Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries
This addresses the challenge of implementing fast-charging protocols for quantum batteries under realistic information constraints, representing an incremental advance in quantum control.
The paper tackled the problem of optimizing charging policies for inhomogeneous Dicke quantum batteries under partial observability, using reinforcement learning to achieve near-optimal performance with second-order correlations, recovering 94%-98% of the full-state baseline ergotropy.
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.