Beyond Rules: LLM-Powered Linting for Quantum Programs

TL;DR

This paper introduces LLM-powered approaches for quantum program linting, outperforming traditional rule-based tools in detecting quantum programming issues with higher accuracy and adaptability.

Contribution

It presents novel LLM-based methods, using Chain-of-Thought prompting and RAG, to improve quantum program analysis over existing rule-based linters.

Findings

LLM-based approaches outperform LintQ in detection accuracy (F1-score 0.70 vs. 0.41)

RAG-enhanced method achieves higher precision, reducing false positives

LLMs offer a scalable, adaptive foundation for future quantum software linters.

Abstract

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.