Quantum federated learning with pole-angle quantum local training and trainable measurement

Recently, quantum federated learning (QFL) has received significant attention as an innovative paradigm. QFL has remarkable features by employing quantum neural networks (QNNs) instead of conventional neural networks owing to quantum supremacy. In order to enhance the flexibility and reliability of classical QFL frameworks, this paper proposes a novel slimmable QFL (SlimQFL) incorporating QNN-grounded slimmable neural network (QSNN) architectures. This innovative design considers time-varying wireless communication channels and computing resource constraints. This framework ensures higher efficiency by using fewer parameters with no performance loss. Furthermore, the proposed QNN is novel according to the implementation of trainable measurement within QFL. The fundamental concept of our QSNN is designed based on the key characteristics of separated training and the dynamic exploitation of joint angle and pole parameters. Our performance evaluation results verify that using both parameters, our proposed QSNN-based SlimQFL achieves higher classification accuracy than QFL and ensures transmission stability, particularly in poor channel conditions.