Bibliometric Survey of Quantum Machine Learning. Issue 4 (1st October 2020)
- Record Type:
- Journal Article
- Title:
- Bibliometric Survey of Quantum Machine Learning. Issue 4 (1st October 2020)
- Main Title:
- Bibliometric Survey of Quantum Machine Learning
- Authors:
- Pande, Mandaar
Mulay, Preeti - Abstract:
- ABSTRACT: Quantum Machine Learning (QML) is one of the core research fields in the larger paradigm of Quantum Computing (also known alternatively as Quantum Information). In recent years, researchers have taken deep interest in QML, given the potential time and cost advantages that solutions to real-life problems using QML algorithms provide, in comparison to their classical (or digital) machine learning equivalents. This is still a very new and exciting area of research with new algorithms and their uses being developed almost every other day. Deep research interest in this area has picked up only in the past 5–6 years. Given the background, this paper focuses on studying Scopus and Web of Science databases for the past 6 years (2014–2019) to identify various publication trends in the areas of Quantum Machine Learning. The authors have done an in-depth study of the Scopus and Web of Science publication data pertaining to this area and have come up with interesting insights. The survey covers 276 publications in Scopus and 154 publications in Web of Science. From the Scopus database, it is found that there has been a consistent growth in the number of publications in this period. Four research areas, namely, Physics, Astronomy, Computer Science, and Mathematics, have contributed 68.1% of the research publications. The USA leads the top 10 countries with nearly half (49.2%) of the research publications. A total of 148 patents have been published with 94 of these beingABSTRACT: Quantum Machine Learning (QML) is one of the core research fields in the larger paradigm of Quantum Computing (also known alternatively as Quantum Information). In recent years, researchers have taken deep interest in QML, given the potential time and cost advantages that solutions to real-life problems using QML algorithms provide, in comparison to their classical (or digital) machine learning equivalents. This is still a very new and exciting area of research with new algorithms and their uses being developed almost every other day. Deep research interest in this area has picked up only in the past 5–6 years. Given the background, this paper focuses on studying Scopus and Web of Science databases for the past 6 years (2014–2019) to identify various publication trends in the areas of Quantum Machine Learning. The authors have done an in-depth study of the Scopus and Web of Science publication data pertaining to this area and have come up with interesting insights. The survey covers 276 publications in Scopus and 154 publications in Web of Science. From the Scopus database, it is found that there has been a consistent growth in the number of publications in this period. Four research areas, namely, Physics, Astronomy, Computer Science, and Mathematics, have contributed 68.1% of the research publications. The USA leads the top 10 countries with nearly half (49.2%) of the research publications. A total of 148 patents have been published with 94 of these being published in the last four years (2016–2019). This essentially translates to one patent for every two publications. The Web of Science database, though bringing out 154 publications in the period, shows similar trends across the metrics. We have carried out a comparative study of some of the metrics in Scopus and Web of Science databases. Overall the study identifies the top 10 Institutions, authors, and research journals. … (more)
- Is Part Of:
- Science & technology libraries. Volume 39:Issue 4(2020)
- Journal:
- Science & technology libraries
- Issue:
- Volume 39:Issue 4(2020)
- Issue Display:
- Volume 39, Issue 4 (2020)
- Year:
- 2020
- Volume:
- 39
- Issue:
- 4
- Issue Sort Value:
- 2020-0039-0004-0000
- Page Start:
- 369
- Page End:
- 382
- Publication Date:
- 2020-10-01
- Subjects:
- Quantum computing -- quantum machine learning -- classical-quantum hybrid algorithms -- quantum deep learning
Science and technology libraries -- Periodicals
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026.5 - Journal URLs:
- http://www.haworthpress.com ↗
http://www.haworthpress.com/store/E-Text/ViewLibraryEText.asp?s=J122&m=0 ↗
http://www.tandfonline.com/loi/wstl20 ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/0194262X.2020.1776193 ↗
- Languages:
- English
- ISSNs:
- 0194-262X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8134.275000
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British Library HMNTS - ELD Digital store - Ingest File:
- 25779.xml