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This paper introduces a novel self-consistent midpoint aggregation method for stable quantum federated learning, addressing challenges like data heterogeneity and quantum noise with validation on real quantum machines.
This paper proposes DUQFL-Prox, a drift-stable quantum federated learning framework that uses deep-unfolded local optimization with adaptive SPSA updates and a proximal term to improve stability, generalization, and client fairness in heterogeneous distributed environments.
QDiffusion-TS is the first quantum generative diffusion model for real-world time series synthesis, replacing feed-forward components in a denoising transformer with quantum neural networks. It reduces trainable parameters by nearly three orders of magnitude and improves Wasserstein distance by 44% on financial data, with downstream forecasting gains up to 71% in RMSE.
This paper presents a rigorous N-qubit theory of stochastic quantum neural networks (SQNNs) for adversarially robust network intrusion detection, proving a decoherence-contraction theorem and showing that depolarising noise provides robustness against adversarial attacks, with experiments on the NSL-KDD dataset.