发表机构
Ludwig Maximilian University of Munich; LMU University Hospital（慕尼黑路德维希-马克西米利安大学; 慕尼黑大学医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究混合量子启发卷积神经网络（HQiCNN）与经典卷积神经网络（CNN）在医学图像分类中的表现，通过系统实验对比两者性能与可解释性，发现特定条件下HQiCNN是可提供益处的替代方案，还提出工具比较模型预测。
AI 中文摘要
众多研究分析了混合量子 - 经典卷积神经网络作为经典深度学习的有前途替代方案。但量子硬件上的网络组件有基本限制，量子电路的可扩展性导致训练问题。本文研究小型经典模拟量子电路组件能否在复杂模型中发挥有意义作用。为此，对混合量子启发卷积神经网络（HQiCNN）与仅在中间密集神经层不同的参数匹配经典卷积神经网络（CNN）进行系统有效性研究。在两个真实医学数据集上评估这两个模型，系统改变不同超参数以确保公平比较。结果表明没有一种架构始终优于另一种：HQiCNN在中间数据区域收益最大，而CNN在两个数据集中最大训练集时准确率最高。此外，去除纠缠产生可比性能并提高量子模拟的可扩展性，只有在有足够训练数据时更丰富的可观集才有益。最后，提出两个基于SHAP的可解释性工具比较两个模型预测，证明两种架构都始终关注解剖学上合理的区域。从而提供了一个全面基准，表明在某些条件下，混合量子启发模型是医学图像分类等实际任务中可提供益处的替代方案。
英文摘要
Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning. However, network components on quantum hardware impose fundamental limitations, while the scalability of quantum circuits leads to trainability issues. In this work, we investigate whether small, classically-emulated quantum circuit components can play a meaningful role within complex models, offering an alternative to purely classical convolutional architectures. To this end, we present a systematic study of the effectiveness of a Hybrid Quantum-inspired Convolutional Neural Network (HQiCNN) compared with a parameter-matched classical Convolutional Neural Network (CNN) that differs only in an intermediate dense neural layer. Both models are evaluated on two real-world medical datasets while systematically varying the different hyperparameters, ensuring a fair model comparison that is both dataset and hyperparameter independent. The results show that no architecture consistently dominates the other: the HQiCNN achieves its largest gains in intermediate-data regimes, whereas the CNN reaches the highest accuracies for the largest training sets in both datasets. Furthermore, removing entanglement produces comparable performance while enabling substantially better scalability of quantum simulations, and richer observable sets become beneficial only when sufficient training data are available. Finally, we propose two SHAP-based explainability tools for comparing the predictions between both models, $|SHAP|$IoU and $EMD_{pos}$ metric, to demonstrate that both architectures consistently attend to anatomically plausible regions. Thus, we provide a comprehensive benchmark showing that, under certain conditions, hybrid quantum-inspired models are an alternative that can offer benefits in practical tasks such as medical image classification.
Comments17 pages, 6 figures, 2 tables
