研究量子电路迁移学习机制，发现可高效适配新任务。
Transfer Learning Analysis of Variational Quantum Circuits
- 用单参数酉子群分析量子电路跨域迁移路径
- 理论证明迁移中损失边界受适应能力约束
- 提出解析微调法，适合相似领域快速适配
本文分析变分量子电路（VQC）的迁移学习。框架从某一领域的预训练VQC出发，计算迁移到新领域所需的单参数酉子群变换。建立形式化理论，研究在损失边界下的适应性与能力。理论观察到VQC中的知识迁移现象，并提供机制的启发式解释。推导出一种解析微调方法，可实现相似领域迁移的最优转换。
原文摘要 · Abstract (English)
This work analyzes transfer learning of the Variational Quantum Circuit (VQC). Our framework begins with a pretrained VQC configured in one domain and calculates the transition of 1-parameter unitary subgroups required for a new domain. A formalism is established to investigate the adaptability and capability of a VQC under the analysis of loss bounds. Our theory observes knowledge transfer in VQCs and provides a heuristic interpretation for the mechanism. An analytical fine-tuning method is derived to attain the optimal transition for adaptations of similar domains.
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