Quantum Computational Intelligence Techniques: A Scientometric Mapping

Abstract

Computational intelligence has previously demonstrated its existence beyond the limitations of binary variables and Turing Machines. Using quantum concepts, Deutsch (1985) and Grover (1996) provide massive parallelism and searching techniques, vastly expanding the computational capacity of soft computing. This paper aims to analyze articles that consider both computational intelligence and quantum computing, referred to here as the quantum computational intelligence (QCI) category, to solve non-deterministic problems efficiently. The category includes 3067 research papers published from 2014 to 2023 that are indexed in high-quality databases like SCI and SCOPUS. This study examines QCI publishing patterns utilizing scientometric analysis employing co-occurrence, co-citation, and bibliographic coupling methodologies. Additionally, it provides insights into the citation patterns of publications, affiliations, and authors. China, USA, and India published more than half (53%) of the articles. The primary emphasis of application fields throughout this decade includes ‘Ground State Preparation’ and ‘Financial Forecasting’ among others. The pertinent keywords that have lately been studied are quantum particle swarm optimization (2022), optimization (2021), quantum circuits (2020), and deep learning (2019). Five quantum-based computation techniques were identified using a mix of critical review and cluster analysis: quantum machine learning, quantum neural networks, quantum particle swarm optimization, quantum variational Monte Carlo, and quantum-inspired evolutionary algorithms. The primary objective of this study is to address key queries that could contribute to future research in this field.