On November 29, 2023, CAIML Colloquium with Francesco Carnazza from the University of Tübingen will take place.

Abstract

In a many body setting, both quantum and classical, full information about the system is often out of reach. In order to understand the complex phenomenology that can be observed in these systems, it is often sufficient to consider dynamical or stationary properties of few body observables.

In the quantum case these quantities are typically studied by singling out a specific subsystem of interest and regarding the remainder of the many-body system as an effective bath. In the simplest scenario, the subsystem dynamics, which is in fact an open quantum dynamics, can be approximated through Markovian quantum master equations. Here, we formulate the problem of finding the generator of the subsystem dynamics as a variational problem, which we solve using the standard toolbox of machine learning for optimization.

In the classical case, the overlooked degrees of freedom give rise to fluctuations in the quantities of interest and their dynamics is given by a Langevin equation. This equation encodes the presence of a directed force, called drift, and a noise term due to fluctuations, called diffusion term. We develop a machine learning routine to optimize a network presenting the directed force and one representing the diffusion term.