Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer

• URL: http://arxiv.org/abs/2107.08710v1
• Date: Mon, 19 Jul 2021 09:35:02 GMT
• Title: Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer
• Authors: Catherine F. Higham and Adrian Bedford
• Abstract summary: We propose approaches to overcome two hurdles for high resolution image classification on a quantum processing unit. We successfully transfer a convolutional neural network to the QPU and show the potential for classification speedup of at least one order of magnitude.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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