A coherent perceptron for all-optical learning
Edward L. Ginzton Laboratory, Stanford University, Stanford, CA, 94305, USA
* e-mail: firstname.lastname@example.org
Accepted: 8 April 2015
Published online: 30 April 2015
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.
Key words: optical information processing / coherent feedback / machine learning / photonic circuits / nonlinear optics / perceptron
© Tezak and Mabuchi; licensee Springer., 2015