  We demonstrate the feasibility of framing a classically learned deep neural
network as an energy based model that can be processed on a one-step quantum
annealer in order to exploit fast sampling times. We propose approaches to
overcome two hurdles for high resolution image classification on a quantum
processing unit (QPU): the required number and binary nature of the model
states. With this novel method we successfully transfer a convolutional neural
network to the QPU and show the potential for classification speedup of at
least one order of magnitude.
