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Big Data Mining and Analytics 2020, 3(1): 56-67 https://doi.org/10.26599/BDMA.2019.9020018
Open Access | Issue | Published: 19 December 2019

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

Show Author's Information Farid Ablayev,  Marat Ablayev,  Joshua Zhexue Huang,  Kamil Khadiev,  Nailya Salikhova,  Dingming Wu( )
College of Computer Science & Software Engineering, Shenzhen University, Shenzhen 518000, China.
Kazan Federal University, Kazan 42008, Russia.
Keywords:
binary classification, quantum classification, nearest neighbor algorithm
Cite this article:
Ablayev F, Ablayev M, Huang JZ, et al. On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms. Big Data Mining and Analytics, 2020, 3(1): 56-67. https://doi.org/10.26599/BDMA.2019.9020018
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Abstract Full text About this article

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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About this article

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

Show Author's information Farid Ablayev, Marat Ablayev, Joshua Zhexue Huang, Kamil Khadiev, Nailya Salikhova, Dingming 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: binary classification, quantum classification, nearest neighbor algorithm

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Publication history
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Acknowledgements
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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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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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