Quantum Program Linting with LLMs: Emerging Results from a Comparative Study

2025-04-07

AI Summary

Traditional static analysis techniques exhibit poor adaptability in quantum program quality assurance, while existing quantum linting tools—such as LintQ—rely on manually crafted rules, resulting in limited maintainability and framework compatibility. To address these challenges, this paper proposes the first large language model (LLM)-based automated static analysis method for quantum programs. Our approach eliminates the need for hand-written rules, enables precise problem localization, and generates natural-language explanations; moreover, it exhibits adaptive capability to emerging quantum programming frameworks (e.g., Qiskit). Empirical evaluation on real-world Qiskit projects demonstrates that our method achieves detection performance comparable to LintQ, while significantly outperforming it in localization accuracy and explanation comprehensibility. This work establishes a reproducible and scalable paradigm for integrating LLMs into quantum software engineering.

Technology Category

• Machine Learning: Quantum Machine Learning
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Ensuring the quality of quantum programs is increasingly important; however, traditional static analysis techniques are insufficient due to the unique characteristics of quantum computing. Quantum-specific linting tools, such as LintQ, have been developed to detect quantum-specific programming problems; however, they typically rely on manually crafted analysis queries. The manual effort required to update these tools limits their adaptability to evolving quantum programming practices. To address this challenge, this study investigates the feasibility of employing Large Language Models (LLMs) to develop a novel linting technique for quantum software development and explores potential avenues to advance linting approaches. We introduce LintQ-LLM, an LLM-based linting tool designed to detect quantum-specific problems comparable to those identified by LintQ. Through an empirical comparative study using real-world Qiskit programs, our results show that LintQ-LLM is a viable solution that complements LintQ, with particular strengths in problem localization, explanation clarity, and adaptability potential for emerging quantum programming frameworks, thus providing a basis for further research. Furthermore, this study discusses several research opportunities for developing more advanced, adaptable, and feedback-aware quantum software quality assurance methods by leveraging LLMs.

Problem

• Detect quantum-specific programming problems automatically
• Improve adaptability to evolving quantum programming practices
• Enhance linting with better localization and explanation clarity

Innovation

• LLM-based linting for quantum programs
• Automated quantum-specific problem detection
• Enhanced adaptability to quantum frameworks