Robust Decentralized Quantum Kernel Learning for Noisy and Adversarial Environment
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910914129690624 |
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| author | Ma, Wenxuan Chen, Kuan-Cheng Yu, Shang Liu, Mengxiang Deng, Ruilong |
| author_facet | Ma, Wenxuan Chen, Kuan-Cheng Yu, Shang Liu, Mengxiang Deng, Ruilong |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13782 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Decentralized Quantum Kernel Learning for Noisy and Adversarial Environment Ma, Wenxuan Chen, Kuan-Cheng Yu, Shang Liu, Mengxiang Deng, Ruilong Quantum Physics Distributed, Parallel, and Cluster Computing 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). |
| title | Robust Decentralized Quantum Kernel Learning for Noisy and Adversarial Environment |
| topic | Quantum Physics Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.13782 |