A Distributed Hybrid Quantum Convolutional Neural Network for Medical Image Classification

2025-01-07

AI Summary

To address the dual challenges of limited representational capacity in classical models for medical image classification and constrained quantum hardware resources, this paper proposes a Distributed Hybrid Quantum Convolutional Neural Network (DH-QCNN). The method introduces a novel distributed architecture based on quantum circuit partitioning, enabling the reconstruction of an original 8-qubit QCNN onto resource-limited 5-qubit devices for lightweight quantum feature extraction. Leveraging hybrid quantum-classical forward propagation, cooperative gradient updates, and distributed training, DH-QCNN achieves superior performance across three medical image datasets—covering both binary and multi-class tasks—outperforming recent quantum-inspired approaches. It delivers significant accuracy improvements while reducing parameter counts by 30%–52%. Experimental results validate the effectiveness and practical viability of quantum-enhanced models under stringent hardware constraints.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Medical and Biological Imaging
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Medical Image Recognition
• Classification Accuracy
• Resource Constraint

Innovation

• Quantum Convolutional Neural Network
• Quantum Circuit Partitioning
• Hybrid Quantum-Classical Computing