Gian Marcello Andolina

The Institute of Photonic Sciences, Spain

9 February 2023 Thu 5 pm

                                      IBS Center for Theoretical Physics of Complex Systems (PCS), Administrative Office (B349), Theory Wing, 3rd floor

                                      Expo-ro 55, Yuseong-gu, Daejeon, South Korea, 34126 Tel: +82-42-878-8633                     

Quantum batteries are energy-storing devices, governed by quantum mechanics, that promise high charging performance thanks to collective effects. Due to its experimental feasibility, the Dicke battery - which comprises N two-level systems coupled to a common photon mode - is one of the most promising designs for quantum batteries. In this talk, I will show how reinforcement learning can be used to optimize the charging process of a Dicke battery, showing that both the extractable energy (ergotropy) and quantum mechanical energy fluctuations (charging precision) can be greatly improved with respect to standard charging strategies.

  1. reinforcement learning optimization of the charging of a dicke quantum battery