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arXiv 2211.09079quant-ph
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通过局域能量优化实现全连接网络中的高效激发转移

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

S. Sgroi, G. Zicari, A. Imparato, M. Paternostro

AI总结:

本文利用自适应梯度下降与自动微分优化全连接量子网络的局域能量,在不同环境和退相条件下实现近乎完美的激发转移,并阐明相干性与退相的作用。

摘要:

我们研究了全连接量子网络中的激发转移,该网络的位点能量可以进行人工设计。从一个被广泛研究的物理系统的简化模型出发,我们采用自适应梯度下降技术和自动微分,系统地优化其局域能量,以在各种环境条件下实现高激发转移效率。我们表明,只要退相率不太大,无论是否存在局域退相,都可以实现近乎完美的转移。我们从网络连接强度或网络规模变化以及相干性损失下的韧性角度研究了所得解。我们重点说明了无退相转移和退相驱动转移的不同特征。本工作进一步揭示了全连接量子网络激发转移现象中相干性与退相效应之间的相互作用。反过来,这将有助于通过简单调控局域能量,在人工开放网络中设计最优转移。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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