量子概率流引导的激发输运耦合控制自由度约简
Quantum Probability Current Guided Reduction of Coupling Control Degrees of Freedom for Excitation Transport
- School of Mathematical Sciences, Hangzhou Dianzi University（杭州电子科技大学数学科学学院）
- School of Mathematical Sciences, Zhejiang University（浙江大学数学科学学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出基于量子概率流梯度的边排序策略，在FMO模型中用少量耦合边保留高输运增强并降低控制努力，显著缩减控制空间。
AI中文摘要：
时间依赖的相干控制可以增强开放量子网络中的激发输运，但独立控制每个位点间耦合会产生高维控制空间，导致优化问题困难。我们提出一种基于控制引起的、通过图霍奇分解获得的时间积分量子概率流梯度分量变化的边排序策略。将该策略应用于七位点芬纳-马修斯-奥尔森（FMO）模型时，六边集合保留了全控制所获增强的99.83%，四边集合保留了97.60%，同时相对于全控制，脉冲通量（此处用作控制努力的代理指标）减少了41.55%。退相干扫描以及与随机边集合和随机网络的比较，为排序的相关性和潜在更广泛实用性提供了数值支持。这些结果表明，由量子概率流引导的边选择可以大幅缩减控制空间，同时以较低的控制努力保持高输运性能。
英文摘要：
Time-dependent coherent control can enhance excitation transport in open quantum networks, but independently controlling every inter-site coupling creates a control space of high dimension and leads to difficult optimization problems. We introduce an edge-ranking strategy based on the control-induced change in the gradient component of the time-integrated quantum probability current, which is obtained via a graph Hodge decomposition. When our strategy is applied to the seven-site Fenna-Matthews-Olson (FMO) model, the six-edge set retains $99.83\%$ of the enhancement achieved by full control, and the four-edge set retains $97.60\%$ while reducing the pulse fluence---used here as a proxy for control effort---by $41.55\%$ relative to full control. Dephasing scans and comparisons with random edge sets and random networks provide numerical support for the relevance and potential broader utility of the ranking. These results show that edge selection guided by the quantum probability current can substantially reduce the control space while preserving high transport performance with lower control effort.
