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.

提出了基于量子张量网络的联邦学习框架，通过多体量子物理原理充分利用，进行了不平衡数据分布下的差分隐私分析，实验结果显示，联邦量子张量网络模型在医疗图像数据集上达到了 0.91-0.98 的平均接收者操作特征曲线下面积（ROC-AUC），在医疗院所之间的不平衡数据分布下，量子联邦全局模型表现出更好的泛化性能和鲁棒性，实现了更高的测试准确率，超过了本地训练客户端的表现。

联邦分层张量网络：面向医疗领域的协作学习量子人工智能框架

Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum  AI-Driven Framework for Healthcare

A key attribute that drives the unprecedented success of modern Recurrent
Neural Networks (RNNs) on learning tasks which involve sequential data, is
their ability to model intricate long-term temporal dependencies. However, a
well established measure of RNNs long-term memory capacity is lacking, and thus
formal understanding of the effect of depth on their ability to correlate data
throughout time is limited. Specifically, existing depth efficiency results on
convolutional networks do not suffice in order to account for the success of
deep RNNs on data of varying lengths. In order to address this, we introduce a
measure of the network's ability to support information flow across time,
referred to as the Start-End separation rank, which reflects the distance of
the function realized by the recurrent network from modeling no dependency
between the beginning and end of the input sequence. We prove that deep
recurrent networks support Start-End separation ranks which are combinatorially
higher than those supported by their shallow counterparts. Thus, we establish
that depth brings forth an overwhelming advantage in the ability of recurrent
networks to model long-term dependencies, and provide an exemplar of
quantifying this key attribute which may be readily extended to other RNN
architectures of interest, e.g. variants of LSTM networks. We obtain our
results by considering a class of recurrent networks referred to as Recurrent
Arithmetic Circuits, which merge the hidden state with the input via the
Multiplicative Integration operation, and empirically demonstrate the discussed
phenomena on common RNNs. Finally, we employ the tool of quantum Tensor
Networks to gain additional graphic insight regarding the complexity brought
forth by depth in recurrent networks.

本文通过引入一种称为 “起始 - 终止分离等级” 的度量方式，证明了深度循环网络相较于其浅层对应版本在建模长时依赖方面具有压倒性的优势，并通过量子张量网络的工具获取了更多的图形洞见。