This research explores how Large Language Models can revolutionize quality assurance for quantum programming, addressing limitations of traditional static analysis techniques.

• Introduces LintQ-LLM, a novel approach that uses LLMs to detect quantum-specific programming issues
• Reduces manual effort in creating and maintaining quantum linting tools
• Shows promising results in adapting to evolving quantum programming practices
• Demonstrates how AI can address the unique quality challenges in quantum software engineering

This innovation matters for engineering teams working with quantum technologies by providing automated tools that can evolve alongside quantum programming paradigms, potentially accelerating development cycles while maintaining code quality.

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