用文献计量法分析量子机器学习发展态势，揭示全球研究格局。
Quantum Machine Learning: Unveiling Trends, Impacts through Bibliometric Analysis
- 基于9493篇论文的文献计量分析，梳理2000-2023年研究脉络。
- 中美在发文量和引用量上领先，体现主导地位。
- 揭示该领域尚处形成期，学术活跃度高，适合关注前沿动态者。
量子机器学习（QML）是量子计算与机器学习两大前沿领域的交叉融合，有望通过量子力学特性实现数据处理、模型构建与问题求解的突破性能力。本研究对2000至2023年间9493篇相关学术文献进行系统的文献计量分析，全面揭示该领域的研究趋势、影响力与资助模式。采用文献计量制图技术，可视化呈现关键国家、机构、作者、专利引用及核心关键词之间的网络关系。分析显示，该领域发表数量持续增长。美国与中国的贡献尤为突出，展现出显著的发文与引文指标。研究结论表明，目前QML仍处于发展初期，学术活动频繁且持续演进。
原文摘要 · Abstract (English)
Quantum Machine Learning (QML) is the intersection of two revolutionary fields: quantum computing and machine learning. It promises to unlock unparalleled capabilities in data analysis, model building, and problem-solving by harnessing the unique properties of quantum mechanics. This research endeavors to conduct a comprehensive bibliometric analysis of scientific information pertaining to QML covering the period from 2000 to 2023. An extensive dataset comprising 9493 scholarly works is meticulously examined to unveil notable trends, impact factors, and funding patterns within the domain. Additionally, the study employs bibliometric mapping techniques to visually illustrate the network relationships among key countries, institutions, authors, patent citations and significant keywords in QML research. The analysis reveals a consistent growth in publications over the examined period. The findings highlight the United States and China as prominent contributors, exhibiting substantial publication and citation metrics. Notably, the study concludes that QML, as a research subject, is currently in a formative stage, characterized by robust scholarly activity and ongoing development.
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