Quantum Split Neural Network Learning using Cross-Channel Pooling. (arXiv:2211.06524v1 [quant-ph])

In recent years, quantum has been attracted by various fields such as quantum
machine learning, quantum communication, and quantum computers. Among them,
quantum federated learning (QFL) has recently received increasing attention,
where quantum neural networks (QNNs) are integrated into federated learning
(FL). In contrast to the existing QFL methods, we propose quantum split
learning (QSL), which is the extension version of split learning. In classical
computing, split learning has shown many advantages in faster convergence,
communication cost, and even privacy. …

arxiv network neural network pooling quantum