Project
AA2-18
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
Description
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.
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.
External Website
Related Publications
- “Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning“
P. A. Erdman, A. Rolandi, P. Abiuso, M. Perarnau-Llobet, F. Noé, Phys. Rev. Res. 5, L022017 (2023). - “Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning“
P. A. Erdman, F. Noé, PNAS Nexus, 2, pgad248 (2023). - “Measurement-based quantum thermal machines with feedback control“
B. Bhandari, R. Czupryniak, P. A. Erdman, A. N. Jordan, Entropy 25, 204 (2023).
Related Pictures
Fig. 1: Schematic representation of the learning process. A computer agent learns how to optimally drive a quantum thermal machines by interacting with it multiple times (panel A). A neural network architecture, based on stacking multiple 1D convolution blocks, is employed to have a model-free method (panels B,C).
Fig. 2: Example of training a Reinforcement Learning agent to optimize the performance of a Quantum Refrigerator based on a Superconducting Qubit. As the training proceeds, the control becomes more deterministic (panel D) and finally converges to the protocol in panel E.
Fig. 3: Example of a Pareto front describing optimal tradeoffs between extracted Power and Efficiency (panel C) of a quantum heat engine based on a collection of non-interacting particles trapped in a harmonic potentian (panel A). Examples of two optimal cycles are shown in panels D and E.