note: GRAPE optimization performed in closed quantum system.
Technology Stack
| Category | Technology |
|---|---|
| Quantum Simulation | QuTiP 5.0+, NumPy, SciPy |
| Optimization | GRAPE, Krotov, L-BFGS |
| RL | Gymnasium, stable-baselines3 |
| Hardware | IQM Resonance API |
| Testing | pytest (822 tests), GitHub Actions CI/CD |
Related projects
This work connects to:
- quantum-circuit-optimizer: gate-level circuit compilation
- qco-integration: end-to-end fidelity analysis from circuit to pulse
Links
Citation
Malarchick, R. (2025). "GRAPE Pulse Optimization for Quantum Gates with Hardware-Representative Noise." Preprint. arXiv:2511.12799.