6533b7d2fe1ef96bd125ed07
RESEARCH PRODUCT
Reinforcement learning approach to nonequilibrium quantum thermodynamics
Pierpaolo SgroiPierpaolo SgroiMauro PaternostroG. Massimo PalmaG. Massimo Palmasubject
---Computer scienceFOS: Physical sciencesGeneral Physics and AstronomyNon-equilibrium thermodynamics01 natural sciencesSettore FIS/03 - Fisica Della Materia010305 fluids & plasmassymbols.namesakeQuantum stateSHORTCUTS0103 physical sciencesQuantum systemReinforcement learningStatistical physics010306 general physicsQuantum thermodynamicsCondensed Matter - Statistical MechanicsADIABATICITYQuantum PhysicsStatistical Mechanics (cond-mat.stat-mech)Entropy productionENTROPYsymbolsQuantum Physics (quant-ph)Hamiltonian (quantum mechanics)description
We use a reinforcement learning approach to reduce entropy production in a closed quantum system brought out of equilibrium. Our strategy makes use of an external control Hamiltonian and a policy gradient technique. Our approach bears no dependence on the quantitative tool chosen to characterize the degree of thermodynamic irreversibility induced by the dynamical process being considered, require little knowledge of the dynamics itself and does not need the tracking of the quantum state of the system during the evolution, thus embodying an experimentally non-demanding approach to the control of non-equilibrium quantum thermodynamics. We successfully apply our methods to the case of single- and two-particle systems subjected to time-dependent driving potentials.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-13 |