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Quantum Support Vector Machine for Classification Task: A Review

Akrom, Muhamad (2024) Quantum Support Vector Machine for Classification Task: A Review. Journal of Multiscale Materials Informatics, 1 (2). pp. 1-8. ISSN 3047-5724

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Abstract

Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines, and review recent advancements and applications. Finally, we highlight the challenges and prospects of QSVM in the context of quantum machine learning.

Item Type: Article
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Depositing User: dladmin fts
Date Deposited: 29 Nov 2024 05:28
Last Modified: 29 Nov 2024 05:28
URI: https://dl.futuretechsci.org/id/eprint/63

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