qutuner-compiler-optimization

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:

1. Limited pass space search: Only explore a small subset of available optimization passes
2. 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:

1. Extract static and dynamic circuit features
2. Use learned model to predict optimal pass sequence
3. Apply passes and measure actual improvement
4. Feed results back to update model

Pattern 2: Hardware-Aware Compilation

When targeting specific quantum hardware:

1. Include hardware topology and noise profile as features
2. Learn hardware-specific pass effectiveness
3. 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

1. Feature computation cost: Dynamic feature extraction can be expensive — cache results for repeated circuits
2. Hardware drift: Calibrated hardware parameters change over time — retrain model periodically
3. Pass interference: Some passes undo the work of others — model must learn interaction effects
4. Small training data: Limited labeled data for rare circuit types — use transfer learning from simulators

Resources

• arXiv: https://arxiv.org/abs/2607.04586
• Related: quantum-compiler-routing, quantum-circuit-compilation-workflow, hardware-aware-quantum-compilation