mlsanityData to 7 Oct 2026

Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding

arXiv 2212.01732 ↗First posted 4 Dec 2022quant-ph cs.LGcited by 4

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Abstract (arXiv)

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 61 references that are in the map. First posted on arXiv 4 Dec 2022.

Builds on

25 of 61 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  4. 2018Supervised learning with quantum enhanced feature spacescited by 654
  5. 2018Barren plateaus in quantum neural network training landscapescited by 624
  6. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  7. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
  8. 2019Universally Slimmable Networks and Improved Training Techniquescited by 219
  9. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  10. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  11. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  12. 2021Federated Quantum Machine Learningcited by 82
13 more
  1. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  2. 2018Continuous-variable quantum neural networkscited by 74
  3. 2020Reinforcement Learning with Quantum Variational Circuitscited by 74
  4. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  5. 2021Quantum Federated Learning with Quantum Datacited by 53
  6. 2022Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Designcited by 25
  7. 2022Slimmable Quantum Federated Learningcited by 23
  8. 2022Quantum Multi-Agent Meta Reinforcement Learningcited by 23
  9. 2021Exponentially Many Local Minima in Quantum Neural Networkscited by 22
  10. 2022An Introduction to Quantum Machine Learning for Engineerscited by 22
  11. 2021Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networkscited by 14
  12. 2022Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applicationscited by 12
  13. 2019Quantum Network: Security Assessment and Key Managementcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2023Towards Quantum Federated Learningcited by 20
  2. 2025Layerwise Federated Learning for Heterogeneous Quantum Clients using Quoruscited by 4
  3. 2025New Insights on Unfolding and Fine-tuning Quantum Federated Learningcited by 1
  4. 2026A Drift Stable Quantum Federated Learning for Intelligent Servicescited by 0