@article{erdman2022identifying,
author = {Erdman, Paolo A. and Noé, Frank},
title = {Identifying optimal cycles in quantum thermal machines with reinforcement-learning},
year = {2022},
month = {January},
abstract = {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 outperforms 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.},
url = {http://approjects.co.za/?big=en-us/research/publication/identifying-optimal-cycles-in-quantum-thermal-machines-with-reinforcement-learning/},
journal = {npj Quantum Information},
volume = {8},
}