Kernel Density Matrices for Probabilistic Deep Learning

arXiv:2305.18204v3 Announce Type: replace
Abstract: This paper introduces a novel approach to probabilistic deep learning, kernel density matrices, which provide a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables. In quantum mechanics, a density matrix is the most general way to describe the state of a quantum system. This work extends the concept of density matrices by allowing them to be defined in a reproducing kernel Hilbert space. This abstraction allows the …

arxiv cs.lg deep learning kernel probabilistic deep learning quant-ph stat.ml type