A Quantum Neural Network for Fraud Detection Using a Data-Driven Priority Entanglement Scheme

This study investigates whether a variational quantum circuit can improve minority-class sensitivity when substituted for a single dense layer in a strong neural network baseline for credit-card fraud detection. Under matched parameter budgets and identical preprocessing, we evaluate a hybrid quantum–classical model against classical baselines on the Kaggle dataset, preserving all 492 fraud cases and downsampling legitimate transactions to 10,000. Our hybrid employs data re-uploading to expose all 30 features with 10–15 qubits. It introduces a data-driven priority-entanglement scheme that couples the most dependent feature pairs before a strongly entangling block. Across 100 (10-qubit) and 20 (15-qubit) randomized runs, the hybrid achieves meaningful and consistent gains in recall and PR-AUC over tuned classical models (e.g., PR-AUC 0.9229±0.006 at 15q/1-layer/10 priority pairs vs 0.9150±0.014 for logistic regression; recall +0.047 over the best classical result).Performance peaks at moderate entanglement budgets, beyond which deeper circuits only exacerbate a precision-recall trade-off, revealing an underlying trainability limit. Results indicate that correlation-guided entanglement provides a useful inductive bias that modestly improves fraud detection under strict capacity parity.