Qiskit Code Migration with LLMs

2017-03-03 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

A new hybrid approach integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to automate Qiskit code migration across versions, addressing technical debt in Quantum Software Engineering (QSE). This methodology uses an automatically generated taxonomy of migration scenarios as a structured, version-specific knowledge source to guide models. An automated workflow evaluates Google Gemini Flash-2.5 and OpenAI Gpt-oss-20b under unconstrained and restrictive retrieval schemes. Results show the taxonomy-based RAG architecture, especially with the restrictive scheme, significantly reduces hallucinations and improves descriptive quality. Google Gemini Flash-2.5 demonstrated superior performance, detecting 32.1% more refactoring scenarios on average and reducing critical errors from 47 to 14.25.

Key takeaway

For Quantum Software Engineers managing Qiskit projects, adopting a taxonomy-driven RAG architecture for code migration is crucial. This approach, particularly with models like Google Gemini Flash-2.5 and a restrictive retrieval scheme, will significantly reduce refactoring errors and ensure long-term algorithm availability, flattening your learning curve in a rapidly evolving ecosystem. Consider integrating this automated workflow to maintain code quality.

Key insights

Taxonomy-guided RAG with LLMs significantly enhances Qiskit code migration accuracy and reduces hallucinations.

Principles

• Domain-specific taxonomies improve LLM reliability in volatile technical fields.
• Restrictive RAG schemes enhance LLM output quality and consistency.

Method

An automated n8n workflow ingests Qiskit release notes into a semantic database, processes code snippets, queries LLMs (Gemini Flash-2.5, Gpt-oss-20b) with RAG, and validates results.

In practice

• Prioritize Google Gemini Flash-2.5 for Qiskit refactoring tasks.
• Implement restrictive RAG for critical code migration scenarios.

Topics

• Quantum Software Engineering
• Qiskit
• Code Migration
• Large Language Models
• Retrieval-Augmented Generation
• API Obsolescence

Code references

• Qiskit/qiskit

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.