Efficient entanglement distribution is a cornerstone of the Quantum Internet. However, physical link parameters such as photon loss, memory coherence time, and gate error rates fluctuate dynamically, rendering static purification strategies suboptimal. In this paper, we propose an Adaptive Purification Controller (APC) that automatically optimizes the entanglement distillation sequence to maximize the goodput, i.e., the rate of delivered pairs meeting a strict fidelity threshold. By treating protocol selection as a resource allocation problem, the APC dynamically switches between purification depths and protocols (BBPSSW vs. DEJMPS) to navigate the trade-off between generation rate and state quality. Using a dynamic programming planner with Pareto pruning, simulation results show that our approach mitigates the "fidelity cliffs" inherent in static protocols and reduces resource wastage in high-noise regimes. Furthermore, we extend the controller to heterogeneous scenarios, and evaluate it for both multipartite GHZ state generation and continuous-variable systems using effective noiseless linear amplification models. We benchmark its computational overhead, showing decision latencies in the millisecond range per link in our implementation.

翻译：高效的纠缠分发是量子互联网的基石。然而，物理链路参数如光子损耗、存储器相干时间及门错误率会动态波动，使得静态纯化策略难以达到最优。本文提出一种自适应纯化控制器（APC），能够自动优化纠缠蒸馏序列，以最大化有效吞吐量，即满足严格保真度阈值的交付纠缠对速率。通过将协议选择视为资源分配问题，APC 动态切换纯化深度与协议（BBPSSW 与 DEJMPS），以权衡生成速率与状态质量。利用带帕累托剪枝的动态规划规划器，仿真结果表明，我们的方法缓解了静态协议固有的“保真度悬崖”现象，并降低了高噪声区域下的资源浪费。此外，我们将控制器扩展至异构场景，并针对多方 GHZ 态生成与连续变量系统（采用有效无噪声线性放大模型）进行了评估。我们对其计算开销进行了基准测试，结果表明在我们的实现中，每条链路的决策延迟在毫秒量级。