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arXiv · 1501.01608

A Coherent Perceptron for All-Optical Learning

Nikolas Tezak·Hideo Mabuchi
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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.

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Nikolas Tezak, Hideo Mabuchi. 2015-01-07. A Coherent Perceptron for All-Optical Learning. https://arxiv.org/abs/1501.01608

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