  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.
