Single-hidden-layer feed-forward quantum neural network based on Grover learning
Single-hidden-layer feed-forward quantum neural network based on Grover learning
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DOI：
10.1016/j.neunet.2013.02.012
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
Cheng-Yi Liu;Chein Chen;Ching-Ter Chang;Lun-Min Shih
中科院分区：
文献类型：
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作者：
Cheng-Yi Liu;Chein Chen;Ching-Ter Chang;Lun-Min Shih
基于量子理论中的一些概念和原理，提出了一种新的单隐层前馈量子神经网络模型。将量子机制与前馈神经网络相结合，定义了量子隐层神经元和量子连接权，并将其作为单隐层前馈神经网络的基本信息处理单元。量子神经元在网络的隐层中使用了大量的非线性函数作为激活函数，Grover搜索算法迭代地突出了最优参数的设置，从而使高效的神经网络学习成为可能。量子神经元和权值的引入，沿着基于Grover搜索算法的学习，使得神经网络具有网络精简、训练效率高的特点，具有前景广阔的应用前景。仿真结果表明，该量子网络能够实现精确学习。
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.