Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine
  Learning
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.
6891ef0e9373cd501b6a2d64
Sebastián Andrés Cajas Ordóñez
claimed_verified
2025-08-06T19:20:27.586Z
628ddf04986ae70e823298f7
https://cdn-avatars.huggingface.co/v1/production/uploads/628ddf04986ae70e823298f7/P6GyCswDo3dDMd59DEkWC.png
Sebastián Andres Cajas Ordóñez
user
sebasmos
6891ef0e9373cd501b6a2d65
Luis Fernando Torres Torres
6891ef0e9373cd501b6a2d66
Mario Bifulco
6891ef0e9373cd501b6a2d67
Carlos Andrés Durán
claimed_verified
2025-08-06T19:20:22.315Z
689231a4772bdf5df19cb8d2
https://cdn-avatars.huggingface.co/v1/production/uploads/689231a4772bdf5df19cb8d2/pgvULNpcHhrwps6AT6DDL.jpeg
Carlos Andres Duran Paredes
user
caduranpd
6891ef0e9373cd501b6a2d68
Cristian Bosch
6891ef0e9373cd501b6a2d69
Ricardo Simón Carbajo
Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine
  Learning
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.
Combining Vision Transformer embeddings with quantum-classical pipelines achieves quantum advantage in classification tasks, demonstrating the importance of embedding choice in quantum machine learning.
quantum support vector machines high-dimensional quantum states class-balanced k-means distillation pretrained Vision Transformer embeddings quantum advantage accuracy improvements Fashion-MNIST MNIST CNN features 16-qubit tensor network simulation cuTensorNet quantum kernel advantage transformer attention quantum feature spaces