arXiv cs.AI· Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler, Andr\'e Brinkmann·· 12 小时前AI 评分36
Claude Opus 4.7 生成离子阱穿梭编译器,穿梭时间步最多减少 72%
Efficient LLM-Generated Shuttling Compilers for Complex Trapped-Ion Architectures
AI 导读
研究首次让单一前沿 LLM Claude Opus 4.7 依据书面规范生成并迭代优化离子阱量子计算机的穿梭编译器 Python 代码,覆盖线性分段阱、带结阱和连通阱图三类架构。
正文 · 原文
Abstract:Trapped-ion quantum computers rely on shuttling compilers, which cast an input algorithm into a sequence of ion-qubit movements within a given architecture. We present the first study in which a single frontier large language model (LLM), Claude Opus 4.7, generates and iteratively refines the Python code of shuttling compilers from written specifications. Starting with a compiler for (i) a linear segmented trap, we extend it to (ii) a trap with junctions, and finally achieve efficient compilation for (iii) a broad class of connected trap graphs. The compilers for the more general cases are seeded with code from the previous ones. We benchmark the LLM-generated compilers against state-of-the-art hand-crafted ones using a common suite of quantum circuits. The number of shuttling timesteps is reduced by up to 72% for (i) and up to 43% for (ii). For the broad case (iii) of freely connected architectures, we find large variations in the required number of shuttling timesteps, depending on the connectivity. A densely connected, junction-rich architecture yields up to an order-of-magnitude reduction in shuttling timesteps compared to a corridor-like one. Repeating the complete generation and evaluation with a second frontier LLM, Claude Fable 5, reproduces these findings, with the Fable 5 compilers surpassing the hand-crafted ones more often on the largest circuits. Our results show that an unmodified frontier LLM can produce working, correct, and competitive shuttling compilers without additional manual algorithmic engineering. Generating compilers this way reduces the development time for new architectures from several months to a few days.
| Comments: | 83 pages, 31 figures, 8 tables |
| Subjects: | Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET) |
| Cite as: | arXiv:2607.24714 [quant-ph] |
| (or arXiv:2607.24714v2 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2607.24714 arXiv-issued DOI via DataCite |
Submission history
From: Fabian Kreppel [view email]
[v1]
Mon, 27 Jul 2026 17:51:18 UTC (152 KB)
[v2]
Fri, 16 Oct 2026 00:57:53 UTC (1,586 KB)
来源:arXiv cs.AI · arxiv.org