Papers › Quantum targeted energy transfer through machine learning tools
Quantum targeted energy transfer through machine learning tools
I. Andronis, G. Arapantonis, G. D. Barmparis, G. P. Tsironis
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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.
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