发表机构
Infleqtion Australia; SoftBank Corp.（澳大利亚Infleqtion公司; 软银集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究动态交易图中稀疏环欺诈检测问题，用合成交易模拟器，将每日交易图聚合到滚动窗口并以多种方式表示。比较GRU基线和量子启发式CML，结果表明拓扑作上下文层有用，CML是检测此类欺诈有前途的候选模型。
AI 中文摘要
我们提出了一个用于动态金融交易图中欺诈筛选的探索性基准和量子启发式建模原型。协同欺诈可能无法仅从单个交易中看出，而是会呈现为多周期关系模式。我们聚焦于稀疏环欺诈，即一个完整的有向环分布在几天内的一种模式，这要求模型整合时间和图结构的证据。我们使用带有完整稀疏环注入和破环诱饵的合成交易模拟器研究此问题。每日有向交易图被聚合到滚动窗口中，并使用原始图特征、持久同调摘要或两者结合的混合特征向量来表示。我们将门控循环单元（GRU）基线与量子启发式上下文机器学习（CML）作为序列级分类器进行比较。由于基准使用合成数据、适度的样本量和序列级标签，结果具有探索性。在此范围内，仅拓扑摘要过于压缩，无法独自解决监督环完成任务，很大程度上是因为它们去除了账户对身份和边的方向。最强的结果来自将保留身份的图特征与拓扑摘要相结合的混合表示。这些发现表明，拓扑作为动态图特征之上的上下文层最有用，并且CML是用于证据分布在时间和关系上下文中的欺诈模式的有前途的候选模型。
英文摘要
We present an exploratory benchmark and quantum-inspired modeling prototype for fraud screening in dynamic financial transaction graphs. Coordinated fraud may not be visible from individual transactions alone, but may emerge as a multi-period relational pattern. We focus on sparse-ring fraud, a stylized pattern in which a completed directed cycle is distributed across several days, requiring models to integrate evidence across both time and graph structure. We study this problem using a synthetic transaction simulator with completed sparse-ring injections and broken-ring decoys. Daily directed transaction graphs are aggregated into rolling windows and represented using raw graph features, persistent-homology summaries, or hybrid feature vectors that combine both. We compare a gated recurrent unit (GRU) baseline with quantum-inspired Contextual Machine Learning (CML) as sequence-level classifiers. Because the benchmark uses synthetic data, a modest sample size, and sequence-level labels, the results are exploratory. Within this scope, topology-only summaries are too compressed to solve the supervised ring-completion task by themselves, largely because they remove account-pair identity and edge direction. The strongest results come from hybrid representations that combine identity-preserving graph features with topological summaries. These findings suggest that topology is most useful as a contextual layer over dynamic graph features, and that CML is a promising candidate model for fraud patterns whose evidence is distributed across temporal and relational context.
Comments23 pages, 4 figures
