Evaluating the Effectiveness of Hybrid Quantum-Classical Convolutional Neural Networks for Image Classification in Multiple Color Spaces (2406.02229v2)

Abstract: As the complexity and scale of image processing tasks continue to expand, quantum convolutional neural networks (QCNNs) have demonstrated the capability to improve image processing performance. These networks can accelerate processing speed and enhance classification accuracy while reducing the number of model parameters. However, these studies restrict images to the RGB color space, and the effectiveness of QCNNs in other color spaces still needs to be explored. In this work, we propose a hybrid quantum-classical convolutional neural network (HQCCNN) model to analyze performance in four different color space images: RGB, Lab, YCrCb, and HSV. Using several multi-qubit entangled gates, HQCCNN is constructed as a parameterized quantum circuit model to evaluate classification performance across these color spaces. Furthermore, we verify the effectiveness of HQCCNN on the CIFAR-100 dataset. The experimental results show that it achieves significantly greater accuracy in binary classification tasks within the RGB color space. For some superclasses, the model performs in the Lab, YCrCb, and HSV color spaces as well as or better than RGB. This serves as an essential reference for QCNN in processing various color space image data for computer vision applications.