Speaker
Description
Ab-initio simulations of multiple heavy quarks propagating in a Quark-Gluon Plasma are computationally difficult to perform due to the large dimension of the space of density matrices. Neural Network Quantum States offer a promising approach to overcoming this numerical difficulty by variationally parametrising quantum states with parameters of a Neural Network. In this talk, I present proof of principle demonstrations of these methods in a QCD-like theory, by solving the Lindblad master equation in the 1+1d lattice Schwinger Model as an Open Quantum System. Neural Network quantum states enable the study of in-medium dynamics on large lattice volumes, where multiple-string interactions and their effects on string-breaking and recombination phenomena can be studied. Thermal properties of the system at equilibrium can also be probed with these methods by variationally constructing the stable state of the Lindblad master equation. Scaling of this approach with system size is presented, and numerical demonstrations on up to 32 spatial lattice sites and with up to 3 interacting strings are presented.