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arXiv 1703.03392quant-phmath-phmath.FAmath.MP
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隐藏在量子力学及其他领域的终极数据

Ultimate data hiding in quantum mechanics and beyond

Ludovico Lami, Carlos Palazuelos, Andreas Winter

摘要:

数据隐藏的现象,即一个二分系统的状态对的存在,通过一般的纠缠测量是完全可区分的,但在LOCC下几乎是不可区分的,是一个独特的签名的非经典性。相关的品质因数是与我们比较的两组测量值(通常是所有测量值与LOCC协议)相关的可验证性范数之间的最大比率(称为数据隐藏比率)。对于二分$n\times n$量子系统,已知数据隐藏比按$n$缩放,即密度矩阵的局部状态空间的实维度的平方根。我们证明了二分$n_A\times n_B$系统对LOCC协议的最大数据隐藏率是$Θ\left(\min\{n_A,n_B\}\right)$。这种缩放比先前获得的上限$O\left(\sqrt{n_A n_B}\right)$和$\min\{n_A^2, n_B^2\}$更好,而且我们直观的论证产生接近最优的常数。在本文中,我们调查数据隐藏在更一般的背景下,一般概率理论(GPT),一个公理化的框架,物理理论,只包含最基本的要求,理论的预测能力。本文的主要结果是确定的最大数据隐藏率可在任意的GPT,这是线性缩放的最小的局部尺寸。我们展示了一个明确的模型,实现这一绑定到添加剂常数,发现量子力学的数据隐藏率仅为最大的平方根的顺序。我们的证明关键在于数据隐藏和Banach空间的射影和内射张量积理论之间的一个意想不到的联系。最后,我们开发了一个身体的技术来计算数据隐藏比率的各种限制类的GPT,支持进一步的对称性。

英文摘要:

The phenomenon of data hiding, i.e. the existence of pairs of states of a bipartite system that are perfectly distinguishable via general entangled measurements yet almost indistinguishable under LOCC, is a distinctive signature of nonclassicality. The relevant figure of merit is the maximal ratio (called data hiding ratio) between the distinguishability norms associated with the two sets of measurements we are comparing, typically all measurements vs LOCC protocols. For a bipartite $n\times n$ quantum system, it is known that the data hiding ratio scales as $n$, i.e. the square root of the real dimension of the local state space of density matrices. We show that for bipartite $n_A\times n_B$ systems the maximum data hiding ratio against LOCC protocols is $Θ\left(\min\{n_A,n_B\}\right)$. This scaling is better than the previously obtained upper bounds $O\left(\sqrt{n_A n_B}\right)$ and $\min\{n_A^2, n_B^2\}$, and moreover our intuitive argument yields constants close to optimal. In this paper, we investigate data hiding in the more general context of general probabilistic theories (GPTs), an axiomatic framework for physical theories encompassing only the most basic requirements about the predictive power of the theory. The main result of the paper is the determination of the maximal data hiding ratio obtainable in an arbitrary GPT, which is shown to scale linearly in the minimum of the local dimensions. We exhibit an explicit model achieving this bound up to additive constants, finding that the quantum mechanical data hiding ratio is only of the order of the square root of the maximal one. Our proof rests crucially on an unexpected link between data hiding and the theory of projective and injective tensor products of Banach spaces. Finally, we develop a body of techniques to compute data hiding ratios for a variety of restricted classes of GPTs that support further symmetries.

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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