A Distributed Hybrid Quantum Convolutional Neural Network for Medical Image Classification
Yangyang Li, Zhengya Qia, Yuelin Lia, Haorui Yanga, Ronghua Shanga, Licheng Jiaoa
TL;DR
The paper tackles the challenge of medical image classification under limited quantum hardware by proposing a distributed hybrid quantum convolutional neural network that uses quantum circuit splitting to execute an $8$-qubit QCNN on smaller $4$- and $5$-qubit circuits. The method combines a lightweight CNN feature extractor (MobileNetV2) with a QCNN that employs angle encoding, Hadamard and $R_Y$ gates, and an MPS-based convolutional kernel, followed by a final classifier; the $8$-qubit circuit is reconstructed via a mid-circuit cut and Pauli-basis tomography for reweighting. Empirical results on HAM10000, ISIC2017, and MedMNIST show high accuracy with far fewer parameters (e.g., $2.16$M) than competing models, confirming strong cross-dataset performance and the practicality of distributed quantum processing for medical imaging. This work demonstrates the viability of quantum-enhanced feature extraction in resource-constrained clinical settings and points to scalable paths for integrating quantum circuits with classical models in medical image analysis.
Abstract
Medical images are characterized by intricate and complex features, requiring interpretation by physicians with medical knowledge and experience. Classical neural networks can reduce the workload of physicians, but can only handle these complex features to a limited extent. Theoretically, quantum computing can explore a broader parameter space with fewer parameters, but it is currently limited by the constraints of quantum hardware.Considering these factors, we propose a distributed hybrid quantum convolutional neural network based on quantum circuit splitting. This model leverages the advantages of quantum computing to effectively capture the complex features of medical images, enabling efficient classification even in resource-constrained environments. Our model employs a quantum convolutional neural network (QCNN) to extract high-dimensional features from medical images, thereby enhancing the model's expressive capability.By integrating distributed techniques based on quantum circuit splitting, the 8-qubit QCNN can be reconstructed using only 5 qubits.Experimental results demonstrate that our model achieves strong performance across 3 datasets for both binary and multiclass classification tasks. Furthermore, compared to recent technologies, our model achieves superior performance with fewer parameters, and experimental results validate the effectiveness of our model.