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.
CITATION STYLE
Tezak, N., & Mabuchi, H. (2015). A coherent perceptron for all-optical learning. EPJ Quantum Technology, 2(1). https://doi.org/10.1140/epjqt/s40507-015-0023-3
Mendeley helps you to discover research relevant for your work.