通用方法用于自测量子资源的优化鲁棒性
Universal method for optimized robustness in self-testing of quantum resources
AI总结：
本文提出一种通用方法，通过优化等价变换来提升自测量子资源的鲁棒性分析，适用于多种自测场景，如贝尔情景中的可 steer 性集合、准备-测量情景中的量子态集合以及引导情景中的纠缠态。
AI中文摘要：
自测是一种现象，其中特定的量子态或测量的使用仅能从它们生成的相关性推断出来。我们介绍了一种通用方法，用于在各种量子资源的自测中进行鲁棒性分析。与以往依赖选择特定等距变换的数值方法不同，我们的方法通过优化等价变换，从而得到更紧的鲁棒性界。这种优化采用了非交换多项式优化中已建立的技术，即半定规划松弛。我们的方法可以普遍应用于多种自测设置，包括贝尔情景中的可 steer 性集合、准备-测量情景中的量子态集合以及引导情景中的纠缠态。我们展示了该方法在多个具体例子中能够超越之前报告的鲁棒性界。
英文摘要：
Self-testing is a phenomenon where the use of specific quantum states or measurements can be inferred solely from the correlations they generate. We introduce a universal method for conducting robustness analysis in the self-testing of various quantum resources. Unlike previous numerical approaches, which rely on selecting specific isometries, our method optimizes over equivalence transformations, thereby leading to tighter robustness bounds. This optimization employs the well-established technique of semidefinite programming relaxations for non-commuting polynomial optimization. Our method can be universally applied to diverse self-testing settings, including steerable assemblages in the Bell scenario, constellations of quantum states in the prepare-and-measure scenario, and entangled states in the steering scenario. We demonstrate the method's capability to surpass previously reported robustness bounds across a range of concrete examples.
