  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 static QFL methods, we propose slimmable QFL
(SlimQFL) in this article, which is a dynamic QFL framework that can cope with
time-varying communication channels and computing energy limitations. This is
made viable by leveraging the unique nature of a QNN where its angle parameters
and pole parameters can be separately trained and dynamically exploited.
Simulation results corroborate that SlimQFL achieves higher classification
accuracy than Vanilla QFL, particularly under poor channel conditions on
average.
