QuTuner: Feature- and Learning-Guided Optimization Pass Tuning for Quantum Compilers

2026-07-05

AI Summary

Existing quantum compilers explore only a limited optimization pass search space and rely solely on static circuit features, making it difficult to accurately predict optimization outcomes. This work proposes QuTuner, a novel framework that, for the first time, integrates static circuit structural features with dynamic optimization-response embeddings to construct an optimization-aware pass representation. QuTuner employs an offline machine learning model to retrieve and rank candidate optimization sequences, augmented by lightweight online fine-tuning to enable adaptive, multi-objective tuning across different compilers. Experimental results on Qiskit and PyTKET demonstrate that QuTuner reduces optimization metrics by 84.85% and 18.68%, respectively, while cutting tuning time by 73.59% and 64.49%, substantially improving both tuning efficiency and effectiveness.

Abstract

Quantum compilers play a key role in transforming quantum circuits into lower-cost implementations with improved execution fidelity. This process is commonly guided by circuit-level metrics, such as gate counts and circuit depth. Although compiler pass tuning has been widely studied in classical compilation, directly transferring these techniques to quantum compilers is challenging, because quantum programs are expressed as circuits and exhibit optimization behaviors that are shaped by quantum-specific structures. Prior quantum compiler tuning approaches have begun to use circuit features to guide pass selection, but they remain limited in two aspects: they search only a small portion of the optimization-pass space, and they mainly rely on static features that do not explicitly reflect how a circuit reacts to compiler optimizations. We present QuTuner, a feature-guided quantum compiler pass tuning framework that generalizes across compilers and tuning objectives. QuTuner first builds a large optimization dataset. It then characterizes each circuit from two complementary views: static circuit features that describe circuit structure, and optimization-aware pass embeddings that summarize the circuit's responses to individual optimization passes. Using these representations, QuTuner trains two offline models to retrieve and rank candidate pass sequences for unseen circuits, followed by lightweight refinement. We evaluate QuTuner on Qiskit and PyTKET using two benchmark suites. On Qiskit, QuTuner improves the evaluation-metric reduction by up to 84.85% over the strongest baseline while reducing tuning time by 73.59%. On PyTKET, it improves metric reduction by up to 18.68% with a 64.49% reduction in tuning time. These results show that QuTuner provides an effective approach to adaptive pass tuning for quantum compilers.

Problem

• quantum compilers
• optimization pass tuning
• circuit features
• optimization-aware embeddings
• adaptive tuning

Innovation

• quantum compiler
• optimization pass tuning
• feature-guided learning
• pass embedding
• adaptive compilation