Efficient quantum state tomography with convolutional neural networks

• Verfasst von: Schmale, Tobias [VerfasserIn]
• Verfasst von: Reh, Moritz [VerfasserIn]
• Verfasst von: Gärttner, Martin [VerfasserIn]
• 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
• DOI: doi:10.1038/s41534-022-00621-4
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Computational science
• Sach-SW: Information theory and computation
• Sach-SW: Quantum simulation
• Sach-SW: Qubits
• K10plus-PPN: 1823826261
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

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.