# Taxonomy of migration scenarios for Qiskit refactoring using LLMs

arXiv: https://arxiv.org/abs/2506.07135 · HTML: https://mlsanity.com/paper/2506.07135/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 8 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves](https://mlsanity.com/paper/2311.04205/) (2023)
- [Design Principles for Generative AI Applications](https://mlsanity.com/paper/2401.14484/) (2024)
- [Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers](https://mlsanity.com/paper/2311.10242/) (2023)
- [Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code](https://mlsanity.com/paper/2405.19495/) (2024)
- [A Study of LLMs' Preferences for Libraries and Programming Languages](https://mlsanity.com/paper/2503.17181/) (2025)
- [An Empirical Study on the Code Refactoring Capability of Large Language Models](https://mlsanity.com/paper/2411.02320/) (2024)
- [Automatic Library Migration Using Large Language Models: First Results](https://mlsanity.com/paper/2408.16151/) (2024)
- [A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG](https://mlsanity.com/paper/2503.02497/) (2025)
- [Quantum Software Engineering: Roadmap and Challenges Ahead](https://mlsanity.com/paper/2404.06825/) (2024)
- [Testing and Debugging Quantum Programs: The Road to 2030](https://mlsanity.com/paper/2405.09178/) (2024)
- [Towards Advancing Code Generation with Large Language Models: A Research Roadmap](https://mlsanity.com/paper/2501.11354/) (2025)
- [A Logic for Recursive Quantum Programs](https://mlsanity.com/paper/1812.00349/) (2018)
- [Proq: Projection-based Runtime Assertions for Debugging on a Quantum Computer](https://mlsanity.com/paper/1911.12855/) (2019)
- [Some Size and Structure Metrics for Quantum Software](https://mlsanity.com/paper/2103.08815/) (2021)
- [On Refactoring Quantum Programs](https://mlsanity.com/paper/2306.10517/) (2023)

## Built on by (most cited first, 2 of 2)

- [Quantum Verifiable Rewards for Post-Training Qiskit Code Assistant](https://mlsanity.com/paper/2508.20907/) (2025)
- [Automatic Qiskit Code Refactoring Using Large Language Models](https://mlsanity.com/paper/2506.14535/) (2025)

## Abstract (arXiv)

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.