Quantum vision transformers (QViTs) build on vision transformers (ViTs) by replacing linear layers within the self-attention mechanism with parameterised quantum neural networks (QNNs), harnessing quantum mechanical properties to improve feature representation. This hybrid approach aims to achieve superior performance, with significantly reduced model complexity as a result of the enriched feature representation, requiring fewer parameters. This paper proposes a novel QViT model for biomedical image classification and investigates its performance against comparable ViTs across eight diverse datasets, encompassing various modalities and classification tasks. We assess models trained from scratch and those pre-trained using knowledge distillation (KD) from high-quality teacher models. Our findings demonstrate that QViTs outperform comparable ViTs with average ROC AUC (0.863 vs 0.846) and accuracy (0.710 vs 0.687) when trained from scratch, and even compete with state-of-the-art classical models in multiple tasks, whilst being significantly more efficient (89% reduction in GFLOPs and 99.99% in parameter number). Additionally, we find that QViTs and ViTs respond equally well to KD, with QViT pre-training performance scaling with model complexity. This is the first investigation into the efficacy of deploying QViTs with KD for computer-aided diagnosis. Our results highlight the enormous potential of quantum machine learning (QML) in biomedical image analysis.

本研究解决了传统视觉变换器在生物医学图像分类中的表现不足问题。提出了一种新颖的量子视觉变换器模型，通过参数化的量子神经网络提升特征表示，从而减少模型复杂度。研究表明，量子视觉变换器在多个数据集上表现优越，显示出量子机器学习在生物医学图像分析中的巨大潜力。

将知识蒸馏应用于量子视觉变换器的生物医学图像分类