Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152419
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dc.contributor.authorGhosh, Sanjiben_US
dc.contributor.authorOpala, Andrzejen_US
dc.contributor.authorMatuszewski, Michalen_US
dc.contributor.authorPaterek, Tomaszen_US
dc.contributor.authorLiew, Timothy Chi Hinen_US
dc.date.accessioned2021-08-11T06:48:39Z-
dc.date.available2021-08-11T06:48:39Z-
dc.date.issued2020-
dc.identifier.citationGhosh, S., Opala, A., Matuszewski, M., Paterek, T. & Liew, T. C. H. (2020). Reconstructing quantum states with quantum reservoir networks. IEEE Transactions On Neural Networks and Learning Systems, 32(7), 3148-3155. https://dx.doi.org/10.1109/TNNLS.2020.3009716en_US
dc.identifier.issn2162-2388en_US
dc.identifier.urihttps://hdl.handle.net/10356/152419-
dc.description.abstractReconstructing quantum states is an important task for various emerging quantum technologies. The process of reconstructing the density matrix of a quantum state is known as quantum state tomography. Conventionally, tomography of arbitrary quantum states is challenging as the paradigm of efficient protocols has remained in applying specific techniques for different types of quantum states. Here, we introduce a quantum state tomography platform based on the framework of reservoir computing. It forms a quantum neural network and operates as a comprehensive device for reconstructing an arbitrary quantum state (finite-dimensional or continuous variable). This is achieved with only measuring the average occupation numbers in a single physical setup, without the need of any knowledge of optimum measurement basis or correlation measurements.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationMOE2019-T2-1-004en_US
dc.relationMOE2017-T2-1-001en_US
dc.relationMOE2015-T2-2-034en_US
dc.relationPoland-2016/22/E/ST3/00045en_US
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TNNLS.2020.3009716.en_US
dc.subjectScience::Physicsen_US
dc.titleReconstructing quantum states with quantum reservoir networksen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.contributor.organizationInstitute of Theoretical Physics and Astrophysics, University of Gda´nsken_US
dc.identifier.doi10.1109/TNNLS.2020.3009716-
dc.description.versionAccepted versionen_US
dc.identifier.pmid32735539-
dc.identifier.issue7en_US
dc.identifier.volume32en_US
dc.identifier.spage3148en_US
dc.identifier.epage3155en_US
dc.subject.keywordsArtificial Neural Networksen_US
dc.subject.keywordsMachine Intelligenceen_US
dc.subject.keywordsQuantum Computingen_US
dc.subject.keywordsTomographyen_US
dc.description.acknowledgementThis work was supported in part by the Singapore Ministry of Education Academic Research Fund Tier 2 under Project MOE2015- T2-2-034, Project MOE2017-T2-1-001, and Project MOE2019-T2-1-004. The work of Andrzej Opala and Michał Matuszewski was supported by the National Science Center, Poland, under Grant 2016/22/E/ST3/00045.en_US
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Reservoir Networks.pdf2.39 MBAdobe PDFView/Open

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