Federated Learning

Chapter 18: Federated quantum natural gradient descent for quantum federated learning

Jun Qi^a; Min-Hsiu Hsieh^b    aFudan University, Shanghai, China^bHon Hai (Foxconn) Quantum Computing Research Center, Taipei, Taiwan

Abstract

The heart of quantum federated learning (QFL) is associated with a distributed learning architecture across several local quantum devices and a more efficient training algorithm for the QFL is expected to minimize the communication overhead among different quantum participants. In this chapter, we introduce an efficient learning algorithm, namely federated quantum natural gradient descent (FQNGD), applied in a QFL framework that consists of the variational quantum circuit (VQC)-based quantum neural networks (QNN). The FQNGD ...