Leveraging Mutation Analysis for LLM-based Repair of Quantum Programs

TL;DR

This paper demonstrates that incorporating mutation analysis as contextual information in prompts significantly improves the success rate and quality of LLM-based automated repair of quantum programs.

Contribution

It introduces a novel framework combining mutation analysis with LLMs for quantum program repair, enhancing success rates and explainability.

Findings

Mutation analysis improves repair success rate to 94.4%.

Including dynamic information enhances explanation quality.

Mutation analysis provides valuable context for quantum program repair.

Abstract

In recent years, Automated Program Repair (APR) techniques specifically designed for quantum programs have been proposed. However, existing approaches often suffer from low repair success rates or poor understandability of the generated patches. In this study, we construct a framework in which a large language model (LLM) generates code repairs along with a natural language explanation of the applied repairs. To investigate how the contextual information included in prompts influences APR performance for quantum programs, we design four prompt configurations with different combinations of static information, dynamic information, and mutation analysis results. Mutation analysis evaluates how small changes to specific parts of a program affect its execution results and provides more detailed dynamic information than simple execution outputs such as stack traces. Our experimental results show that mutation analysis can provide valuable contextual information for LLM-based APR of quantum programs, improving repair success rates (achieving 94.4% in our experiment) and in some cases also improving the quality of generated explanations. Our findings point toward new directions for developing APR techniques for quantum programs that enhance both reliability and explainability.