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.
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.
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.
Abstract Quantum cryptography (QC) provides security to information through laws of nature, particularly quantum mechanics. The necessity of making QC a living reality has provided the impetus to advancement in material science, which led to many publications since 1996. Therefore, in this work, we analyze the articles in the Scopus database concerning the contribution of material science in the development of QC through scientometric methods. A total of 1395 articles published from 1996 to 2022 were retrieved from the Scopus database. Subsequently, these articles were analyzed to retrieve publication patterns, major collaborating nations, significant documents, recent trends, and future directions. A comprehensive introduction to QC is also included to facilitate a better grasp of this field for a wide range of readers. The findings of this study show that China is the leading nation in developing QC through material science, closely followed by the United States and India. Moreover, the study indicates significant areas of QC where extensive work is being done from the material science standpoint through keyword and significant document analysis. Hence, the outcomes of this article can be valuable for researchers, newcomers, technology managers, and policymakers interested in the future of QC.
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.
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.
The machines can handle tasks that require thinking and decisive capacity by using Artificial Intelligence. But these systems require higher resources for their operations. As such, Quantum Machine Learning (QML) has been explored as a parallel solution for such problems. In our study, we comprehensively analyzed the research evolution within the QML domain. We integrated literature from Scopus and Web of Science to investigate underlying relationships through bibliometric information. The publication and citation trends, country collaboration, keyword co-occurrence, and thematic relationships helped to introspect the growth of QML. Our results show that quantum kernels, quantum natural language processing, contrastive learning, quantum reservoir computing, and explainable QML are gaining attention within academia. It has been expanding across a diverse set of problem spaces like management of resources, intrusion detection, process control, cloud computing, molecular and materials modeling, simulation of integrated circuits, and nuclear physics, etc. The findings also highlight the existing challenges that are limiting the practical implementation of solutions within the NISQ era. In response, we also identified actionable directives that can be followed for advancing the current state of research. These insights will provide future researchers with a single-point reference for guiding their exploration of QML.
Quantum computing is a rapidly growing technology that exploits the principles of conventional information theory, computer science, and quantum mechanics principles to address complicated computational problems. The recent breakthroughs in information and communication technologies have yielded profound and intriguing insights into quantum computing research. For more advances in quantum computing, researchers are currently concentrating on exploiting material science in this burgeoning research domain to achieve outperform processing power. Henceforth, a scientometric analysis is undertaken to extract the role of material science in quantum computing by analyzing the published academic bibliometric data using CiteSpace. It appraises the research status quo of the field by analyzing yearly publication growth rate, average citation structure analysis, international research collaboration analysis, most cited publication analysis, and keyword co-occurrence network analysis. It deduced that two-qubit gates, quantum entanglement, and spin fluctuation are the research hotspots. Superconducting qubits and spin-orbit coupling are the current most active research areas in material science assisted-quantum computing research. China, the United States, and Japan are mediating in this field. Furthermore, it offers future research directives and significant clues to researchers for the further diffusion of knowledge in the research domain.