Jakub Lengiewicz

Abstract: In this talk, the speaker will discuss a data-driven strategy parallel to quantum tomography. Thus, offering an alternative approach to the study of quantum dynamics directed at quantum computing, condensed matter, among other applications. Starting with time series data, possibly noisy, and a class of universal differential equations parameterised by feed-forward neural networks, the dynamical picture of a non-relativistic, charged micro particle moving in a magnetic ion trap is reconstructed. This strategy proves the quantum dynamics conveyed by quadratic Hamiltonians can be identified from data. Even more, the long-term motion beyond the neural network’s training interval is predicted at a good approximation. We study both stable and unstable motion of the particle inside the ion trap and confirm that quantum effects, such as quantum squeezing and parametric resonance of massive particles, can be captured by the method.

Jesús Fuentes is a Postdoctoral researcher in the Systems Control group (Gonçalves Lab), in the Luxembourg Centre for Systems Biomedicine, University of Luxembourg.