论文标题
通过机器学习工具进行量子目标传输
Quantum targeted energy transfer through machine learning tools
论文作者
论文摘要
在量子靶向能量转移中，利用非线性共振构型从特定的晶体位点转移到替代晶体位点，类似于经典的靶向能传递。我们使用一种基于机器学习算法的新型计算方法，以便在二聚体和三聚体系统的背景下研究选择性以及量子传输的效率。我们发现我们的方法确定了允许玻色子转移一致的谐振量子传递路径。该方法很容易扩展到涉及非线性共振的较大晶格系统。
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.
