关键词: 量子计算, 量子神经网络, 卷积神经网络, 量子门

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.

Key words: quantum computation, quantum neural networks, convolutional neural networks, quantum gates