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arXiv 2201.08986quant-ph
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超越随机访问码模型的信息因果性

Information causality beyond the random access code model

Centre for Quantum Technologies, National University of Singapore(新加坡国立大学量子技术中心);Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology(南方科技大学深圳量子科学与工程研究院);Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area(粤港澳大湾区量子科学中心);Department of Physics, National University of Singapore(新加坡国立大学物理系)

Baichu Yu, Valerio Scarani

AI总结:

本文提出基于冗余信息的新量化器,用于缩小信息因果性原则在量子相关性中的理论间隙。

摘要:

信息因果性(IC)是最早被用来限制量子相关性的原则之一。对于某些相关性家族,这一原则恰好恢复了量子集的边界;对于其他家族,仍存在差距。我们利用一个新的基于『冗余信息』概念的IC量化器来缩小这些差距。这一进展得益于认识到IC原则可以不依赖随机访问码的成功标准来捕捉。我们提供了强有力的数值证据,表明新的定义在相同场景下仍被量子相关性所遵守。

英文摘要:

Information causality (IC) was one of the first principles that have been invoked to bound the set of quantum correlations. For some families of correlations, this principle recovers exactly the boundary of the quantum set; for others, there is still a gap. We close some of these gaps using a new quantifier for IC, based on the notion of ``redundant information''. This progress was made possible by the recognition that the principle of IC can be captured without referring to the success criterion of random access codes. We give strong numerical evidence that the new definition is still obeyed by quantum correlations in the same scenario.

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。

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