Abstract: Quantum neural network combines the advantages of quantum computing and classical neural network model,and provides a new idea for the development of artificial intelligence in the future.Although quantum neural networks have been widely studied,the impact of data encoding methods and different training circuits on model performance has not yet been fully explored.Therefore,this paper proposes a new quantum neural network model for data classification,which explores the influence of diffe-rent data encoding methods and different structure training layers on classification tasks.The method first preprocesses the classical image,uses different data encoding methods to encode it into different parameterized quantum circuits for training,measures the output of the model,and uses the parameter shift rule to update the training parameters to complete the data classification.Experimental results on MNIST handwritten dataset show that the proposed model achieves more than 97% classification accuracy on digit {3,6} classification task.Compared with the current mainstream methods,the proposed method has a significant improvement in classification accuracy.

Key words: Quantum neural networks, Quantum computing, Data classification, Quantum circuit, Parameter shift rules