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Performance of Domain-Wall Encoding for Quantum Annealing [dataset] Open Access

In this paper we experimentally test the performance of the recently proposed domain-wall en-coding of discrete variables from [Chancellor Quantum Sci. Technol. 4 045004] on Ising model fluxqubit quantum annealers. We compare this encoding with the traditional one-hot methods and findthat they outperform the one-hot encoding for three different problems at different sizes both of theproblem and of the variables. From these results we conclude that the domain-wall encoding yieldssuperior performance against a variety of metrics furthermore, we do not find a single metric bywhich one hot performs better. We even find that a 2000Q quantum annealer with a drastically lessconnected hardware graph but using the domain-wall encoding can outperform the next generationAdvantage processor if that processor uses one-hot encoding.

Descriptions

Resource type
Dataset
Contributors
Creator: Jie Chen 1
Editor: Jie Chen 1
Editor: Tobias Stollenwerk 2
Creator: Nicholas Chancellor 1
Data collector: Nicholas Chancellor 1
Data curator: Nicholas Chancellor 1
Editor: Nicholas Chancellor 1
1 Durham University, United Kingdom
2 DLR, Germany
Funder
Engineering and Physical Sciences Research Council
Research methods
Remote quantum annealing experiments
Other description
Deposit contains dataset and Python code.
Keyword
Quantum annealing
Quantum computing
Subject
Location
Language
Cited in
arxiv:2102.12224
Identifier
ark:/32150/r11j92g748d
doi:10.15128/r11j92g748d
Rights
Creative Commons Attribution 4.0 International (CC BY)

Publisher
Durham University
Date Created

File Details

Depositor
N.G. Chancellor
Date Uploaded
Date Modified
1 March 2021, 09:03:31
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Characterization
File format: zip (ZIP Format)
Mime type: application/zip
File size: 382327180
Last modified: 2021:02:26 15:49:52+00:00
Filename: Domain_wall_experiments_repository.zip
Original checksum: c00f2d8991e8893a6a54bf30779f8a37
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User N.G. Chancellor has updated Domain_wall_experiments_repository.zip over 3 years ago
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