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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

quantum classificationbinary classificationnearest neighbor algorithm

References

[1]
C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref
[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.
Crossref
[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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
[15]
C. A. Trugenberger, Probabilistic quantum memories, Physical Review Letters, vol. 87, no. 6, p. 067901, 2001.
Crossref Google Scholar
[16]
C. A. Trugenberger, Quantum pattern recognition, Quantum Information Processing, vol. 1, no. 6, pp. 471-493, 2002.
Crossref Google Scholar
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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