arXiv Machine Learning By Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler, Andr\'e Brinkmann

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

Read the original on arXiv Machine Learning →

arXiv:2512. 18021v3 Announce Type: replace-cross Abstract: We present the first shuttling compiler based on large language models (LLMs) for trapped-ion quantum computers, where qubits are shuttled between segments for gate execution and qubit storage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Sep 16

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

The paper reports on shuttling compilers for trapped‑ion quantum computers that are built using five large language models (LLMs) fine‑tuned on hand‑crafted shuttling schedules for linear and branched one‑dimensional trap architectures. For circuits up to 16 qubits, the fine‑tuned LLMs produce valid schedules on the training architectures, and in 12% of compilations the best of ten runs achieves up to 21% fewer operations than heuristic baselines after rule‑based post‑processing. A single run of one LLM also generates a valid schedule for a previously unseen four‑way branched architecture, providing preliminary evidence of cross‑architecture generalization, though no LLM succeeded on two other unseen architectures.

By Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler, Andr\'e Brinkmann
arXiv Machine Learning
Sep 25

MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline

The paper introduces MQSS-Selector, a reinforcement‑learning guided pass selection system for an MLIR compilation pipeline aimed at unified High Performance Computing‑Quantum Computing (HPCQC) infrastructures. It addresses the challenges of Noisy Intermediate‑Scale Quantum (NISQ) devices by integrating device selection, compiler‑pass optimization, and job queue scheduling into a single learning‑based framework. The selector can simultaneously optimize multiple objectives—fidelity, compilation time, and scheduling latency—while adapting to circuit characteristics and device conditions.

By Andre Youssefi (Leibniz Supercomputing Centre), Erc\"ument Kaya (Leibniz Supercomputing Centre, Technical University of Munich), Minh Chung (Leibniz Supercomputing Centre), Jorge Echavarria (Munich Quantum Valley), Laura B. Schulz (Argonne National Laboratory), Martin Schulz (Leibniz Supercomputing Centre, Technical University of Munich)
arXiv AI
Jul 13

QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming

arXiv:2508. 20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration.

By Zhenxiao Fu, Lei Jiang, Yilun Xu, Gang Huang, Fan Chen