Quantum targeted energy transfer through machine learning tools

TL;DR

This paper introduces a machine learning-based computational approach to analyze quantum targeted energy transfer, demonstrating its effectiveness in identifying resonant transfer paths in bosonic systems.

Contribution

The paper presents a novel machine learning method for studying quantum energy transfer, capable of identifying resonant paths and scalable to larger nonlinear lattice systems.

Findings

Identifies resonant quantum transfer paths for boson transfer.

Method is effective for dimer and trimer systems.

Scalable to larger lattice systems.

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.