Star network non-n-local correlations can resist consistency noises better

TL;DR

This paper demonstrates that star network quantum non-n-local correlations are more resilient to consistency noises than polygon and linear networks, with the persistency of non-n-locality increasing with the number of sources.

Contribution

It reveals the superior noise resistance of star network non-n-local correlations and analyzes their persistency under consistency noises, extending understanding of network robustness.

Findings

Star networks can resist consistency noises better than polygon and linear networks.

Persistency number of sources n approaches infinity under consistent noise in star networks.

Maximal sources nmax for demonstrating non-nmax-local correlation is affected by partial consistency noises.

Abstract

Imperfections from devices can result in the decay or even vanish of non-n-local correlations as the number of parties n increases in the polygon and linear quantum networks ([Phys. Rev. A 106, 042206 (2022)] and [Phys. Rev. A 107, 032404 (2023)]). Even so this phenomenon is also for the special kind of noises, including consistency noises of a sequence of devices, which means the sequence of devices have the same probability fails to detect. However, in the paper, we discover that star network quantum non-n-local correlations can resist better consistency noises than these in polygon and linear networks. We first calculate the noisy expected value o f star network non-n-locality and analyze the persistency conditions theoretically. When assume that congener devices have the consistency noise, the persistency number of sources n has been rid of such noises, and approximates to the infinity. Polygon and linear network non-n-local correlations can not meet the requirements. Furthermore, we explore the change pattern of the maximal number of sources nmax such that non-nmax-local correlation can be demonstrated in the star network under the influence of partially consistent noises, which is more general than consistent ones.