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Quantum nonlocality offers a secure way to produce random numbers: their unpredictability is intrinsic and can be certified just by observing the statistic of the measurement outcomes, without assumptions on how they are produced. To do…

We consider differentially private approximate singular vector computation. Known worst-case lower bounds show that the error of any differentially private algorithm must scale polynomially with the dimension of the singular vector. We are…

Data Structures and Algorithms · Computer Science 2012-11-06 Moritz Hardt , Aaron Roth

We consider asymptotic dimension of coarse spaces. We analyse coarse structures induced by metrisable compactifications. We calculate asymptotic dimension of coarse cell complexes. We calculate the asymptotic dimension of certain negatively…

Metric Geometry · Mathematics 2007-05-23 Bernd Grave

Borel probability measures living on metric spaces are fundamental mathematical objects. There are several meaningful distance functions that make the collection of the probability measures living on a certain space a metric space. We are…

Functional Analysis · Mathematics 2018-06-14 Dániel Virosztek

Recent theoretical and experimental studies have given raise to new aspects in quantum measurements and error-disturbance uncertainty relations. After a brief review of these issues, we present an experimental test of the error-disturbance…

Quantum Physics · Physics 2016-06-24 Keiichi Edamatsu

One obtains Bell's inequalities if one posits a hypothetical joint probability distribution, or {\it measure}, whose marginals yield the probabilities produced by the spin measurements in question. The existence of a joint measure is in…

Quantum Physics · Physics 2010-04-14 David Craig , Fay Dowker , Joe Henson , Seth Major , David Rideout , Rafael D. Sorkin

When observations must come from incompatible devices and cannot be produced by compatible devices? This question motivates two integer valued quantifications of incompatibility, called incompatibility dimension and compatibility dimension.…

Quantum Physics · Physics 2021-12-02 Teiko Heinosaari , Takayuki Miyadera , Ryo Takakura

We investigate the compression of quantum information with respect to a given set $\mathcal{M}$ of high-dimensional measurements. This leads to a notion of simulability, where we demand that the statistics obtained from $\mathcal{M}$ and an…

Let $X=\bigcup\varphi_{i}X$ be a strongly separated self-affine set in $\mathbb{R}^2$ (or one satisfying the strong open set condition). Under mild non-compactness and irreducibility assumptions on the matrix parts of the $\varphi_{i}$, we…

Metric Geometry · Mathematics 2017-12-21 Balázs Bárány , Michael Hochman , Ariel Rapaport

We imagine an experiment on an unknown quantum mechanical system in which the system is prepared in various ways and a range of measurements are performed. For each measurement M and preparation rho the experimenter can determine, given…

Quantum Physics · Physics 2009-01-20 Stephanie Wehner , Matthias Christandl , Andrew C. Doherty

We propose a scheme to measure the parity of two distant qubits, while ensuring that losses on the quantum channel between them does not destroy coherences within the parity subspaces. This capability enables deterministic preparation of…

Quantum Physics · Physics 2017-04-05 Alain Sarlette , Mazyar Mirrahimi

In this paper we develop a general framework of badly approximable points in a metric space $X$ equipped with a $\sigma$-finite doubling Borel regular measure $\mu$. We establish that under mild assumptions the $\mu$-measure of the set of…

Number Theory · Mathematics 2023-07-20 Victor Beresnevich , Shreyasi Datta , Anish Ghosh , Benjamin Ward

By weakly measuring the polarization of a photon between two strong polarization measurements, we experimentally investigate the correlation between the appearance of anomalous values in quantum weak measurements, and the violation of…

Quantum Physics · Physics 2011-03-30 M. E. Goggin , M. P. Almeida , M. Barbieri , B. P. Lanyon , J. L. O'Brien , A. G. White , G. J. Pryde

A quasi-metric is a distance function which satisfies the triangle inequality but is not symmetric: it can be thought of as an asymmetric metric. The central result of this thesis, developed in Chapter 3, is that a natural correspondence…

Information Retrieval · Computer Science 2008-10-31 Aleksandar Stojmirovic

Since weak measurements are known to produce measurement values that can be much larger than the maximal eigenvalues of the measured observable, it is an interesting question how this enhancement of the measurement signal relates to the…

Quantum Physics · Physics 2012-11-21 Holger F. Hofmann

Fractal Lipschitz-Killing curvature measures C^f_k(F,.), k = 0, ..., d, are determined for a large class of self-similar sets F in R^d. They arise as weak limits of the appropriately rescaled classical Lipschitz-Killing curvature measures…

Metric Geometry · Mathematics 2010-09-29 Steffen Winter , Martina Zähle

We characterize the subsets $E$ of a metric space $X$ with doubling measure whose distance function to some negative power $\textrm{dist}(\cdot,E)^{-\alpha}$ belongs to the Muckenhoupt $A_1$ class of weights in $X$. To this end, we…

Classical Analysis and ODEs · Mathematics 2025-12-01 Carlos Mudarra

We consider iterated function systems (IFS) in ${\mathbb R}^d$ for $d\ge 3$ of the form $\{f_j(x) = \lambda {\mathcal O} x + a_j\}_{j=0}^m$, with $a_0=0$ and $m\ge 1$. Here $\lambda\in (0,1)$ is the contraction ratio and ${\mathcal O}$ is…

Dynamical Systems · Mathematics 2025-08-21 Boris Solomyak

The WKB approximation for deformed space with minimal length is considered. The Bohr-Sommerfeld quantization rule is obtained. A new interesting feature in presence of deformation is that the WKB approximation is valid for intermediate…

Quantum Physics · Physics 2009-11-11 T. V. Fityo , I. O. Vakarchuk , V. M. Tkachuk

Many signal processing and machine learning methods share essentially the same linear-in-the-parameter model, with as many parameters as available samples as in kernel-based machines. Sparse approximation is essential in many disciplines,…

Machine Learning · Statistics 2023-07-19 Paul Honeine