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This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms. This second part of the review presents several classification problems in machine learning that can be accelerated with quantum subroutines. We have chosen supervised learning tasks as typical classification problems to illustrate the use of quantum methods for classification.


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On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms

Show Author's information Farid AblayevMarat AblayevJoshua Zhexue HuangKamil KhadievNailya SalikhovaDingming Wu( )
College of Computer Science & Software Engineering, Shenzhen University, Shenzhen 518000, China.
Kazan Federal University, Kazan 42008, Russia.

Abstract

This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms. This second part of the review presents several classification problems in machine learning that can be accelerated with quantum subroutines. We have chosen supervised learning tasks as typical classification problems to illustrate the use of quantum methods for classification.

Keywords:

quantum classification, binary classification, nearest neighbor algorithm
Received: 10 September 2019 Accepted: 25 September 2019 Published: 19 December 2019 Issue date: March 2020
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Publication history

Received: 10 September 2019
Accepted: 25 September 2019
Published: 19 December 2019
Issue date: March 2020

Copyright

© The author(s) 2020

Acknowledgements

This work was supported in part by the Russian Science Foundation (No. 19-19-00656) and the Natural Science Foundation of Guangdong Province, China (No. 2019A1515011721).

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