1 citations · 1 across the 4 of their papers we have counts for
5 papers
AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
Mateusz Krawczyk, JarosÅaw PawÅowski
We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this knowledge to autotune these devices - b…
Information in Many-body Eigenstates: A Question of Learnability
Maksymilian Kliczkowski, JarosÅaw PawÅowski, 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 Hamiltonians for solid-state quantum simulators
JarosÅaw PawÅowski, Mateusz Krawczyk
We introduce a generalizable framework for learning to identify effective Hamiltonians directly from experimental data in solid-state quantum systems. Our approach is based on a ph…
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, JarosÅaw PawÅowski +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…