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Open Access Research Article

Define, refined and re-defined concepts of quantum machine learning  : A review

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pp. 1229–1262Vol. 45Issue 5July 2024DOI: 10.47974/JIOS-1320XML
Received:
10 May 2022
Accepted:
12 Sep 2022
Published Online:
12 Aug 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1320
Pages:
1229–1262

Abstract

Quantum Machine Learning (QML) is a new phraseology in the world today. With various upcoming Artificial Intelligence (AI) technologies, everyday data scientists and corporate around the world are trying to harness its power. QML borrows concepts of Quantum Computing and QML algorithms enhance the existing Machine Learning (ML) algorithms by processing datasets more efficiently. Quantum Computers are a powering force driving QML algorithms that boosts computational power and helps analyse data. QML algorithms can be applied in sectors involving number crunching, pattern identification and so on. In the existing world, QML can be a game-changer for organizations that work on identifying product correlations and customer behaviour in a brief time. This paper discusses studies related to principles of Quantum Mechanics, Quantum Computing and QML algorithm. Additionally, it explores opportunities and challenges faced in the field of QML. Lastly, the paper talks about QML applications and compares various available QML toolkits from the market. Future direction and exhaustive survey of papers from various spectrums related to Quantum Computing is presented in this paper.

Keywords

Subject Classifications

81T25

References

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