Libraries at University of Nebraska-Lincoln
Date of this Version
Winter 1-1-2021
Document Type
Article
Abstract
The paper presents a bibliometric analysis from 2014 to 2020 of the emerging and engaging field of quantum computing called Quantum Machine Learning (QML). The study discusses the analysis results from the comprehensive high indexed databases worldwide such as Institute of Electrical and Electronics Engineers (IEEE), Scopus, Web of Science (WOS), Google Scholar and the Association for Computing Machinery (ACM). Tools like iMapbuilder, IBM and SPSS Statistics are used to provide meaningful insights and flawless representations of the extracted data. There has been little research to provide a macroscopic overview of renowned authors, subject areas, funding agencies and patent applications related to Quantum Clustering (QC). The result and analysis of this study show an interesting fact, most researchers are now aware of quantum technology from the past few years. The purpose of bibliometric and patentometric analysis papers is to figure out the importance and utility of the QC research area. Most of the countries are taking an initiative to seek attention towards QC but the analysis shows that China and the US are leading. The survey revealed that the maximum numbers of publications of QC are from Physics and Astronomy followed by Computer Science.