用大模型辅助量子系统设计，效果堪比研究生。
Are LLMs Good For Quantum Software, Architecture, and System Design?
- 测试9个前沿大模型在量子系统推理任务中的表现。
- 部分模型表现接近德克萨斯大学奥斯汀分校研究生水平。
- 为量子软件与系统开发提供新思路，适合相关研究者参考。
量子计算机在物理、化学、密码分析、医疗等领域有望实现巨大计算加速，但历经数十年发展仍远未进入实用阶段。其核心瓶颈在于缺乏成熟的软件、架构与系统解决方案，难以将量子算法的物理特性转化为实际量子比特设备上的状态变换。该问题因对领域专业知识的高度依赖而加剧，尤其影响软件开发者、计算机架构师与系统工程师。为突破此限制并加速大规模高性能量子系统的设计，本文探讨大语言模型（LLMs）在解决量子软件、架构与系统问题上的潜力。我们开展案例研究，评估9个前沿大模型在量子系统推理任务中的表现，并与德克萨斯大学奥斯汀分校研究生进行对比。结果表明，部分模型已展现出接近人类专家的推理能力。最后，本文提出未来研究与工程发展的若干关键方向。
原文摘要 · Abstract (English)
Quantum computers promise massive computational speedup for problems in many critical domains, such as physics, chemistry, cryptanalysis, healthcare, etc. However, despite decades of research, they remain far from entering an era of utility. The lack of mature software, architecture, and systems solutions capable of translating quantum-mechanical properties of algorithms into physical state transformations on qubit devices remains a key factor underlying the slow pace of technological progress. The problem worsens due to significant reliance on domain-specific expertise, especially for software developers, computer architects, and systems engineers. To address these limitations and accelerate large-scale high-performance quantum system design, we ask: Can large language models (LLMs) help with solving quantum software, architecture, and systems problems? In this work, we present a case study assessing the performance of LLMs on quantum system reasoning tasks. We evaluate nine frontier LLMs and compare their performance to graduate UT Austin students on a set of quantum computing problems. Finally, we recommend several directions along which research and engineering development efforts must be pursued.
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