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arXiv:2511.12176 · Cybernetics and Intelligence

Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries

Xiaobin Song, Siyuan Bai, Da-Wei Wang, Hanxiao Tao, Xizhe Wang, Rebing Wu, Benben Jiang

quant-phcs.AISubmitted Nov 15, 2025• Announced Nov 18, 2025• 0 citations• Revised Jan 23, 2026 (v2)
v1 · Nov 15, 2025v2 · Jan 23, 2026
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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.

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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.
Quantum batteries are energy-storing devices, governed by quantum mechanics, that promise high charging performance thanks to collective effects. Because of 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.
Quantum batteries have emerged as a next-generation energy storage solution, leveraging quantum phenomena such as superabsorption to overcome the limitations of conventional energy technologies. However, noise arising from interactions with the external environment degrades the charging efficiency and stability of the battery by disrupting the system's quantum coherence. To address this challenge, this study proposes a robust charging framework for a single-qubit quantum battery based on the Jaynes-Cummings (JC) model. The proposed framework combines the Proximal Policy Optimization (PPO) algorithm with a multi-stage reinforcement learning structure. The agent first learns fundamental control principles in a noise-free, ideal environment and subsequently performs robust learning in progressively noisier and more complex settings. Simulation results demonstrate that the trained agent navigates a stable charging trajectory on the Bloch sphere, thereby achieving high ergotropy even in the presence of noise. These findings suggest that multi-stage reinforcement learning is an effective solution for control problems in noisy quantum systems and provides a theoretical foundation for designing charging protocols for multi-qubit systems.
Phase transitions (PTs) in quantum many-body systems offer a powerful route to manipulate the performance of quantum batteries (QBs). Here, we investigate an anisotropic Dicke QB with Kerr nonlinearity, focusing on the role of PTs in the charging process of the QB system. By analyzing how the Kerr nonlinearity reshapes the cavity level-spacing structure, we find that multiple near-degeneracies emerge at specific nonlinearity strengths. Away from the region of multiple near-degeneracies, the stable stored energy is governed primarily by the ergodic-nonergodic transition (ENET), whereas within this region it is controlled by first- and second-order dissipative phase transitions (DPTs). We employ Liouvillian spectral theory to quantify the critical slowing down associated with the DPTs, and use the Wigner function to characterize the properties of the cavity field in the vicinity of criticality. We optimize the charging process via reinforcement learning (RL) and reveal the physical mechanism underlying performance enhancement from the perspective of ergodic and nonergodic phase dynamics. Our results offer a feasible pathway for manipulating and optimizing quantum battery performance based on intrinsic PT behaviors.
Abstract Achieving rapid charging in quantum batteries requires the efficient harnessing of quantum resources, which is a task with great challenge experimentally. Here we propose and experimentally realize a quantum battery charging protocol that enables fast injection of substantial energy using a trapped-ion platform governed by the quantum Rabi model (QRM). The core of the protocol lies in the employment of the squeezing effect and the QRM criticality. Reinforcement learning further improves the charging performance by optimizing the preparation of the squeezed and anti-squeezed states and accelerating energy inflow near criticality. Experimental implementation with a single 171Yb+{}^{171}\mathrm{Yb}^+171Yb+ ion demonstrates the feasibility of the protocol, where the vibrational mode (i.e., quantized phonons) acts as the quantum battery. We witness that both the maximally extractable work (i.e., ergotropy) and the charging power exhibit criticality-induced enhancement. This work provides a practical pathway toward criticality-enhanced quantum batteries, underscoring the potential of squeezing-mediated amplification and displacement-based energy storage in realistic quantum systems.
We investigate open-system charging protocols for Rabi and Dicke quantum batteries under direct incoherent pumping. The charging dynamics follows a Lindblad master equation, while effective non-Hermitian generators describe the conditioned no-jump mechanism. For a single Rabi battery, this pumping suppresses coherent energy backflow, broadens the high-energy charging window, and improves robustness against both timing errors and detuning. At the selected Rabi operating points, pumping also increases ergotropy and the extractable fraction of the stored energy. For finite Dicke batteries, direct incoherent pumping likewise stabilizes the charging dynamics. Across the finite-size scan, the strongest pump considered improves the peak stored energy and ergotropy per cell and near-peak timing robustness relative to the Hermitian reference. The maximum average charging power also increases, although this metric includes pump-assisted jump contributions. Equal-input comparisons show that the relative performance of common-cavity Dicke and independent Rabi batteries varies with the pump-input bound. Taken together, these results show that direct incoherent pumping provides a route to stabilized charging in Rabi and finite Dicke quantum batteries.
Controlling the charging process of a quantum battery involves strategies to efficiently transfer, store, and retain energy, while mitigating decoherence, energy dissipation, and inefficiencies caused by surrounding interactions. We develop a model to study the charging process of a quantum battery in an open quantum setting, where the battery interacts with a charger and a structured reservoir. To overcome the limitations of static charging protocols, a reinforcement learning (RL) charging strategy is proposed, which utilizes the deep deterministic policy gradient algorithm alongside long short-term memory (LSTM) networks. The LSTM networks enable the RL model to capture temporal correlations driven by non-Markovian dynamics, facilitating a continuous, adaptive charging strategy. The RL protocols consistently outperform conventional fixed heuristic strategies by real-time controlling the driving field amplitude and coupling parameters. By penalizing battery-to-charger backflow in the reward function, the RL-optimized charging strategy promotes efficient unidirectional energy transfer from charger to battery, achieving higher and more stable extractable work. The proposed RL controller would provide a framework for designing efficient charging schemes in broader configurations and multi-cell quantum batteries.