mlsanityData to 7 Oct 2026

Federated Quantum Natural Gradient Descent for Quantum Federated Learning

arXiv 2209.00564 ↗First posted 15 Aug 2022quant-ph cs.DC cs.LGcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 18 of its 35 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

18 of 35 references.

References · 18

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  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
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6 more
  1. 2020Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognitioncited by 56
  2. 2021An end-to-end trainable hybrid classical-quantum classifiercited by 49
  3. 2022When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computingcited by 40
  4. 2021QTN-VQC: An End-to-End Learning framework for Quantum Neural Networkscited by 27
  5. 2022Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regressioncited by 26
  6. 2021Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networkscited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 2; 2025: 6; 2026: 1.

14 papers.

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  1. 2022PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protectioncited by 26
  2. 2023Towards Quantum Federated Learningcited by 20
  3. 2025Quantum Federated Learning: A Comprehensive Surveycited by 7
  4. 2023Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementationcited by 5
  5. 2025QFAL: Quantum Federated Adversarial Learningcited by 5
  6. 2025When Federated Learning Meets Quantum Computing: Survey and Research Opportunitiescited by 5
  7. 2024From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networkscited by 4
  8. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
  9. 2023Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuitscited by 1
  10. 2024Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuitscited by 1
  11. 2025Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcarecited by 1
  12. 2023Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Frameworkcited by 0
2 more
  1. 2025LLM-QFL: Distilling Large Language Model for Quantum Federated Learningcited by 0
  2. 2026Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processingcited by 0