{"ID":2827259,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18021","arxiv_id":"2512.18021","title":"Shuttling Compiler for Trapped-Ion Quantum Computers Based on Large Language Models","abstract":"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).","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2512.18021","url_pdf":"https://arxiv.org/pdf/2512.18021v2","authors":"[\"Fabian Kreppel\",\"Reza Salkhordeh\",\"Ferdinand Schmidt-Kaler\",\"André Brinkmann\"]","published":"2025-12-19T19:29:09Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.ET\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}