Scalable quantum state tomography with artificial neural networks
Modern day quantum simulators can prepare a wide variety of quantum states but extracting observables from the resulting "quantum data" often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
September 29, 2021
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| In: |
Arxiv
Year: 2021, Pages: 1-9 |
| DOI: | 10.48550/arXiv.2109.13776 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2109.13776 Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2109.13776 
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| Verfasserangaben: | Tobias Schmale, Moritz Reh, and Martin Gärttner |
| Zusammenfassung: | Modern day quantum simulators can prepare a wide variety of quantum states but extracting observables from the resulting "quantum 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 root mean square errors of observables by up to an order of magnitude compared to their direct estimation from experimental data. |
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| Beschreibung: | Gesehen am 13.07.2022 |
| Beschreibung: | Online Resource |
| DOI: | 10.48550/arXiv.2109.13776 |