量子模型在数据稀缺时比经典模型更稳定，更具鲁棒性。
Quantum Transfer Learning Shows Improved Robustness in Low-Data Regimes
- 对比量子与经典模型在低数据下的迁移学习表现
- 量子模型数据减少时性能下降更小，相对保留率更高
- 适合关注数据效率与模型鲁棒性的研究者
在数据有限的迁移学习场景中，模型需在极少标注数据下适应新任务。以往研究多聚焦于提升绝对准确率，但对量子与经典模型在真实迁移设置中的对比，尤其是低数据情形下的表现，仍缺乏实证。本文系统评估了多种量子与经典架构在不同迁移任务和重训练配置下的表现，采用准确率下降和相对性能保留率（RPR）量化鲁棒性。结果表明，尽管经典模型在峰值性能上占优，但在数据受限时表现出显著性能下降；而量子模型在不同数据规模下保持更稳定的性能，展现出更强的鲁棒性与数据效率。该发现为量子模型在低资源迁移学习中的优势提供了实证支持。
原文摘要 · Abstract (English)
Transfer learning under limited data is a challenging setting, where models must adapt to new tasks with minimal supervision. Prior work has primarily focused on improving absolute accuracy in transfer learning. However, empirical evidence comparing quantum and classical models in realistic transfer learning settings remains limited, especially in low-data regimes. In this work, we systematically study the robustness of quantum models under reduced training data. We evaluate multiple quantum and classical architectures across diverse transfer tasks and retraining configurations, and quantify robustness using accuracy degradation and relative performance retention (RPR). Our results show that, although classical models often achieve higher peak performance, they exhibit significantly larger degradation when training data is limited. In contrast, quantum models maintain more stable performance across data regimes, indicating improved robustness and data efficiency. These findings provide empirical evidence that quantum models can offer improved robustness in low-resource transfer learning scenarios.
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