A Cognitive-Aware QML-CRL Framework for Detecting Affinity and Romance-Investment Fraud
Abstract
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.
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BibTeX
@misc{adhikari2026cognitiveaware,
title = {{A Cognitive-Aware QML-CRL Framework for Detecting Affinity and Romance-Investment Fraud}},
author = {Bibhas Adhikari and Ramya Srinivasan},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.09141},
eprint = {2610.09141},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.09141},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - A Cognitive-Aware QML-CRL Framework for Detecting Affinity and Romance-Investment Fraud
AU - Bibhas Adhikari
AU - Ramya Srinivasan
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2610.09141
UR - https://arxiv.org/abs/2610.09141
AB - 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.
ER -