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Open Access

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

College of Computer Science & Software Engineering, Shenzhen University, Shenzhen 518000, China.
Kazan Federal University, Kazan 42008, Russia.
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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 classificationquantum classificationnearest neighbor algorithm

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Big Data Mining and Analytics
Volume 3 Issue 1,
March 2020
Pages 56-67
DOI: 10.26599/BDMA.2019.9020018
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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Received: 10 September 2019
Accepted: 25 September 2019
Published: 19 December 2019
© The author(s) 2020

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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