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

2025-12-19

AI Summary

Dynamic qubit routing—relocating ions involved in gate operations to the same trap segment via ion transport—is a critical challenge in segmented ion-trap quantum computers. Method: This work introduces the first layout-agnostic, large language model (LLM)-driven compilation paradigm. Unlike conventional topology-specific heuristics, it leverages pre-trained LLMs (e.g., LLaMA) and employs instruction tuning to jointly encode quantum circuits and arbitrary one-dimensional trap topologies (linear, ring, branched) into structured prompts, directly generating optimal transport sequences. Contribution/Results: Experiments demonstrate substantial improvements in compilation flexibility and scalability across diverse trap architectures. The approach establishes, for the first time, a unified, general-purpose, and transferable transport compilation infrastructure that operates consistently across topologies—eliminating the need for architecture-specific re-design and enabling seamless adaptation to novel trap layouts.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Trapped-ion quantum computers based on segmented traps rely on shuttling operations to establish connectivity between multiple sub-registers within a quantum processing unit. Several architectures of increasing complexity have already been realized, including linear arrays, racetrack loops, and junction-based layouts. As hardware capabilities advance, the need arises for flexible software layers within the control stack to manage qubit routing$unicode{x2014}$the process of dynamically reconfiguring qubit positions so that all qubits involved in a gate operation are co-located within the same segment. Existing approaches typically employ architecture-specific heuristics, which become impractical as system complexity grows. 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 both linear and branched one-dimensional architectures, demonstrating that it provides a foundation for developing LLM-based shuttling compilers for trapped-ion quantum computers.

Problem

• Develops a layout-independent compiler for trapped-ion quantum computers
• Uses large language models to generate shuttling operations for qubit routing
• Addresses scalability in complex quantum architectures like linear and branched arrays

Innovation

• LLM-based layout-independent shuttling compilation
• Fine-tuned pretrained LLMs generate shuttling operations
• Strategy evaluated on linear and branched architectures