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.
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 Natural Language Processing (QNLP) is an emerging interdisciplinary field that connects quantum computing and natural language processing (NLP), with the goal of improving language models through quantum algorithms. Although the field has gained increasing attention, research in QNLP remains fragmented, with limited studies mapping its evolution or practical implementations. This article presents a comprehensive scientometric analysis of QNLP research, revealing its conceptual foundations, research dynamics, and emerging directions. Using bibliographic data from the Scopus (2014-2024) and Web of Science (2014-2024) databases and analytical tools such as VOSviewer and Biblioshiny, we examined 116 publications following the PRISMA guidelines. The analysis covers publication growth, author productivity, leading journals, influential countries, and keyword trends. Network visualizations highlight the intellectual structure of the field, co-authorship patterns, and thematic clusters. The results indicate a sharp increase in QNLP studies since 2021, driven by advances in quantum hardware, frameworks such aslambeq, and growing interdisciplinary collaborations. However, during the literature mapping, we identified a significant gap regarding the applications of QNLP in various specific domains such as healthcare field. This study offers a quantitative mapping of the QNLP landscape, providing a structured overview of its development and outlining promising avenues for future exploration.
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 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.
This paper presents a comprehensive systematic and bibliometric review of the rapidly expanding literature on quantum computing. The study addresses six research questions concerning prominent themes, temporal evolution, types of key findings, frequently reported research gaps, higher-order gap typologies, and future research directions. Utilizing a researcher-curated database, the final corpus comprises 153 studies compiled from non-Scopus, Scopus and Web of Science. The results demonstrate that quantum hardware and architecture, algorithms, and theoretical foundations dominate the current literature. Furthermore, publication output has risen significantly between 2018 and 2025. Among the reported findings, algorithmic innovations, performance claims, and hardware advancements are the most common. Despite this progress, the field faces substantial barriers; technical, scalability, fault-tolerance, and methodological-standardization gaps are the most frequently reported. Based on these identified gaps, eight actionable future research directions emerge to guide the scientific community. Ultimately, this review concludes that quantum computing is actively transitioning from a purely physics-driven domain into a multidisciplinary, engineering- and application-oriented science. However, its continued advancement is presently constrained by a hardware-methodology-application triad, wherein reporting practices often outpace empirical verification and standardized benchmarking.
This survey report presents an overview of the field of quantum computing, focusing on the application of quantum technologies in machine learning, deep learning, image processing, and other related areas. A comprehensive analysis of research papers published between 2005 and 2022 is conducted, covering the latest advancements in quantum-based techniques. The report provides insights into various types of quantum technologies, including quantum machine learning, deep learning, computing, learning, and image processing. The survey encompasses a wide range of topics, such as quantum algorithms, quantum data encoding, quantum feature extraction, quantum neural networks, quantum image recognition, quantum optimization, and quantum simulations. It also identifies the emerging trends, challenges, and future research directions in this rapidly evolving field. The findings of this survey report are expected to provide valuable insights for researchers, practitioners, and decision-makers interested in the field of quantum and its applications in machine learning, deep learning, image processing, and related areas. Comprehensive analysis of the research papers and the identification of the current trends and challenges in this field can serve as a valuable reference for future research and development in the quantum computing domain.