Healthcare industries frequently handle sensitive and proprietary data, and due to strict privacy regulations, they are often reluctant to share data directly. In today's context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning while effectively managing critical concerns regarding data privacy and governance. The fusion of federated learning and quantum computing represents a groundbreaking interdisciplinary approach with immense potential to revolutionize various industries, from healthcare to finance. In this work, we proposed a federated learning framework based on quantum tensor networks, which leverages the principles of many-body quantum physics. Currently, there are no known classical tensor networks implemented in federated settings. Furthermore, we investigated the effectiveness and feasibility of the proposed framework by conducting a differential privacy analysis to ensure the security of sensitive data across healthcare institutions. Experiments on popular medical image datasets show that the federated quantum tensor network model achieved a mean receiver-operator characteristic area under the curve (ROC-AUC) between 0.91-0.98. Experimental results demonstrate that the quantum federated global model, consisting of highly entangled tensor network structures, showed better generalization and robustness and achieved higher testing accuracy, surpassing the performance of locally trained clients under unbalanced data distributions among healthcare institutions.

翻译：医疗行业经常处理敏感和专有数据，且由于严格的隐私法规，往往不愿直接共享数据。在当今背景下，联邦学习作为关键解决方案脱颖而出，它既能促进分布式机器学习的快速发展，又能有效管理数据隐私与治理的关键问题。联邦学习与量子计算的融合代表了一种突破性的跨学科方法，具有彻底改变从医疗到金融等各个行业的巨大潜力。本研究提出了一种基于量子张量网络的联邦学习框架，该框架利用了多体量子物理原理。目前，尚未有已知的经典张量网络在联邦设置中得以实现。此外，我们通过差分隐私分析研究了该框架的有效性和可行性，以确保跨医疗机构敏感数据的安全性。在主流医学图像数据集上的实验表明，联邦量子张量网络模型的平均受试者工作特征曲线下面积达到0.91–0.98。实验结果显示，由高度纠缠的张量网络结构组成的量子联邦全局模型展现出更好的泛化能力和鲁棒性，并在医疗机构间数据分布不均衡的情况下实现了更高的测试精度，超越了本地训练客户端的性能。