A Coherent Perceptron for All-Optical Learning

2015/01/07 by Nikolas Tezak, Hideo Mabuchi, Tezak, Nikolas +1
Computer Science · Engineering · #FOS: Physical sciences #Neural Networks and Reservoir Computing #Optical Network Technologies #Optics (physics.optics) #Photonic and Optical Devices #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.1501.01608

openalex publication_date 2015/01/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• The perceptron: A probabilistic model for information storage and organization in the brain.

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