Optimizing the Charging of Open Quantum Batteries using Long Short-Term Memory-Driven Reinforcement Learning

• URL: http://arxiv.org/abs/2504.19840v1
• Date: Mon, 28 Apr 2025 14:40:11 GMT
• Title: Optimizing the Charging of Open Quantum Batteries using Long Short-Term Memory-Driven Reinforcement Learning
• Authors: Shadab Zakavati, Shahriar Salimi, Behrouz Arash,
• Abstract summary: We study the charging process of a quantum battery in an open quantum setting, where the battery interacts with a charger and a structured reservoir.<n>A reinforcement learning (RL) charging strategy is proposed, which utilizes the deep deterministic policy gradient algorithm alongside long short-term memory (LSTM) networks.<n>The RL protocols consistently outperform conventional fixed strategies by real-time controlling the driving field amplitude and coupling parameters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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