Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach

• URL: http://arxiv.org/abs/2601.07882v1
• Date: Sun, 11 Jan 2026 23:29:08 GMT
• Title: Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach
• Authors: Ratun Rahman, Shaba Shaon, Dinh C. Nguyen,
• Abstract summary: We propose a novel integrated sporadic-personalized approach called SPQFL that simultaneously handles quantum noise and data heterogeneity in a single QFL framework.<n>We conduct a rigorous convergence analysis for the proposed SPQFL framework, with both sporadic and personalized learning considerations.<n> Extensive simulation results in real-world datasets also illustrate that the proposed SPQFL approach yields significant improvements in terms of training performance and convergence stability.
• Score: 7.995944232955725
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve optimal model training performance primarily due to inherent heterogeneity in terms of (i) quantum noise where current quantum devices are subject to varying levels of noise due to varying device quality and susceptibility to quantum decoherence, and (ii) heterogeneous data distributions where data across participating quantum devices are naturally non-independent and identically distributed (non-IID). To address these challenges, we propose a novel integrated sporadic-personalized approach called SPQFL that simultaneously handles quantum noise and data heterogeneity in a single QFL framework. It is featured in two key aspects: (i) for quantum noise heterogeneity, we introduce a notion of sporadic learning to tackle quantum noise heterogeneity across quantum devices, and (ii) for quantum data heterogeneity, we implement personalized learning through model regularization to mitigate overfitting during local training on non-IID quantum data distributions, thereby enhancing the convergence of the global model. Moreover, we conduct a rigorous convergence analysis for the proposed SPQFL framework, with both sporadic and personalized learning considerations. Theoretical findings reveal that the upper bound of the SPQFL algorithm is strongly influenced by both the number of quantum devices and the number of quantum noise measurements. Extensive simulation results in real-world datasets also illustrate that the proposed SPQFL approach yields significant improvements in terms of training performance and convergence stability compared to the state-of-the-art methods.

Related papers

• Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model [0.0]
  We propose a framework that generates projected ensembles via chaotic Hamiltonian time evolution.<n>This method improves trainability and robustness, broadening the applicability of quantum generative modeling.
  arXiv  Detail & Related papers  (2026-02-25T16:09:50Z)
• Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions [47.08625631041616]
  Quantum federated learning (QFL) combines quantum computing and federated learning to enable decentralized model training while maintaining data privacy.<n>Existing QFL frameworks largely focus on homogeneity among quantum textcolorblackclients, and they do not account for real-world variances in quantum data distributions, encoding techniques, hardware noise levels, and computational capacity.<n>These differences can create instability during training, slow convergence, and reduce overall model performance.
  arXiv  Detail & Related papers  (2025-11-27T06:35:45Z)
• Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise [1.2026018242953707]
  SpoQFL dynamically adjusts training strategies based on noise fluctuations.<n>Experiments on real-world datasets demonstrate that SpoQFL significantly outperforms conventional QFL approaches.
  arXiv  Detail & Related papers  (2025-07-15T20:30:11Z)
• A purely Quantum Generative Modeling through Unitary Scrambling and Collapse [6.647966634235082]
  Quantum Scrambling and Collapse Generative Model (QGen) is a purely quantum paradigm that eliminates classical dependencies.<n>We introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus.<n> Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling.
  arXiv  Detail & Related papers  (2025-06-12T11:00:21Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
  Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
  arXiv  Detail & Related papers  (2025-05-24T02:51:34Z)
• Mixed-State Quantum Denoising Diffusion Probabilistic Model [0.40964539027092906]
  We propose a mixed-state quantum denoising diffusion probabilistic model (MSQuDDPM) to eliminate the need for scrambling unitaries.<n>MSQuDDPM integrates depolarizing noise channels in the forward diffusion process and parameterized quantum circuits with projective measurements in the backward denoising steps.<n>We evaluate MSQuDDPM on quantum ensemble generation tasks, demonstrating its successful performance.
  arXiv  Detail & Related papers  (2024-11-26T17:20:58Z)
• Multimodal deep representation learning for quantum cross-platform verification [60.01590250213637]
  Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms. We introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities. We devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation.
  arXiv  Detail & Related papers  (2023-11-07T04:35:03Z)
• On-the-fly Tailoring towards a Rational Ansatz Design for Digital Quantum Simulations [0.0]
  It is imperative to develop low depth quantum circuits that are physically realizable in quantum devices. We develop a disentangled ansatz construction protocol that can dynamically tailor an optimal ansatz. The construction of the ansatz may potentially be performed in parallel quantum architecture through energy sorting and operator commutativity prescreening.
  arXiv  Detail & Related papers  (2023-02-07T11:22:01Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.