Identifying optimal cycles in quantum thermal machines with reinforcement-learning
This work addresses the challenge of improving quantum information processing technologies by optimizing thermodynamic cycles, representing an incremental advancement through the application of reinforcement learning to specific quantum systems.
The authors tackled the problem of optimal control in open quantum systems by introducing a reinforcement learning framework to discover thermodynamic cycles that maximize power in quantum heat engines and refrigerators, achieving performance improvements such as outperforming previous cycles in a superconducting qubit refrigerator and finding a cycle that beats the optimized Otto cycle in a quantum harmonic oscillator engine.
The optimal control of open quantum systems is a challenging task but has a key role in improving existing quantum information processing technologies. We introduce a general framework based on Reinforcement Learning to discover optimal thermodynamic cycles that maximize the power of out-of-equilibrium quantum heat engines and refrigerators. We apply our method, based on the soft actor-critic algorithm, to three systems: a benchmark two-level system heat engine, where we find the optimal known cycle; an experimentally realistic refrigerator based on a superconducting qubit that generates coherence, where we find a non-intuitive control sequence that outperform previous cycles proposed in literature; a heat engine based on a quantum harmonic oscillator, where we find a cycle with an elaborate structure that outperforms the optimized Otto cycle. We then evaluate the corresponding efficiency at maximum power.