This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) to innovate financial fraud detection. Using quantum technologies' computational power and FL's data privacy, QFNN-FFD presents a secure, efficient method for identifying fraudulent transactions. Implementing a dual-phase training model across distributed clients surpasses existing methods in performance. QFNN-FFD significantly improves fraud detection and ensures data confidentiality, marking a significant advancement in fintech solutions and establishing a new standard for privacy-focused fraud detection.

翻译：本研究提出了一种用于金融欺诈检测的量子联邦神经网络（QFNN-FFD），这是一个融合量子机器学习（QML）、量子计算与联邦学习（FL）的前沿框架，旨在革新金融欺诈检测技术。通过利用量子技术的计算能力和联邦学习的数据隐私保护特性，QFNN-FFD提供了一种安全高效的欺诈交易识别方法。该框架在分布式客户端上实施双阶段训练模型，其性能超越了现有方法。QFNN-FFD在显著提升欺诈检测能力的同时确保数据机密性，这标志着金融科技解决方案的重大进步，并为以隐私为核心的欺诈检测树立了新的基准。