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Scientometric Analysis of Quantum Algorithms for VANET Optimization
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DOI：10.1109/TSMC.2024.3428707.png)
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摘要
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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.
Keyword:
Quantum computing
Optimization
Vehicular ad hoc networks
Machine learning algorithms
Quantum mechanics
Market research
Bibliometrics
CiteSpace
quantum algorithm taxonomy
quantum optimization
vehicular ad-hoc network (VANET)
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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