用视觉变压器嵌入提升量子支持向量机性能，实现可扩展量子机器学习。
Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
- 结合平衡聚类与预训练ViT嵌入，构建量子-经典混合流水线。
- 在Fashion-MNIST上比经典SVM高8.02%，在MNIST上高4.42%。
- 首次证明量子优势依赖嵌入选择，适合研究量子计算与深度学习融合者。
量子支持向量机因高维量子态和硬件限制面临可扩展性挑战。本文提出一种嵌入感知的量子-经典混合流水线，结合类平衡k-means蒸馏与预训练视觉变压器（Vision Transformer）嵌入。关键发现：仅ViT嵌入能实现量子优势，在Fashion-MNIST上相比经典SVM提升8.02%准确率，在MNIST上提升4.42%；而CNN特征反而导致性能下降。通过16量子比特张量网络模拟（cuTensorNet），首次系统揭示量子核优势高度依赖嵌入选择，揭示了变换器注意力与量子特征空间间的根本协同效应。该方法为利用现代神经架构实现可扩展量子机器学习提供了实用路径。
原文摘要 · Abstract (English)
Quantum Support Vector Machines face scalability challenges due to high-dimensional quantum states and hardware limitations. We propose an embedding-aware quantum-classical pipeline combining class-balanced k-means distillation with pretrained Vision Transformer embeddings. Our key finding: ViT embeddings uniquely enable quantum advantage, achieving up to 8.02% accuracy improvements over classical SVMs on Fashion-MNIST and 4.42% on MNIST, while CNN features show performance degradation. Using 16-qubit tensor network simulation via cuTensorNet, we provide the first systematic evidence that quantum kernel advantage depends critically on embedding choice, revealing fundamental synergy between transformer attention and quantum feature spaces. This provides a practical pathway for scalable quantum machine learning that leverages modern neural architectures.
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