Learning to stabilize nonequilibrium phases of matter with active feedback using partial information
Abstract Recent advances in quantum computing experiments have enabled new pathways for feedback control. Here, we investigate the role of information in interactive quantum many-body dynamics to engineer out-of-equilibrium phases of matter. To this end, we train reinforcement learning agents to prevent entanglement sp...
Giovanni Cemin, Markus Schmitt, Marin Bukov
· Machine Learning Science and... · 0 citations