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Quantum targeted energy transfer through machine learning tools.
- Source :
-
Physical review. E [Phys Rev E] 2023 Jun; Vol. 107 (6-2), pp. 065301. - Publication Year :
- 2023
-
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 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.
Details
- Language :
- English
- ISSN :
- 2470-0053
- Volume :
- 107
- Issue :
- 6-2
- Database :
- MEDLINE
- Journal :
- Physical review. E
- Publication Type :
- Academic Journal
- Accession number :
- 37464680
- Full Text :
- https://doi.org/10.1103/PhysRevE.107.065301