Quantum deep learning by sampling neural nets with a quantum annealer

Higham, C. F. and Bedford, A. (2023) Quantum deep learning by sampling neural nets with a quantum annealer. Scientific Reports, 13, 3939. (doi: 10.1038/s41598-023-30910-7)

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 the binary nature of the model states. With this novel method we successfully transfer a pretrained convolutional neural network to the QPU. By taking advantage of the strengths of quantum annealing, we show the potential for classification speedup of at least one order of magnitude.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Higham, Dr Catherine
• Authors: Higham, C. F., and Bedford, A.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Scientific Reports
• Publisher: Nature Research
• ISSN: 2045-2322
• ISSN (Online): 2045-2322
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Scientific Reports 13: 3939
• Publisher Policy: Reproduced under a Creative Commons License
• Data DOI: 10.5525/gla.researchdata.1409

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
190841		UK Quantum Technology Hub in Enhanced Quantum Imaging	Miles Padgett	Engineering and Physical Sciences Research Council (EPSRC)	EP/M01326X/1	P&S - Physics & Astronomy
305567		QuantIC - The UK Quantum Technoogy Hub in Quantum Enhanced Imaging	Miles Padgett	Engineering and Physical Sciences Research Council (EPSRC)	EP/T00097X/1	P&S - Physics & Astronomy