研究量子中继网络路由的对抗学习与可解释鲁棒性，提升安全通信性能。
When Routes Run Out: Adversarial Co-Learning and Explainable Robustness in Quantum Repeater Networks
- 通过对抗共学习模拟敌手攻击，优化量子纠缠路由策略。
- 学习到的保真度轨迹与理论最优值高度一致（相关系数0.99）。
- 构建可解释工作流，支持语言模型生成安全分析摘要，适合安全研究人员。
本文研究在中等规模图谱上的基于纠缠的量子网络路由对抗性带通问题。爱丽丝选择一条用于Ekert-91协议（E91）的端到端中继路径作为其行动，而夏娃选择攻击面，包括边截获重发或中继记忆退化。收益来自缓存的SeQUeNCe仿真E91传输记录，当有限样本统计量违反贝尔不等式CHSH时，爱丽丝接受该回合。在50种结构化拓扑上进行对抗共学习，发现学习到的保真度轨迹紧密追踪全矩阵极小极大参考值（皮尔逊相关系数r=0.99）：在单一攻击面模型下，瓶颈族保留率为零，非瓶颈族遵循1−1/N覆盖原则。随后，我们为图、攻击和路径级拓扑数据集拟合决策树解释模型，并报告其忠实度。最后，构建提示记录以供本地语言模型总结树状证据，形成开源的量子中继网络博弈解释工作流。
原文摘要 · Abstract (English)
We study an adversarial bandit problem for entanglement-based quantum-network routing over a modest graph corpus. Alice selects an end-to-end repeater route for an Ekert-91 protocol (E91) representing her move, while Eve selects an attack surface, either edge intercept--resend or repeater memory degradation. Payoffs are drawn from cached SeQUeNCe-simulated E91 transcripts, and Alice accepts a turn when the finite-sample statistic violates the Clauser-Horne-Shimony-Holt (CHSH) bound. Performing adversarial co-learning across 50 structured topologies, we find that learned retention tracks a full-matrix minimax reference closely (Pearson $r=0.99$): under a one-surface Eve action model, bottleneck families have zero retention, while non-bottleneck families follow a $1-1/N$ coverage principle. We then fit decision-tree explanation models to graph-, attack-, and route-level topology-corpus targets and report their faithfulness. Finally, we construct prompt records for local language models to summarize the tree evidence, resulting in an open-source explanation workflow for quantum-repeater network games.
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