作　　者： ; ; ; ;

机构地区： 华南农业大学信息学院

出　　处： 《现代计算机》 2010年第11期3-6,共4页

摘　　要： 提出一种量子BP网络模型及改进学习算法,该BP网络模型首先基于量子学中一位相移门和两位受控非门的通用性,构造出一种量子神经元,然后由该量子神经元构造隐含层,采用梯度下降法进行学习。输出层采用传统神经元构造,采用基于改进的带动量自适应学习率梯度下降法学习。在UCI两个数据集上采用该模型及算法,实验结果表明该方法比传统的BP网络具有较好的收敛速度和正确率。 Presents a quantum BP neural network model and its improved learning algorithm.Firstly a quantum neuron is constructed based on the universality of single qubit rotation gate and two-qubit controlled not gate.Secondly,the hide layer is constructed and uses these quantum neurons with the application of the gradient descent algorithm and the output layer is made with traditional neurons,in which weight and bias values are updated according to the improved algorithm of gradient descent momentum and an adaptive learning rate.It is shown that this model and algorithm are superior to the conventional BP networks in two aspects：convergence speed and accuracy by two UCI＇s data sets examples.

关 键 词： 量子计算 量子神经元 量子 神经网络 学习算法 收敛速度

领　　域： [自动化与计算机技术] [自动化与计算机技术]