用大模型训练出可通用的离子阱量子计算机调度编译器，减少搬运操作。
Shuttling Compiler for Trapped-Ion Quantum Computers Based on Large Language Models
- 基于大模型学习离子阱中量子比特搬运策略，实现布局无关编译
- 16量子比特电路调度成功率提升，搬运量最多减少15%
- 首次在未见过的四岔路口布局上生成有效调度方案
我们提出首个基于大语言模型（LLMs）的离子阱量子计算机搬运编译器，其中量子比特通过在不同区域间搬运执行门操作和存储。通过对线性与分支型一维搬运架构的数据进行微调，得到一种无需依赖具体布局的编译策略，直接从数据中学习所需搬运操作。在包含最多16个量子比特的基准电路测试中，微调后的LLM能生成有效的搬运调度。值得注意的是，该模型还成功为此前未见过的四路交汇布局生成了有效调度方案，表明训练后的模型具备良好泛化能力。对于多种架构，基于LLM的调度方案优于现有最优基线，搬运操作量最多降低15%。
原文摘要 · Abstract (English)
We present the first shuttling compiler based on large language models (LLMs) for trapped-ion quantum computers, where qubits are shuttled between segments for gate execution and qubit storage. We fine-tune pre-trained LLMs on examples from linear and branched one-dimensional shuttling architectures. Thus, we obtain a layout-independent compilation strategy that learns the required shuttling operations directly from data. Using benchmark circuits with up to 16 qubits, such fine-tuned LLMs can now generate valid schedules for shuttling architectures. Notably, we also obtain a valid schedule for a previously unseen four-way junction layout. This demonstrates that trained LLMs can generalize to layouts not encountered during training. For various architectures, LLM-based schedules improve upon state-of-the-art baseline compiler results, reducing the shuttling effort by up to 15%.
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