The present scientometric analysis evaluates the interaction between quantum computing, security, and machine learning by analyzing 495 peer-reviewed articles published in 2018-mid 2025, as indexed on Scopus and Web of Science. The findings reveal an increasing trend in the research output from year to year, from five papers in 2018 to 165 in 2024, while the latter number is provisional due to incomplete data available for the year 2025. As can be seen from citation data, citation rate reached its peak in 2021, declining afterwards, which may be explained by citation delay and the fast growing volume of published studies. A source analysis demonstrates a clustered distribution of sources and reveals that a Bradford core consisting of 12 titles covers a large percentage of output and is represented primarily by IEEE Access and Lecture Notes in Networks and Systems. Author productivity is also very skewed with most authors publishing one article in total, as expected of Lotka productivity distribution. At the national level, the USA, India, and China are among the top countries producing publications, whereas the citation distribution is relatively less balanced amongst nations. Keyword analysis shows that quantum computation, machine learning, and security concepts are at the heart of the quantum machine learning literature, and quantum cryptography, quantum machine learning, and privacy concepts have been gaining prominence in recent times. From the above results, it can be concluded that although this field is growing very fast, it is doing so in a relatively consolidated manner. The future directions in this field would comprise topics such as quantum-hybrid systems, quantum resilient security, noise-resistant quantum learning, and secure IoT.
The present scientometric analysis evaluates the interaction between quantum computing, security, and machine learning by analyzing 495 peer-reviewed articles published in 2018-mid 2025, as indexed on Scopus and Web of Science. The findings reveal an increasing trend in the research output from year to year, from five papers in 2018 to 165 in 2024, while the latter number is provisional due to incomplete data available for the year 2025. As can be seen from citation data, citation rate reached its peak in 2021, declining afterwards, which may be explained by citation delay and the fast growing volume of published studies. A source analysis demonstrates a clustered distribution of sources and reveals that a Bradford core consisting of 12 titles covers a large percentage of output and is represented primarily by IEEE Access and Lecture Notes in Networks and Systems. Author productivity is also very skewed with most authors publishing one article in total, as expected of Lotka productivity distribution. At the national level, the USA, India, and China are among the top countries producing publications, whereas the citation distribution is relatively less balanced amongst nations. Keyword analysis shows that quantum computation, machine learning, and security concepts are at the heart of the quantum machine learning literature, and quantum cryptography, quantum machine learning, and privacy concepts have been gaining prominence in recent times. From the above results, it can be concluded that although this field is growing very fast, it is doing so in a relatively consolidated manner. The future directions in this field would comprise topics such as quantum-hybrid systems, quantum resilient security, noise-resistant quantum learning, and secure IoT.
Technological and scientific advancements in recent years have enabled us to manipulate technologies that were once considered unimaginable. Among these, Artificial Intelligence (AI) and quantum physics have emerged as pivotal forces, significantly enhancing our capacity to achieve goals and solve complex problems. Given their growing relevance, a comprehensive review of the scientific output underpinning this new era is both timely and necessary. Such a review allows us to trace the increasing scholarly interest in these domains and to identify emerging trends in the synergy between them. Accordingly, the objective of this article is to present a scientometric analysis of the academic literature related to both AI and quantum science. To achieve this, we examined peerreviewed articles indexed in the Web of Science (WoS) and Scopus databases, focusing on key indicators such as publication volume, citation patterns, and collaboration networks among countries, journals, and authors. The findings reveal a notable increase in interest toward the integration of AI and quantum technologies in recent years, driven by their combined potential to deliver enhanced performance across a wide range of disciplines.
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 Machine Learning (QML) opens up exciting possibilities for tackling problems that are incredibly complex and consume a lot of time. The drive to make QML a reality has sparked significant progress in material science, inspiring a growing number of research publications in the field. In this study, we extracted articles from the Scopus database to understand the contribution of material science in the advancement of QML. This scientometric analysis accumulated 1926 extracted publications published over 11 years spanning from 2014 to 2024. A total of 55 countries contributed to this domain of QML, among which the top 10 countries contributed 65.7% out of the total number of publications; the USA is on top, with 19.47% of the publications globally. A total of 57 authors contributed to this research area from 55 different countries. From 2014 to 2024, publications had an average citation impact of 32.12 citations per paper; the year 2015 received 16.7% of the total citations, which is the highest in the 11 years, and the year 2014 had the highest number of citations per paper, which is 61.4% of the total. The study also identifies the most significant document in the year 2017, with the source title Journal of Physics Condensed Matter, having a citation count of 2649 and a normalized citation impact index (NCII) of 91.34.
Technological and scientific advancements in recent years have enabled us to manipulate technologies that were once considered unimaginable. Among these, Artificial Intelligence (AI) and quantum physics have emerged as pivotal forces, significantly enhancing our capacity to achieve goals and solve complex problems. Given their growing relevance, a comprehensive review of the scientific output underpinning this new era is both timely and necessary. Such a review allows us to trace the increasing scholarly interest in these domains and to identify emerging trends in the synergy between them. Accordingly, the objective of this article is to present a scientometric analysis of the academic literature related to both AI and quantum science. To achieve this, we examined peer-reviewed articles from prestigious databases such as Scopus and Web of Science. The study spans the period from 2004 to 2024, analyzing key metrics such as publication growth, influential authors, leading institutions, and the geographic distribution of research. The results reveal an exponential growth trajectory, with a marked increase in interdisciplinary publications over the last decade. This analysis provides a structured overview of the intersection between quantum computing and AI, offering a solid foundation for researchers and practitioners to identify priority areas for future innovation.
Quantum Machine Learning is an interdisciplinary field that combines the principles of quantum physics, quantum computers, and machine learning to enhance computational performance. In recent years, there have been significant advancements in the field of quantum machine learning. Researchers have developed quantum algorithms for various tasks. Furthermore, quantum machine learning has been applied to various domains, such as chemistry, cryptography, and finance. Previous bibliometric surveys in this area covered the period from 2014 to 2020. However, as demonstrated in this paper, the pace of publication has increased significantly from 2018 to the present. Therefore, it has become essential to provide a new analysis of the current state of research in this field. In this paper, we deployed visualization tools to analyze co-authorships, co-occurrences, and keyword density in this research area. Our study covers and analyzes a total of 918 publications from the Web of Science database and 1171 publications from the Scopus database from 2006 to 2022. Following the analysis, we identify research questions, opportunities, and research gaps in the field of Quantum Machine Learning.
Quantum Machine Learning (QML) merges Quantum Computing (QC) and Artificial Intelligence (AI) to solve problems that are intractable by classical computers. At the same time, domains such as finance, healthcare, manufacturing, and transportation domains are transitioning into Internet of Things (IoT)-enabled cyber physical systems generating streams of heterogeneous data with high dimensionality and volume. This work presents a unified scientometric and qualitative review of QML across these sectors. A PRISMA-based screening is followed by a Keyword Co-occurrence Cluster Analysis (KCCA), a Keyword Burst Analysis (KBA), and a Citation Burst Analysis (CBA) using CiteSpace on a Scopus-derived corpus of literature. An evidence-based Technology Readiness Level (TRL) framework is introduced to assess the technology’s maturity. Our findings indicate that the volume of publications has increased after 2022, with healthcare dominating the volume and manufacturing the impact. Furthermore, a clear trend towards the development of hybrid quantum classical architectures is present across all domains. Security applications such as anomaly detection are the most developed QML entry point to IoT systems. All four domains are at a TRL of 3-4, indicating that QML research is still at the concept-validation stage. Challenges include hardware noise, barren plateaus, absence of standardized benchmarks, and encoding heterogeneous data from IoT devices for training QML applications.