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We use a reinforcement learning approach to reduce entropy production in aclosed quantum system brought out of equilibrium. Our strategy makes use of anexternal control Hamiltonian and a policy gradient technique. Our approachbears no dependence on the quantitative tool chosen to characterize the degreeof thermodynamic irreversibility induced by the dynamical process beingconsidered, require little knowledge of the dynamics itself and does not needthe tracking of the quantum state of the system during the evolution, thusembodying an experimentally non-demanding approach to the control ofnon-equilibrium quantum thermodynamics. We successfully apply our methods tothe case of single- and two-particle systems subjected to time-dependentdriving potentials.

Reinforcement learning approach to non-equilibrium quantum thermodynamics