发表机构
The University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究在PAC学习框架中，通过构造相对预言机的分布，证明能访问量子示例的量子学习器可高效生成某些分布，而仅能访问经典示例的量子学习器无法做到，为量子示例能力优于经典示例提供了依据。
AI 中文摘要
我们在PAC学习框架中研究量子示例与经典示例相比的能力。现有两个均具备量子计算能力的学习算法，一个可获取量子示例，另一个仅能获取经典示例。此前未知是否存在某学习任务可由前者高效完成但后者无法完成。我们的主要结果表明，相对于一个预言机，存在一些分布可由能访问量子示例的量子学习器高效生成，却无法由仅能访问经典示例的量子学习器生成，这为该问题给出了肯定解答。
英文摘要
Consider two PAC learning algorithms, both having access to quantum computation, but differing in the types of examples they obtain: one is provided with classical samples, while the other is given quantum samples. Are there any learning tasks that can be efficiently performed by the latter, but not by the former? This question, the focus of our work, is surprisingly still open. Our main result is to show that \emph{relative to an oracle}, there are distributions that can be efficiently generated by a quantum learner with access to quantum samples, but not by a quantum learner with access to only classical samples, making progress to answering this question in the affirmative.
Comments24 pages, 3 figures; significant updates to abstract, introduction and related works. Substantive contents remain the same
