Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine
  Learning
Authors: name: Sebastián Andrés Cajas Ordóñez, name: Luis Fernando Torres Torres, name: Mario Bifulco, name: Carlos Andrés Durán, name: Cristian Bosch, name: Ricardo Simón Carbajo

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.