Quantum Kerr Learning
Abstract
Quantum machine learning is a rapidly evolving area that could facilitate important applications for quantum computing and significantly impact data science. In our work, we argue that a single Kerr mode might provide some extra quantum enhancements when using quantum kernel methods based on various reasons from complexity theory and physics. Furthermore, we establish an experimental protocol, which we call \emph{quantum Kerr learning} based on circuit QED. A detailed study using the kernel method, neural tangent kernel theory, firstorder perturbation theory of the Kerr nonlinearity, and nonperturbative numerical simulations, shows quantum enhancements could happen in terms of the convergence time and the generalization error, while explicit protocols are also constructed for higherdimensional input data.
 Publication:

arXiv eprints
 Pub Date:
 May 2022
 arXiv:
 arXiv:2205.12004
 Bibcode:
 2022arXiv220512004L
 Keywords:

 Quantum Physics;
 Computer Science  Artificial Intelligence;
 Computer Science  Machine Learning;
 Statistics  Machine Learning
 EPrint:
 19 pages, many figures