提出新方法评估量子神经网络在噪声下的鲁棒性，关键发现是几何特征可预测抗噪能力。
JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks
- 通过雅可比几何分析参数扰动，量化噪声对量子模型的影响
- 在真实噪声下验证，几何描述符能准确预测模型鲁棒性
- 适合关注量子机器学习抗噪设计的研究者
NISQ时代对量子计算提出严苛要求，噪声与退相干从根本上限制性能。经典深度学习中，模型对剪枝、噪声注入和结构扰动具有鲁棒性，源于表示中的内在冗余。但量子神经网络（QNN）在真实噪声下如何实现类似鲁棒性仍不明确。本文提出JGRA：一种基于雅可比几何的噪声感知QNN鲁棒性评估框架，包含熵匹配的噪声校准、噪声感知训练和噪声条件下的雅可比提取，生成连接干净状态结构与噪声推理行为的几何描述符。实验表明，这些描述符能编码未见噪声下的鲁棒性预测信息。
原文摘要 · Abstract (English)
The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance. In classical deep learning, model robustness and resilience to perturbations are well studied: deep neural networks (DNNs) maintain high performance despite pruning, noise injection, and structural perturbations due to inherent redundancy in their representations. A central challenge in quantum machine learning is to transfer this notion of robustness to quantum neural networks (QNNs) under realistic NISQ noise. While classical deep learning exhibits robustness through structural redundancy, analogous principles for QNNs remain underdeveloped. We propose JGRA: a framework for assessing robustness in noise-aware QNNs via Jacobian geometry, capturing model sensitivity to parameter perturbations induced by noise. Our method includes entropy-matched noise calibration, noise-aware training, and noise-conditioned Jacobian extraction, yielding geometric descriptors that link clean-regime structure to noisy inference behaviour. We also empirically demonstrate that these descriptors encode predictive information about robustness under unseen noise.
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