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Performance of Qubit Neural Network in Chaotic Time Series Forecasting

  • Taisei UeguchiEmail author
  • Nobuyuki Matsui
  • Teijiro Isokawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

In recent years, quantum inspired neural networks have been applied to various practical problems since their proposal. Here we investigate whether our qubit neural network(QNN) leads to an advantage over the conventional (real-valued) neural network(NN) in the forecasting of chaotic time series. QNN is constructed from a set of qubit neuron, of which internal state is a coherent superposition of qubit states. In this paper, we evaluate the performance of QNN through a prediction of well-known Lorentz attractor, which produces chaotic time series by three dynamical systems. The experimental results show that QNN can forecast time series more precisely, compared with the conventional NN. In addition, we found that QNN outperforms the conventional NN by reconstructing the trajectories of Lorentz attractor.

Keywords

Quantum information processing Qubit Neural network Chaotic time series forecasting 

Notes

Acknowledgment

This study was financially supported by Japan Society for the Promotion of Science (Scientific Research (C) 16K00337).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Taisei Ueguchi
    • 1
    Email author
  • Nobuyuki Matsui
    • 1
  • Teijiro Isokawa
    • 1
  1. 1.Graduate School of EngineeringUniversity of HyogoHimejiJapan

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