Exploring the features of quanvolutional neural networks for improved image classification

Image classification has an important role in many machine learning applications. Numerous classification techniques based on quantum machine learning have been reported recently. In this article, we investigate the features of the quanvolutional neural network—a hybrid quantum-classical image classification technique, which is inspired by the convolutional neural network and has the potential to outperform current image processing techniques. We improve the training strategy and evaluate the classification tasks on three traditional public datasets in terms of different topologies, sizes, and depths of filters. Finally, we propose four efficient configurations for the quanvolutional neural network to improve image classification.