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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

References(16)

[1]
C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
DOI Google Scholar
[2]
F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review, vol. 65, no. 6, pp. 386-408, 1958.
DOI Google Scholar
[3]
J. A. K. Suykens and J. Vandewalle, Least squares support vector machine classifiers, Neural Processing Letters, vol. 9, no. 3, pp. 293-300, 1999.
DOI Google Scholar
[4]
D. Anguita, S. Ridella, F. Rivieccio, and R. Zunino, Quantum optimization for training support vector machines, Neural Networks, vol. 16, nos. 5&6, pp. 763-770, 2003.
DOI Google Scholar
[5]
C. Durr and P. Høyer, A quantum algorithm for finding the minimum, arXiv preprint arXiv: quant-ph/9607014, 1996.
Google Scholar
[6]
L. K. Grover, A fast quantum mechanical algorithm for database search, in ACM Symp. on Theory of Computing, Philadelphia, PA, USA, 1996, pp. 212-219.
DOI
[7]
A. Kapoor, N. Wiebe, and K. Svore, Quantum perceptron models, in Advances in Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 3999-4007.
[8]
N. Wiebe, A. Kapoor, and K. M. Svore, Quantum perceptron models, in Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 4006-4014.
[9]
M. Schuld, I. Sinayskiy, and F. Petruccione, Quantum computing for pattern classification, in Pacific Rim International Conference on Artificial Intelligence, Springer, 2014, pp. 208-220.
DOI
[10]
Y. Ruan, X. L. Xue, H. Liu, J. N. Tan, and X. Li, Quantum algorithm for k-nearest neighbors classification based on the metric of hamming distance, International Journal of Theoretical Physics, vol. 56, no. 11, pp. 3496-3507, 2017.
DOI Google Scholar
[11]
N. Wiebe, A. Kapoor, and K. M. Svore, Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning, Quantum Information & Computation, vol. 15, nos. 3&4, pp. 316-356, 2015.
DOI Google Scholar
[12]
T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21-27, 1967.
DOI Google Scholar
[13]
K. Fukunaga and P. M. Narendra, A branch and bound algorithm for computing k-nearest neighbors, IEEE Transactions on Computers, vol. C-24, no. 7, pp. 750-753, 1975.
DOI Google Scholar
[14]
P. Rebentrost, M. Mohseni, and S. Lloyd, Quantum support vector machine for big data classification, Physical Review Letters, vol. 113, no. 13, p. 130503, 2014.
DOI Google Scholar
[15]
C. A. Trugenberger, Probabilistic quantum memories, Physical Review Letters, vol. 87, no. 6, p. 067901, 2001.
DOI Google Scholar
[16]
C. A. Trugenberger, Quantum pattern recognition, Quantum Information Processing, vol. 1, no. 6, pp. 471-493, 2002.
DOI Google Scholar
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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