迈向量子互联网上的联邦学习
Towards Federated Learning on the Quantum Internet
AI总结：
该研究评估了量子互联网上的量子联邦学习应用，通过多场景多数据集实验证明其是常规训练的有效替代方案，并发现网络拓扑与训练性质会显著影响模型性能。
摘要：
尽管迄今为止量子计算的大部分焦点都集中在单片量子系统上，但量子通信网络，尤其是量子互联网，正越来越受到研究人员和工业界的关注。量子互联网可能允许大量应用，例如分布式或盲量子计算，不过其物理实现和算法的研究仍处于早期阶段；因此，合适的应用仍是一个开放的研究问题。我们评估了量子互联网的一个潜在应用，即量子联邦学习。我们在各种场景（例如网络约束）下的不同设置中使用来自不同领域的多个数据集进行实验，并表明：（1）量子联邦学习是常规训练的有效替代方案，以及（2）网络拓扑和训练性质是关键的考量因素，因为它们可能极大地影响模型的性能。结果表明，需要在潜在的量子互联网上最优地部署量子联邦学习，仍需进行更全面的研究。
英文摘要：
While the majority of focus in quantum computing has so far been on monolithic quantum systems, quantum communication networks and the quantum internet in particular are increasingly receiving attention from researchers and industry alike. The quantum internet may allow a plethora of applications such as distributed or blind quantum computing, though research still is at an early stage, both for its physical implementation as well as algorithms; thus suitable applications are an open research question. We evaluate a potential application for the quantum internet, namely quantum federated learning. We run experiments under different settings in various scenarios (e.g. network constraints) using several datasets from different domains and show that (1) quantum federated learning is a valid alternative for regular training and (2) network topology and nature of training are crucial considerations as they may drastically influence the models performance. The results indicate that more comprehensive research is required to optimally deploy quantum federated learning on a potential quantum internet.
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