Abstract Quantum algorithms, based on the principles of quantum mechanics, offer significant parallel processing capabilities with a wide range of applications. Nature-inspired stochastic optimization algorithms have long been a research hotspot. The fusion of quantum mechanics with optimization methods can potentially address NP-hard problems more efficiently and exponentially faster. The potential advantages provided by the ground-breaking paradigm have expedited the scientific output of quantum-inspired optimization algorithms locale. Consequently, a pertinent investigation is required to explain how ground-breaking scientific advancements have evolved. The scientometric approach utilizes quantitative and qualitative techniques to analyze research publications to evaluate the structure of scientific knowledge. Henceforth, the current research presents a scientometric and systematic analysis of quantum-inspired metaheuristic algorithms (QiMs) literature from the Scopus database since its inception. The scientometric implications of the article offer a detailed exploration of the publication patterns, keyword co-occurrence network analysis, author co-citation analysis and country collaboration analysis corresponding to each opted category of QiMs. The analysis reveals that QiMs solely account to 26.66% of publication share in quantum computing and have experienced an impressive 42.59% growth rate in the past decade. Notably, power management, adiabatic quantum computation, and vehicle routing are prominent emerging application areas. An extensive systematic literature analysis identifies key insights and research gaps in the QiMs knowledge domain. Overall, the findings of the current article provide scientific cues to researchers and the academic fraternity for identifying the intellectual landscape and latest research trends of QiMs, thereby fostering innovation and informed decision-making.
Abstract Quantum algorithms, based on the principles of quantum mechanics, offer significant parallel processing capabilities with a wide range of applications. Nature-inspired stochastic optimization algorithms have long been a research hotspot. The fusion of quantum mechanics with optimization methods can potentially address NP-hard problems more efficiently and exponentially faster. The potential advantages provided by the ground-breaking paradigm have expedited the scientific output of quantum-inspired optimization algorithms locale. Consequently, a pertinent investigation is required to explain how ground-breaking scientific advancements have evolved. The scientometric approach utilizes quantitative and qualitative techniques to analyze research publications to evaluate the structure of scientific knowledge. Henceforth, the current research presents a scientometric and systematic analysis of quantum-inspired metaheuristic algorithms (QiMs) literature from the Scopus database since its inception. The scientometric implications of the article offer a detailed exploration of the publication patterns, keyword co-occurrence network analysis, author co-citation analysis and country collaboration analysis corresponding to each opted category of QiMs. The analysis reveals that QiMs solely account to 26.66% of publication share in quantum computing and have experienced an impressive 42.59% growth rate in the past decade. Notably, power management, adiabatic quantum computation, and vehicle routing are prominent emerging application areas. An extensive systematic literature analysis identifies key insights and research gaps in the QiMs knowledge domain. Overall, the findings of the current article provide scientific cues to researchers and the academic fraternity for identifying the intellectual landscape and latest research trends of QiMs, thereby fostering innovation and informed decision-making.
The rapid proliferation of quantum information technologies, spanning theoretical investigations to practical experiments, has generated a number of research papers and documents in quantum algorithms. Consequently, the current research serves as a gateway for interested readers to comprehend the status quo of quantum algorithms, with a specific focus on vehicular network optimization. It aims to explore the research patterns and latest trends by analyzing the dataset sourced from the Scopus and Web of Science databases. The scientometric implications offer valuable insights into publication patterns, keyword co-occurrence, author co-citation, country collaboration, and burst reference. These analyses delineate the temporal progression, prominent research topics, emerging research areas, leading collaborative nations, prolific authors, and research trends within this knowledge domain. The results reveal that smart power grids, traveling salesman problem, electric vehicle charging, battery life estimation, and air traffic control are emerging research areas. Similarly, quantum approximate optimization algorithms, adiabatic quantum computing, quantum-inspired evolutionary algorithms, and quantum annealing emerge as prominent quantum algorithms employed for vehicular network optimization problems. In addition, systematic literature analysis is objectively conducted to discern key insights, research challenges and future research directions in the current knowledge domain.
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.
Quantum computing (QC) has the potential to be the next abstruse technology, with a wide range of possible applications and ramifications for organizations and markets. QC provides an exponential speedup by employing quantum mechanics principles, including superposition and entanglement. The potential advantages offered by the revolutionary paradigm have propelled scientific productions. Therefore, a highly pertinent investigation is required to elucidate the evolution of path-breaking trajectories for scientific advances. This study confronts the idea by presenting a scientometric analysis for the recent decade of literature collected from the Web of Science database in the computer science discipline. The scientometric implications of the article identify the significant research domains and provide an intensive insight into the publication patterns, country collaboration, geographical analysis, citation patterns, eminent journals, and research frontiers of each domain of QC. The scholarly literature analysis identifies key challenges in the QC knowledge domain. Overall, the inference reveals an evolutionary pathway for future research directives and collaboration in the domains of QC research. The research findings provide innovative significance to information scientists by presenting a comprehensive overview of QC research to help them find relevant applications, research topics, and key challenges.
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 computing (QC) has emerged as a promising research area transitioning from theory to practical realization, driven by interdisciplinary integration. To gain a comprehensive understanding of the evolving research landscape in QC, this study conducts a scientometric analysis of scientific literature retrieved from the Scopus database between 2010 and 2023. Employing the CiteSpace visualization tool, this study examines publication trends, citation patterns, and identifies the prestigious journals in literature. Furthermore, collaboration analysis reveals the top collaborating countries highlighting the active global network of research in QC. Moreover, author cocitation analysis identifies prominent contributors, underscoring their impact and influence in the field. In addition, keyword cooccurrence analysis and thematic analysis pinpoints the areas of significant interest and emerging research frontiers. The content analysis further explores these findings to provide in-depth insights in to themes and trends shaping the field. By providing insights into the evolution of QC, this study contributes to a deeper understanding of technological advancements and determines the future pathway of the field.
Abstract Quantum computing has rapidly emerged as one of the fastest-growing research domains, driven by both technological breakthroughs and the promise of computational advantages over classical systems. This study presents a comprehensive bibliometric analysis of the global quantum computing literature from 1980 to 2025, comprising 31,662 publications indexed in Scopus and accumulating over 800,000 citations. Using advanced bibliometric techniques—including co-authorship mapping, keyword co-occurrence networks, co-citation analysis, and centrality measures—we characterize publication trends, institutional contributions, collaborative structures, and thematic evolution across the field. The results reveal exponential growth in output since 2015, led primarily by the United States, China, and Europe, with key institutions such as MIT, the Chinese Academy of Sciences, CNRS, and the University of Waterloo at the forefront. The intellectual structure exhibits a dual-core configuration, with foundational algorithmic work (e.g. Shor, Grover, Nielsen & Chuang) tightly connected to emerging NISQ-era applications such as quantum machine learning, cryptography, and hybrid algorithms. Keyword analysis reveals the progressive expansion from core quantum algorithms toward increasingly diverse applied domains in healthcare, finance, chemistry, energy, and climate modeling. Public funding bodies (e.g. NSFC, NSF, European Commission, DOE, DoD, MoST) and private-sector investment from major technology firms are jointly driving this translational acceleration. Together, these findings provide an integrated quantitative map of the field’s evolution, offering valuable insight for future scientific policy, international collaboration, and strategic funding initiatives.