摘要
为提高人工神经网络的逼近能力,该文从研究隐层神经元的映射机制入手,提出基于量子比特在Bloch球面的绕轴旋转构造神经网络模型的新思想。首先将样本线性变换为量子比特的相位,并使量子比特在Bloch球面上分别绕着3个坐标轴旋转,旋转角度即为网络参数。然后通过投影测量可以得到量子比特的球面坐标,将这些坐标值提交到隐层激励函数,可得隐层神经元的输出。输出层采用普通神经元。基于L-M(Levenberg-Marquardt)算法设计了该模型的学习算法。实验结果表明,该文提出的模型在逼近能力、泛化能力、鲁棒性能方面,均优于采用L-M算法的普通神经网络。
To enhance the mapping ability of artificial neural networks, by studying the mapping mechanism of hidden layer neurons, a new idea of designing neural networks model based on rotation of qubits in the Bloch sphere is proposed in this paper. In the proposed approach, the samples are linearly transformed to quantum bit phase, and the qubits are rotated about three coordinate axes, respectively. The network parameters of hidden layer are the rotation angles. The spherical coordinates of qubits can be obtained by the projection measurement. The output of hidden layer neurons can be concluded by submitting these coordinates to excitation functions in hidden layer. The general neurons are applied to the output layer. The learning algorithms of the proposed model are designed based on the Levenberg-Marquardt（L-M） algorithm. The experimental results show that the proposed model is superior to the classical（L-M） algorthm in approximation ability, generalization ability, and robust performance.
出处
《电子与信息学报》
EI
CSCD
北大核心
2016年第1期111-118,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61170132)
黑龙江省自然科学基金(F2015021)
黑龙江省教育厅科学技术研究项目(12541059)~~
