mlsanityData to 14 Oct 2026

Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries

arXiv 2511.12176 ↗First posted 15 Nov 2025quant-ph cs.AIcited by 0

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 36 references that are in the map. First posted on arXiv 15 Nov 2025.

Builds on

5 of 36 references.

References · 5

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  4. 2018Universal Quantum Control through Deep Reinforcement Learningcited by 44
  5. 2021Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPCcited by 11

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Reinforcement Learning for Quantum Technology
  2. 2026Reinforcement learning for ion shuttling on trapped-ion quantum computers
  3. 2025A Physics-Informed Reinforcement Learning Approach for Degradation-Aware Long-Term Charging Optimization in Batteries
  4. 2025A Hybrid Physics-Based and Reinforcement Learning Framework for Electric Vehicle Charging Time Prediction
  5. 2025Quantum state-agnostic work extraction (almost) without dissipation
  6. 2025Reinforcement Learning for Robust Ageing-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verification
  7. 2024Time-Varying Constraint-Aware Reinforcement Learning for Energy Storage Control
  8. 2024Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols