Effective quantum convolutional neural network on symmetric complex-phase modulated RBS gate in pneumonia medical imaging diagnosis

Abstract Pneumonia is a prevalent respiratory disease for which medical imaging plays an important role in diagnosis. With the growing integration of quantum computing and deep learning, Quantum Convolutional Neural Networks (QCNNs) have emerged as a promising framework for medical image analysis by exploiting quantum parallelism. However, their further development is constrained by the limitations of core quantum gates. To address key shortcomings in conventional QCNNs, particularly the error coupling caused by the asymmetric two-angle rotation in the Reconfigurable Beam Splitter (RBS) gate and the confinement of quantum states to real-valued space, this study proposes an optimized RBS gate with symmetric complex-phase modulation. Specifically, the proposed design replaces the asymmetric two-angle rotation with a symmetric single-angle rotation to alleviate rotation-mismatch-induced error coupling, and introduces an R z gate to enable complex-phase control. The optimized gate is then integrated into a baseline QCNN for pneumonia image diagnosis. Simulations on MNIST, Fashion-MNIST, and PneumoniaMNIST show that the proposed model achieves classification accuracies of 95.4%, 87.8%, and 82.1%, respectively, and outperforms the compared QCNN variants under the current setting. These findings suggest that gate-level optimization of the RBS gate may serve as an effective design strategy for enhancing QCNN-based medical image classification under controlled simulation settings.