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dc.contributor.authorBausch, Johannes
dc.contributor.authorLeditzky, Felix
dc.date.accessioned2020-02-05T15:38:06Z
dc.date.available2020-02-05T15:38:06Z
dc.date.issued2020-02-04
dc.date.submitted2019-11-16
dc.identifier.othernjpab6cdd
dc.identifier.otherab6cdd
dc.identifier.othernjp-111330
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301745
dc.descriptionFunder: Draper’s Company Research Fellowship
dc.description.abstractAbstract: We examine the usefulness of applying neural networks as a variational state ansatz for many-body quantum systems in the context of quantum information-processing tasks. In the neural network state ansatz, the complex amplitude function of a quantum state is computed by a neural network. The resulting multipartite entanglement structure captured by this ansatz has proven rich enough to describe the ground states and unitary dynamics of various physical systems of interest. In the present paper, we initiate the study of neural network states in quantum information-processing tasks. We demonstrate that neural network states are capable of efficiently representing quantum codes for quantum information transmission and quantum error correction, supplying further evidence for the usefulness of neural network states to describe multipartite entanglement. In particular, we show the following main results: (a) neural network states yield quantum codes with a high coherent information for two important quantum channels, the generalized amplitude damping channel and the dephrasure channel. These codes outperform all other known codes for these channels, and cannot be found using a direct parametrization of the quantum state. (b) For the depolarizing channel, the neural network state ansatz reliably finds the best known codes given by repetition codes. (c) Neural network states can be used to represent absolutely maximally entangled states, a special type of quantum error-correcting codes. In all three cases, the neural network state ansatz provides an efficient and versatile means as a variational parametrization of these highly entangled states.
dc.languageen
dc.publisherIOP Publishing
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectPaper
dc.subjectquantum capacity
dc.subjectneural networks states
dc.subjectglobal optimization techniques
dc.subjectquantum information transmission
dc.subjectsuperadditivity of coherent information
dc.subjectquantum error-correcting codes
dc.titleQuantum codes from neural networks
dc.typeArticle
dc.date.updated2020-02-05T15:38:06Z
prism.publicationNameNew Journal of Physics, volume 22, issue 2
dc.identifier.doi10.17863/CAM.48816
dcterms.dateAccepted2020-01-16
rioxxterms.versionofrecord10.1088/1367-2630/ab6cdd
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidBausch, Johannes [0000-0003-3189-9162]
dc.contributor.orcidLeditzky, Felix [0000-0002-1073-9795]
dc.identifier.eissn1367-2630
pubs.funder-project-idNational Science Foundation (PHY 1734006)


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)