关键词: 量子计算, 混合量子神经网络, 迁移学习, 特征融合

Abstract: In order to explore the possibility of cross fusion between quantum computing and neural networks, a digital recognition model based on hybrid quantum classical neural networks is proposed. In this model, the parametric quantum circuit is added to the convolution model to realize the mixing of embedding and direct output. The convolution full connection layer information is taken as the control parameter of the quantum layer, and the feature is processed through a series of unitary transformations. The potential of this structure is demonstrated by digit recognition experiments on MNIST datasets. Combined with transfer learning, convolution networks and hybrid networks are used to extract deep-seated features of images and perform feature fusion to improve the overfitting problem of single convolution networks on small sample datasets. The experimental results show that the model has validity, practicability and high generalization performance. On MNIST datasets, the recognition rate of direct output hybrid network and embedded hybrid network is 0.039 8 and 0.021 1 higher than that of convolution network, respectively. After transfer learning and feature fusion, the recognition rate of the hybrid model in small sample datasets is better than that of the single network, reaching 83%, which verifies the learning and generalization ability of the model.

Key words: quantum computing, hybrid quantum neural network, transfer learning, feature fusion