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Quantum Machine Learning Algorithms

  • Renata Wong
  • , Tanya Garg
  • , Ritu Thombre
  • , Alberto Maldonado Romo
  • , P. N. Niranjan
  • , Pinaki Sen
  • , Mandeep Kaur Saggi
  • , Amandeep Singh Bhatia
  • National Center for Theoretical Sciences
  • Indian Institute of Technology Roorkee
  • Visvesvaraya National Institute of Technology
  • Instituto Politécnico Nacional
  • Amrita Vishwa Vidyapeetham
  • National Institute of Technology Agartala
  • Thapar Institute of Engineering & Technology
  • Purdue University

研究成果: 圖書/報告稿件的類型 › 章節 › 同行評審

4 引文 斯高帕斯(Scopus)

摘要

In recent times, the combination of machine learning and quantum computing has been applied greatly to solve the problems of intelligent computing. The new emerging area of quantum machine learning can be exploited to great extent for accelerating the existing classical machine learning algorithms for accurate classifications and better predictions of massive data. Quantum computing and machine learning are anticipated to play a crucial part in how the community deals with information in the future. The objective of this chapter is to highlight the present evolution of quantum computers in the setting of intelligent data mining. Recent progress in quantum algorithms can act as a stepping stone for quantum machine learning models. The determination relative merits of classical and quantum machine learning models would depend on the current and future prospects of quantum computing. However, the implementation of quantum algorithms needs quantum hardware that is not yet accessible on a wide scale.

原文英語
主出版物標題Emerging Computing Paradigms
主出版物子標題Principles, Advances and Applications
發行者wiley
頁面79-98
頁數20
ISBN(電子)9781119813439
ISBN(列印)9781119813415
DOIs
出版狀態已出版 - 01 01 2022
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Publisher Copyright:
© 2022 John Wiley & Sons, Ltd.

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