基于参数化量子电路的量子卷积神经网络模型及应用

Quantum neural networks have provided entirely new insight into the future of artificial intelligence by combining the advantages of quantum computing technologies and classical neural network models. In this paper, a parameterized quantum circuit based quantum convolutional neural network model is proposed, which can deal with both Euclidean data and non-Euclidean data and accelerate classical machine learning tasks by taking the computational advantages of quantum systems. Simulation results on the MNIST data set show that the model has strong learning ability and good generalization performance.