Status: Submitted for publication
GitHub: github.com/rylanmalarchick/qco-integration
Paper: arXiv:2601.20871 — "End-to-End Fidelity Analysis of Quantum Circuit Optimization: From Gate-Level Transformations to Pulse-Level Control"
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.