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

arXiv: https://arxiv.org/abs/2512.18021 · HTML: https://mlsanity.com/paper/2512.18021/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 86 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [Training Deep Nets with Sublinear Memory Cost](https://mlsanity.com/paper/1604.06174/) (2016)
- [PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel](https://mlsanity.com/paper/2304.11277/) (2023)
- [Ring Attention with Blockwise Transformers for Near-Infinite Context](https://mlsanity.com/paper/2310.01889/) (2023)
- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://mlsanity.com/paper/2101.06840/) (2021)
- [Reducing Activation Recomputation in Large Transformer Models](https://mlsanity.com/paper/2205.05198/) (2022)
- [Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives](https://mlsanity.com/paper/2505.21627/) (2025)
- [Enhancing Training Efficiency Using Packing with Flash Attention](https://mlsanity.com/paper/2407.09105/) (2024)
- [On the qubit routing problem](https://mlsanity.com/paper/1902.08091/) (2019)
- [Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search](https://mlsanity.com/paper/2104.01992/) (2021)
- [AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search](https://mlsanity.com/paper/2410.05115/) (2024)
- [Quantum Circuit Compiler for a Shuttling-Based Trapped-Ion Quantum Computer](https://mlsanity.com/paper/2207.01964/) (2022)
- [Compiler for Distributed Quantum Computing: a Reinforcement Learning Approach](https://mlsanity.com/paper/2404.17077/) (2024)
- [MUSS-TI: Multi-level Shuttle Scheduling for Large-Scale Entanglement Module Linked Trapped-Ion](https://mlsanity.com/paper/2509.25988/) (2025)
- [Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures](https://mlsanity.com/paper/2406.11452/) (2024)

## Abstract (arXiv)

In trapped-ion quantum computers, qubits must be shuttled between segments to interact. The routing logic that schedules these movements is written by hand for every new trap architecture. We present shuttling compilers based on five large language models (LLMs). Each LLM is fine-tuned on shuttling schedules produced by hand-coded heuristics for linear and branched one-dimensional trap architectures. We investigate how the shuttling operation counts of their schedules compare with those of the heuristics and how far they generalize to unseen architectures. For circuits of up to 16 qubits, the fine-tuned LLMs generate valid schedules on both training architectures, more often the fewer qubits a circuit has. In 12% of the compilations yielding a schedule, the best of ten runs needs up to 21% fewer operations than the heuristic baselines, after a rule-based post-processing step. A single run of one fine-tuned LLM produces a valid shuttling schedule for a previously unseen four-way branched architecture. This is preliminary evidence of cross-architecture generalization. On two other unseen architectures no LLM produces a valid schedule. Thus, LLM-learned shuttling compilation is feasible, and we show how far it currently reaches.