Efficient entanglement distribution is the foundational challenge in realizing large-scale Quantum Networks. However, state-of-the-art solutions are frequently limited by restrictive operational assumptions, prohibitive computational complexities, and performance metrics that misalign with practical application needs. To overcome these barriers, this paper addresses the entanglement distribution problem by introducing four pivotal advances. First, recognizing that the primary application of quantum communication is the transmission of private information, we derive the Ensemble Capacity (EC), a novel metric that explicitly quantifies the secure classical information enabled by the entanglement distribution. Second, we propose a generalized mathematical formulation that removes legacy structural restrictions in the solution space. Our formulation supports an unconstrained, arbitrary sequencing of entanglement swapping and purification. Third, to efficiently navigate the resulting combinatorial optimization space, we introduce a novel Dynamic Programming (DP)-based hypergraph generation algorithm. Unlike prior methods, our approach avoids artificial fidelity quantization, preserving exact, continuous fidelities while proactively pruning sub-optimal trajectories. Finally, we encapsulate these algorithmic solutions into CODE, a system-level, two-tiered orchestration framework designed to enable near-real-time network responsiveness. Extensive evaluations confirm that our DP-driven architecture yields superior private classical information capacity and significant reductions in computational complexity, successfully meeting the strict sub-second latency thresholds required for dynamic QN operation.

翻译：高效的纠缠分发是实现大规模量子网络的基础性挑战。然而，现有最先进解决方案常受限于严格的操作假设、高昂的计算复杂度以及与实际应用需求错位的性能指标。为突破这些障碍，本文通过引入四项关键进展来解决纠缠分发问题。首先，鉴于量子通信的主要应用是传输私密信息，我们推导出集合容量这一新型指标，该指标明确量化了纠缠分发所实现的经典安全信息。其次，我们提出一种广义数学表述，消除了解空间中的遗留结构限制，支持无约束、任意顺序的纠缠交换与纯化操作。第三，为高效搜索由此产生的组合优化空间，我们引入了一种基于动态规划的超图生成算法。与现有方法不同，该算法避免人工保真度量化，保持精确的连续保真度值，并主动剪除次优路径。最后，我们将这些算法解决方案封装到CODE系统中——一个支持近实时网络响应的双层编排框架。大量评估证实，我们的动态规划驱动架构能提供更优的私密经典信息容量，显著降低计算复杂度，成功满足动态量子网络运行所需的亚秒级严格延迟阈值。