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arXiv 1504.07524quant-ph
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矩阵乘积态的纠缠分类

Entanglement classification with matrix product states

M. Sanz, I. L. Egusquiza, R. Di Candia, H. Saberi, L. Lamata, E. Solano

摘要:

纠缠被广泛认为是量子信息的基石,也是相关量子效应的重要资源,如量子隐形传态、量子密码学或量子计算的加速,如Shor算法。然而,到目前为止,对于多体系统的纠缠还没有一个普遍的描述。在这个意义上,它是令人鼓舞的,量子态连接随机本地操作与经典通信(SLOCC),执行概率相同的量子任务,可以收集到纠缠类。然而,对于四个或更多的参与方来说,有无限多的类可以依次聚集成有限数量的纠缠族。不幸的是,我们还不能把所有的类和族与特定的性质或量子信息任务联系起来,尽管其中一些确实引起了实验的兴趣。在这里,我们提出了一种新的纠缠分类的量子态根据其矩阵产品的状态结构,对称子空间的例子。该分类将纠缠族与哈密顿量的相互作用长度联系起来,建立了纠缠分类与凝聚态之间的第一个联系。此外,我们还发现了一个自然的嵌套属性,其中$N$方的族延续到$N+1$的情况。我们希望我们的建议是一个起点,探索纠缠分类属性和凝聚态模型之间的联系。

英文摘要:

Entanglement is widely considered the cornerstone of quantum information and an essential resource for relevant quantum effects, such as quantum teleportation, quantum cryptography, or the speed-up of quantum computing, as in Shor's algorithm. However, up to now, there is no general characterization of entanglement for many-body systems. In this sense, it is encouraging that quantum states connected by stochastic local operations assisted with classical communication (SLOCC), which perform probabilistically the same quantum tasks, can be collected into entanglement classes. Nevertheless, there is an infinite number of classes for four or more parties that may be gathered, in turn, into a finite number of entanglement families. Unfortunately, we have not been able to relate all classes and families to specific properties or quantum information tasks, although a few of them have certainly raised experimental interest. Here, we present a novel entanglement classification for quantum states according to their matrix-product-state structure, exemplified for the symmetric subspace. The proposed classification relates entanglement families to the interaction length of Hamiltonians, establishing the first connection between entanglement classification and condensed matter. Additionally, we found a natural nesting property in which the families for $N$ parties carry over to the $N+1$ case. We anticipate our proposal to be a starting point for the exploration of the connection between entanglement classification properties and condensed-matter models.

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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