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%.

翻译：我们提出了首个基于大型语言模型(LLMs)的离子阱量子计算机穿梭编译器，其中量子比特在门操作执行与量子比特存储时于不同区域间穿梭。我们在线性及分支式一维穿梭架构的示例上对预训练语言模型进行微调，由此获得一种与布局无关的编译策略，该策略可直接从数据中学习所需的穿梭操作。通过使用含多达16个量子比特的基准测试电路，经过微调的语言模型现能为穿梭架构生成有效调度。值得注意的是，我们还在一种此前未见过的四路交叉布局上获得了有效调度，这表明经训练的LLMs可泛化至训练过程中未遭遇的布局。针对多种架构，基于LLMs的调度方法相较于现有最优基线编译器结果实现了性能提升，穿梭开销降低幅度最高达15%。