QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
Quantum computing in the NISQ era requires hardware-level capabilities such as mid-circuit measurement, classical feedback, precise timing, and pulse-level access, all exposed by OpenQASM-3. However, no dataset existed to train LLMs on these hardware-oriented features. QASM-Eval fills this gap with an expert-verified test set of 100 tasks and a training set of 4,000 tasks covering classical logic, timing scheduling, pulse control, and complex workflows. An extended verifier automatically checks syntax, quantum states, and timeline. Evaluation shows that state-of-the-art LLMs struggle significantly on OpenQASM-3 tasks, but fine-tuning on QASM-Eval yields substantial improvements, providing a crucial benchmark for developing reliable LLM assistants in quantum programming.
[2605.30358] QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
[Submitted on 28 Apr 2026]
Title:QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
View a PDF of the paper titled QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits, by Zhenxiao Fu and 2 other authors
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Abstract:Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limitation often requires hardware-facing capabilities beyond gate-sequence circuit specification, including mid-circuit measurement and classical feedback for quantum error correction (QEC), precise timing control for dynamical decoupling (DD), and pulse-level waveform access for calibration. OpenQASM-3 was introduced to expose exactly these capabilities, providing a hardware-level programming interface. However, despite the rapid progress of large language models in code generation, there is still no dataset specifically designed to train and evaluate LLMs on OpenQASM-3 programs that involve its advanced hardware-oriented features. To address this gap, we introduce QASM-Eval, the first comprehensive dataset designed to train and evaluate LLMs on OpenQASM-3. Rather than focusing on quantum algorithm design or reasoning, QASM-Eval explicitly targets the language's hardware-facing features. QASM-Eval comprises an expert-verified test set of 100 tasks and a training set of 4,000 tasks, systematically covering classical logic, timing scheduling, pulse control, and complex real-world workflows. To automatically validate generated programs, we check syntax, quantum states and program timeline using an extended verifier. Our evaluation reveals that while state-of-the-art LLMs struggle heavily in OpenQASM-3 coding tasks, targeted fine-tuning on QASM-Eval yields significant gains. QASM-Eval provides a crucial benchmark and training foundation to accelerate the development of reliable LLM assistants for hardware-facing quantum programming in NISQ era. Data and code: this https URL
Subjects:
Machine Learning (cs.LG); Quantum Physics (quant-ph)
Cite as: arXiv:2605.30358 [cs.LG]
(or arXiv:2605.30358v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.30358
arXiv-issued DOI via DataCite
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From: Zhenxiao Fu [view email] [v1] Tue, 28 Apr 2026 19:01:24 UTC (195 KB)
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