Summary
Choosing the best settings for quantum computer programs is tricky because you need to pick both the right hardware and the right compiler options to get accurate results. The authors point out that current methods pick hardware first, which can limit how well the program runs even if the compiler settings are perfect. They propose looking at all options together from the start using a complete set of tested configurations. They built a large benchmark and found that simply asking large language models (LLMs) to pick settings doesn’t work well. But by using LLMs to help create simple rules combined with machine learning, their system called QuTOP quickly picks good settings without much searching.
What this means in practice
- •For quantum software engineers: Quickly select effective quantum compiler and hardware settings to improve program execution quality without costly iterative searches.
- •For high performance computing teams: Incorporate rule-guided ML-based configuration methods to optimize quantum workloads more efficiently than traditional sequential tuning.
Authors
Ming Zhong, Xiangyu Ren, Kangyu Zheng, Zhi Zeng, Shenggen Zheng, Shaohua Li, Zhiding Liang
Abstract
Selecting an effective quantum compiler configuration requires coordinated decisions over both hardware targets and compiler settings, with the ultimate goal of improving circuit execution fidelity. Existing SOTA approaches typically decompose this problem into two sequential stages: a device is chosen first, after which compiler options are tuned on that device through RL-based search. This bottom-up strategy can be fundamentally limited by its initial device decision, since a poorly matched backend may constrain the best fidelity achievable regardless of how effectively later compiler options are optimized. In contrast to classical compiler tuning, where the search space is often prohibitively large, the configuration space of quantum compilers remains manageable enough to support exhaustive search. Consequently, rather than repeatedly searching from candidate configurations, one can derive reusable configuration knowledge from offline oracle results and apply it directly to new circuits. In this paper, we introduce a top-down configuration-selection paradigm and build QuOPTBench, an exhaustively searched benchmark containing oracle configurations for 268 quantum circuits. We first study whether LLMs can directly infer suitable configurations and evaluate ten representative LLMs. The results reveal that vanilla LLMs remain far from the oracle. We then employ an LLM to extract transferable rules from sampled circuits governing configuration selection. Using these rules together with predictors trained on the enumerated configuration data of the sampled circuits, we design QuTOP, a rule-guided ML framework that performs fast configuration selection without iterative online search. Across our evaluation, the distilled rules consistently strengthen LLM-based selection, while QuTOP provides the strongest overall balance between solution quality and selection latency among the baselines.