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Quantum Machine Learning : A Modern Approach

By: D. Sumathi (Edited by) , M Akila (Edited by) , S Karthikeyan (Edited by) , T Poongodi (Edited by)

Manufacture on Demand

Ksh 22,800.00

Format: Hardback or Cased Book

ISBN-10: 1032544716

ISBN-13: 9781032544717

Publisher: Taylor & Francis Ltd

Imprint: Chapman & Hall/CRC

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Oct 28th, 2024

Publication Status: Active

Product extent: 288 Pages

Weight: 580.00 grams

Product Classification / Subject(s): Computer science
Machine learning

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This text 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.

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.


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