Overview
This project integrates my quantum-circuit-optimizer (C++) with QubitPulseOpt (Python) to analyze how gate-level optimization affects pulse-level fidelity across the full compilation stack. Unlike existing work that evaluates optimization passes in isolation, this framework measures end-to-end fidelity from OpenQASM input to simulated pulse execution.
Key Research Question: Do gate-level optimization metrics (gate count, circuit depth) reliably predict pulse-level fidelity? Our analysis reveals that pulse duration—not gate count—is the strongest fidelity predictor (r=-0.74).
Key Results
| Metric |
Value |
| Circuits analyzed |
371 |
| Mean gate reduction |
23.1% |
| Max gate reduction |
96.2% |
| Mean process fidelity |
0.680 ± 0.224 |
| Hardware validation |
IQM Garnet (20 qubits) |
| Job success rate |
100% (8/8) |
| Tests |
252 |
Optimization Pass Effectiveness
| Pass |
Gates Removed |
% Improved |
| Gate Cancellation |
14,024 |
68% |
| Rotation Merging |
6,512 |
29% |
| Identity Elimination |
55 |
9% |
| Commutation |
0 (enables others) |
0% |
Fidelity Correlations
| Parameter |
Pearson r |
R² |
| Pulse duration |
-0.743 |
0.553 |
| Input gates |
-0.606 |
0.368 |
| Input depth |
-0.585 |
0.342 |
| Input qubits |
-0.569 |
0.324 |
Architecture
| Stage |
Description |
| 1. Parse & Validate |
Extract initial metrics (gates, depth, qubits) |
| 2. Optimize (C++) |
Apply configurable pass sequence, track per-pass gate changes |
| 3. SABRE Routing |
Map to hardware topology, insert SWAP gates |
| 4. Pulse Compilation |
Native gate decomposition, pulse schedule generation |
| 5. Lindblad Noise Simulation |
T₁/T₂ decoherence modeling, process and state fidelity computation |
Hardware Validation
Validated simulation results on the IQM Resonance Garnet 20-qubit superconducting processor:
| Circuit |
Gates (Orig → Opt) |
Reduction |
Fidelity (Orig) |
Fidelity (Opt) |
| GHZ 4q |
4 → 4 |
0% |
0.494 |
0.469 |
| GHZ 8q |
8 → 8 |
0% |
0.406 |
0.375 |
| GHZ 12q |
12 → 12 |
0% |
0.256 |
0.288 (+12%) |
| QFT 4q |
30 → 9 |
70% |
0.100 |
0.088 |
Key Findings:
- Optimizer correctly identifies GHZ circuits as already minimal (0% reduction)
- QFT circuits achieve 70% gate reduction with significant rotation merging
- 12-qubit GHZ showed 12% fidelity improvement after optimization
Benchmark Corpus
| Circuit Type |
Qubit Range |
Description |
| GHZ states |
2–12 |
Entanglement preparation |
| QFT |
2–8 |
Quantum Fourier Transform |
| QAOA |
configurable |
MaxCut optimization |
| Random |
4–8 qubits, depth 5–30 |
Stress testing |
Technology Stack
| Category |
Technology |
| Languages |
Python 3.11, C++17 |
| Integration |
Subprocess via OpenQASM/JSON |
| Simulation |
Lindblad master equation (IQM Garnet params) |
| Hardware |
IQM Resonance (free tier cloud access) |
| Testing |
252 tests, mypy, ruff |
| Principles |
AgentBible |
Research Impact
This work provides actionable guidance for quantum compiler design:
- Prioritize cancellation: Gate cancellation provides the largest fidelity gains (68% improvement rate)
- Commutation enables cancellation: While commutation provides no direct reduction, it creates opportunities for subsequent passes
- Minimize pulse duration: The strong correlation (r=-0.74) emphasizes decoherence-aware optimization over pure gate count reduction
- Optimal pass sequence:
cancel → commute → rotate
Links
This project completes my full-stack quantum compilation pipeline: from high-level circuits through optimization, routing, and noise-robust pulse synthesis.