发表机构
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出QSA，一种量子感知工具，通过LLM引导进化搜索自动化量子软件设计，利用多种量子特定指导，在IBM平台上显著提升QPU利用率和保真度，并大幅降低错误缓解时间。
AI 中文摘要
量子软件对于提高稀缺量子硬件的效率和可靠性至关重要。然而，其设计仍严重依赖于临时、手工制作的启发式方法，这些方法往往次优，并且随着量子硬件的演进很快变得过时。LLM引导的进化搜索为自动探索复杂软件设计提供了一种有前景的方式，但现有的搜索框架缺乏量子特定的支持，无法实现高效进化：验证成本高昂，反馈稀疏，且异构量子程序需要不同的优化目标。在本文中，我们提出了QSA，一个用于LLM引导进化搜索的量子感知工具，旨在实现量子软件设计的自动化。QSA为搜索配备了三种形式的量子特定指导：基于进化难度引导的核心集和近似评分以减少验证成本，静态和快照分析以提供细粒度的执行上下文，以及针对编译器传递和运行时策略的任务特定奖励。我们在IBM量子平台上跨三个基准套件评估了QSA。对于多道程序设计，QSA将QPU利用率提高了4.2%-9.5%，将Hellinger保真度提高了15.2%-19.5%，优于现有技术水平。对于错误缓解，QSA将缓解时间减少了至少96.8%，同时实现了相当或更好的保真度。这些收益仅需6.9美元的LLM API成本，耗时11.3小时。
英文摘要
Quantum software is critical for improving the efficiency and reliability of scarce quantum hardware. However, its design still relies heavily on ad-hoc, handcrafted heuristics that are often suboptimal and quickly become obsolete as quantum hardware evolves. LLM-guided evolutionary search offers a promising way to automatically explore complex software designs, but existing search frameworks lack the quantum-specific support needed for efficient evolution: verification is expensive, feedback is sparse, and heterogeneous quantum programs require different optimization objectives. In this paper, we present QSA, a quantum-aware harness for LLM-guided evolutionary search toward automating quantum software design. QSA equips the search with three forms of quantum-specific guidance: an evolution-hardness-guided coreset and approximate scoring to reduce verification cost, static and snapshot analyses to provide fine-grained execution context, and task-specific rewards for compiler passes and runtime policies. We evaluate QSA on the IBM Quantum platform across three benchmark suites. For multiprogramming, QSA improves QPU utilization by 4.2%-9.5% and Hellinger fidelity by 15.2%-19.5% over the state of the art. For error mitigation, QSA reduces mitigation time by at least 96.8% while achieving comparable or better fidelity. These gains require only $6.9 in LLM API cost over 11.3 hours.
Comments25 pages, 41 figures
