Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent

Jun Qi*, Xiao Lei Zhang, Javier Tejedor

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)

Abstract

Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is always expected to minimize the communication overhead among different quantum participants. In this work, we propose an efficient optimization algorithm, namely federated quantum natural gradient descent (FQNGD), and further, apply it to a QFL framework that is com-posed of a variational quantum circuit (VQC)-based quantum neural networks (QNN). Compared with stochastic gradient descent methods like Adam and Adagrad, the FQNGD algorithm admits much fewer training iterations for the QFL to get converged. Moreover, it can significantly reduce the total communication overhead among local quantum devices. Our experiments on a handwritten digit classification dataset justify the effectiveness of the FQNGD for the QFL framework in terms of a faster convergence rate on the training set and higher accuracy on the test set.

Original languageEnglish
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Number of pages5
Edition1st
ISBN (Electronic)9781728163277
ISBN (Print)9781728163284
DOIs
Publication statusPublished - 4 Jun 2023
Event2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023
https://ieeexplore.ieee.org/xpl/conhome/10094559/proceeding

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2023-June
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Competition

Competition2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/TerritoryGreece
CityRhodes Island
Period4/06/2310/06/23
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Quantum neural network
  • variational quantum circuit
  • quantum federated learning
  • federated quantum natural gradient descent

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