嵌入感知的量子-經典支持向量機：面向可擴展的量子機器學習

摘要

量子支持向量機面臨著由於高維量子態和硬體限制帶來的可擴展性挑戰。我們提出了一種結合類別平衡k均值蒸餾與預訓練視覺Transformer嵌入的嵌入感知量子-經典混合管道。我們的核心發現是：ViT嵌入獨特地實現了量子優勢，在Fashion-MNIST上相比經典SVM提升了高達8.02%的準確率，在MNIST上提升了4.42%，而CNN特徵則表現出性能下降。通過使用cuTensorNet進行的16量子位張量網絡模擬，我們首次系統性地證明了量子核優勢關鍵依賴於嵌入選擇，揭示了Transformer注意力機制與量子特徵空間之間的根本協同效應。這為利用現代神經架構實現可擴展的量子機器學習提供了一條實用路徑。

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.