对比量子与经典学习在标注查询下的复杂度差异。
The Quantum Learning Menagerie (A survey on Quantum learning for Classical concepts)
- 基于概率近似正确框架研究量子编码的古典概念学习
- 揭示量子与经典学习在查询、样本和时间复杂度上的分离
- 提出23个开放问题，指引未来研究方向
本文综述了量子学习理论领域的多项成果，重点聚焦于在概率近似正确（PAC）框架下，学习量子编码的古典概念。核心在于分析不同标注预言机下的查询、样本和时间复杂度分离现象。本文旨在整合该领域所有已知结果，并通过留下23个未解问题，凸显当前理解的局限性。
原文摘要 · Abstract (English)
This paper surveys various results in the field of Quantum Learning theory, specifically focusing on learning quantum-encoded classical concepts in the Probably Approximately Correct (PAC) framework. The cornerstone of this work is the emphasis on query, sample, and time complexity separations between classical and quantum learning that emerge under learning with query access to different labeling oracles. This paper aims to consolidate all known results in the area under the above umbrella and underscore the limits of our understanding by leaving the reader with 23 open problems.
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