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arXiv quant-ph/0603199quant-phcs.CC
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量子可分性问题的计算复杂性

Computational complexity of the quantum separability problem

Lawrence M. Ioannou

摘要:

自从纠缠被确定为一种计算和密码资源以来,研究人员一直在寻找有效的方法来判断给定的密度矩阵是否代表一种非纠缠或可分离的状态。本文给出了第一个系统的和全面的处理这个(二分)量子可分性问题,专注于其确定性(而不是随机)的计算复杂性。首先,我回顾了单侧测试的可分性,特别注意半定规划方法。然后,我讨论了制定量子可分性问题的各种方式,从精确到近似的配方,后者是本文的主要重点。然后,我给出了一个彻底的处理问题的复杂性类NP,NP-完全,和co-NP的关系。本文还讨论了Gurvits的NP-困难性结果对某些相关问题的强NP-困难性的推广。一个主要的开放问题是量子可分性问题的NP-包含公式(QSEP)是否是Karp-NP-完全的; QSEP可能是图灵-NP-完全但不是Karp-NP-完全的问题的第一个自然例子。最后,我调查所有提出的(确定性)算法的量子可分性问题,包括有界搜索对称扩展(通过半定规划),最近的量子de Finetti定理的基础上;和纠缠见证搜索(通过邻居点算法和全局优化)。这两个算法具有最低的复杂度,后者是最好的建议下的渐近最优点覆盖的领域。

英文摘要:

Ever since entanglement was identified as a computational and cryptographic resource, researchers have sought efficient ways to tell whether a given density matrix represents an unentangled, or separable, state. This paper gives the first systematic and comprehensive treatment of this (bipartite) quantum separability problem, focusing on its deterministic (as opposed to randomized) computational complexity. First, I review the one-sided tests for separability, paying particular attention to the semidefinite programming methods. Then, I discuss various ways of formulating the quantum separability problem, from exact to approximate formulations, the latter of which are the paper's main focus. I then give a thorough treatment of the problem's relationship with the complexity classes NP, NP-complete, and co-NP. I also discuss extensions of Gurvits' NP-hardness result to strong NP-hardness of certain related problems. A major open question is whether the NP-contained formulation (QSEP) of the quantum separability problem is Karp-NP-complete; QSEP may be the first natural example of a problem that is Turing-NP-complete but not Karp-NP-complete. Finally, I survey all the proposed (deterministic) algorithms for the quantum separability problem, including the bounded search for symmetric extensions (via semidefinite programming), based on the recent quantum de Finetti theorem; and the entanglement-witness search (via interior-point algorithms and global optimization). These two algorithms have the lowest complexity, with the latter being the best under advice of asymptotically optimal point-coverings of the sphere.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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