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Verfasst von:Schmale, Tobias [VerfasserIn]   i
 Reh, Moritz [VerfasserIn]   i
 Gärttner, Martin [VerfasserIn]   i
Titel:Efficient quantum state tomography with convolutional neural networks
Verf.angabe:Tobias Schmale, Moritz Reh and Martin Gärttner
E-Jahr:2022
Jahr:23 September 2022
Umfang:8 S.
Fussnoten:Gesehen am 29.11.2022
Titel Quelle:Enthalten in: npj Quantum information
Ort Quelle:London : Nature Publ. Group, 2015
Jahr Quelle:2022
Band/Heft Quelle:8(2022), Artikel-ID 115, Seite 1-8
ISSN Quelle:2056-6387
Abstract:Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement data often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution over the outcomes of an informationally complete measurement in a variational manifold represented by a convolutional neural network. We show an excellent representability of prototypical ground- and steady states with this ansatz using a number of variational parameters that scales polynomially in system size. This compressed representation allows us to reconstruct states with high classical fidelities outperforming standard methods such as maximum likelihood estimation. Furthermore, it achieves a reduction of the estimation error of observables by up to an order of magnitude compared to their direct estimation from experimental data.
DOI:doi:10.1038/s41534-022-00621-4
URL:kostenfrei: Volltext: https://doi.org/10.1038/s41534-022-00621-4
 kostenfrei: Volltext: https://www.nature.com/articles/s41534-022-00621-4
 DOI: https://doi.org/10.1038/s41534-022-00621-4
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Computational science
 Information theory and computation
 Quantum simulation
 Qubits
K10plus-PPN:1823826261
Verknüpfungen:→ Zeitschrift
 
 
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