Driving Enhanced Exciton Transfer by Automatic Differentiation

TL;DR

This paper models excitation transfer in quantum networks and demonstrates that introducing optimized driving fields significantly enhances transfer efficiency across various network configurations.

Contribution

It introduces a novel approach using automatic differentiation to optimize driving fields for improved exciton transfer in complex quantum networks.

Findings

Driving fields markedly increase transfer probability.

Optimization remains effective across different network types.

Few parameters are needed for significant improvement.

Abstract

We model and study the processes of excitation, absorption, and transfer in various networks. The model consists of a harmonic oscillator representing a single-mode radiation field, a qubit acting as an antenna, a network through which the excitation propagates, and a qubit at the end serving as a sink. We investigate how off-resonant excitations can be optimally absorbed and transmitted through the network. Three strategies are considered: optimising network energies, adjusting the couplings between the radiation field, the antenna, and the network, or introducing and optimising driving fields at the start and end of the network. These strategies are tested on three different types of network with increasing complexity: nearest-neighbour and star configurations, and one associated with the Fenna-Matthews-Olson complex. The results show that, among the various strategies, the introduction of driving fields is the most effective, leading to a significant increase in the probability of reaching the sink in a given time. This result remains stable across networks of varying dimensionalities and types, and the driving process requires only a few parameters to be effective.