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Qubit neural network and its learning efficiency

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Abstract

Neural networks have attracted much interest in the last two decades for their potential to realistically describe brain functions, but so far they have failed to provide models that can be simulated in a reasonable time on computers; rather they have been limited to toy models. Quantum computing is a possible candidate for improving the computational efficiency of neural networks. In this framework of quantum computing, the Qubit neuron model, proposed by Matsui and Nishimura, has shown a high efficiency in solving problems such as data compression. Simulations have shown that the Qubit model solves learning problems with significantly improved efficiency as compared to the classical model. In this paper, we confirm our previous results in further detail and investigate what contributes to the efficiency of our model through 4-bit and 6-bit parity check problems, which are known as basic benchmark tests. Our simulations suggest that the improved performance is due to the use of superposition of neural states and the use of probability interpretation in the observation of the output states of the model.

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Acknowledgements

This study is financially supported by Grant-in-Aid for Scientific Research (C-13680458), Japan Society for the Promotion of Science.

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Authors and Affiliations

  1. Hyogo Prefectural Institute of Technology, 3-1-12 Yukihira-cho, Suma, Kobe, Hyogo, 654-0037, Japan

    Noriaki Kouda

  2. Division of Computer Engineering, Graduate School of Engineering, Himeji Institute of Technology, University of Hyogo, 2167 Shosha, Himeji, Hyogo, 671-2201, Japan

    Nobuyuki Matsui

  3. Graduate School of Applied Informatics, University of Hyogo, 1-3-3 Higashikawasaki-cho, Chuo-ku, Kobe, 650-0044, Japan

    Haruhiko Nishimura

  4. National Institute of Information and Communications Technology, 588-2 Iwaoka, Iwaoka-cho, Nishi-ku, Kobe, 651-2492, Japan

    Ferdinand Peper

Authors
  1. Noriaki Kouda
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  2. Nobuyuki Matsui
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  3. Haruhiko Nishimura
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  4. Ferdinand Peper
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Correspondence to Nobuyuki Matsui.

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Kouda, N., Matsui, N., Nishimura, H. et al. Qubit neural network and its learning efficiency. Neural Comput & Applic 14, 114–121 (2005). https://doi.org/10.1007/s00521-004-0446-8

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  • DOI: https://doi.org/10.1007/s00521-004-0446-8

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