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Statistical Online Learning in Recurrent and Feedforward Quantum Neural Networks

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Abstract

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 two most common architectures of quantum neural networks: feedforward and recurrent. 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. Online learning algorithms for recurrent and feedforward quantum neural network are presented and experimentally confirmed. 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.

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Notes

  1. In fact, there are some limitations, but they can be overcome by changing the attribute value ranges.

  2. https://www.kaggle.com/datasets/jimschacko/airlines-dataset-to-predict-a-delay. Cited August 13, 2023.

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Funding

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.

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  1. Shukhov Belgorod State Technological University, Belgorod, Russia

    S. V. Zuev

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Correspondence to S. V. Zuev.

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Zuev, S.V. Statistical Online Learning in Recurrent and Feedforward Quantum Neural Networks. Dokl. Math. 108 (Suppl 2), S317–S324 (2023). https://doi.org/10.1134/S1064562423701557

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