標題: Single-hidden-layer feed-forward quantum neural network based on Grover learning
作者: Liu, Cheng-Yi
Chen, Chein
Chang, Ching-Ter
Shih, Lun-Min
資訊工程學系
Department of Computer Science
關鍵字: Neural network;Quantum computing;Grover algorithm
公開日期: 1-九月-2013
摘要: In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. (C) 2013 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.neunet.2013.02.012
http://hdl.handle.net/11536/22790
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2013.02.012
期刊: NEURAL NETWORKS
Volume: 45
Issue: 
起始頁: 144
結束頁: 150
顯示於類別:期刊論文


文件中的檔案:

  1. 000323589200014.pdf