Quantum Neural Network (QNN) Architectures and Their Potential in High-Dimensional Medical Image Classification

Abstract

The rapid expansion of medical imaging modalities (high-resolution CT, MRI, whole-slide histopathology, and multi-omics image representations) stresses current classical deep-learning pipelines with rising dimensionality, complex feature manifolds, and often limited labeled data. Quantum neural networks (QNNs) an umbrella term for quantum-native and hybrid variational models offer new representational primitives (superposition, entanglement) and feature-mapping strategies that may yield advantages for high-dimensional classification tasks, especially in low-data regimes or where quantum-enhanced feature spaces better separate classes. This paper provides an in-depth, journal-ready treatment of QNN architectures tailored to medical imaging: variational quantum circuits (VQCs), quantum convolutional neural networks (QCNNs), quanvolutional layers, quantum kernel methods and hybrid classical–quantum pipelines. We develop rigorous mathematical formulations (state and operator models, data encoding maps, loss functions, gradient estimators), analyze expressivity and trainability (including barren plateau phenomena and mitigation), and present architecture design patterns for integrating quantum layers with standard convolutional backbones. This paper proposes evaluation protocols, benchmarks, and realistic experimental blueprints (datasets, classical baselines, hardware vs. simulator tradeoffs), and discuss regulatory, privacy, and deployment concerns in clinical settings. The literature review synthesizes reviewed works and standards, situating QNNs within the current trajectory of quantum machine learning and clinical AI. Finally, we offer practical roadmaps: short-term hybrid pilots on cloud quantum resources and mid-term device-aware retrospectives as qubit counts and fidelities improve. While conclusive quantum advantage for large-scale medical imaging is yet unproven, carefully designed QNN architectures present a promising research direction that merits rigorous empirical and theoretical exploration.