Taxonomy of migration scenarios for Qiskit refactoring using LLMs

• Autores: Suárez, José Manuel; Bibbo, Luis Mariano; Bogado, Joaquín; Fernández, Alejandro
• Año de publicación: 2025
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias de la Computación e Información; Quantum Computing (QC); Quantum Software Engineering (QSE); Large Language Models (LLMs); Generative AI; Qiskit; Migration Code
• Institución: Comisión de Investigaciones Científicas de la Provincia de Buenos Aires

As quantum computing advances, quantum programming libraries’ heterogeneity and steady evolution create new challenges for software developers. Frequent updates in software libraries break working code that needs to be refactored, thus adding complexity to an already complex landscape. These refactoring challenges are, in many cases, fundamentally different from those known in classical software engineering due to the nature of quantum computing software. This study addresses these challenges by developing a taxonomy of quantum circuit’s refactoring problems, providing a structured framework to analyze and compare different refactoring approaches. Large Language Models (LLMs) have proven valuable tools for classic software development, yet their value in quantum software engineering remains unexplored. This study uses LLMs to categorize refactoring needs in migration scenarios between different Qiskit versions. Qiskit documentation and release notes were scrutinized to create an initial taxonomy of refactoring required for migrating between Qiskit releases. Two taxonomies were produced: one by expert developers and one by an LLM. These taxonomies were compared, analyzing differences and similarities, and were integrated into a unified taxonomy that reflects the findings of both methods. By systematically categorizing refactoring challenges in Qiskit, the unified taxonomy is a foundation for future research on AI-assisted migration while enabling a more rigorous evaluation of automated refactoring techniques. Additionally, this work contributes to quantum software engineering (QSE) by enhancing software development workflows, improving language compatibility, and promoting best practices in quantum programming. This research marks the first step in a broader effort to assess various refactoring strategies, ultimately guiding the development of AI-powered tools to support quantum software engineers.