中文摘要：

量子神经网络是一种借鉴量子理论中的态叠加思想而设计的单隐层前馈神经网络,其主要用于数据分类。由于采用多层激励函数神经元,并且在量子间隔训练中采用了新的目标函数,即同类输入数据的隐层节点输出方差最小,从而使量子神经网络具备了发掘不同类别数据间模糊性的能力。但由于训练时对量子神经网络权值和量子间隔使用了不同的目标函数,使迭代过程中两者不可避免的会出现相互冲突,从而导致训练迭代次数的增加和网络性能的下降。本文借鉴约束优化理论,在两个目标函数的梯度下降求解中引入了惩罚函数,提出了一种新的量子神经网络训练算法,消除了两个目标函数间的冲突。实验结果表明,本文提出的训练算法可以显著提升训练的速度和网络的性能。

英文摘要：

Inspired by the principle of superposing states in quantum mechanics,a new kind of feedforward neural network with single hidden layer was designed and named as Quantum Neural Networks.It has mainly been used for data classification.Multilevel activation functions is adopted in Quantum Neural Networks and a different objective function is used for the learning of quantum intervals of the multilevel hidden units;minimizing the class-conditional variances at the outputs of the hidden units.Quantum Neural Networks is endowed with a special ability of identifying uncertainty in data classification by its unique design.However,as different objective functions are used for the learning of synaptic weights and quantum intervals,conflict is observed in the iterative training process which will cause more iteration times for training and degrades the training results.Using the theory of Constrained Optimization,a new training algorithm is proposed in this paper by introducing penalty function into the steepest descent method.In the new training algorithm, the conflict is removed.Experimental results indicate that the new algorithm can increase the training speed and improve the performance of the trained network remarkably.