提出量子神经网络的可信训练方法，确保对抗扰动下预测准确
Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks
- 用区间与仿射算术追踪量子模型边界，实现可信训练
- 实验证明模型在扰动范围内决策边界稳健可靠
- 适合关注量子机器学习安全性的研究者和工程师
量子机器学习有望高效学习数据特征以完成分类等任务。区间边界传播（IBP）是经典机器学习中一种流行的可信训练方法，通过全程追踪模型的上下界，在训练中确保模型在对抗扰动下仍能正确预测。尽管IBP在经典领域取得成功，但量子领域的可信训练研究仍有限。本文提出量子区间边界传播（QIBP），为量子机器学习建立可信训练流程，保证模型在对抗扰动下的预测准确性。我们采用区间算术和仿射算术两种方式实现QIBP，比较两者在精度及其他设计因素上的权衡。大量实验表明，经认证训练的模型具有稳健的决策边界，能在训练所得的对抗鲁棒性范围内确保样本正确分类。
原文摘要 · Abstract (English)
Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Interval bound propagation (IBP) is a popular certified training method in classical machine learning, where the lower and upper bounds are tracked throughout the model. These bounds are used during training to ensure that the model is certified to predict the correct label even under adversarial perturbations. While IBP is successful in classical domain, there are limited certified training efforts in quantum domain. In this paper, we present quantum interval bound propagation (QIBP) to establish a certified training routine for quantum machine learning, certifying the accuracy of models under adversarial perturbations. We implement QIBP using both interval and affine arithmetic to explore the tradeoffs between the two implementations in terms of accuracy and other design considerations. Extensive evaluation demonstrates that the resulting certified trained models have robust decision boundaries, guaranteed to predict the correct class for the samples within the trained adversarial robustness bounds.
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