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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
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- 건국대학교 지식콘텐츠연구소 International Journal of Knowledge Content Development & Technology International Journal of Knowledge Content Development & Technology Vol.11 No.3
- 발행연도
- 2021.9
- 수록면
- 29 - 44 (16page)
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초록· 키워드
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The study provides a quantitative and qualitative description of global research in the domain of quantum machine learning (QML) as a way to understand the status of global research in the subject at the global, national, institutional, and individual author level. The data for the study was sourced from the Scopus database for the period 1999-2020. The study analyzed global research output (1374 publications) and global citations (22434 citations) to measure research productivity and performance on metrics. In addition, the study carried out bibliometric mapping of the literature to visually represent network relationship between key countries, institutions, authors, and significant keyword in QML research. The study finds that the USA and China lead the world ranking in QML research, accounting for 32.46% and 22.56% share respectively in the global output. The top 25 global organizations and authors lead with 35.52% and 16.59% global share respectively. The study also tracks key research areas, key global players, most significant keywords, and most productive source journals. The study observes that QML research is gradually emerging as an interdisciplinary area of research in computer science, but the body of its literature that has appeared so far is very small and insignificant even though 22 years have passed since the appearance of its first publication. Certainly, QML as a research subject at present is at a nascent stage of its development.
목차
ABSTRACT
1. Introduction
2. Literature Review
3. Objectives of the Study
4. Methodology
5. Analysis and Results
6. Summary and Conclusion
7. Limitations of the Study
References
참고문헌 (13)
참고문헌 신청
Carlo Ciliberto / 2018 / Quantum machine learning: a classical perspective / Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2209):20170551
Tânia F. G. G. Cova / 2019 / Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns / Frontiers in Chemistry 7:809
S. M. Dhawan / 2018 / Global Publications Output in Quantum Computing Research: A Scientometric Assessment during 2007-16 / Emerging Science Journal 2(4)
Dhawan, S. M. / 2020 / Global Machine-learning Research : A Scientometric Assessment of Global Literature during 2009–18 / World Digital Libraries-An International Journal 13(2):105~120
B. M. Gupta / 2019 / Deep Learning Research: Scientometric Assessment of Global Publications Output during 2004 -17 / Emerging Science Journal 3(1):23
Tânia F. G. G. Cova / 2019 / Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns / Frontiers in Chemistry 7:809
S. M. Dhawan / 2018 / Global Publications Output in Quantum Computing Research: A Scientometric Assessment during 2007-16 / Emerging Science Journal 2(4)
Dhawan, S. M. / 2020 / Global Machine-learning Research : A Scientometric Assessment of Global Literature during 2009–18 / World Digital Libraries-An International Journal 13(2):105~120
B. M. Gupta / 2019 / Deep Learning Research: Scientometric Assessment of Global Publications Output during 2004 -17 / Emerging Science Journal 3(1):23
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