Parallel structure of hybrid quantum-classical neural networks for image classification

Hybrid quantum-classical neural networks (QCNNs) integrate principles from quantum computing principle and classical neural networks, offering a novel computational approach for image classification tasks. However, current QCNNs with sequential structures encounter limitations in accuracy and robustness, especially when dealing with tasks involving numerous classes. In this study, we propose a novel solution-the hybrid parallel quantum-classical neural network (PQCNN)-for image classification tasks. This architecture seamlessly integrates the parallel processing capabilities of quantum computing with the hierarchical feature extraction abilities of classical neural networks, aiming to overcome the constraints of conventional sequential structures in multi-class classification tasks. Extensive experimentation demonstrates the superiority of PQCNN over traditional concatenative structures in binary classification datasets, displaying heightened accuracy and robustness against noise. Particularly noteworthy is PQCNN's significantly improved accuracy on datasets with 5 and 10 classes. These findings underscore the transformative potential of the PQCNN architecture as an advanced solution for enhancing the performance of quantum-classical-based classifiers, particularly in the domain of image classification.