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Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia, 2023, Volume 514, Number 2, Pages 177–186
DOI: https://doi.org/10.31857/S268695432360129X
(Mi danma463)
 

This article is cited in 1 scientific paper (total in 1 paper)

SPECIAL ISSUE: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGIES

Statistical online learning in recurrent and feedforward quantum neural networks

S. V. Zuev

Belgorod Shukhov State Technological University, Belgorod, Russian Federation
Citations (1)
References:
Abstract: Purpose. For adaptive artificial intelligence systems, the question of the possibility of online learning is especially important, since such training provides adaptation. The purpose of the work is to consider methods of quantum machine online learning for the most common two architectures of quantum neural networks: feedforward and recurrent.
Methods. The work uses the quantumz module available on PyPI to emulate quantum computing and create artificial quantum neural networks. In addition, the genser module is used to transform data dimensions, which provides reversible transformation of dimensions without loss of information. The data for the experiments are taken from open sources. The paper implements the machine learning method without optimization, proposed by the author earlier.
Results. Online learning algorithms for recurrent and feedforward quantum neural network are presented and experimentally confirmed.
Conclusions. The proposed learning algorithms can be used as data science tools, as well as a part of adaptive intelligent control systems. The developed software can fully unleash its potential only on quantum computers, but, in the case of a small number of quantum registers, it can also be used in systems that emulate quantum computing, or in photonic computers.
Keywords: online learning, adaptive artificial intelligence, quantum machine learning, quantum entanglement.
Funding agency Grant number
Priority 2030 Program
The work was carried out within the framework of the Priority-2030 development program on the material base of the Center for High Technologies of the Shukhov Belgorod State Technological University.
Presented: A. L. Semenov
Received: 24.08.2023
Revised: 15.09.2023
Accepted: 24.10.2023
English version:
Doklady Mathematics, 2023, Volume 108, Issue suppl. 2, Pages S317–S324
DOI: https://doi.org/10.1134/S1064562423701557
Bibliographic databases:
Document Type: Article
UDC: 004.032.26
Language: Russian
Citation: S. V. Zuev, “Statistical online learning in recurrent and feedforward quantum neural networks”, Dokl. RAN. Math. Inf. Proc. Upr., 514:2 (2023), 177–186; Dokl. Math., 108:suppl. 2 (2023), S317–S324
Citation in format AMSBIB
\Bibitem{Zue23}
\by S.~V.~Zuev
\paper Statistical online learning in recurrent and feedforward quantum neural networks
\jour Dokl. RAN. Math. Inf. Proc. Upr.
\yr 2023
\vol 514
\issue 2
\pages 177--186
\mathnet{http://mi.mathnet.ru/danma463}
\crossref{https://doi.org/10.31857/S268695432360129X}
\elib{https://elibrary.ru/item.asp?id=56717810}
\transl
\jour Dokl. Math.
\yr 2023
\vol 108
\issue suppl. 2
\pages S317--S324
\crossref{https://doi.org/10.1134/S1064562423701557}
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  • This publication is cited in the following 1 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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