发表机构
Fujitsu Research of America, Inc.（富士通美国研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出混合量子-经典框架，通过量子电路建模认知偏差并联合强化学习智能体逐轮决策，用于检测亲和与婚恋投资欺诈，在含硬负样本的合成对话上评估性能。
AI 中文摘要
我们提出了一种混合量子-经典框架，通过建模操纵性对话中的认知偏差来检测亲和与婚恋投资欺诈。在我们提出的框架中，此类欺诈核心的认知偏差由结构化参数化量子电路中的专用量子比特承载，同时一个框架量子比特使编码对操纵性重构的时间顺序敏感，一个叙事量子比特通过可训练的纠缠层聚合共现信息。电路参数与一个经典强化学习智能体联合训练，该智能体逐轮决定是否标记对话，这被建模为一个最优停止问题。我们在包含硬负样本、合法但紧急以及合法但强势推销的合成对话上评估了模型性能。
英文摘要
We present a hybrid quantum-classical framework that detects affinity and romance-investment fraud by modelling the cognitive biases in a manipulative conversation. In our proposed framework, cognitive biases central to this fraud class are carried by dedicated qubits in a structured parameterized quantum circuit, together with a frame qubit makes the encoding sensitive to the temporal order of manipulative reframing, and a narrative qubit that aggregates co-occurrence through a trainable entanglement layer. The circuit parameters are trained jointly with a classical reinforcement-learning agent that decides, turn by turn, whether to flag the conversation, modeled as an optimal stopping problem. We evaluate the model's performance on synthetic conversations that include hard negatives, legitimate but urgent, and legitimate but pushy sales conversations.
