Exploring the features of quanvolutional neural networks for improved image classification

Abstract In several machine learning applications, image classification is a crucial problem. Numerous classification techniques based on quantum machine learning have been presented recently. In this article, we investigate the features of the quanvolutional neural network - a hybrid quantum-classical image classification technique inspired by the convolutional neural network which has the potential to outperform current image processing techniques. We improve the training strategy and evaluate the classification tasks on traditional datasets by different topologies, sizes, and depth of filters. Finally, we propose an efficient configuration for the quanvolutional neural network, making it a promising solution for image classification problems.