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Quantum Machine Learning: Unveiling Trends, Impacts through Bibliometric Analysis

Riya Bansal, Nikhil Kumar Rajput

TL;DR

This study maps the growth and structure of Quantum Machine Learning (QML) research from 2000 to 2023 using a Lens-based bibliometric analysis of 9493 journal articles. It analyzes publications, citations, fields of study, institutions, authors, journals, keywords, patent citations, and funding patterns to reveal a rapidly evolving, multidisciplinary field with the United States and China as dominant players. Key findings include rising publication momentum after 2015, a prominent role for leading institutions such as the Chinese Academy of Sciences and MIT, and substantial patent activity linked to QML-related research. The work demonstrates the value of bibliometric mapping (via VOSviewer) for understanding emerging, interdisciplinary domains and informs researchers and funders about current trends and strategic priorities.

Abstract

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.

Quantum Machine Learning: Unveiling Trends, Impacts through Bibliometric Analysis

TL;DR

This study maps the growth and structure of Quantum Machine Learning (QML) research from 2000 to 2023 using a Lens-based bibliometric analysis of 9493 journal articles. It analyzes publications, citations, fields of study, institutions, authors, journals, keywords, patent citations, and funding patterns to reveal a rapidly evolving, multidisciplinary field with the United States and China as dominant players. Key findings include rising publication momentum after 2015, a prominent role for leading institutions such as the Chinese Academy of Sciences and MIT, and substantial patent activity linked to QML-related research. The work demonstrates the value of bibliometric mapping (via VOSviewer) for understanding emerging, interdisciplinary domains and informs researchers and funders about current trends and strategic priorities.

Abstract

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.

Paper Structure

This paper contains 15 sections, 7 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Results
  4. Total Publications and Citations per year
  5. Fields of study
  6. Most Productive Countries and Institutions
  7. Most Productive Authors
  8. Top 10 most highly cited articles by researchers
  9. Most Productive Journals
  10. Significant Keywords
  11. Total Patent Citations by Year of Publications
  12. Top patent cited works over time
  13. Top Funding Organizations
  14. Conclusion
  15. Limitations

Figures (7)

  • Figure 1: Workflow of bibliometric analysis
  • Figure 2: Total Publications and citations per year
  • Figure 3: Fields of study
  • Figure 4: Top 20 most productive authors ranked by (a) number of documents and (b) cumulative citations.
  • Figure 5: Keyword co-occurrence network visualization
  • ...and 2 more figures