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dc.contributor.author
Chancellor, Nicholas
dc.contributor.author
Szoke, Szilard
dc.contributor.author
Vinci, Walter
dc.contributor.author
Aeppli, Gabriel
dc.contributor.author
Warburton, Paul A.
dc.date.accessioned
2018-09-18T15:59:05Z
dc.date.available
2017-06-12T02:43:50Z
dc.date.available
2018-09-18T15:59:05Z
dc.date.issued
2016-03-03
dc.identifier.issn
2045-2322
dc.identifier.other
10.1038/srep22318
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/114222
dc.identifier.doi
10.3929/ethz-b-000114222
dc.description.abstract
Optimisation problems typically involve finding the ground state (i.e. the minimum energy configuration) of a cost function with respect to many variables. If the variables are corrupted by noise then this maximises the likelihood that the solution is correct. The maximum entropy solution on the other hand takes the form of a Boltzmann distribution over the ground and excited states of the cost function to correct for noise. Here we use a programmable annealer for the information decoding problem which we simulate as a random Ising model in a field. We show experimentally that finite temperature maximum entropy decoding can give slightly better bit-error-rates than the maximum likelihood approach, confirming that useful information can be extracted from the excited states of the annealer. Furthermore we introduce a bit-by-bit analytical method which is agnostic to the specific application and use it to show that the annealer samples from a highly Boltzmann-like distribution. Machines of this kind are therefore candidates for use in a variety of machine learning applications which exploit maximum entropy inference, including language processing and image recognition.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Publishing Group
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Information theory and computation
en_US
dc.subject
Mathematics and computing
en_US
dc.subject
Quantum information
en_US
dc.title
Maximum-Entropy Inference with a Programmable Annealer
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Scientific Reports
ethz.journal.volume
6
en_US
ethz.journal.abbreviated
Sci Rep
ethz.pages.start
22318
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
006751867
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-12T02:44:43Z
ethz.source
ECIT
ethz.identifier.importid
imp59365439a8a4c37506
ethz.ecitpid
pub:175996
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-14T13:25:41Z
ethz.rosetta.lastUpdated
2021-02-15T01:47:39Z
ethz.rosetta.versionExported
true
ethz.COinS
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