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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", 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.


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

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", 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 algorithm, quantum programming, machine learning
Received: 10 September 2019 Accepted: 25 September 2019 Published: 19 December 2019 Issue date: March 2020
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Publication history
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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 Natural Science Foundation of Guangdong Province, China (No. 2019A1515011721).

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