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) for financial fraud detection. Using quantum technologies' computational power and the robust data privacy protections offered by FL, QFNN-FFD emerges as a secure and efficient method for identifying fraudulent transactions within the financial sector. Implementing a dual-phase training model across distributed clients enhances data integrity and enables superior performance metrics, achieving precision rates consistently above 95%. Additionally, QFNN-FFD demonstrates exceptional resilience by maintaining an impressive 80% accuracy, highlighting its robustness and readiness for real-world applications. This combination of high performance, security, and robustness against noise positions QFNN-FFD as a transformative advancement in financial technology solutions and establishes it as a new benchmark for privacy-focused fraud detection systems. This framework facilitates the broader adoption of secure, quantum-enhanced financial services and inspires future innovations that could use QML to tackle complex challenges in other areas requiring high confidentiality and accuracy.

翻译：本研究提出了用于金融欺诈检测的量子联邦神经网络（QFNN-FFD），这是一个将量子机器学习（QML）和量子计算与联邦学习（FL）相结合的前沿框架，旨在进行金融欺诈检测。通过利用量子技术的计算能力以及联邦学习所提供的强大数据隐私保护，QFNN-FFD成为金融领域内识别欺诈交易的一种安全且高效的方法。在分布式客户端上实施双阶段训练模型，增强了数据完整性，并实现了卓越的性能指标，其精确率持续保持在95%以上。此外，QFNN-FFD展现出卓越的鲁棒性，保持了高达80%的准确率，突显了其稳健性及其在实际应用中的准备就绪程度。这种高性能、安全性以及对噪声的强鲁棒性的结合，使QFNN-FFD成为金融科技解决方案中的一项变革性进展，并为其确立了以隐私为中心的欺诈检测系统的新基准。该框架促进了安全、量子增强型金融服务的更广泛采用，并激励未来可能利用QML来解决其他需要高度保密性和准确性的领域中复杂挑战的创新。