Shuttling Compiler for Trapped-Ion Quantum Computers Based on Large Language Models

TL;DR

This paper introduces a novel layout-independent shuttling compiler for trapped-ion quantum computers using large language models, achieving up to 15% reduction in shuttle overhead for certain architectures.

Contribution

It presents a new LLM-based approach for qubit routing in trapped-ion systems, improving efficiency over previous methods and demonstrating potential for scalable quantum compilation.

Findings

LLMs can generate valid shuttling schedules for quantum circuits.

The approach reduces shuttle overhead by approximately 15% in some cases.

Performance degrades with increased circuit width and depth.

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).