arXiv Machine Learning By Zhenxiao Fu, Lei Jiang, Fan Chen

QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits

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arXiv:2605. 30358v2 Announce Type: replace Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, with performance constrained by noise.

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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
arXiv AI
Sep 17

BadQubits: An LLM-Based Framework for Static Pre-Execution Detection of Structurally Harmful Quantum Circuits

BadQubits is an LLM-based framework that statically analyzes OpenQASM 2.0 circuits before execution to detect structurally harmful patterns. The system evaluates four large language model architectures on 1,500 circuits, achieving 92.67% classification accuracy and 96.1% recall for harmful circuits with a fine‑tuned Qwen Coder 2.5 7B model. Comparative experiments show that LLMs retain sequential token structure, outperforming a bag‑of‑gates CNN, and that model decisions correlate with threat‑defining features such as SWAP density and measurement timing.

By Justin Woodring, Lamine Noureddine, Aisha Ali-Gombe
arXiv AI
Jul 24

PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation

arXiv:2605. 25572v2 Announce Type: replace-cross Abstract: The growing complexity of quantum programming frameworks has exposed a critical limitation in existing large language model (LLM)-based code assistants: general-purpose models hallucinate PennyLane-specific gate names, misplace device configurations, and produce structurally invalid circuits when faced with specialized quantum coding challenges.

By Minghao Shao, Nouhaila Innan, Hariharan Janardhanan, Muhammad Kashif, Alberto Marchisio, Muhammad Shafique