Efficient excitation-transfer across fully connected networks via local-energy optimization

TL;DR

This paper demonstrates how optimizing local site energies in fully connected quantum networks can significantly enhance excitation transfer efficiency, even under environmental disturbances, by employing gradient-based optimization techniques.

Contribution

It introduces a systematic method to optimize local energies for high transfer efficiency in quantum networks using adaptive gradient descent and automatic differentiation.

Findings

Near-perfect transfer achieved with controlled dephasing

Resilience demonstrated against network variations

Insights into coherence and dephasing interplay

Abstract

We study the excitation transfer across a fully connected quantum network whose sites energies can be artificially designed. Starting from a simplified model of a broadly-studied physical system, we systematically optimize its local energies to achieve high excitation transfer for various environmental conditions, using an adaptive Gradient Descent technique and Automatic Differentiation. We show that almost perfect transfer can be achieved with and without local dephasing, provided that the dephasing rates are not too large. We investigate our solutions in terms of resilience against variations in either the network connection strengths, or size, as well as coherence losses. We highlight the different features of a dephasing-free and dephasing-driven transfer. Our work gives further insight into the interplay between coherence and dephasing effects in excitation-transfer phenomena across fully connected quantum networks. In turn, this will help designing optimal transfer in artificial open networks through the simple manipulation of local energies.