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Open Access

On Quantum Methods for Machine Learning Problems Part I: Quantum Tools

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", presents the fundamentals of qubits, quantum registers, and quantum states, introduces important quantum tools based on known quantum search algorithms and SWAP-test, and discusses the basic quantum procedures used for quantum search methods. The second part, "quantum classification algorithms", introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification.

Keywords

quantum algorithmquantum programmingmachine learning

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Big Data Mining and Analytics
Volume 3 Issue 1,
March 2020
Pages 41-55
DOI: 10.26599/BDMA.2019.9020016
Cite this article:
Ablayev F, Ablayev M, Huang JZ, et al. On Quantum Methods for Machine Learning Problems Part I: Quantum Tools. Big Data Mining and Analytics, 2020, 3(1): 41-55. https://doi.org/10.26599/BDMA.2019.9020016

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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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