“…The reason for this often times relies on the fact that the underlying physics of these problems cannot be explained without taking into consideration the contribution from high-energy states excited during the nonequilibrium process. Some prominent examples of such problems include the study of the many-body localisation (MBL) transition [24,25,26,27,28], the Eigenstate Thermalisation hypothesis [29], ergodicity breaking, thermalization and scrambling [30,31,32], quantum quench dynamics [33], periodically-driven systems [34,35,36,37,38,39,40,41,42], non-demolition measurements in many-body systems [43], long-range quantum coherence [44], dynamics-induced instabilities [45,46,47,48,49,50,51,52], adiabatic and counter-diabatic state preparation [53,54,55,56,57], dynamical [58,59] and topological [60] phase transitions applications of Machine Learning to (non-equilibrium) physics [61,49,62,63,64,65,66], optimal control [67,<...>…”