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Entropy of entanglement and multifractal exponents for random states  [PDF]
Olivier Giraud,John Martin,Bertrand Georgeot
Physics , 2008, DOI: 10.1103/PhysRevA.79.032308
Abstract: We relate the entropy of entanglement of ensembles of random vectors to their generalized fractal dimensions. Expanding the von Neumann entropy around its maximum we show that the first order only depends on the participation ratio, while higher orders involve other multifractal exponents. These results can be applied to entanglement behavior near the Anderson transition.
Weak and strong moments of random vectors  [PDF]
Rafa? Lata?a
Mathematics , 2010, DOI: 10.4064/bc95-0-8
Abstract: We discuss a conjecture about comparability of weak and strong moments of log-concave random vectors and show the conjectured inequality for unconditional vectors in normed spaces with a bounded cotype constant.
On the concentration of measure phenomenon for stable and related random vectors  [PDF]
Christian Houdre,Philippe Marchal
Mathematics , 2004, DOI: 10.1214/009117904000000289
Abstract: Concentration of measure is studied, and obtained, for stable and related random vectors.
Approximation of projections of random vectors  [PDF]
Elizabeth Meckes
Mathematics , 2009,
Abstract: Let $X$ be a $d$-dimensional random vector and $X_\theta$ its projection onto the span of a set of orthonormal vectors $\{\theta_1,...,\theta_k\}$. Conditions on the distribution of $X$ are given such that if $\theta$ is chosen according to Haar measure on the Stiefel manifold, the bounded-Lipschitz distance from $X_\theta$ to a Gaussian distribution is concentrated at its expectation; furthermore, an explicit bound is given for the expected distance, in terms of $d$, $k$, and the distribution of $X$, allowing consideration not just of fixed $k$ but of $k$ growing with $d$. The results are applied in the setting of projection pursuit, showing that most $k$-dimensional projections of $n$ data points in $\R^d$ are close to Gaussian, when $n$ and $d$ are large and $k=c\sqrt{\log(d)}$ for a small constant $c$.
Concurrence Vectors for Entanglement of High-dimensional Systems  [PDF]
You-Quan Li,Guo-Qiang Zhu
Physics , 2003, DOI: 10.1007/s11467-008-0022-2
Abstract: The concurrence vectors are proposed by employing the fundamental representation of $A_n$ Lie algebra, which provides a clear criterion to evaluate the entanglement of bipartite system of arbitrary dimension for both pure and mixed states. Accordingly, a state is separable if the norm of its concurrence vector vanishes. The state vectors related to SU(3) states and SO(3) states are discussed in detail. The sign situation of nonzero components of concurrence vectors of entangled bases presents a simple criterion to judge whether the whole Hilbert subspace spanned by those bases is entangled, or there exists entanglement edge. This is illustrated in terms of the concurrence surfaces of several concrete examples.
Concentration Inequalities for Bounded Random Vectors  [PDF]
Xinjia Chen
Computer Science , 2013,
Abstract: We derive simple concentration inequalities for bounded random vectors, which generalize Hoeffding's inequalities for bounded scalar random variables. As applications, we apply the general results to multinomial and Dirichlet distributions to obtain multivariate concentration inequalities.
Asymptotics of the Norm of Elliptical Random Vectors  [PDF]
Enkelejd Hashorva
Mathematics , 2008, DOI: 10.1016/j.jmva.2009.10.004
Abstract: In this paper we consider elliptical random vectors X in R^d,d>1 with stochastic representation A R U where R is a positive random radius independent of the random vector U which is uniformly distributed on the unit sphere of R^d and A is a given matrix. The main result of this paper is an asymptotic expansion of the tail probability of the norm of X derived under the assumption that R has distribution function is in the Gumbel or the Weibull max-domain of attraction.
Detecting entanglement of random states with an entanglement witness  [PDF]
Marko Znidaric,Tomaz Prosen,Giuliano Benenti,Giulio Casati
Physics , 2007, DOI: 10.1088/1751-8113/40/45/017
Abstract: The entanglement content of high-dimensional random pure states is almost maximal, nevertheless, we show that, due to the complexity of such states, the detection of their entanglement using witness operators is rather difficult. We discuss the case of unknown random states, and the case of known random states for which we can optimize the entanglement witness. Moreover, we show that coarse graining, modeled by considering mixtures of m random states instead of pure ones, leads to a decay in the entanglement detection probability exponential with m. Our results also allow to explain the emergence of classicality in coarse grained quantum chaotic dynamics.
On Weak Tail Domination of Random Vectors  [PDF]
Rafa? Lata?a
Mathematics , 2007, DOI: 10.4064/ba57-1-8
Abstract: Motivated by a question of Krzysztof Oleszkiewicz we study a notion of weak tail domination of random vectors. We show that if the dominating random variable is sufficiently regular weak tail domination implies strong tail domination. In particular positive answer to Oleszkiewicz question would follow from the so-called Bernoulli conjecture.
Maximal vectors in Hilbert space and quantum entanglement  [PDF]
William Arveson
Mathematics , 2008,
Abstract: Let $V$ be a norm-closed subset of the unit sphere of a Hilbert space $H$ that is stable under multiplication by scalars of absolute value 1. A {\em maximal vector} (for $V$) is a unit vector $\xi\in H$ whose distance to $V$ is maximum $d(\xi,V)=\sup_{\|\eta\|=1}d(\eta,V)$, $d(\xi,V)$ denoting the distance from $\xi$ to the set $V$. Maximal vectors generalize the {\em maximally entangled} unit vectors of quantum theory. In general, under a mild regularity hypothesis on $V$, there is a {\em norm} on $H$ whose restriction to the unit sphere achieves its minimum precisely on $V$ and its maximum precisely on the set of maximal vectors. This "entanglement-measuring norm" is unique. There is a corresponding "entanglement-measuring norm" on the predual of $\mathcal B(H)$ that faithfully detects entanglement of normal states. We apply these abstract results to the analysis of entanglement in multipartite tensor products $H=H_1\otimes ...\otimes H_N$, and we calculate both entanglement-measuring norms. In cases for which $\dim H_N$ is relatively large with respect to the others, we describe the set of maximal vectors in explicit terms and show that it does not depend on the number of factors of the Hilbert space $H_1\otimes...\otimes H_{N-1}$.
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