论文标题
通过局部能源优化,有效跨连接网络的有效激发转移
Efficient excitation-transfer across fully connected networks via local-energy optimization
论文作者
论文摘要
我们研究了可以人为设计的站点能量的完全连接的量子网络的激发转移。从广泛研究的物理系统的简化模型开始,我们使用自适应梯度下降技术和自动分化来系统地优化其局部能量,以实现各种环境条件的高激发转移。我们表明,只要倾向率不太大,就可以在有和没有局部的情况下实现几乎完美的转移。我们从网络连接强度或大小的变化以及相干损失的差异方面研究了解决方案。我们强调了无摘要和dephasing驱动的转移的不同特征。我们的工作进一步深入了解了在完全连接的量子网络中激发转移现象中相干性和脱落效应之间的相互作用。反过来,这将有助于通过简单地操纵当地能量来设计人工开放网络中的最佳传输。
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.