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arXiv 0901.4510quant-ph
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利用精确凸优化操纵非线性和线性纠缠见证的通用算法

A general algorithm for manipulating non-linear and linear entanglement witnesses by using exact convex optimization

M. A. Jafarizadeh, K. Aghayar, A. Heshmati

摘要:

提出了一种遗传算法,将获取给定量子系统的线性和非线性纠缠证据的问题归结为凸优化问题。该方法具有很好的普适性,可以应用于任意N体量子系统的纠缠检测。为此,定义了一个从可分密度矩阵的凸空间到一个称为可行域的凸区域的映射,其中利用精确凸优化方法,可以从多边形可行域中得到线性纠缠证据,而对于曲面可行域,线性纠缠证据族的包络可以看作是非线性纠缠证据.这种方法提出了一个新的方法框架内,大多数以前的电子战可以研究。最后,为了证明所提出的方法的能力,除了为不可扩展乘积基、W-态和W-态GHZ中的密度矩阵的纠缠检测提供了一些非线性证据外,还包括了一些三量子比特系统及其分类和纠缠检测的例子.同时也解释了如何用本文提出的方法操纵我们和其他作者发表的一些论文中出现的大多数不可分解的线性和非线性三量子比特纠缠见证。关键词:非线性与线性纠缠见证,凸最优化。PACS编号:03.67.Mn,03.65.Ud

英文摘要:

A generic algorithm is developed to reduce the problem of obtaining linear and nonlinear entanglement witnesses of a given quantum system, to convex optimization problem. This approach is completely general and can be applied for the entanglement detection of any N-partite quantum system. For this purpose, a map from convex space of separable density matrices to a convex region called feasible region is defined, where by using exact convex optimization method, the linear entanglement witnesses can be obtained from polygonal shape feasible regions, while for curved shape feasible regions, envelope of the family of linear entanglement witnesses can be considered as nonlinear entanglement witnesses. This method proposes a new methodological framework within which most of previous EWs can be studied. To conclude and in order to demonstrate the capability of the proposed approach, besides providing some nonlinear witnesses for entanglement detection of density matrices in unextendible product bases, W-states, and GHZ with W-states, some further examples of three qubits systems and their classification and entanglement detection are included. Also it is explained how one can manipulate most of the non-decomposable linear and nonlinear three qubits entanglement witnesses appearing in some of the papers published by us and other authors, by the method proposed in this paper. Keywords: non-linear and linear entanglement witnesses, convex optimization. PACS number(s): 03.67.Mn, 03.65.Ud

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