Operator formalism for optical neural network based on the parametrical four-wave mixing process

TL;DR

This paper introduces a formalism for optical neural networks utilizing parametric four-wave mixing, demonstrating enhanced storage capacity over traditional models, with potential implications for nonlinear optical computing.

Contribution

The paper develops a novel formalism for optical neural networks based on four-wave mixing, showing improved storage capacity compared to existing models.

Findings

Higher storage capacity than Potts-glass models

Formalism applicable to networks with multiple frequencies

Potential for advanced nonlinear optical computing

Abstract

In this paper we develop a formalism allowing us to describe operating of a network based on the parametrical four-wave mixing process that is well-known in nonlinear optics. The recognition power of a network using parametric neurons operating with q different frequencies is considered. It is shown that the storage capacity of such a network is higher compared with the Potts-glass models.