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.
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.
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.
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 Traffic optimization remains a significant challenge in urban planning and transportation management. While efficient traffic optimization is crucial for enhancing urban mobility, reducing congestion, and promoting environmental sustainability, traditional computational methods often struggle with the complex, dynamic nature of traffic systems. Recent advances in quantum computing, particularly quantum annealing, offer promising new techniques that could revolutionize traffic flow optimization. This work systematically reviews the literature, starting with search term formulation and ending with the final set of articles. These articles are categorized into three groups: (1) traffic signal control, (2) traffic flow optimization, and (3) routing problems optimization (including vehicle routing problem and traveling salesman problem). The review critically examines current studies on quantum annealing-based traffic optimization, focusing on contributions, methods, solvers, problem suitability, key findings, benchmark fairness, and limitations. It identifies key challenges and provides recommendations for future research. Insights from this work offer researchers and practitioners a concise overview of current challenges and future directions in traffic optimization.
We explore how quantum computing (QC) can advance transportation optimization, with a focus on two high-impact areas: traffic signal control and vehicle electrification with grid integration. As transportation systems grow in complexity, classical optimization methods increasingly struggle to deliver scalable and efficient solutions, particularly for real-time environments. This work identifies key challenges within these two domains where QC may offer advantages, particularly in handling combinatorial decision spaces and dynamic constraints. We begin by outlining the limitations of classical approaches for traffic signal control optimization and electric vehicle charging coordination, highlighting where computational limitations arise. Previous quantum formulations are discussed to demonstrate how emerging quantum algorithms, including quantum annealing and hybrid algorithms, could be leveraged to reformulate and address these problems. We also evaluate the suitability of current quantum hardware and discuss the advantages that QC could bring to these domains. While acknowledging the present limitations of QC technologies, this poster emphasizes the importance of preparing quantum-compatible models today. By reviewing and establishing formulations that align with the strengths of quantum algorithms, researchers and practitioners can better position themselves to take advantage of QC advancements as they occur. This work aims to provide a practical, forward-looking perspective on the near-term potential of quantum computing in transportation optimization. Quantum Computing Transportation Optimization Traffic Signal Control Electric Vehicle Charging
IntroductionQuantum Internet has emerged as a transformative technological frontier, attracting increasing global research attention. Its development spans foundational physics, cryptography, network engineering, and applied technologies, demanding systematic evaluation of scholarly progress. ObjectiveThis study aims to present a comprehensive bibliometric analysis of Quantum Internet research, identifying publication trends, influential contributors, collaborative networks, and evolving thematic directions. MethodData were retrieved from the Scopus database and analyzed using Biblioshiny, VOSviewer, and CiteSpace. The study examined publication growth, author and journal influence, country-level collaborations, bibliographic coupling, keyword co-occurrence, and thematic evolution. ResultsFindings indicate steady growth in publications since 2016, peaking in 2024 with 130 documents. Leading contributors include Angela Sara Cacciapuoti, Marcello Caleffi, Laszlo Gyongyosi, and Stephanie Wehner, while prominent publication outlets are Physical Review Letters, Physical Review A, Scientific Reports, and New Journal of Physics. Country-level analysis highlights the United States, China, the United Kingdom, Italy, and Japan as primary research hubs, with strong international collaborations. Network analyses reveal 15 author and 10 journal clusters, underscoring interdisciplinary connections. Bibliographic coupling and keyword co-occurrence identify critical themes such as quantum key distribution, entanglement distribution, quantum memory, blockchain, and cybersecurity, alongside emerging areas like quantum IoT and quantum machine learning. Thematic maps show a shift from theoretical constructs to application-driven studies, integrating artificial intelligence and advanced sensing. Trend analyses confirm growing attention to scalability, security, and interdisciplinary applications, though gaps remain in resource optimization, experimental validation, and integration with classical infrastructures. ConclusionsThis analysis provides a knowledge framework and practical insights into the intellectual and conceptual structure of Quantum Internet research. By mapping influential contributors, core themes, and research gaps, the study supports academics, policymakers, and industry in making strategic investments and advancing future research in this rapidly evolving field.
Purpose: This study explores the improvement in the area of Quantum computing techniques and natural language algorithm, to assist researchers to earn new awareness and detect emerging areas of growth for further research in this field. Methodology: In this study, we apply bibliometric analysis by using R Studio and VOS viewer software, we got our data from the Scopus database after searching via keywords, and before the exclusion, we got 292 papers, then we start to apply the exclusion criteria such as (time limit from 1990 to 2024, Document type: Article, Publication stage: Final, Source type: Journal, and Language: English) then after we applied those criteria we reach to 273 papers. Finding: The results of this study established the development status of the field of Quantum computing techniques and natural language algorithm, we demonstrate the growth rate, and emerging thematic areas for further research, we also identified the most impactful and leading authors, topic trends, keywords, sources, publications, and the relationship between co-authors. These findings assist researchers to guide new research to concentrate on future topic trends. Originality: This study contributes to the literature by identifying the growing interest in Quantum computing techniques and natural language algorithm.