Hybrid Quantum Networks Lift Fraud Detection Accuracy to 85%

Epameinondas Douros and colleagues from National Technical University of Athens and New York University Abu Dhabi, have achieved 85% recall for the Deep QLayer using QuantumChain, a new system that improves upon classical methods. The framework integrates hybrid quantum-classical neural networks with robust security features to address challenges in decentralized financial data. QuantumChain protects model updates through homomorphic encryption, threshold secret sharing, and quantum key distribution, while a permissioned blockchain ensures auditability and trust among participants. In federated deployments with diverse clients, global accuracy increases from 70% to 85% over five rounds, demonstrating a rapid convergence speed for this complex, secure pipeline. These results show QuantumChain can integrate depth-aware hybrid quantum models into a secure federated fraud-detection pipeline while maintaining stable global convergence. The ability to discern subtle patterns in complex financial data is being enhanced by a novel approach to quantum neural networks. Researchers are demonstrating that even relatively shallow quantum…