[论文] Discovering state-of-the-art quantum circuits with large language mode...

论文概要

研究领域: ML
作者: Andi Gu, Alek H. Kemeny, Susanne F. Yelin, Mikhail D. Lukin
发布时间: 2026-10-15
arXiv: 2610.22367

中文摘要

当前大量工作正致力于实现大规模量子信息处理器，但实现有用的量子优势还需要开发高效的量子算法。虽然许多实际问题的主要资源成本与一小部分计算原语相关，但在量子环境中优化它们面临独特挑战，包括经典计算中不存在的可逆性约束。我们引入了一个进化框架，其中大型语言模型（LLM）提出电路设计，独立评估器验证和评分。我们将该框架应用于数值计算、化学、优化、量子模拟和数论任务。在八个任务中的七个中，发现的电路相比最先进水平显著减少了 Toffoli 门和量子比特数量。在第八个任务中，搜索发现了用于素数域模逆运算的新可逆架构，使 divsteps——一种此前对可逆整数运算不切实际的经典算法——变得可行。这些结果为评估器引导的 LLM 协同设计量子计算新算法结构打开了大门。

原文摘要

Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many practical problems the dominant resource costs are associated with a small set of computational primitives, optimizing them in the quantum setting presents unique challenges, including a reversibility constraint absent from ordinary classical computation. Here we introduce an evolutionary framework in which large language models (LLMs) propose circuit designs and an independent evaluator verifies and scores them. We apply this framework to tasks spanning numerical computation, chemistry, optimization, quantum simulation, and number theory. In seven of the...

自动采集于 2026-10-17

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