Separability discrimination and decomposition of $m$-partite quantum mixed states

TL;DR

This paper introduces a unified tensor optimization algorithm that efficiently detects separability and provides decomposition of $m$-partite quantum mixed states, advancing quantum information theory methods.

Contribution

It proposes a novel tensor-based optimization approach for separability detection and decomposition, unifying previous methods into a single algorithm.

Findings

The algorithm can detect separability of mixed states.

It provides a decomposition for separable states.

Not all symmetric separable states have symmetric decompositions.

Abstract

The separability detecting problem of mixed states is one of the fundamental problems in quantum information theory. In the last 20 years, almost all methods are based on the sufficient or necessary conditions for entanglement. However, in this paper, we only need one algorithm to solve the problem. We propose a tensor optimization method to check whether an $m$-partite quantum mixed state is separable or not and give a decomposition for it if it is. We first convert the separability discrimination problem of mixed states to the positive Hermitian decomposition problem of Hermitian tensors. Then, employing the $E$-truncated $K$-moment method, we obtain an optimization model for discriminating separability. Moreover, applying semidefinite relaxation method, we get a hierarchy of semidefinite relaxation optimization models and propose an $E$-truncated $K$-moment and semidefinite relaxations (ETKM-SDR) algorithm for detecting the separability of mixed states. The algorithm can also be used for symmetric and non-symmetric decomposition of separable mixed states. By numerical examples, we find that not all symmetric separable states have symmetric decompositions. The algorithm can be used for studying properties of mixed states in the future.