量子电子学报2017，Vol.34Issue(4)：467-472,6.DOI:10.3969/j.issn.1007-5461.2017.04.015
连续变量相干态量子神经网络模型的构建
Construction of continuous-variable coherent state quantum neural network model
摘要
Abstract
In order to apply a powerful neural network to the continuous-variable quantum information processing,it is necessary to construct the continuous-variable quantum neural network (QNN) model.Coherent state quantum logic gates are taken as basic elements.Quantum circuit composed of input layer,hidden layer and output layer is constructed based on QNN principle,and the function of continuous-variable coherent state quantum neural network (CSQNN) is realized.The model realizes quantum state operation by using multi-bit CNOT gate,and the learning training of network parameters is completed by using phase rotation gates.Simulation results show that under the assistance of CSQNN,the quantum teleportation fidelity of amplitude damping channel with damping coefficient of 0.5 is significantly improved,and its value approaches 1.It's shown that the proposed CSQNN model can effectively deal with the continuous-variable quantum information.关键词
量子信息/量子神经网络/学习训练/连续变量/量子隐形传态Key words
quantum information/quantum neural network/learning training/continuous-variable/quantum teleportation分类
信息技术与安全科学引用本文复制引用
陈珊琳,黄春晖..连续变量相干态量子神经网络模型的构建[J].量子电子学报,2017,34(4):467-472,6.基金项目
Supported by National Natural Science Foundation of China(国家自然科学基金,61177072) （国家自然科学基金,61177072）
