Provable compressed sensing quantum state tomography via non-convex methods

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dc.citation.articleNumber36en_US
dc.citation.journalTitlenpj Quantum Informationen_US
dc.citation.volumeNumber4en_US
dc.contributor.authorKyrillidis, Anastasiosen_US
dc.contributor.authorKalev, Amiren_US
dc.contributor.authorPark, Dohyungen_US
dc.contributor.authorBhojanapalli, Srinadhen_US
dc.contributor.authorCaramanis, Constantineen_US
dc.contributor.authorSanghavi, Sujayen_US
dc.date.accessioned2018-11-09T15:00:02Zen_US
dc.date.available2018-11-09T15:00:02Zen_US
dc.date.issued2018en_US
dc.description.abstractWith nowadays steadily growing quantum processors, it is required to develop new quantum tomography tools that are tailored for high-dimensional systems. In this work, we describe such a computational tool, based on recent ideas from non-convex optimization. The algorithm excels in the compressed sensing setting, where only a few data points are measured from a low-rank or highly-pure quantum state of a high-dimensional system. We show that the algorithm can practically be used in quantum tomography problems that are beyond the reach of convex solvers, and, moreover, is faster and more accurate than other state-of-the-art non-convex approaches. Crucially, we prove that, despite being a non-convex program, under mild conditions, the algorithm is guaranteed to converge to the global minimum of the quantum state tomography problem; thus, it constitutes a provable quantum state tomography protocol.en_US
dc.identifier.citationKyrillidis, Anastasios, Kalev, Amir, Park, Dohyung, et al.. "Provable compressed sensing quantum state tomography via non-convex methods." <i>npj Quantum Information,</i> 4, (2018) Springer Nature: https://doi.org/10.1038/s41534-018-0080-4.en_US
dc.identifier.digitals41534-018-0080-4en_US
dc.identifier.doihttps://doi.org/10.1038/s41534-018-0080-4en_US
dc.identifier.urihttps://hdl.handle.net/1911/103310en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleProvable compressed sensing quantum state tomography via non-convex methodsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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