01.01.2023 − 31.12.2024
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.