112 citations · 985 across the 32 of their papers we have counts for
6 papers · 2 filters
Reinforcement learning optimization of the charging of a Dicke quantum battery
Paolo Andrea Erdman, Gian Marcello Andolina, Vittorio Giovannetti +1
Quantum batteries are energy-storing devices, governed by quantum mechanics, that promise high charging performance thanks to collective effects. Due to its experimental feasibilit…
Measurement-based quantum thermal machines with feedback control
Bibek Bhandari, Robert Czupryniak, Paolo Andrea Erdman +1
We investigate coupled-qubit-based thermal machines powered by quantum measurements and feedback. We consider two different versions of the machine: 1) a quantum Maxwell's demon wh…
Optimal Thermometers with Spin Networks
Paolo Abiuso, Paolo Andrea Erdman, Michael Ronen +3
The heat capacity of a given probe is a fundamental quantity that determines, among other properties, the maximum precision in temperature estimation. In turn, $\math…
Optimal Control Methods for Quantum Batteries
Francesco Mazzoncini, Vasco Cavina, Gian Marcello Andolina +2
We investigate the optimal charging processes for several models of quantum batteries, finding how to maximize the energy stored in a given battery with a finite-time modulation of…
Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning
Paolo Andrea Erdman, Alberto Rolandi, Paolo Abiuso +2
The full optimization of a quantum heat engine requires operating at high power, high efficiency, and high stability (i.e. low power fluctuations). However, these three objectives…
Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning
Paolo Andrea Erdman, Frank Noé
A quantum thermal machine is an open quantum system that enables the conversion between heat and work at the micro or nano-scale. Optimally controlling such out-of-equilibrium syst…