Nonlocality in Continuous-Variable Quantum Networks

TL;DR

This paper develops a formalism to analyze nonlocality in continuous-variable quantum networks, revealing how nonlocal correlations persist under various conditions and can be enhanced by non-Gaussian resources, with feasible experimental implementations.

Contribution

It introduces a new formalism for studying CV network nonlocality using pseudospin measurements and explores the effects of network configuration, temperature, and non-Gaussianity on nonlocal correlations.

Findings

Nonlocality persists in star networks regardless of size.

Nonlocal correlations remain at high temperatures if squeezing exceeds a threshold.

Non-Gaussian resources can enhance network nonlocality.

Abstract

Quantum networks enable forms of nonlocality beyond the standard Bell scenario, with a multitude of potential applications. Continuous-variable (CV) platforms are particularly attractive for large-scale networks, offering deterministic entanglement generation and favorable prospects for long-distance distribution. Here we present a formalism to study CV network nonlocality using pseudospin measurements. Considering the linear chain and star configurations, we derive the maximal violations of the corresponding network locality inequalities for arbitrary two-mode states. Using two-mode squeezed vacuum states, we show that the strength of nonlocality in the star configuration remains independent of the network size. Moreover, the nonlocal correlations persist even at arbitrarily high temperatures provided the squeezing exceeds a critical threshold. Further, we demonstrate non-Gaussianity as an enhancer of network nonlocality through illustrations of various classes of non-Gaussian resources. Remarkably, a coherent superposition of single-photon subtractions across modes achieves maximal violation for vanishing squeezing. Finally, we provide schematics of an experimentally feasible implementation of CV network nonlocality based on the isomorphism between pseudospin and spatial parity observables.