EPJ Quantum Technol. (2015) 2: 10
https://doi.org/10.1140/epjqt/s40507-015-0023-3

Research

A coherent perceptron for all-optical learning

Edward L. Ginzton Laboratory, Stanford University, Stanford, CA, 94305, USA

^* e-mail: ntezak@stanford.edu

Received: 2 January 2015
Accepted: 8 April 2015
Published online: 30 April 2015

Abstract

We present nonlinear photonic circuit models for constructing programmable linear transformations and use these to realize a coherent perceptron, i.e., an all-optical linear classifier capable of learning the classification boundary iteratively from training data through a coherent feedback rule. Through extensive semi-classical stochastic simulations we demonstrate that the device nearly attains the theoretical error bound for a model classification problem.