QC-Stark: A Multi-Task Benchmark Revealing Capability Dissociations in LLMs Evaluated on Quantum Computing Tasks

Abstract:We introduce QC-Stark, a benchmark for evaluating large language models (LLMs) on 11 quantum computing (QC) tasks, spanning circuit construction, debugging, compilation, error correction, and simulation. Across 2,750 evaluations (10 models $\times$ 11 tasks x 5 difficulty levels x 5 seeds), we find that overall rankings mask substantial per-task variation. The Spearman correlation between overall and per-task rankings is statistically insignificant for 4 out of the 11 tasks included in this benchmark. A 2-parameter Item Response Theory (IRT) model validates measurement quality, and prompt sensitivity analysis confirms ranking robustness across prompt conditions. All tasks are auto-verifiable via execution, thus not requiring any manual evaluation. We make the code and data publicly available on Huggingface.

Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.35581 [quant-ph]

(or arXiv:2609.35581v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.35581 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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