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Neural-network quantum state tomography in a two-qubit experiment

We study the performance of efficient quantum state tomography methods based on neural-network quantum states using measured data from a two-photon experiment. Machine-learning-inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. Whil...

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Bibliographic Details
Main Authors: Neugebauer, Marcel (Author) , Fischer, Laurin (Author) , Jäger, Alexander (Author) , Czischek, Stefanie (Author) , Jochim, Selim (Author) , Weidemüller, Matthias (Author) , Gärttner, Martin (Author)
Format: Article (Journal)
Language:English
Published: 9 October 2020
In: Physical review
Year: 2020, Volume: 102, Issue: 4
ISSN:2469-9934
DOI:10.1103/PhysRevA.102.042604
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1103/PhysRevA.102.042604
Verlag, lizenzpflichtig, Volltext: https://link.aps.org/doi/10.1103/PhysRevA.102.042604
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Author Notes:Marcel Neugebauer, Laurin Fischer, Alexander Jäger, Stefanie Czischek, Selim Jochim, Matthias Weidemüller, and Martin Gärttner
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Summary:We study the performance of efficient quantum state tomography methods based on neural-network quantum states using measured data from a two-photon experiment. Machine-learning-inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. While the power of these methods has been demonstrated on synthetic data, applications to real experimental data remain scarce. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states, i.e., to positive semidefinite density matrices, greatly improves the quality of the reconstructed states but renders the learning procedure more demanding. Including additional, possibly unjustified, constraints, such as assuming pure states, facilitates learning, but also biases the estimator.
Item Description:Gesehen am 12.11.2020
Physical Description:Online Resource
ISSN:2469-9934
DOI:10.1103/PhysRevA.102.042604

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