Semidefinite programming lower bounds on the squashed entanglement

TL;DR

This paper introduces a hierarchy of semidefinite programming-based lower bounds to better approximate the squashed entanglement, a key quantum entanglement measure, addressing computational challenges.

Contribution

The authors develop a new hierarchy of semidefinite programming bounds that improve the computability of the squashed entanglement measure.

Findings

Provides a systematic way to compute lower bounds

Enhances understanding of entanglement quantification

Potentially improves algorithms for quantum information tasks

Abstract

The squashed entanglement is a widely used entanglement measure that has many desirable properties. However, as it is based on an optimization over extensions of arbitrary dimension, one drawback of this measure is the lack of good algorithms to compute it. Here, we introduce a hierarchy of semidefinite programming lower bounds on the squashed entanglement.