Reinforcement Learning Optimization of Charging a Dicke Quantum Battery

Explore the optimization of Dicke quantum batteries through reinforcement learning in this one-hour lecture by Gian Marcello Andolina from PCS Institute for Basic Science. Delve into the world of quantum batteries, energy-storing devices governed by quantum mechanics that offer enhanced charging performance due to collective effects. Learn about the Dicke battery design, comprising N two-level systems coupled to a common photon mode, and its experimental feasibility. Discover how reinforcement learning techniques can be applied to optimize the charging process of a Dicke battery, resulting in significant improvements in both extractable energy (ergotropy) and quantum mechanical energy fluctuations (charging precision) compared to standard charging strategies.