Quantum deep learning by sampling neural nets with a quantum annealer

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 quant...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2023-03, Vol.13 (1), p.3939-3939, Article 3939
Main Authors: Higham, Catherine F., Bedford, Adrian
Format: Article
Language:English
Subjects:
639/705
639/705/1042
Annealing
Classification
Connectivity
Deep learning
Energy
Humanities and Social Sciences
Machine learning
multidisciplinary
Neural networks
Sampling
Science
Science (multidisciplinary)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-30910-7