  In this work we develop a quantum field theory formalism for deep learning,
where input signals are encoded in Gaussian states, a generalization of
Gaussian processes which encode the agent's uncertainty about the input signal.
We show how to represent linear and non-linear layers as unitary quantum gates,
and interpret the fundamental excitations of the quantum model as particles,
dubbed ``Hintons''. On top of opening a new perspective and techniques for
studying neural networks, the quantum formulation is well suited for optical
quantum computing, and provides quantum deformations of neural networks that
can be run efficiently on those devices. Finally, we discuss a semi-classical
limit of the quantum deformed models which is amenable to classical simulation.
