Quantum Transfer Learning Shows Improved Robustness in Low-Data Regimes
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 t...
| Authors | Li-An Lo, Li-Yi Hsu, Hsien-Yi Hsieh |
|---|---|
| Primary category | quant-ph |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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