Research evolution in security involved for quantum computing and machine learning through scientometric review analysis
Abstract
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…
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