Efficient entanglement distribution is the 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 autonomously optimizes the entanglement distillation sequence to maximize the "goodput," 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 protocol families (e.g., BBPSSW vs. DEJMPS) to navigate the trade-off between generation rate and state quality. Using a dynamic programming planner with Pareto pruning, simulation results demonstrate that our approach eliminates the "fidelity cliffs" inherent in static protocols and prevents resource wastage in high-noise regimes. Furthermore, we extend the controller to heterogeneous scenarios, demonstrating robustness for both multipartite GHZ state generation and continuous variable systems using effective noiseless linear amplification models. We benchmark its computational overhead, confirming real-time feasibility with decision latencies in the millisecond range per link.

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、存储器相干时间和门错误率）会动态波动，使得静态纯化策略难以达到最优。本文提出了一种自适应纯化控制器（APC），它能够自主优化纠缠蒸馏序列，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 动态地在不同纯化深度和协议族（例如 BBPSSW 与 DEJMPS）之间切换，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，我们的方法消除了静态协议固有的"保真度悬崖"，并防止了高噪声区域中的资源浪费。此外，我们将控制器扩展至异构场景，证明了其在多方 GHZ 态生成和使用有效无噪声线性放大模型的连续变量系统中均具有鲁棒性。我们对其计算开销进行了基准测试，确认了其在每链路毫秒量级决策延迟下的实时可行性。