发表机构
RFI-IRFOS and TU Graz; openmaind FlexCo and TU Graz; Know Center Research GmbH and University of Graz; Atominstitut, TU Wien and IQOQI, ÖAW（RFI-IRFOS和格拉茨技术大学; openmaind FlexCo和格拉茨技术大学; Know Center Research GmbH和格拉茨大学; 原子院、维也纳技术大学和ÖAW IQOQI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究量子中继器网络路由对抗博弈问题，通过对抗协同学习，发现保留率与参考值紧密跟踪，拟合决策树解释模型并报告忠实度，构建提示记录形成开源解释工作流程。
AI 中文摘要
我们研究了在适度图语料库上基于纠缠的量子网络路由的对抗性博弈问题。爱丽丝为埃克特91协议（E91）选择端到端中继器路线，而伊芙选择攻击面，包括边拦截重发或中继器内存退化。收益来自缓存的SeQUeNCe模拟的E91转录本，当有限样本统计违反克劳泽-霍恩-希莫尼-霍尔特（CHSH）界时，爱丽丝接受一轮。在50种结构化拓扑上进行对抗协同学习，发现学习到的保留率与全矩阵极小极大参考值紧密跟踪（皮尔逊r = 0.99）。然后将决策树解释模型拟合到图、攻击和路由级拓扑语料库目标并报告其忠实度。最后，为本地语言模型构建提示记录以总结树证据，形成量子中继器网络游戏的开源解释工作流程。
英文摘要
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.
Comments4 pages, 5 figures, submitted to IEEE QCE26, Workshop on Q-GenAI: Synergies between QC & GenAI
