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.
Original language | English |
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Article number | 020601 |
Number of pages | 6 |
Journal | Physical Review Letters |
Volume | 126 |
Issue number | 2 |
DOIs | |
Publication status | Published - 13 Jan 2021 |
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Dive into the research topics of 'Reinforcement learning approach to nonequilibrium quantum thermodynamics'. Together they form a unique fingerprint.Student theses
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Reinforcement learning based methods for optimal control and design of quantum systems
Sgroi, S. (Author), Paternostro, M. (Supervisor) & Ferraro, A. (Supervisor), Jul 2024Student thesis: Doctoral Thesis › Doctor of Philosophy
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