Background: One of the main areas of study within the broader paradigm of quantum machine learning is quantum clustering (QC). Considering the potential time and cost savings that solutions to realworld issues employing QC algorithms bring, in comparison to their classical methods, researchers have recently developed a keen interest in QC. With new algorithms and their applications being invented virtually every other day, this is still a highly young and fascinating area of research. Based on the background information provided, this work aims to analyze research and patent databases spanning twelve years (2010 to mid-2022) to identify and understand the publishing and patent trends in the field of QC. This study aims to study the topological analyses, important study areas, relationships, and collaboration patterns that distinguish traditional and developing research clusters. The graphical representation of the progress of publications and patents over time depends on such rigorous field mapping. This paper presents a comprehensive list of all the sources through the network, bibliometric and patentometric (BibPat) analysis, and future research scope in the QC. The top authors, universities, and research fields were listed after the primary and secondary keywords connected to the quantum K-means clustering algorithm in the analysis design. Reviewing the articles and then delving into the specifics of the patents will help us evaluate the total body of work on the quantum K-means clustering technique. Using the thorough BibPat tools and numerous research and patent databases like Scopus, IEEE, ACM, Google Scholar, Lens, Google Patents, and Espacenet, the analysis design displays the patents and journal papers that have been published. Additionally, it is crucial for later research because it aids in the identification of areas for current research interests and possible avenues for future study. QC offers various studies in disciplines from computer science to psychology. The Ministry of Education, China, produced most publications. Since 2014, the trend has been up, and experts continue studying the issue. The BibPat analysis shows that the Chinese National Natural Science Foundation has facilitated funding for cutting-edge research. In order to open the door for future research and investigation on the substantial amount of unstructured real-time data, the report concluded by proposing an incremental QC approach.
Background: One of the main areas of study within the broader paradigm of quantum machine learning is quantum clustering (QC). Considering the potential time and cost savings that solutions to realworld issues employing QC algorithms bring, in comparison to their classical methods, researchers have recently developed a keen interest in QC. With new algorithms and their applications being invented virtually every other day, this is still a highly young and fascinating area of research. Based on the background information provided, this work aims to analyze research and patent databases spanning twelve years (2010 to mid-2022) to identify and understand the publishing and patent trends in the field of QC. This study aims to study the topological analyses, important study areas, relationships, and collaboration patterns that distinguish traditional and developing research clusters. The graphical representation of the progress of publications and patents over time depends on such rigorous field mapping. This paper presents a comprehensive list of all the sources through the network, bibliometric and patentometric (BibPat) analysis, and future research scope in the QC. The top authors, universities, and research fields were listed after the primary and secondary keywords connected to the quantum K-means clustering algorithm in the analysis design. Reviewing the articles and then delving into the specifics of the patents will help us evaluate the total body of work on the quantum K-means clustering technique. Using the thorough BibPat tools and numerous research and patent databases like Scopus, IEEE, ACM, Google Scholar, Lens, Google Patents, and Espacenet, the analysis design displays the patents and journal papers that have been published. Additionally, it is crucial for later research because it aids in the identification of areas for current research interests and possible avenues for future study. QC offers various studies in disciplines from computer science to psychology. The Ministry of Education, China, produced most publications. Since 2014, the trend has been up, and experts continue studying the issue. The BibPat analysis shows that the Chinese National Natural Science Foundation has facilitated funding for cutting-edge research. In order to open the door for future research and investigation on the substantial amount of unstructured real-time data, the report concluded by proposing an incremental QC approach.
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.
Purpose: This study explores the improvement in the area of Quantum computing techniques and natural language algorithm, to assist researchers to earn new awareness and detect emerging areas of growth for further research in this field. Methodology: In this study, we apply bibliometric analysis by using R Studio and VOS viewer software, we got our data from the Scopus database after searching via keywords, and before the exclusion, we got 292 papers, then we start to apply the exclusion criteria such as (time limit from 1990 to 2024, Document type: Article, Publication stage: Final, Source type: Journal, and Language: English) then after we applied those criteria we reach to 273 papers. Finding: The results of this study established the development status of the field of Quantum computing techniques and natural language algorithm, we demonstrate the growth rate, and emerging thematic areas for further research, we also identified the most impactful and leading authors, topic trends, keywords, sources, publications, and the relationship between co-authors. These findings assist researchers to guide new research to concentrate on future topic trends. Originality: This study contributes to the literature by identifying the growing interest in Quantum computing techniques and natural language algorithm.
Quantum computing is the next-generation technology for future human society. This article aims to give a comprehensive and in-depth analysis of the global development status of quantum computing. First, the latest research hotspots of quantum computing are reviewed. Then multiple bibliometric metrics were used to analyse the current development status, research hotspots, technological trends, and the contribution of authors, institutions, and countries in quantum computing from 2016 to 2020. The results confirm that quantum computing is still under development, with the United States and China in the leading position, from where most critical scholars came. The most important institutions are scattered in the United States, China, and France. The characteristics of international cooperation, research impact, topics with the highest Prominence percentile, distinctive keyphrases, and leading source journals within the field were also analysed. The results may provide a quantitative basis for scientific research and decision-making in quantum computing.
Quantum computing is the next-generation technology for future human society. This article aims to give a comprehensive and in-depth analysis of the global development status of quantum computing. First, the latest research hotspots of quantum computing are reviewed. Then multiple bibliometric metrics were used to analyse the current development status, research hotspots, technological trends, and the contribution of authors, institutions, and countries in quantum computing from 2016 to 2020. The results confirm that quantum computing is still under development, with the United States and China in the leading position, from where most critical scholars came. The most important institutions are scattered in the United States, China, and France. The characteristics of international cooperation, research impact, topics with the highest Prominence percentile, distinctive keyphrases, and leading source journals within the field were also analysed. The results may provide a quantitative basis for scientific research and decision-making in quantum computing.
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.
Quantum computing is a revolutionary field of research in science and technology, which provides sophisticated processing capacity and computation in a finite time frame based on quantum mechanics. It has the great potential to offer more effective and faster solutions to overcome current processing constraints. Therefore, to address the global significance, relevance, and exponential growth, it is imperative to conduct a thorough investigation and promote advancements through rigorous scientometric analysis. The current article presents the knowledge mapping of rapidly expanding quantum computing research using the CiteSpace visualization tool. The foundation of research is the scientific literature on quantum computing extracted from the Scopus database, covering the period of 2008–2022. It provides publication patterns, citation structure, and in-depth keyword co-occurrence network analysis insights to extract emerging research areas. It illustrates a variety of empirical approaches to discover the status quo and evolution. The findings reveal that quantum algorithms serve as the fundamental basis for all research fields, and quantum machine learning is the most active research area nowadays.