Abstract
This paper investigates quantum neural networks and discusses its application to controlling systems. Multi-layer quantum neural networks having qubit neurons as its information processing unit are considered and a direct neural network controller using the multi-layer quantum neural networks is proposed. A real-coded genetic algorithm is applied instead of a back-propagation algorithm for the supervised training of the multi-layer quantum neural networks to improve learning performance. To evaluate the capability of the direct quantum neural network controller, computational experiments are conducted for controlling a discrete-time system and a nonholonomic system - in this study a two-wheeled robot. Experimental results confirm the effectiveness of the real-coded genetic algorithm for the training of the quantum neural networks and show both feasibility and robustness of the direct quantum neural control system.
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Takahashi, K., Kurokawa, M., Hashimoto, M. (2012). Remarks on Multi-layer Quantum Neural Network Controller Trained by Real-Coded Genetic Algorithm. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_7
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DOI: https://doi.org/10.1007/978-3-642-31919-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
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