2022/08/15 by Jun Qi, Qi, Jun · 5 citations
Computer Science · Engineering · Physics and Astronomy · #Advancements in Semiconductor Devices and Circuit Design #Distributed #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Parallel #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Quantum and electron transport phenomena #and Cluster Computing (cs.DC) #cs.DC #cs.LG #quant-ph
paper · pdf · doi:10.48550/arxiv.2209.00564
Published parts of book in Federated Learning
arxiv created 2022/08/15 · openalex publication_date 2022/08/15 · arxiv updated 2022/09/02 · openalex created_date 2022/09/03 · openalex updated_date 2026/07/28
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 work, we put forth an efficient learning algorithm, namely federated quantum natural gradient descent (FQNGD), applied in a QFL framework which consists of the variational quantum circuit (VQC)-based quantum neural networks (QNN). The FQNGD algorithm admits much fewer training iterations for the QFL model to get converged and it can significantly reduce the total communication cost among local quantum devices. Compared with other federated learning algorithms, our experiments on a handwritten digit classification dataset corroborate the effectiveness of the FQNGD algorithm for the QFL in terms of a faster convergence rate on the training dataset and higher accuracy on the test one.