发表机构
University of Jyväskylä（于韦斯屈莱大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对量子软件开发中实验跟踪等实践难推行的问题，提出嵌入开发环境的检索增强生成式AI助手Qlippy，其基于量子软件工程知识语料库，为Qiskit程序添加MLflow实验跟踪，可低成本本地部署。
AI 中文摘要
量子软件开发具有迭代性且易出错，有噪声的硬件与重复重新执行使得实验跟踪、来源追溯和可复现性至关重要，但由于工具复杂度及所需专业知识，这些实践难以推行。通用语言模型虽有帮助，但易产生幻觉且缺乏领域特定工具的支撑。本文提出Qlippy，这是一款嵌入开发环境的检索增强生成式AI助手，其响应基于精选的量子软件工程知识语料库。Qlippy会在上下文中解释可复现性与来源追溯概念，并为现有Qiskit程序添加基于MLflow的、符合QProv模式的实验跟踪功能。通过将知识与模型参数分离，该支撑机制可对助手响应的范围与来源进行显式控制，减少对模型规模的依赖，指向低成本、隐私保护型的本地部署。
英文摘要
Quantum software development is iterative and error-prone. Noisy hardware and repeated re-execution make experiment tracking, provenance, and reproducibility essential, yet these practices are hard to adopt because of tooling complexity and the specialized knowledge they demand. General-purpose language models can help but tend to hallucinate and lack grounding in domain-specific tooling. We present Qlippy, a retrieval-augmented GenAI assistant embedded in the development environment that grounds its responses in a curated corpus of quantum-software-engineering knowledge. Qlippy explains reproducibility and provenance concepts in context and augments existing Qiskit programs with MLflow-based experiment tracking aligned to the QProv schema. By separating knowledge from model parameters, grounding gives explicit control over the scope and provenance of the assistant's responses and reduces reliance on model scale, which points toward low-cost, privacy-preserving local deployment.
CommentsAccepted for publication in the QGenAI Workshop at IEEE QCE 2026
