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Learning efficient erasure protocols for an underdamped memory

Barros, Nicolas [Centre National de la Recherche Scientifique (CNRS) (France); École normale supérieure de Lyon (France)] (ORCID:0009000013487725)·Whitelam, Stephen [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry] (ORCID:0000000200866803)·Ciliberto, Sergio [Centre National de la Recherche Scientifique (CNRS) (France); École normale supérieure de Lyon (France)] (ORCID:0000000243666094)·Bellon, Ludovic [Centre National de la Recherche Scientifique (CNRS) (France); École normale supérieure de Lyon (France)] (ORCID:0000000224998106)
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Abstract

Here we apply evolutionary reinforcement learning to a simulation model to identify efficient time-dependent erasure protocols for a physical realization of a 1-bit memory using an underdamped mechanical cantilever. We show that these protocols, when applied to the cantilever in the laboratory, are considerably more efficient than our best hand-designed protocols. The learned protocols allow reliable high-speed erasure by minimizing the heating of the memory during its operation. More generally, the combination of methods used here opens the door to the rational design of efficient protocols for various physics applications.

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74 ATOMIC AND MOLECULAR PHYSICSArtificial neural networksOptimization problemsStochastic thermodynamics

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Barros, Nicolas [Centre National de la Recherche Scientifique (CNRS) (France); École normale supérieure de Lyon (France)] (ORCID:0009000013487725), Whitelam, Stephen [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry] (ORCID:0000000200866803), Ciliberto, Sergio [Centre National de la Recherche Scientifique (CNRS) (France); École normale supérieure de Lyon (France)] (ORCID:0000000243666094), Bellon, Ludovic [Centre National de la Recherche Scientifique (CNRS) (France); École normale supérieure de Lyon (France)] (ORCID:0000000224998106). 2025-04-10. Learning efficient erasure protocols for an underdamped memory. https://doi.org/10.1103/physreve.111.044114

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