用可控引导机制增强量子机器学习抗干扰能力
Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
- 用被动引导取代传统编码，使量子态向预设中间态收敛
- 在多种模型和数据集上提升对抗准确率，最高达40.19%改善
- 适合关注量子模型安全性的研究者与实际部署开发者
量子机器学习（QML）利用量子效应提升学习性能，但对对抗扰动敏感仍是实际应用的障碍。经典输入的小幅扰动会经量子编码阶段传播，扭曲输出量子态，导致模型性能下降。本文提出一种防御机制，将传统量子编码替换为基于被动引导的受控状态制备，引导编码态趋向一个受控的中间态。通过调节引导强度与迭代次数，该方法在保持高纯净准确率的同时，有效抑制对抗扰动影响，提升对抗准确率。实验表明，在不同QML模型与数据集上，面对梯度类对抗攻击，该方法稳定提升对抗准确率，最高可达40.19%。
原文摘要 · Abstract (English)
Quantum machine learning (QML) provides a promising framework for leveraging quantum-mechanical effects in learning tasks. However, its vulnerability to adversarial perturbations remains a major challenge for practical deployment. In QML systems, small perturbations applied to classical inputs can propagate through the quantum encoding stage and distort the resulting quantum state, thereby degrading model performance. In this work, we propose a defense mechanism that replaces the conventional quantum encoding stage of a QML model with passive steering-based controlled state preparation, which guides the encoded state toward a controlled intermediate state. By tuning the steering strength and the number of steering iterations, the proposed method suppresses the influence of adversarial perturbations while maintaining high clean accuracy and improving adversarial accuracy. Experimental results demonstrate that the passive steering-based defense consistently improves adversarial accuracy across different QML models and datasets under gradient-based adversarial attacks, achieving adversarial accuracy improvements of up to 40.19%.
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