Nonclassical kernels in continuous-variable systems

Kernel methods are ubiquitous in classical machine learning, and recently their formal similarity with quantum mechanics has been established. To grasp the potential advantage of it is necessary to understand distinction between nonclassical kernel functions kernels. This paper builds on a proposed phase-space nonclassicality witness [Bohmann Agudelo, Phys. Rev. Lett. 124, 133601 (2020)] derive for kernel's quantumness continuous-variable systems. We discuss role data distribution feature space effect imperfect state preparation. Furthermore, we show that kernels lead parameter estimation. Our work highlights correlation understanding learning from learning.