Performance analysis of quantum convolutional layers for image classification

Abstract In recent years, with the rapid development of quantum computing technology, the fusion of quantum computing and machine learning techniques is becoming a research hotspot in the field of machine learning. This article aims to explore the impact of the depth and width of quantum convolutional layers on image classification tasks in Quantum−Classical Hybrid Convolutional Neural Networks. To this end, a model combining parameterized quantum circuits and classical neural networks is designed, and a series of experiments are conducted on the MNIST dataset to assess the specific effects of different configurations of quantum convolutional layers on model performance. The research results indicate that simply increasing the depth or width of quantum convolutional layers does not guarantee performance improvement and sometimes may even lead to performance degradation. Therefore, when designing quantum convolutional layers, we should make reasonable choices based on the actual needs of the application scenarios. Finally, based on these findings, a multidimensional optimization strategy is proposed to enhance the overall performance of the model. The achievements of this research not only provide important guidance for the design and optimization of Quantum−Classical Hybrid Convolutional Neural Networks but also offer new research perspectives for researchers in the field of quantum machine learning.