最小误差和最大置信度镜像态区分中情境优势的实验演示
Experimental demonstration of Contextual Advantage in minimum error and maximum confidence mirror-state discrimination
AI总结：
本文首次实验演示了在最小误差和最大置信度区分三个镜像对称态时的情境优势，利用干涉量子行走实现，结果与理论模拟一致，并可推广至其他品质因数。
摘要：
情境性众所周知是划定经典理论与量子理论边界的重要资源，也是识别展现量子优势任务的关键。在近期的一系列工作中 [Schmid 和 Spekkens, Phys.Rev.X 8, 011015 (2018); Mukherjee, Naonit 和 Pan, Phys.Rev.A 106, 012216 (2022); Flatt, Lee, Carceller, Brask 和 Bae, PRX QUANTUM 3, 030337 (2022)]，也已证明情境性是量子态区分（QSD）任务中的关键资源，包括最小误差区分（MED）和最大置信度区分（MCD），以及许多其他品质因数。尽管上述工作取得了根本性进展，但它们均未提及如何实现其精巧方案，而这对于最终在真实QSD任务中应用该资源无疑是必要的。在本文中，我们报告了利用干涉量子行走对三个镜像对称态在MED和MCD中情境优势的首次实验演示，该方法可轻松推广到QSD中的任何品质因数。我们的实验与理论模拟结果高度一致，并展示了利用该方法探索更简单的情境性见证版本的巨大潜力，以及展示需要QSD的各种任务的量子优势。
英文摘要：
Contextuality is well known as a vital resource for locating the boundary between classical and quantum theories, as well as identifying tasks showing quantum advantage. In a surge of recent works [Schmid and Spekkens, Phys.Rev.X 8, 011015 (2018); Mukherjee, Naonit and Pan, Phys.Rev.A 106, 012216 (2022); Flatt, Lee, Carceller, Brask and Bae, PRX QUANTUM 3, 030337 (2022)], it has also been shown that contextuality is the crucial resource in quantum state discrimination (QSD) tasks, including minimum error discrimination (MED) and maximum confidence discrimination (MCD), together with many other figure-of-merits. Despite the fundamental progress made by those aforementioned works, none of them mention about how to realize their fancy proposals, which is doubtlessly necessary for the final goal of applying this resource in real QSD tasks. In this paper, we report the first experimental demonstration of contextual advantage in both MED and MCD for three mirror-symmetric states using interferometric quantum walk, which can be easily generalized to any figure-of-merit in QSD. Our experiment agrees well with the result of theoretical simulation, and also shows the great potentiality of leveraging this method to explore a simpler version for the witness of contextuality, as well as demonstrating quanutm advantage of various tasks that require QSD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
