Large Language Models for Quantum Software Engineering: Opportunities and Challenges in Modeling, Generation, Testing, and Repair

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Abstract
Large Language Models (LLMs) have already reshaped many aspects of traditional software engineering, from code generation and debugging to automated testing and maintenance. As an important research sub-domain in the software engineering area, quantum software engineering (QSE) is poised to undergo a similar transformation. As QSE moves toward broader adoption, including the generation of quantum code, program testing, and automated program repair, LLMs will likely play a key supporting role in its development. However, despite encouraging early attempts, the community still lacks a roadmap for using LLMs in QSE. Without such a roadmap, opportunities may be missed, challenges may remain unrevealed, and risks may be overlooked. In this article, we present our vision for how LLMs can advance QSE, focusing on four core tasks: program modeling and analysis, program generation, testing, and repair. We outline the opportunities and unique challenges, identify the risks inherent in this integration, and propose a research agenda to guide the community toward realizing the full potential of LLM-powered QSE.
Funding Information
  • Fundamental Research Funds for the Central Universities (Grant 2682026CX206)
  • National Natural Science Foundation of China (Grants U23B204, U24A6009)
  • JSPS KAKENHI (JP26K02892, JP24K14908)
  • JST SPRING (JPMJSP2136)
  • JST BOOST (Grant JPMJBS2406)

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