All-optical graphical models for probabilistic inference

Abstract

Considering that high performance electronic computation has become extremely efficient, for an optical hardware accelerator to be relevant, it must solve a type or a set of problems where its electronic counterpart is still struggling in term of size, energy, or time. We have identified one such challenge as the minimization of large scale Ising Hamiltonians when the number of particles is on the order of a million. Here we discuss an algorithmic approach based on probabilistic inference using graphical model and message passing.