[论文解读] State of the Quantum Software Engineering Ecosystem

Nazanin Siavash, Armin Moin|arXiv (Cornell University)|Jan 5, 2026

Quantum Computing Algorithms and Architecture被引用 0

一句话总结

本论文综述当前的 Quantum Software Engineering (QSE) 生态系统，利用基于 GPT-5 的 AI 方法识别 QSE 中活跃的学术机构、产业参与者和初创公司资金情况。它分析学术界与产业界的互动并概述了值得关注的初创公司和产业参与者清单。

ABSTRACT

We study the current state of the Quantum Software Engineering (QSE) ecosystem, focusing on the achievements, activities, and engagements from academia and industry, with a special focus on successful entrepreneurial endeavors in this arena. Our research methodology is a novel one, featuring the state-of-the-art in Artificial Intelligence (AI), namely Large Language Models (LLMs), especially Generative Pretrained Transformers (GPT). We use one of such models, namely the OpenAI GPT-5 model, through the ChatGPT tool. The goal is to identify institutions and companies that are highly active and have achieved distinguished results in QSE, evidenced by peer-reviewed publications or raised capital in the venture capital market.

研究动机与目标

• Identify active institutions and companies in the QSE ecosystem with peer-reviewed publications or capital raised.
• Assess academia and industry engagement in QSE to enable future collaborations.
• Highlight entrepreneurial activities and notable startups in the QSE space.
• Evaluate methodological challenges of using LLMs (GPT-5) for ecosystem mapping in QSE.

提出的方法

• Use OpenAI GPT-5 via ChatGPT to extract affiliations and activities from selected journals and conferences focused on quantum software.
• Prompt GPT-5 to compile lists of institutions, academic centers, and industrial participants with respect to QSE.
• Manually review a subset of results to resolve ambiguities and ensure accuracy.
• Label and summarize industrial players and startups with reported capital or notable collaborations.
• Compare results to existing reports (e.g., MIT Quantum Index Report) and discuss limitations of LLM-based extraction.

实验结果

研究问题

• RQ1 Which universities and research centers are most active in publishing QSE-related work?
• RQ2 Which industrial players and startups are most engaged in the QSE ecosystem and what capital or collaborations do they have?
• RQ3 What are the major gaps and challenges in mapping the QSE ecosystem using LLM-based methods?
• RQ4 How does academia–industry collaboration appear across identified institutions and companies?
• RQ5 What is the overall trajectory and growth indicators of QSE based on publications and funding activity?

主要发现

• The study identifies numerous universities, research centers, and industrial participants engaged in QSE, highlighting a broad, multi-national ecosystem.
• It documents a range of industrial players and startups, with a selection of companies and artifacts named (e.g., Qiskit, Azure Quantum QDK/Q#, Cirq, PennyLane, etc.).
• The authors use GPT-5 via ChatGPT to extract affiliations and collaborations, acknowledging reproducibility challenges and occasional name ambiguities.
• The paper presents Tables 1–6 summarizing academia engagement and industry participants, and provides a summarized startup/funding list.
• The authors note that QSE is growing rapidly but not yet fully established across all SE and QC-related venues, and that arXiv and csrankings do not yet fully index QSE due to categorization issues.