Quantum Kerr Learning
Abstract
Quantum machine learning is a rapidly evolving field of research that could facilitate important applications for quantum computing and also significantly impact data-driven sciences. In our work, based on various arguments from complexity theory and physics, we demonstrate that a single Kerr mode can provide some "quantum enhancements" when dealing with kernel-based methods. Using kernel properties, neural tangent kernel theory, first-order perturbation theory of the Kerr non-linearity, and non-perturbative numerical simulations, we show that quantum enhancements could happen in terms of convergence time and generalization error. Furthermore, we make explicit indications on how higher-dimensional input data could be considered. Finally, we propose an experimental protocol, that we call \emph{quantum Kerr learning}, based on circuit QED.
Cite
@article{arxiv.2205.12004,
title = {Quantum Kerr Learning},
author = {Junyu Liu and Changchun Zhong and Matthew Otten and Anirban Chandra and Cristian L. Cortes and Chaoyang Ti and Stephen K Gray and Xu Han},
journal= {arXiv preprint arXiv:2205.12004},
year = {2023}
}
Comments
20 pages, many figures. v2: significant updates, author added