Quantifying Nonlocality Based on Local Hidden Variable Models

TL;DR

This paper presents a new method to quantify quantum nonlocality in high-dimensional systems by determining the minimal white noise needed to render the system local, using convex optimization techniques.

Contribution

It introduces a novel scheme based on local hidden variable models that is applicable to high-dimensional quantum systems and is computationally feasible.

Findings

The scheme effectively quantifies nonlocality in complex quantum systems.

It leverages semidefinite programming for numerical computation.

The approach has a clear geometric interpretation.

Abstract

We introduce a fresh scheme based on the local hidden variable models to quantify nonlocality for arbitrarily high-dimensional quantum systems. Our scheme explores the minimal amount of white noise that must be added to the system in order to make the system local and realistic. Moreover, the scheme has a clear geometric significance and is numerically computable due to powerful computational and theoretical methods for the class of convex optimization problems known as semidefinite programs.