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Towards Quantum Federated Learning

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arxiv 2306.09912 v4 pith:OY3MHOAC submitted 2023-06-16 cs.LG quant-ph

Chao Ren , Rudai Yan , Huihui Zhu , Han Yu , Minrui Xu , Yuan Shen , Yan Xu , Ming Xiao
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Zhao Yang Dong Mikael Skoglund Dusit Niyato Leong Chuan Kwek
This is my paper
classification cs.LGquant-ph
keywords fieldquantumlearningcomprehensivefederatedtechniquesapplicationschallenges
verification ladder T0 review T1 audit T2 compute T3 formal
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Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. New Insights on Unfolding and Fine-tuning Quantum Federated Learning

    cs.LG 2025-06 reject novelty 5.0 of 10

    Deep unfolding with client-learned hyperparameters is claimed to improve quantum federated learning accuracy from roughly 55% to 90%, but the supporting proof and baseline data are not established.

  2. Universal Fluctuations in the Tail Probability for d=2 Random Walks in Space-Time Random Environments

    cond-mat.stat-mech 2025-08 reject novelty 4.0 of 10

    The reported d=2 random-walk universality result is unsupported: the full text is a quantum federated learning survey that never mentions random walks, tail probabilities, or lambda_ext.

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