Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer

Catherine F. Higham; Adrian Bedford

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arXiv · 2107.08710

Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer

Catherine F. Higham·Adrian Bedford

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Abstract

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.

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Catherine F. Higham, Adrian Bedford. 2021-07-19. Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer. https://arxiv.org/abs/2107.08710

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