Multiclassification quantum neural network based on variational quantum circuits

Multi classification tasks are an important direction for the application of classical convolutional neural networks. However, in the application research of quantum convolutional neural networks, due to limitations such as quantum bits, a large amount of research has focused on binary and ternary classification tasks, while few have studied the classification of four or more categories. This article presents an improved quantum neural network and uses it to accomplish multi classification tasks. We use the Pennylane library to implement our hybrid quantum classical neural network model, and experimental results show that our model performs slightly better than classical convolutional neural networks with comparable parameter quantities. We hope that our findings can provide some inspiration for the application research of quantum convolutional neural networks in the future.