Taxonomy of migration scenarios for Qiskit refactoring using LLMs

TL;DR

This paper develops a taxonomy of quantum circuit refactoring challenges in Qiskit, using LLMs to categorize migration needs, thereby aiding future AI-assisted quantum software migration and improving development workflows.

Contribution

It introduces a unified taxonomy of Qiskit refactoring scenarios derived from expert and LLM analyses, advancing quantum software engineering and AI-assisted migration techniques.

Findings

Created initial taxonomies from documentation and release notes

Compared expert and LLM taxonomies to identify differences

Unified taxonomy supports future research and tooling in quantum software migration

Abstract

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.