States as a Neural Networks and Gates as Pullbacks

We represent the characteristic function of a state as a neural network. We detail the case of Gaussian states. We deepen linear transformations and show how to map cascaded gates in neural network models. We introduce the bug train representation and the concept of “pullback” for concatenating transformations. We present layered models in the phase space.