Quantum Machine Learning for Computational Methods in Engineering: A Systematic Review

Quantum Machine Learning (QML) has emerged as a unique computing area. The utilization of quantum technology in machine learning can solve complex problems (unsolvable using classical computational methodologies). The revolutionary paradigms potential has spurred scientific research and progress. Th...

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Vydáno v:Archives of computational methods in engineering Ročník 31; číslo 3; s. 1555 - 1577
Hlavní autoři: Sood, Sandeep Kumar, Agrewal, Monika
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.04.2024
Springer Nature B.V
Témata:
Algorithms
Bibliometrics
Computers
Engineering
Error correction
Geographical distribution
Information processing
Machine learning
Mathematical and Computational Engineering
Network analysis
Neural networks
Pattern analysis
Quantum computing
Quantum physics
Review Article
Scientometrics
Support vector machines
Systematic review
Taxonomy
ISSN:1134-3060, 1886-1784
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Shrnutí:Quantum Machine Learning (QML) has emerged as a unique computing area. The utilization of quantum technology in machine learning can solve complex problems (unsolvable using classical computational methodologies). The revolutionary paradigms potential has spurred scientific research and progress. Therefore, a highly essential exploration is needed to extract scientific breakthrough paths. The proposed work supports the concept by providing a scientometric analysis of QML scientific literature for the period 2003–2023, gathered from the Web of Science database. The study explores the powerful machine learning techniques in the quantum realm. The scientometric implication of the article provides deep insights into the publication and citation pattern, geographical distribution analysis, document co-citation, and keyword co-occurrence network analysis. The research findings highlight the predominant use of algorithms such as quantum support vector machines, quantum neural networks, and Q-learning. Notably active research hotspots in this field include drug design and discovery, quantum control, optimization, error-correction, and quantum state tomography. Additionally, collaborative efforts are evident in the domains of quantum unsupervised and reinforcement machine learning. The overall inference of QML literature portrays insightful recommendations and research directions for the academic community.
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ISSN:1134-3060
1886-1784
DOI:10.1007/s11831-023-10027-w