| name | qutuner-compiler-optimization |
| description | Feature- and learning-guided quantum compiler pass tuning methodology — moves beyond static circuit features to capture how circuits react to compiler optimizations, enabling more effective pass sequence selection. |
QuTuner: Feature- and Learning-Guided Quantum Compiler Optimization Pass Tuning
Description
QuTuner methodology for feature- and learning-guided optimization pass tuning in quantum compilers. Addresses two key limitations of prior work: (1) searching only a small portion of the optimization-pass space, and (2) relying mainly on static features that don't explicitly reflect how a circuit reacts to compiler optimizations. arXiv:2607.04586.
Activation Keywords
- qutuner
- quantum compiler optimization pass tuning
- learning-guided compiler passes
- dynamic circuit features for quantum compilation
- quantum pass sequence optimization
- 量子编译器优化调优
- feature-guided quantum compilation
Core Concepts
Problem Statement
Quantum compilers transform high-level quantum circuits into hardware-executable implementations. The quality of compilation depends heavily on the sequence and parameters of optimization passes applied. Prior approaches have two limitations:
- Limited pass space search: Only explore a small subset of available optimization passes
- Static feature reliance: Use static circuit features (gate count, depth) that don't capture how circuits actually react to compiler optimizations
Key Innovation
QuTuner introduces learning-guided pass tuning that:
- Dynamically captures circuit reactions to optimization passes
- Uses learned features to guide pass selection in a larger search space
- Balances exploration (trying new pass combinations) with exploitation (using known good sequences)
Methodology
Step 1: Feature Extraction
Extract circuit features at multiple stages:
- Pre-optimization: Gate types, connectivity, depth, qubit count
- Post-optimization: Reduction ratios, gate cancellations, SWAP insertions
- Cross-pass: How one pass affects the effectiveness of subsequent passes
Step 2: Learning Model
Train a model to predict:
- Which optimization passes are most effective for a given circuit
- Optimal pass ordering based on circuit characteristics
- Parameter tuning for each pass (e.g., commutation depth, optimization level)
Step 3: Guided Search
Use the learned model to guide search through the pass space:
- Greedy selection: Choose passes with highest predicted benefit
- Beam search: Explore top-K pass sequences in parallel
- Reinforcement learning: Learn from execution outcomes on real hardware
Step 4: Validation
- Compare compiled circuit metrics: gate count, depth, fidelity, execution time
- Validate on multiple hardware backends (IBM, IonQ, Rigetti)
- Measure generalization to unseen circuits
Usage Patterns
Pattern 1: Compiler Pass Selection
When building or tuning a quantum compiler:
- Extract static and dynamic circuit features
- Use learned model to predict optimal pass sequence
- Apply passes and measure actual improvement
- Feed results back to update model
Pattern 2: Hardware-Aware Compilation
When targeting specific quantum hardware:
- Include hardware topology and noise profile as features
- Learn hardware-specific pass effectiveness
- Optimize for hardware-native gates and connectivity
Error Handling
Model Overfitting
- Use cross-validation across different circuit families
- Regularize to prefer simpler pass sequences
- Monitor generalization on held-out circuits
Feature Engineering Failure
- Fall back to static features if dynamic extraction is too expensive
- Use ablation studies to identify which features are most predictive
Pitfalls
- Feature computation cost: Dynamic feature extraction can be expensive — cache results for repeated circuits
- Hardware drift: Calibrated hardware parameters change over time — retrain model periodically
- Pass interference: Some passes undo the work of others — model must learn interaction effects
- Small training data: Limited labeled data for rare circuit types — use transfer learning from simulators
Resources
