量子支持向量回归在异常检测中表现稳定，抗噪能力强但易受对抗攻击。
Quantum Support Vector Regression for Robust Anomaly Detection
- 基于量子支持向量回归，在11个数据集上实测其异常检测性能。
- 模型在部分数据集上优于无噪声模拟，且对多种量子噪声具有鲁棒性。
- 尽管抗噪性强，但对对抗攻击敏感，现有方法无法提升防御能力。
异常检测（AD）在数据分析中至关重要，尤其在信息技术安全领域。本文研究量子机器学习在异常检测中的应用潜力，重点关注其对噪声和对抗攻击的鲁棒性。我们基于先前关于半监督异常检测的量子支持向量回归（QSVR）工作，在IBM量子硬件上对11个数据集进行了全面基准测试。结果表明，QSVR展现出优异的分类性能，甚至在两个数据集上超越了无噪声仿真结果。此外，我们研究了在当前NISQ时代不可避免的量子噪声对QSVR性能的影响。发现该模型对去极化、相位阻尼、相位翻转和比特翻转噪声具有鲁棒性，而振幅阻尼与校准误差噪声则更具破坏性。最后，通过探索量子对抗机器学习，证明QSVR对对抗攻击高度敏感，无论引入量子噪声或对抗训练均无法改善其防御能力。
原文摘要 · Abstract (English)
Anomaly Detection (AD) is critical in data analysis, particularly within the domain of IT security. In this study, we explore the potential of Quantum Machine Learning for application to AD with special focus on the robustness to noise and adversarial attacks. We build upon previous work on Quantum Support Vector Regression (QSVR) for semisupervised AD by conducting a comprehensive benchmark on IBM quantum hardware using eleven datasets. Our results demonstrate that QSVR achieves strong classification performance and even outperforms the noiseless simulation on two of these datasets. Moreover, we investigate the influence of - in the NISQ-era inevitable - quantum noise on the performance of the QSVR. Our findings reveal that the model exhibits robustness to depolarizing, phase damping, phase flip, and bit flip noise, while amplitude damping and miscalibration noise prove to be more disruptive. Finally, we explore the domain of Quantum Adversarial Machine Learning by demonstrating that QSVR is highly vulnerable to adversarial attacks, with neither quantum noise nor adversarial training improving the model's robustness against such attacks.
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