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Controller application of a multi-layer quantum neural network trained by a conjugate gradient algorithm

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3 Author(s)
Takahashi, K. ; Inf. Syst. Design, Doshisha Univ., Kyotanabe, Japan ; Kurokawa, M. ; Hashimoto, M.

This paper investigates a quantum neural network and discusses its application to control systems. A learning-type neural control system that uses a multi-layer quantum neural network having qubit neurons as its information processing unit is proposed. A conjugate gradient algorithm is applied instead of the back-propagation algorithm for the supervised training of the multi-layer quantum neural network in order to improve learning performance. To evaluate the capability of the learning-type quantum neural control system, computational experiments are conducted for controlling a nonholonomic system - in this study a two-wheeled robot. Simulation results confirm both feasibility and robustness of the learning-type quantum neural control system.

Published in:

IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society

Date of Conference:

7-10 Nov. 2011