Driving Enhanced Exciton Transfer by Automatic Differentiation

• URL: http://arxiv.org/abs/2411.17906v1
• Date: Tue, 26 Nov 2024 21:42:14 GMT
• Title: Driving Enhanced Exciton Transfer by Automatic Differentiation
• Authors: E. Ballarin, D. A. Chisholm, A. Smirne, M. Paternostro, F. Anselmi, S. Donadi,
• Abstract summary: We study the processes of excitation, absorption, and transfer in various networks.<n>We consider how off-resonant excitations can be optimally absorbed and transmitted through the network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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