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Information in Many-body Eigenstates: A Question of Learnability
Maksymilian Kliczkowski, Jarosław Pawłowski, Masudul Haque
To what extent do individual eigenstates encode information about their parent Hamiltonian, and how does this encoding vary across the spectrum? We introduce \emph{learnability} as…
Learning quantum tomography from incomplete measurements
Mateusz Krawczyk, Pavel Baláž, Katarzyna Roszak +1
We revisit quantum tomography in an informationally incomplete scenario and propose improved state reconstruction methods using deep neural networks. In the first approach, the tra…
Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests
Pavel Baláž, Mateusz Krawczyk, Jarosław Pawłowski +1
We study the effectiveness of two distinct machine learning techniques, neural networks and random forests, in the quantification of entanglement from two-qubit tomography data. Al…