Interdisciplinary research has a significant impact on invention. Quantum computing, a cutting-edge emerging technology in this area, has shown remarkable growth and development in the current time. Therefore, an inclusive analysis of the academic literature is needed to understand the research advancement in the domain. The present study fulfills the need by analyzing published academic work from the Scopus database using the CiteSpace visualization tool. The research findings classify the quantum domain into five stages for research exploration, viz. quantum state preparation, quantum entanglement, decoherence, quantum error correction, and quantum optimization. Keyword co-occurrence, document cocitation, and burst analysis of the network pinpoint nascent research topics, inclinations, and advancements in each stage. Inclusively, the systematized and comprehensive study presents an evolutionary pathway for future researchers to gain in-depth knowledge, identify essential research subjects, and identify challenging issues.
Interdisciplinary research has a significant impact on invention. Quantum computing, a cutting-edge emerging technology in this area, has shown remarkable growth and development in the current time. Therefore, an inclusive analysis of the academic literature is needed to understand the research advancement in the domain. The present study fulfills the need by analyzing published academic work from the Scopus database using the CiteSpace visualization tool. The research findings classify the quantum domain into five stages for research exploration, viz. quantum state preparation, quantum entanglement, decoherence, quantum error correction, and quantum optimization. Keyword co-occurrence, document cocitation, and burst analysis of the network pinpoint nascent research topics, inclinations, and advancements in each stage. Inclusively, the systematized and comprehensive study presents an evolutionary pathway for future researchers to gain in-depth knowledge, identify essential research subjects, and identify challenging issues.
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.
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.
Abstract The field of quantum computing has undergone significant expansion and development in recent years. A quantitative and comprehensive examination of the research domain is essential to comprehend research trends and directions in the field. Henceforth, the current paper conducts a scientometric analysis of the academic literature on quantum computing retrieved from the Scopus database to understand the recent advances made by researchers in the domain. The analysis evaluates the state of the discipline's research by examining the most prominent journals and highly referenced articles. CiteSpace is leveraged to visualize the intellectual landscape of quantum state preparation by performing document co‐citation and author co‐citation analysis. The analysis reveals that trapped ions, photonic qubits, superconducting qubits, nitrogen‐vacancy centers, and nuclear spin qubits are the primary research topics in quantum computing. Furthermore, the most current research issues that require attention in the field are measurement‐based qubits, Majorana fermions, and quantum dots.
This chapter discusses a systematic review of the literature available on quantum computers from their theoretical bases through their current advances to their trends in the future. It uses the PRISMA approach to collect such writings from many fields, including physics, computer science, and engineering. It identifies crucial breakthroughs, including Feynman’s conjecture and Shor’s algorithm, and examines how they have affected modern quantum computing paradigms. The said focus areas will center on quantum algorithms, hardware developments in quantum information technology, cryptography, and machine learning, all with an emphasis on their combined contribution to the growth of the field. Identified milestones include achievements of the so-called quantum supremacy and advances made possible by qubit technologies, such as superconducting circuits and ion traps. The review not only presents future challenges, such as scalability, error correction, and noise management, but also pinpoints research gaps and possible avenues for subsequent exploration. The chapter examines the themes of interdisciplinary collaboration and funding trends as well as emerging issues such as quantum cloud computing and hybrid quantum-classical models. Such an overview of the trajectory of quantum computing by this review is also an academic reference as well as a strategic guide for researchers, policymakers, and technologists traversing this fast-evolving field.
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.
Abstract Quantum computing has emerged as a promising field with the potential to revolutionize information processing and storage. Understanding its current state and research trajectory is crucial. This study aims to map the current literature on quantum computing, to identify how quantum computing is being used in solving quantum software problems and improving quantum algorithms. Moreover, it examines evaluation parameters and the challenges faced in this field. A systematic mapping study was conducted using the SCOPUS database, including all papers up to December 2024. Relevant articles were selected based on inclusion criteria, and a content analysis was performed to categorize and synthesize the findings. Information was gathered from 33 primary studies. Findings show that quantum computing is mainly applied in machine learning and optimization, though classical methods still outperform it. IBM Quantum is the most used platform. Research focuses on algorithm efficiency, verification over validation, and resource optimization, but lacks standardized evaluation methods. Key limitations include hardware constraints, noise, and scalability, while challenges involve integration, error correction, and problem formulation. Quantum computing continues to face significant technological challenges, but current research lays the groundwork for future advances. More practical studies on real hardware and strategies to improve the implementation and performance of quantum algorithms are needed. Effective evaluation, verification, and validation of quantum solutions must be further investigated.