arXiv 论文：暗网链接图谱上的量子-经典链路预测对比
Quantum-Classical Link Prediction on Dark Web Hyperlink Graphs
一篇量化对比 VQC 与 GAE、VGAE 等经典方法的论文，数据是 383 个 Tor 主机的真实链接图，想看量子模型和经典图模型差距的可以细读。
该研究基于一份包含 383 个 onion 主机和 2,067 条跨主机有向链接的 Tor 离线爬取数据，将语义分析与度控制的量子-经典链路预测结合。有向优先连接在随机非边下 ROC-AUC 达 0.976，但在源条件目标度匹配下降至 0.550±0.014。在不相交训练/测试对上，VGAE 取得 0.906±0.016，GAE 为 0.899±0.016，多模态逻辑回归为 0.672±0.021。四量子比特变分量子分类器（VQC）在 20 组图划分上的最佳精确模拟 ROC-AUC 为 0.549±0.023，与同输入下的 RBF SVC（0.551±0.016）和 16 参数 MLP（0.573±0.029）相当。
Quantum-Classical Link Prediction on Dark Web Hyperlink Graphs
We analyze HTML content and hyperlinks from an offline Tor crawl containing 383 onion hosts and 2,067 directed cross-host links. The study combines semantic analysis with degree-controlled quantum--classical link prediction. Two directory groups emit 94.8\% of the observed host links. Directed preferential attachment reaches ROC-AUC 0.976 with random non-edges, but falls to $0.550\pm0.014$ under source-conditioned target-degree matching. With disjoint training and test pairs, multimodal logistic regression reaches $0.672\pm0.021$, GAE $0.899\pm0.016$, and VGAE $0.906\pm0.016$. A four-qubit variational quantum classifier (VQC) comparison tests two encodings across 20 disjoint-pair graph splits under exact and 1,024-shot simulation, with classical controls using the same inputs and training examples. Its best exact mean ROC-AUC is $0.549\pm0.023$, compared with $0.551\pm0.016$ for RBF SVC and $0.573\pm0.029$ for a 16-parameter MLP on the same inputs and training examples. These results support predictive signal in combined graph/content features and show why degree control, feature compression, and optimization conditions matter in quantum--classical comparisons.