Cycle Flux Ranking of Network Analysis in Quantum Thermal Device

TL;DR

This paper introduces a cycle flux ranking method using network analysis to identify key operational cycles in quantum thermal devices, aiding understanding and optimization of their heat transport mechanisms.

Contribution

It applies cycle flux ranking to quantum thermal devices, revealing principal working cycles and mechanisms through graph-theoretic analysis, which is a novel approach in this context.

Findings

Dominant cycle trajectories elucidate device mechanisms

Cycle flux ranking identifies principal operational cycles

Method demonstrated on spin-Seebeck pump and quantum transistor

Abstract

Manipulating quantum thermal transport relies on uncovering the principle working cycles of quantum devices. Here, we apply the cycle flux ranking of network analysis to nonequilibrium thermal devices described by graphs of quantum state transitions. To excavate the principal mechanism out of complex transport behaviors, we decompose the quantum-transition network into cycles, calculate the cycle flux by algebraic graph theory, and pick out the dominant cycles with top-ranked fluxes, i.e., the cycle trajectories with highest probabilities. We demonstrate the cycle flux ranking in typical quantum device models, such as a thermal-drag spin-Seebeck pump, and a quantum thermal transistor as thermal switch or heat amplifier. The dominant cycle trajectories indeed elucidate the principal working mechanisms of those quantum devices. The cycle flux analysis provides an alternative perspective that naturally describes the working cycle corresponding to the main functionality of quantum thermal devices, which would further guide the device optimization with desired performance