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This paper introduces LintQ-LLM+CoT and LintQ-LLM+RAG, two novel LLM-based approaches for linting quantum programs, addressing the limitations of traditional rule-based methods. The LLMs are enhanced with Chain-of-Thought prompting and Retrieval-Augmented Generation using a knowledge base of quantum programming problems. Experiments on 55 Qiskit programs demonstrate that both LLM-based approaches significantly outperform the state-of-the-art rule-based linter, LintQ, in both precision and recall, with the RAG-enhanced variant showing improved precision.
LLMs can catch more bugs in quantum code than traditional rule-based linters, suggesting a new path to more reliable quantum software.
As quantum computing transitions from theoretical experimentation to its practical application, the reliability of quantum software has become a critical bottleneck. Traditional static analysis techniques for quantum programs, primarily rule-based linters, are increasingly inadequate; they struggle to keep pace with rapidly evolving APIs and fail to capture complex, context-dependent quantum programming problems. This results in high maintenance overhead and limited detection capabilities. In this paper, we introduce LintQ-LLM+CoT and LintQ-LLM+RAG, novel approaches that redefine the detection of quantum programming problems by employing Large Language Models (LLMs) specialized, respectively, via Chain-of-Thought (CoT) prompting and a Retrieval-Augmented Generation (RAG) system that grounds the model's reasoning in a curated knowledge base of verified quantum programming problems and best practices. We conducted a rigorous and manual comparative evaluation against the state-of-the-art rule-based tool, LintQ, using a corpus of 55 Qiskit programs. Our results show that LLM-based approaches, with and without RAG, outperform LintQ in terms of quantum programming problems detection correctness (precision) and completeness (recall). Overall, LLM-based approaches were more effective than LintQ (F1-score equal to 0.70 and 0.68 vs. 0.41). Furthermore, the RAG-enhanced variant demonstrated a slightly superior precision, effectively reducing false positives. Our findings suggest that LLMs provide a scalable and adaptive foundation for the next generation of linters in quantum software engineering.
