Reinforcement learning optimization of the charging of a Dicke quantum battery

TL;DR

This paper employs reinforcement learning to optimize the charging process of a Dicke quantum battery, significantly enhancing energy extraction and charging speed by mitigating quantum chaos effects.

Contribution

It introduces reinforcement learning techniques to optimize Dicke quantum battery charging, improving ergotropy and charging precision over standard methods.

Findings

Enhanced ergotropy and reduced energy fluctuations achieved

Charging speedup maintained even at full charge

Reinforcement learning effectively counters quantum chaos effects

Abstract

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. However, the chaotic nature of the model severely hinders the extractable energy (ergotropy). Here, we use reinforcement learning to optimize the charging process of a Dicke battery either by modulating the coupling strength, or the system-cavity detuning. We find that the ergotropy and quantum mechanical energy fluctuations (charging precision) can be greatly improved with respect to standard charging strategies by countering the detrimental effect of quantum chaos. Notably, the collective speedup of the charging time can be preserved even when nearly fully charging the battery.