arXiv Machine Learning June 15, 2026 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 28

Efficient LLM-Generated Shuttling Compilers for Complex Trapped-Ion Architectures

arXiv:2607. 24714v1 Announce Type: cross 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.

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

llms benchmarks

arXiv Machine Learning

Aug 3

Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization

arXiv:2607. 29145v1 Announce Type: cross Abstract: Quantum software engineering is an emerging research field focusing on efficiently embedding the quantum programming paradigm into existing software ecosystems.

By Piotr Malkowski, Domenik Eichhorn, Joshua Ammermann, Rinor Kelmendi, Nick Poser, Patrick Hopf, Ina Schaefer

rag benchmarks

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

llms rag agents

arXiv AI

Jul 29

OmniQEC: discovering practical quantum error-correcting codes by an AI scientist

arXiv:2607. 25865v1 Announce Type: cross Abstract: Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing.

By Ge Yan, Shanchuan Li, Pengyue Ma, Qixin Zhang, Pingchuan Ma, Jianping Wang, Min-Hsiu Hsieh, Yuxuan Du

llms

arXiv AI

Aug 3

InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation

arXiv:2607. 29134v1 Announce Type: cross Abstract: Recent work suggests that relational database management systems (RDBMSs) can execute quantum circuit simulation by compiling the simulation into SQL workloads (primarily join-and-aggregate tensor contractions).

By Andrei Ilinescu, Aadi Patwardhan, Rihan Hai

benchmarks data-engineering

arXiv Machine Learning

Jun 9

Hardware-aware Low-latency Quantum Compilation with Data-driven Lightweight Error Detection for Early Fault-Tolerant Systems

arXiv:2606. 07666v1 Announce Type: cross Abstract: Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightweight error detection can meaningfully improve algorithmic success rates.

By Sumit Chongder (Indian Institute of Technology Jodhpur)

benchmarks

Related stories

Efficient LLM-Generated Shuttling Compilers for Complex Trapped-Ion Architectures

Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization

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

OmniQEC: discovering practical quantum error-correcting codes by an AI scientist

InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation

Hardware-aware Low-latency Quantum Compilation with Data-driven Lightweight Error Detection for Early Fault-Tolerant Systems