Andronis I, Arapantonis G, Barmparis G D, Tsironis G P
Department of Physics, University of Crete, Heraklion 70013, Greece.
William H. Miller III Department of Physics & Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Phys Rev E. 2023 Jun;107(6-2):065301. doi: 10.1103/PhysRevE.107.065301.
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.
在量子靶向能量转移中，玻色子从特定的晶体位点转移到另一个位点，利用一种类似于经典靶向能量转移的非线性共振配置。我们使用基于机器学习算法的计算方法，以便在二聚体和三聚体系统的背景下研究量子转移的选择性和效率。我们发现我们的方法识别出允许玻色子同步转移的共振量子转移路径。该方法很容易扩展到涉及非线性共振的更大晶格系统。
