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Robust Decentralized Quantum Kernel Learning for Noisy and Adversarial Environment

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arxiv 2504.13782 v1 pith:KGTCDB4E submitted 2025-04-18 quant-ph cs.DC

Wenxuan Ma , Kuan-Cheng Chen , Shang Yu , Mengxiang Liu , Ruilong Deng This is my paper
classification quant-phcs.DC
keywords quantumdecentralizedadversariallearningnoiserobustapproachimpact
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This paper proposes a general decentralized framework for quantum kernel learning (QKL). It has robustness against quantum noise and can also be designed to defend adversarial information attacks forming a robust approach named RDQKL. We analyze the impact of noise on QKL and study the robustness of decentralized QKL to the noise. By integrating robust decentralized optimization techniques, our method is able to mitigate the impact of malicious data injections across multiple nodes. Experimental results demonstrate that our approach maintains high accuracy under noisy quantum operations and effectively counter adversarial modifications, offering a promising pathway towards the future practical, scalable and secure quantum machine learning (QML).

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Cited by 3 Pith papers

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

  1. Quantum Adaptive Excitation Network with Variational Quantum Circuits for Channel Attention

    quant-ph 2025-07 reject novelty 5.0 of 10

    A hybrid CNN that uses a small trainable quantum circuit for channel attention claims large accuracy gains, but the evidence is statistically thin.

  2. Quantum Machine Learning for UAV Swarm Intrusion Detection

    quant-ph 2025-09 conditional novelty 4.0 of 10

    A hybrid quantum neural network with 8 qubits and a small classical head outperforms quantum kernels, pure variational QNNs, and classical SVM on the UAVIDS-2025 UAV intrusion detection benchmark.

  3. Special-Unitary Parameterization for Trainable Variational Quantum Circuits

    quant-ph 2025-07 reject novelty 4.0 of 10

    SUN-VQC claims to avoid barren plateaus by using SU(4) exponential blocks, but the dynamical-Lie-algebra argument is invalid for the brick-wall circuit in the experiments.

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