  In recent years, the field of quantum science has attracted significant
interest across various disciplines, including quantum machine learning,
quantum communication, and quantum computing. Among these emerging areas,
quantum federated learning (QFL) has gained particular attention due to the
integration of quantum neural networks (QNNs) with traditional federated
learning (FL) techniques. In this study, a novel approach entitled quantum
split learning (QSL) is presented, which represents an advanced extension of
classical split learning. Previous research in classical computing has
demonstrated numerous advantages of split learning, such as accelerated
convergence, reduced communication costs, and enhanced privacy protection. To
maximize the potential of QSL, cross-channel pooling is introduced, a technique
that capitalizes on the distinctive properties of quantum state tomography
facilitated by QNNs. Through rigorous numerical analysis, evidence is provided
that QSL not only achieves a 1.64\% higher top-1 accuracy compared to QFL but
also demonstrates robust privacy preservation in the context of the MNIST
classification task.

Quantum Physics;Artificial Intelligence;Machine Learning