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- W4310631704 abstract "In quantum targeted energy transfer, bosons are transferred from a certain crystal site to an alternative one, utilizing a nonlinear resonance configuration similar to the classical targeted energy transfer. We use a novel computational method based on machine learning algorithms in order to investigate selectivity as well as efficiency of the quantum transfer in the context of a dimer and a trimer system. We find that our method identifies resonant quantum transfer paths that allow boson transfer in unison. The method is readily extensible to larger lattice systems involving nonlinear resonances." @default.
- W4310631704 created "2022-12-13" @default.
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- W4310631704 date "2022-12-01" @default.
- W4310631704 modified "2023-09-26" @default.
- W4310631704 title "Quantum targeted energy transfer through machine learning tools" @default.
- W4310631704 doi "https://doi.org/10.48550/arxiv.2212.00556" @default.
- W4310631704 hasPublicationYear "2022" @default.
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