Coherent optical neural network that learns desirable phase values in the frequency domain by use of multiple optical-path differences

Abstract

A coherent optical neural network is proposed that has the learning ability to achieve desirable phase values in the frequency domain. It is composed of multiple optical-path differences whose lengths are different from one another. The system learns a phase value at each discrete position in the frequency domain by obeying the complex-valued Hebbian rule. The learning curve also agrees with theoretical evolution.