UID:
almahu_9949926928602882
Format:
1 online resource
ISBN:
9781040116104
,
1040116108
,
9781003429654
,
1003429653
,
9781040116142
,
1040116140
Content:
This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies. In summary, the book: Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects. This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.
Additional Edition:
Print version: ISBN 1032544716
Additional Edition:
ISBN 9781032544717
Language:
English
DOI:
10.1201/9781003429654
URL:
https://www.taylorfrancis.com/books/9781003429654
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