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The Wasserstein distance quantifies the distance between two probability measures on a metric space. We prove an analogue of the Berry-Esseen inequality for the Wasserstein distance on a finite area hyperbolic surface. This inequality…

Number Theory · Mathematics 2025-12-18 Peter Humphries

Controlling the $\mathcal W_\infty$ Wasserstein distance by the $\mathcal W_p$ Wasserstein distance is interesting both for theorical and numerical applications. A first paper on this problem was written several years ago [3]. Some year…

Optimization and Control · Mathematics 2026-01-22 Luigi De Pascale , Igor Pinheiro

We study a class of quadratic Wasserstein distances on spaces consisting of generalized dynamical systems on a von Neumann algebra. We emphasize how symmetry of such a Wasserstein distance arises, but also study the asymmetric case. This…

Operator Algebras · Mathematics 2024-11-05 Rocco Duvenhage

The measures of distances between points in a Hilbert space are one of the basic theoretical concepts used to characterize properties of a quantum system with respect to some etalon state. These are not only used in studying fidelity of…

Quantum Physics · Physics 2019-04-03 Karol Bartkiewicz , Vojtěch Trávníček , Karel Lemr

Suppose that a system is known to be in one of two quantum states, $|\psi_1 > $ or $|\psi_2 >$. If these states are not orthogonal, then in conventional quantum mechanics it is impossible with one measurement to determine with certainty…

High Energy Physics - Theory · Physics 2018-11-28 Carl M. Bender , Dorje C. Brody , Joao Caldeira , Bernard K. Meister

A geometric approach to formulate the uncertainty principle between quantum observables acting on an $N$-dimensional Hilbert space is proposed. We consider the fidelity between a density operator associated with a quantum system and a…

Quantum Physics · Physics 2015-06-16 G. M. Bosyk , T. M. Osán , P. W. Lamberti , M. Portesi

The Quasi Manhattan Wasserstein Distance (QMWD) is a metric designed to quantify the dissimilarity between two matrices by combining elements of the Wasserstein Distance with specific transformations. It offers improved time and space…

Machine Learning · Computer Science 2023-10-20 Evan Unit Lim

Optimal Transport (OT) metrics allow for defining discrepancies between two probability measures. Wasserstein distance is for longer the celebrated OT-distance frequently-used in the literature, which seeks probability distributions to be…

Machine Learning · Computer Science 2021-10-14 Mokhtar Z. Alaya , Gilles Gasso , Maxime Berar , Alain Rakotomamonjy

We reveal strong and weak inequalities relating two fundamental macroscopic quantum geometric quantities, the quantum distance and Berry phase, for closed paths in the Hilbert space of wavefunctions. We recount the role of quantum geometry…

Quantum Physics · Physics 2026-03-31 Praveen Pai , Fan Zhang

In this manuscript we investigate the equivalence of Fourier-based metrics on discrete state spaces with the well-known Wasserstein distances. While the use of Fourier-based metrics in continuous state spaces is ubiquitous since its…

Probability · Mathematics 2024-04-09 Fei Cao , Xiaoqian Gong

Standard quantum mechanics unquestionably violates the separability principle that classical physics (be it point-like analytic, statistical, or field-theoretic) accustomed us to consider as valid. In this paper, quantum nonseparability is…

Quantum Physics · Physics 2007-05-23 Vassilios Karakostas

We study the structure of the support of a doubling measure by analyzing its self-similarity properties, which we estimate using a variant of the $L^1$ Wasserstein distance. We show that measure satisfying certain self-similarity conditions…

Metric Geometry · Mathematics 2014-11-11 Jonas Azzam , Guy David , Tatiana Toro

The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying…

Machine Learning · Computer Science 2017-06-01 Marc G. Bellemare , Ivo Danihelka , Will Dabney , Shakir Mohamed , Balaji Lakshminarayanan , Stephan Hoyer , Rémi Munos

The Hilbert-Schmidt distance between a mixed three-qubit state and its closest state is used to quantify the amount of pairwise quantum correlations in a tripartite system. Analytical expressions of geometric quantum discord are derived. A…

Quantum Physics · Physics 2016-01-20 M. Daoud , R. Ahl Laamara , S. Seddik

Combining gravity with quantum theory is still work in progress. On the one hand, classical gravity, is the geometry of space-time determined by the energy-momentum tensor of matter and the resulting nonlinear equations; on the other hand,…

Quantum Physics · Physics 2024-01-26 P. Gusin , D. Burys , A. Radosz

In this paper, we establish sharp upper and lower bounds on the convergence rate of the empirical measures of point processes under the Wasserstein distance. To this end, we first introduce a new metric on the space of counting measures…

Statistics Theory · Mathematics 2026-04-28 Dongzhou Huang , Tianyi Jiang , Haonan Wang

In this article, we study Wasserstein-type metrics and corresponding barycenters for mixtures of a chosen subset of probability measures called atoms hereafter. In particular, this works extends what was proposed by Delon and Desolneux [A…

Optimization and Control · Mathematics 2023-01-20 Geneviève Dusson , Virginie Ehrlacher , Nathalie Nouaime

In this paper we introduce a Wasserstein-type distance on the set of Gaussian mixture models. This distance is defined by restricting the set of possible coupling measures in the optimal transport problem to Gaussian mixture models. We…

Optimization and Control · Mathematics 2020-06-15 Julie Delon , Agnes Desolneux

Adrian Kent has recently presented a critique [arXiv:2307.06191] of our paper [Nat. Comms. 10, 1361 (2019)] in which he claims to refute our main result: the measurement postulates of quantum mechanics can be derived from the rest of…

Quantum Physics · Physics 2025-01-15 Lluís Masanes , Thomas D. Galley , Markus P. Müller

We introduce the observable Wasserstein distance, a framework for deriving lower bounds on the Wasserstein distance between probability measures on Polish metric spaces, designed to bypass the computational intractability of exact optimal…

Metric Geometry · Mathematics 2026-05-12 Edivaldo Lopes dos Santos , Leandro Vicente Mauri , Washington Mio , Tom Needham