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, requires 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 nondemanding approach to the control of nonequilibrium quantum thermodynamics. We successfully apply our methods to the case of single- and two-particle systems subjected to time-dependent driving potentials.

Sgroi P., Palma G.M., Paternostro M. (2021). Reinforcement Learning Approach to Nonequilibrium Quantum Thermodynamics. PHYSICAL REVIEW LETTERS, 126(2), 020601 [10.1103/PhysRevLett.126.020601].

Reinforcement Learning Approach to Nonequilibrium Quantum Thermodynamics

Palma G. M.;Paternostro M.
2021-01-01

Abstract

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, requires 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 nondemanding approach to the control of nonequilibrium quantum thermodynamics. We successfully apply our methods to the case of single- and two-particle systems subjected to time-dependent driving potentials.
2021
Settore FIS/03 - Fisica Della Materia
Sgroi P., Palma G.M., Paternostro M. (2021). Reinforcement Learning Approach to Nonequilibrium Quantum Thermodynamics. PHYSICAL REVIEW LETTERS, 126(2), 020601 [10.1103/PhysRevLett.126.020601].
File in questo prodotto:
File Dimensione Formato  
PhysRevLett.126.020601.pdf

Solo gestori archvio

Tipologia: Versione Editoriale
Dimensione 482.95 kB
Formato Adobe PDF
482.95 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
reinforcement learning.pdf

accesso aperto

Descrizione: Peer reviewed version
Tipologia: Pre-print
Dimensione 1.77 MB
Formato Adobe PDF
1.77 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/494945
Citazioni
  • ???jsp.display-item.citation.pmc??? 3
  • Scopus 30
  • ???jsp.display-item.citation.isi??? 26
social impact