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.
@article{arxiv.2107.08710,
title = {Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer},
author = {Catherine F. Higham and Adrian Bedford},
journal= {arXiv preprint arXiv:2107.08710},
year = {2021}
}