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，当前最先进的解决方案常受限于严苛的运行假设、过高的计算复杂度以及与实用需求不匹配的性能指标。为突破这些瓶颈，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，我们推导出系综容量（EC），这是一个明确量化纠缠分发所实现的保密经典信息的新指标。其次，我们提出一种泛化的数学表述，消除了解空间中遗留的结构性限制。该表述支持不受约束、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，我们引入一种基于动态规划（DP）的超图生成算法。与先前方法不同，本方法避免了人为保真度量化，在主动剪除次优轨迹的同时保留了精确、连续的保真度。最后，我们将这些算法方案封装进CODE——一个面向近实时网络响应的系统级双层编排框架。广泛评估证实，我们基于DP的架构可提供更优的私密经典信息容量，并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时延阈值。