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arXiv · 1707.02958

Convex optimization over classes of multiparticle entanglement

Jiangwei Shang·Otfried Gühne
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Abstract

A well-known strategy to characterize multiparticle entanglement utilizes the notion of stochastic local operations and classical communication (SLOCC), but characterizing the resulting entanglement classes is difficult. Given a multiparticle quantum state, we first show that Gilbert's algorithm can be adapted to prove separability or membership in a certain entanglement class. We then present two algorithms for convex optimization over SLOCC classes. The first algorithm uses a simple gradient approach, while the other one employs the accelerated projected-gradient method. For demonstration, the algorithms are applied to the likelihood-ratio test using experimental data on bound entanglement of a noisy four-photon Smolin state [Phys. Rev. Lett. 105, 130501 (2010)].

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Jiangwei Shang, Otfried Gühne. 2018-02-02. Convex optimization over classes of multiparticle entanglement. https://doi.org/10.1103/physrevlett.120.050506

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