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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…

Quantum Physics · Physics 2007-10-24 Marko Znidaric , Tomaz Prosen , Giuliano Benenti , Giulio Casati

We establish that an optimistic variant of Q-learning applied to a fixed-horizon episodic Markov decision process with an aggregated state representation incurs regret $\tilde{\mathcal{O}}(\sqrt{H^5 M K} + \epsilon HK)$, where $H$ is the…

Machine Learning · Statistics 2020-02-20 Shi Dong , Benjamin Van Roy , Zhengyuan Zhou

We show that states on tensor products of matrix algebras whose ranks are relatively small are {\em almost surely} entangled, but that states of maximum rank are not. More precisely, let $M=M_m(\mathbb C)$ and $N=M_n(\mathbb C)$ be full…

Operator Algebras · Mathematics 2009-11-13 William Arveson

We study random walks in a balanced, i.i.d. random environment in $\mathbb Z^d$ for $d\geq 3$. We establish improved convergence rates for the homogenization of the Dirichlet problem associated with the corresponding non-divergence form…

Probability · Mathematics 2025-12-09 Xiaoqin Guo , Timo Sprekeler , Hung V. Tran

Minimum error state discrimination between two mixed states \rho and \sigma can be aided by the receipt of "classical side information" specifying which states from some convex decompositions of \rho and \sigma apply in each run. We…

Quantum Physics · Physics 2012-10-22 Matthew F. Pusey , Terry Rudolph

The orthant model is a directed percolation model on $\mathbb{Z}^d$, in which all clusters are infinite. We prove a sharp threshold result for this model: if $p$ is larger than the critical value above which the cluster of $0$ is contained…

Probability · Mathematics 2021-11-03 Thomas Beekenkamp

While it is known that Tr(\rho^n) can be measured directly (i.e., without first reconstructing the density matrix) by performing joint measurements on n copies of the same state rho, it is shown here that random measurements on single…

Quantum Physics · Physics 2012-03-20 S. J. van Enk , C. W. J. Beenakker

Unpredictability, or randomness, of the outcomes of measurements made on an entangled state can be certified provided that the statistics violate a Bell inequality. In the standard Bell scenario where each party performs a single…

Quantum Physics · Physics 2017-03-01 F. J. Curchod , M. Johansson , R. Augusiak , M. J. Hoban , P. Wittek , A. Acín

We prove a \emph{query complexity} lower bound on rank-one principal component analysis (PCA). We consider an oracle model where, given a symmetric matrix $M \in \mathbb{R}^{d \times d}$, an algorithm is allowed to make $T$ \emph{exact}…

Machine Learning · Computer Science 2017-04-18 Max Simchowitz , Ahmed El Alaoui , Benjamin Recht

More than two multipartite orthogonal states cannot always be discriminated (with certainty) if only local operations and classical communication (LOCC) are allowed. Using an existing inequality among the measures of entanglement, we show…

Quantum Physics · Physics 2009-11-07 Sibasish Ghosh , Guruprasad Kar , Anirban Roy , Aditi Sen De , Ujjwal Sen

Quantum information is nonlocal in the sense that local measurements on a composite quantum system, prepared in one of many mutually orthogonal states, may not reveal in which state the system was prepared. It is shown that in the many copy…

Quantum Physics · Physics 2015-05-28 Somshubhro Bandyopadhyay

This paper addresses the detection of a low rank high-dimensional tensor corrupted by an additive complex Gaussian noise. In the asymptotic regime where all the dimensions of the tensor converge towards $+\infty$ at the same rate, existing…

Signal Processing · Electrical Eng. & Systems 2018-02-21 Antoine Chevreuil , Philippe Loubaton

We consider one copy of a quantum system prepared with equal prior probability in one of two non-orthogonal entangled states of multipartite distributed among separated parties. We demonstrate that these two states can be optimally…

Quantum Physics · Physics 2013-05-29 Yi-Xin Chen , Dong Yang

The effective application of representation learning to real-world problems requires both techniques for learning useful representations, and also robust ways to evaluate properties of representations. Recent work in disentangled…

Machine Learning · Computer Science 2020-12-16 Salman Mohammadi , Anders Kirk Uhrenholt , Bjørn Sand Jensen

Distance covariance and distance correlation have been widely adopted in measuring dependence of a pair of random variables or random vectors. If the computation of distance covariance and distance correlation is implemented directly…

Computation · Statistics 2014-10-07 Xiaoming Huo , Gabor J. Szekely

We show how to extend several basic concentration inequalities for simple random tensors $X = x_1 \otimes \cdots \otimes x_d$ where all $x_k$ are independent random vectors in $\mathbb{R}^n$ with independent coefficients. The new results…

Probability · Mathematics 2025-10-07 Roman Vershynin

The (im)possibility of local distinguishability of orthogonal multipartite quantum states still remains an intriguing question. Beyond $\mathbb{C}^{3}\otimes\mathbb{C}^{3}$, the problem remains unsolved even for maximally entangled states…

Quantum Physics · Physics 2017-04-11 Tanmay Singal , Ramij Rahman , Sibasish Ghosh , Guruprasad Kar

Order of magnitude reasoning - reasoning by rough comparisons of the sizes of quantities - is often called 'back of the envelope calculation', with the implication that the calculations are quick though approximate. This paper exhibits an…

Artificial Intelligence · Computer Science 2011-05-30 E. Davis

We consider the problem of low-rank rectangular matrix completion in the regime where the matrix $M$ of size $n\times m$ is ``long", i.e., the aspect ratio $m/n$ diverges to infinity. Such matrices are of particular interest in the study of…

Statistics Theory · Mathematics 2024-06-24 Ludovic Stephan , Yizhe Zhu

A distance matrix $A \in \mathbb R^{n \times m}$ represents all pairwise distances, $A_{ij}=\mathrm{d}(x_i,y_j)$, between two point sets $x_1,...,x_n$ and $y_1,...,y_m$ in an arbitrary metric space $(\mathcal Z, \mathrm{d})$. Such matrices…

Data Structures and Algorithms · Computer Science 2019-06-05 Piotr Indyk , Ali Vakilian , Tal Wagner , David Woodruff