AA2 – Nano and Quantum Technologies



Pareto-Optimal Control of Quantum Thermal Devices with Deep Reinforcement Learning

Project Heads

Paolo Erdman

Project Members

Paolo Erdman

Project Duration

01.01.2023 − 31.12.2024

Located at

FU Berlin


Quantum thermal machines are micro-scale devices that convert between heat and work exploiting quantum effects. Optimally controlling such systems as to maximize their performance is an extremely challenging task. Here we develop a mathematical
framework, based on Reinforcement Learning, to optimally control Quantum thermal machines exploiting quantum measurements and feedback. The method finds Pareto-optimal tradeoffs between high power, high efficiency and low power fluctuations.
Applications to real-world quantum devices are foreseen.

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