Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
Abstract
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.
Cite
@article{arxiv.2508.00024,
title = {Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning},
author = {Sebastián Andrés Cajas Ordóñez and Luis Fernando Torres Torres and Mario Bifulco and Carlos Andrés Durán and Cristian Bosch and Ricardo Simón Carbajo},
journal= {arXiv preprint arXiv:2508.00024},
year = {2025}
}
Comments
Accepted for Poster, Presentation and Proceedings at: 3rd International Workshop on AI for Quantum and Quantum for AI (AIQxQIA 2025), co-located with ECAI 2025, Bologna, Italy, 25-30 October 2025