Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries

TL;DR

This paper uses reinforcement learning to optimize charging protocols for inhomogeneous quantum batteries, demonstrating near-optimal performance even with limited observability by leveraging second-order correlations.

Contribution

It introduces a reinforcement learning approach for charging optimization of inhomogeneous Dicke quantum batteries under various observability regimes, including partial and full information.

Findings

Full observability achieves near-optimal ergotropy with low variability.

Partial observability with second-order correlations recovers most performance.

Learned schedules are nonmyopic, balancing short-term plateaus with long-term gains.

Abstract

Charging optimization is a key challenge to the implementation of quantum batteries, particularly under inhomogeneity and partial observability. This paper employs reinforcement learning to optimize piecewise-constant charging policies for an inhomogeneous Dicke battery. We systematically compare policies across four observability regimes, from full-state access to experimentally accessible observables (energies of individual two-level systems (TLSs), first-order averages, and second-order correlations). Simulation results demonstrate that full observability yields near-optimal ergotropy with low variability, while under partial observability, access to only single-TLS energies or energies plus first-order averages lags behind the fully observed baseline. However, augmenting partial observations with second-order correlations recovers most of the gap, reaching 94%-98% of the full-state baseline. The learned schedules are nonmyopic, trading temporary plateaus or declines for superior terminal outcomes. These findings highlight a practical route to effective fast-charging protocols under realistic information constraints.