Learning quantum fuzzy orbits from coherent data

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.

The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences. More information about the ML Seminar, together with video recordings from past meetings you will find here: https://legato-team.eu/seminars/