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Optimized observable readout from single-shot images of ultracold atoms via machine learning

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cam.issuedOnline2021-10-08
cam.orpheus.success2021-11-15 - Embargo set during processing via Fast-track
dc.contributor.authorLode, Axel UJ
dc.contributor.authorLin, Rui
dc.contributor.authorBuettner, Miriam
dc.contributor.authorPapariello, Luca
dc.contributor.authorLeveque, Camille
dc.contributor.authorChitra, R
dc.contributor.authorTsatsos, Marios C
dc.contributor.authorJaksch, Dieter
dc.contributor.authorMolignini, Paolo
dc.contributor.orcidMolignini, Paolo [0000-0001-6294-3416]
dc.date.accessioned2021-11-16T00:30:53Z
dc.date.available2021-11-16T00:30:53Z
dc.date.issued2021
dc.description.abstractSingle-shot images are the standard readout of experiments with ultracold atoms, the imperfect reflection of their many-body physics. The efficient extraction of observables from single-shot images is thus crucial. Here we demonstrate how artificial neural networks can optimize this extraction. In contrast to standard averaging approaches, machine learning allows both one- and two-particle densities to be accurately obtained from a drastically reduced number of single-shot images. Quantum fluctuations and correlations are directly harnessed to obtain physical observables for bosons in a tilted double-well potential at an extreme accuracy. Strikingly, machine learning also enables a reliable extraction of momentum-space observables from real-space single-shot images and vice versa. With this technique, the reconfiguration of the experimental setup between in situ and time-of-flight imaging is required only once to obtain training data, thus potentially granting an outstanding reduction in resources.
dc.identifier.doi10.17863/CAM.78109
dc.identifier.eissn2469-9934
dc.identifier.issn2469-9926
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330664
dc.language.isoeng
dc.publisherAmerican Physical Society (APS)
dc.publisher.urlhttp://dx.doi.org/10.1103/physreva.104.l041301
dc.rightsAll rights reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.subject5108 Quantum Physics
dc.subject5102 Atomic, Molecular and Optical Physics
dc.subject51 Physical Sciences
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.titleOptimized observable readout from single-shot images of ultracold atoms via machine learning
dc.typeArticle
dcterms.dateAccepted2021-09-08
prism.issueIdentifier4
prism.numberARTN L041301
prism.publicationDate2021
prism.publicationNamePHYSICAL REVIEW A
prism.volume104
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P009565/1)
rioxxterms.licenseref.startdate2021-10-08
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1103/PhysRevA.104.L041301

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