Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan.
Neural Netw. 2013 Sep;45:144-50. doi: 10.1016/j.neunet.2013.02.012. Epub 2013 Mar 14.
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.
本文提出了一种基于量子理论的新概念和原理的新型单隐层前馈量子神经网络模型。通过将量子机制与前馈神经网络相结合，我们定义了量子隐层神经元和连接量子权值，并将其作为单隐层前馈神经网络的基本信息处理单元。量子神经元使广泛的非线性函数作为网络隐层的激活函数，而 Grover 搜索算法则突出了最优参数设置的迭代，从而使神经网络学习变得非常高效。量子神经元和权值，以及基于 Grover 搜索算法的学习，导致了一种新型的高效神经网络，具有减少网络、高效训练和未来应用前景的特点。进行了一些模拟以研究所提出的量子网络的性能，结果表明它可以实现准确的学习。
