Shuttling Compiler for Trapped-Ion Quantum Computers Based on Large Language Models
This addresses the challenge of scaling qubit connectivity in quantum computing, though it is incremental as it builds on existing LLM methods with specific architectural improvements.
The authors tackled the problem of qubit routing in trapped-ion quantum computers by proposing a layout-independent compilation strategy using fine-tuned large language models (LLMs) to generate shuttling operations, resulting in up to 15% less shuttle overhead in some cases compared to previous compilers.
Trapped-ion quantum computers based on segmented traps rely on shuttling operations to establish long-range connectivity between sub-registers. Qubit routing dynamically reconfigures qubit positions so that all qubits involved in a gate operation are co-located within the same segment, a task whose complexity increases with system size. To address this challenge, we propose a layout-independent compilation strategy based on large language models (LLMs). Specifically, we fine-tune pretrained LLMs to generate the required shuttling operations. We evaluate this approach on linear and branched one-dimensional architectures using quantum circuits of up to $16$ qubits. Our results show that the fine-tuned LLMs generate valid shuttling schedules and, in some cases, outperform previous shuttling compilers by requiring approximately $15\,\%$ less shuttle overhead. However, results degrade as the algorithms increase in width and depth. In future, we plan to improve LLM-based shuttle compilation by enhancing our training pipeline using Direct Preference Optimization (DPO) and Gradient Regularized Policy Optimization (GRPO).