Exploring the Role of Material Science in Advancing Quantum Machine Learning: A Scientometric Study

Manish Tomar, Sunil Prajapat, Dheeraj Kumar, Pankaj Kumar (), Rajesh Kumar and Athanasios V. Vasilakos ()
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Manish Tomar: Department of Physics and Astronomical Sciences, Central University of Himachal Pradesh, Dharamshala 176215, India
Sunil Prajapat: Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India
Dheeraj Kumar: Department of Computer Science, Hansraj College, University of Delhi, New Delhi 110007, India
Pankaj Kumar: Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India
Rajesh Kumar: Department of Physics and Astronomical Sciences, Central University of Himachal Pradesh, Dharamshala 176215, India
Athanasios V. Vasilakos: Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam 31441, Saudi Arabia

Mathematics, 2025, vol. 13, issue 6, 1-20

Abstract: Quantum Machine Learning (QML) opens up exciting possibilities for tackling problems that are incredibly complex and consume a lot of time. The drive to make QML a reality has sparked significant progress in material science, inspiring a growing number of research publications in the field. In this study, we extracted articles from the Scopus database to understand the contribution of material science in the advancement of QML. This scientometric analysis accumulated 1926 extracted publications published over 11 years spanning from 2014 to 2024. A total of 55 countries contributed to this domain of QML, among which the top 10 countries contributed 65.7% out of the total number of publications; the USA is on top, with 19.47% of the publications globally. A total of 57 authors contributed to this research area from 55 different countries. From 2014 to 2024, publications had an average citation impact of 32.12 citations per paper; the year 2015 received 16.7% of the total citations, which is the highest in the 11 years, and the year 2014 had the highest number of citations per paper, which is 61.4% of the total. The study also identifies the most significant document in the year 2017, with the source title Journal of Physics Condensed Matter , having a citation count of 2649 and a normalized citation impact index (NCII) of 91.34.

Keywords: quantum computing; machine learning; scientometric; material science; Scopus database (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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