### Design and implementation of convolution neural network based on quantum gate group

XU Xingyang, LIU Hongzhi

1. School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
• Online:2018-10-15 Published:2018-10-19

### 基于量子门组的卷积神经网络设计与实现

1. 北京工商大学 计算机信息与工程学院，北京 100048

Abstract: In order to improve the training speed of convolution neural network and reduce the training cost, combined with the latest research progress of convolution neural network and quantum computation, a model of Quantum Gate Convolutional Neural Network（QGCNN） is proposed. In order to construct QGCNN network structure, firstly, according to the characteristics of traditional CNN structure, the definition of convolution arithmetic circuit is given. Secondly, the relationship between the weight coefficients of ConvAC is explained by tensor decomposition, which provides the theoretical basis for constructing QGCNN. Then, QGCNN is divided into input presentation layer, hidden layer and output layer, and on this basis, the data is quantized, and a series of operations such as data initialization and network parameter updating are completed by using quantum gate group. Finally, QGCNN is applied to digital handwriting recognition. The experimental results show that the method has a good effect on the accuracy and convergence speed of handwriting recognition.