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A general connection between the characteristic function of a L\'evy process and loss of coherence of the statistical operator describing the center of mass degrees of freedom of a quantum system interacting through momentum transfer events…

Quantum Physics · Physics 2007-05-23 Bassano Vacchini

We discuss a few mathematical aspects of random dynamical decoupling, a key tool procedure in quantum information theory. In particular, we place it in the context of discrete stochastic processes, limit theorems and CPT semigroups on…

Quantum Physics · Physics 2015-06-19 Robin Hillier , Christian Arenz , Daniel Burgarth

In this paper, we provide bounds in Wasserstein and total variation distances between the distributions of the successive iterates of two functional autoregressive processes with isotropic Gaussian noise of the form $Y_{k+1} =…

Probability · Mathematics 2023-11-30 Alain Durmus , Andreas Eberle , Aurélien Enfroy , Arnaud Guillin , Pierre Monmarché

Spontaneous symmetry breaking is a cornerstone of modern physics, defining a wealth of phenomena in condensed-matter and high-energy physics, and beyond. It requires an infinite number of degrees of freedom, and even then, for continuous…

Disordered Systems and Neural Networks · Physics 2026-04-10 Oleg Evnin

The paper studies the relationship between diffraction and dynamics for uniformly discrete ergodic point processes in real spaces. This relationship takes the form of an isometric embedding of two L^2 spaces. Diffraction (or equivalently…

Dynamical Systems · Mathematics 2009-11-13 Xinghua Deng , Robert V. Moody

We develop a mathematical formalism that allows to study decoherence with a great level generality, so as to make it appear as a geometrical phenomenon between reservoirs of dimensions. It enables us to give quantitative estimates of the…

Mathematical Physics · Physics 2024-01-30 Antoine Soulas

We study the problem of approximately recovering a probability distribution given noisy measurements of its Chebyshev polynomial moments. This problem arises broadly across algorithms, statistics, and machine learning. By leveraging a…

Data Structures and Algorithms · Computer Science 2026-05-20 Cameron Musco , Christopher Musco , Lucas Rosenblatt , Apoorv Vikram Singh

We consider the fully-coupled McKean-Vlasov equation with multi-time-scale potentials, and all the coefficients depend on the distributions of both the slow component and the fast motion. By studying the smoothness of the solution of the…

Probability · Mathematics 2022-04-28 Yun Li , Fuke Wu , Longjie Xie

A new Wasserstein multi-element polynomial chaos expansion (WPCE) is proposed, which is inspired by recent advances in computational optimal transport for estimating Wasserstein distances. The developed method combines unsupervised learning…

Numerical Analysis · Mathematics 2024-10-17 Robert Gruhlke , Martin Eigel

We provide a new existence result for weak solutions to the one-dimensional Euler equations with a maximal density constraint, corresponding to a unilateral constraint on the density. Such models arise in the description of congestion…

Analysis of PDEs · Mathematics 2026-04-06 Charlotte Perrin

We study the joint asymptotic behavior of spacings between particles at the edge of multilevel Dyson Brownian motions, when the number of levels tends to infinity. Despite the global interactions between particles in multilevel Dyson…

Probability · Mathematics 2014-09-09 Vadim Gorin , Mykhaylo Shkolnikov

Renyi's "thinning" operation on a discrete random variable is a natural discrete analog of the scaling operation for continuous random variables. The properties of thinning are investigated in an information-theoretic context, especially in…

Information Theory · Computer Science 2010-08-17 Peter Harremoes , Oliver Johnson , Ioannis Kontoyiannis

The sliced-Wasserstein flow is an evolution equation where a probability density evolves in time, advected by a velocity field computed as the average among directions in the unit sphere of the optimal transport displacements from its 1D…

Optimization and Control · Mathematics 2024-05-13 Giacomo Cozzi , Filippo Santambogio

We provide new convergence guarantees in Wasserstein distance for diffusion-based generative models, covering both stochastic (DDPM-like) and deterministic (DDIM-like) sampling methods. We introduce a simple framework to analyze…

Machine Learning · Computer Science 2025-11-14 Eliot Beyler , Francis Bach

We study the particle method to approximate the gradient flow on the $L^p$-Wasserstein space. This method relies on the discretization of the energy introduced by [3] via nonoverlapping balls centered at the particles and preserves the…

Numerical Analysis · Mathematics 2025-01-08 Rong Lei

Wasserstein gradient flows on probability measures have found a host of applications in various optimization problems. They typically arise as the continuum limit of exchangeable particle systems evolving by some mean-field interaction…

Probability · Mathematics 2023-06-30 Sewoong Oh , Soumik Pal , Raghav Somani , Raghavendra Tripathi

We characterize lower bounds for the Bakry-Emery Ricci tensor of nonsymmetric diffusion operators by convexity of entropy on the $L^2$-Wasserstein space, and define a curvature-dimension condition for general metric measure spaces together…

Differential Geometry · Mathematics 2017-02-10 Christian Ketterer

In this article, exponential contraction in Wasserstein distance for heat semigroups of diffusion processes on Riemannian manifolds is established under curvature conditions where Ricci curvature is not necessarily required to be…

Differential Geometry · Mathematics 2020-01-20 Li-Juan Cheng , Anton Thalmaier , Shao-Qin Zhang

We briefly review some equilibrium and nonequilibrium properties of systems with long-range interactions. Such systems, which are characterized by a potential that weakly decays at large distances, have striking properties at equilibrium,…

Statistical Mechanics · Physics 2009-11-13 Stefano Ruffo

In this paper, we investigate the Hausdorff dimension of naturally occurring sets of inhomogeneous well-approximable points with a sequence of real invertible matrices $\mathcal{A}=(A_n)_{n\in\mathbb{N}}$. Specifically, for a given point…

Number Theory · Mathematics 2025-12-17 Zhang-nan Hu , Junjie Huang , Bing Li , Jun Wu