mlsanityData to 14 Oct 2026

Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus

arXiv 2510.06228 ↗First posted 30 Sep 2025quant-ph cs.DC cs.ETcited by 5

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Abstract (arXiv)

Quantum machine learning (QML) holds the promise to solve classically intractable problems, but, as critical data can be fragmented across private clients, there is a need for distributed QML in a quantum federated learning (QFL) format. However, the quantum computers that different clients have access to can be error-prone and have heterogeneous error properties, requiring them to run circuits of different depths. We propose a novel solution to this QFL problem, Quorus, that utilizes a layerwise loss function for effective training of varying-depth quantum models, which allows clients to choose models for high-fidelity output based on their individual capacity. Quorus also presents various model designs based on client needs that optimize for shot budget, qubit count, midcircuit measurement, and optimization space. Our simulation and real-hardware results show the promise of Quorus: it increases the magnitude of gradients of higher depth clients and improves testing accuracy by 12.4% on average over the state-of-the-art.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 19 of its 51 references that are in the map. First posted on arXiv 30 Sep 2025.

Builds on

19 of 51 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
  3. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  4. 2021Federated Quantum Machine Learningcited by 82
  5. 2018Continuous-variable quantum neural networkscited by 74
  6. 2024Curriculum reinforcement learning for quantum architecture search under hardware errorscited by 37
  7. 2019Input Redundancy for Parameterized Quantum Circuitscited by 33
  8. 2022Systematic Literature Review: Quantum Machine Learning and its applicationscited by 32
  9. 2021Local Adaptivity in Federated Learning: Convergence and Consistencycited by 31
  10. 2024Recurrent Early Exits for Federated Learning with Heterogeneous Clientscited by 12
  11. 2024A Unified Theory of Quantum Neural Network Loss Landscapescited by 12
  12. 2025Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noisecited by 8
7 more
  1. 2025Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecastingcited by 7
  2. 2024NAC-QFL: Noise Aware Clustered Quantum Federated Learningcited by 5
  3. 2022Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Codingcited by 4
  4. 2024Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industriescited by 3
  5. 2021Quantum circuits with classical channels and the principle of deferred measurementscited by 2
  6. 2025EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Datacited by 2
  7. 2025ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computerscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correctioncited by 0
  2. 2026PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneitycited by 0
  3. 2026Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clientscited by 0
  4. 2026QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAGcited by 0
  5. 2026SQUARE: Structured Quantum Representation Adapters as Compact Quadratic Feature Maps for Frozen Language Modelscited by 0