Efficient LLM-Generated Shuttling Compilers for Complex Trapped-Ion Architectures
2026-07-27 • Artificial Intelligence
Artificial IntelligenceEmerging Technologies
AI summaryⓘ
The authors studied how large language models (LLMs) can automatically write software that plans how to move ion-qubits in trapped-ion quantum computers. They showed that an LLM called Claude Opus 4.7 could create and improve full Python compilers that translate movement instructions for different trap setups. These AI-made compilers performed as well as or better than hand-made ones in tests, often reducing the time steps needed for moving ions. The study was repeated with another LLM, Claude Fable 5, which confirmed the results and sometimes did even better. This suggests LLMs can speed up the development of quantum computer control software significantly.
trapped-ion quantum computersshuttling compilerion-qubit movementlarge language model (LLM)quantum circuit compilationtrap architecturesjunction trapsconnected trap graphsPython code generationquantum hardware control
Authors
Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler, André Brinkmann
Abstract
Trapped-ion quantum computers rely on shuttling compilers, which cast an input algorithm into a sequence of ion-qubit movements within a given architecture. We present the first study in which a single frontier large language model (LLM), Claude Opus 4.7, generates and iteratively refines the full Python code of shuttling compilers from written specifications. We start with a compiler for (i) a linear segmented trap, extend it to (ii) a trap with junctions, and finally achieve efficient compilation for (iii) a broad class of connected trap graphs. The compilers for the more general cases are seeded with code from the previous ones. We benchmark the LLM-generated compilers against state-of-the-art hand-crafted ones using a common suite of quantum circuits. The number of shuttling timesteps is reduced by up to 76% for (i) and up to 39% for (ii). For the broad case (iii) of freely connected architectures, we find large variations in the required number of shuttling timesteps, depending on the connectivity. A densely connected, junction-rich architecture yields an order-of-magnitude reduction in shuttling timesteps compared to a corridor-like one. Repeating the complete generation and evaluation with a second frontier LLM, Claude Fable 5, reproduces these findings, with the Fable 5 compilers surpassing the hand-crafted ones more often on the largest circuits. Our results show that an unmodified frontier LLM can produce working, correct, and competitive shuttling compilers without additional manual algorithmic engineering, thus reducing the development time for new architectures from several months to a few days.