mlsanityData to 7 Oct 2026

Quantum Federated Learning With Quantum Networks

arXiv 2310.15084 ↗First posted 23 Oct 2023quant-ph cs.LGcited by 8

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Abstract (arXiv)

A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable information that is vulnerable to access by third parties. Ideas of using the quantum internet to address this issue have been previously proposed, which would enable fast and completely secure online communications. Previous work has yielded a hybrid quantum-classical transfer learning scheme for classical data and communication with a hub-spoke topology. While quantum communication is secure from eavesdrop attacks and no measurements from quantum to classical translation, due to no cloning theorem, hub-spoke topology is not ideal for quantum communication without quantum memory. Here we seek to improve this model by implementing a decentralized ring topology for the federated learning scheme, where each client is given a portion of the entire dataset and only performs training on that set. We also demonstrate the first successful use of quantum weights for quantum federated learning, which allows us to perform our training entirely in quantum.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

23 of 33 references.

References · 23

  1. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  2. 2020The power of quantum neural networkscited by 377
  3. 2021Generalization in quantum machine learning from few training datacited by 214
  4. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  5. 2021Federated Quantum Machine Learningcited by 82
  6. 2021The Dawn of Quantum Natural Language Processingcited by 60
  7. 2020Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognitioncited by 56
  8. 2021Quantum Federated Learning with Quantum Datacited by 53
  9. 2021An end-to-end trainable hybrid classical-quantum classifiercited by 49
  10. 2021Quantum federated learning through blind quantum computingcited by 44
  11. 2022When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computingcited by 40
  12. 2021Quantum machine learning with differential privacycited by 30
11 more
  1. 2022Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regressioncited by 26
  2. 2022PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protectioncited by 26
  3. 2022Slimmable Quantum Federated Learningcited by 23
  4. 2022Quantum deep recurrent reinforcement learningcited by 23
  5. 2021QuantumFed: A Federated Learning Framework for Collaborative Quantum Trainingcited by 22
  6. 2022FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneitycited by 20
  7. 2021Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networkscited by 19
  8. 2023Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descentcited by 14
  9. 2020TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecturecited by 12
  10. 2022A Ring Topology-based Communication-Efficient Scheme for D2D Wireless Federated Learningcited by 2
  11. 2021QuISP: a Quantum Internet Simulation Packagecited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 1.

8 papers.

  1. 2024Consensus-based Distributed Quantum Kernel Learning for Speech Recognitioncited by 10
  2. 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
  3. 2025Experimentally validated quantum-secure federated learning over a multi-user quantum networkcited by 5
  4. 2025When Federated Learning Meets Quantum Computing: Survey and Research Opportunitiescited by 5
  5. 2025Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomycited by 4
  6. 2025Towards Personalized Quantum Federated Learning for Anomaly Detectioncited by 4
  7. 2025How Can Quantum Deep Learning Improve Large Language Models?cited by 0
  8. 2026Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networkscited by 0