PDB-HQCNN: a parallel dual-branch hybrid quantum-classical convolutional neural network

Abstract

With the rapid advancement of quantum machine learning, numerous hybrid quantum–classical convolutional neural network (HQCNN) architectures have been proposed. However, most existing HQCNNs adopt serial topologies-either classical convolution followed by quantum processing or vice versa-which suffer from low feature propagation efficiency, limited representational capacity, and poor robustness due to inherent information bottlenecks. To address these limitations, this paper proposes a novel parallel dual-branch HQCNN (PDB-HQCNN). The proposed architecture comprises two mutually independent branches: a classical feature branch for extracting local structural features and a quantum feature branch for capturing global high-dimensional correlations. Features from both branches are concatenated and fed into a fully connected layer to generate classification predictions based on quantum measurement outcomes. Extensive experiments on MNIST, Fashion-MNIST, and Medical MNIST datasets demonstrate that the proposed PDB-HQCNN achieves classification accuracy exceeding 96% across all benchmarks. Compared with existing serial hybrid architectures, pure classical convolutional neural networks, and pure quantum neural networks, the proposed parallel dual-branch design attains state-of-the-art classification accuracy on all test datasets, validating its effectiveness in leveraging complementary classical and quantum representational strengths.