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Turning Quantum Compiler Configuration Selection Upside Down: A Top-Down Paradigm

Zhong Ming, Xiangyu Ren, Kangyu Zheng, Zhi Zeng, 鄭盛根, Shaohua Li, Zhiding Liang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29170 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

A seleção de uma configuração de circuito quântico eficaz requer decisões coordenadas sobre ambos os alvos de hardware e as configurações de compilador, com o objetivo final de melhorar a fidelidade da execução de circuito. As abordagens SOTA existentes normalmente dividem este problema em duas etapas sequenciais: um dispositivo é escolhido primeiro, após o qual as opções de compilador são ajustadas nesse dispositivo através de pesquisas baseadas em RL. Esta estratégia de fundo para cima pode ser fundamentalmente limitada por sua decisão inicial de dispositivo, uma vez que um backend mal ajustado pode restringir a melhor fidelidade alcançável, independentemente de quão eficazmente as opções de compilador mais tarde são otimizadas. Em contraste com o ajustamento de compilador clássico, onde o espaço de pesquisa é muitas vezes proib

Machine translation (Meta M2M100); the English original follows.

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.

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Topics: Compiler, Computer science, Optimizing compiler, Benchmark (surveying), Oracle, Selection (genetic algorithm)

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