4 papers
Learning Hamiltonians for solid-state quantum simulators
Jarosław Pawłowski, Mateusz Krawczyk
We introduce a generalizable framework for identifying effective Hamiltonians directly from experimental data in solid-state quantum systems. Our unsupervised autoencoder-based app…
AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
Mateusz Krawczyk, Jarosław Pawłowski
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…
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…