A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability.
@article{arxiv.2206.14507,
title = {Variational Quantum Approximate Support Vector Machine with Inference Transfer},
author = {Siheon Park and Daniel K. Park and June-Koo Kevin Rhee},
journal= {arXiv preprint arXiv:2206.14507},
year = {2023}
}