量子链：用于金融欺诈检测的区块链支持的量子联邦学习
QuantumChain: Blockchain-Backed Quantum Federated Learning for Financial Fraud Detection
AI总结：
针对金融欺诈检测面临的挑战，提出量子链框架，结合混合量子 - 经典神经网络等技术。通过局部混合模型训练、加密保护模型更新，利用区块链记录聚合事件。实验表明其能集成量子模型到检测管道，保持全局收敛，提升欺诈类召回率和准确率。
AI中文摘要：
金融欺诈检测面临分散数据、严重类别不平衡和隐私限制等挑战。本文提出了量子链，这是一个安全的量子联邦学习（QFL）框架，它结合了混合量子 - 经典神经网络、加密联邦聚合、基于区块链的可审计性和量子安全通信。每个客户端训练一个局部混合模型，其中变分量子电路嵌入在经典神经层之间，同时模型更新通过同态加密、阈值秘密共享和基于量子密钥分发（QKD）的密钥生成来保护。一个许可区块链记录聚合事件并支持参与者之间基于声誉加权的信任。我们使用紧凑的、大小匹配的经典基线在金融交易数据上评估量子链，以隔离量子层的影响。结果表明，混合量子神经网络（HQNN）在大多数设置下实现了可比的准确率，同时提高了欺诈类召回率，与经典模型的93.2%相比，召回率达到94.6%。深度量子层在全数据设置下提高了性能，表明当浅电路受限，增加电路深度有助于恢复表征能力。混合态模拟进一步表明，在非理想量子演化下召回趋势仍然存在。在具有10个异构客户端的联邦部署中，全局准确率在五轮后从97.7%提高到98.8%，然后稳定下来。这些结果表明，量子链可以将深度感知混合量子模型集成到安全的联邦欺诈检测管道中，同时保持稳定的全局收敛。
英文摘要：
Financial fraud detection is challenged by decentralized data, severe class imbalance, and privacy constraints. This paper presents QuantumChain, a secure Quantum Federated Learning (QFL) framework that combines hybrid quantum-classical neural networks, encrypted federated aggregation, blockchain-based auditability, and quantum-secure communication. Each client trains a local hybrid model in which a variational quantum circuit is embedded between classical neural layers, while model updates are protected through homomorphic encryption, threshold secret sharing, and QKD-based keying. A permissioned blockchain records aggregation events and supports reputation-weighted trust among participants. We evaluate QuantumChain on financial transaction data using a compact, size-matched classical baseline to isolate the effect of the quantum layer. Results show that the HQNN achieves comparable accuracy while improving fraud-class recall in most settings, reaching 94.6% recall compared with 93.2% for the classical model. The Deep QLayer improves performance in full-data settings, suggesting that added circuit depth helps recover representational capacity when the shallow circuit becomes limited. Mixed-state simulations further show that the recall trend persists under non-ideal quantum evolution. In federated deployment with 10 heterogeneous clients, global accuracy increases from 97.7% to 98.8% over five rounds before stabilizing. These results show that QuantumChain can integrate depth-aware hybrid quantum models into a secure federated fraud-detection pipeline while maintaining stable global convergence.
