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Two dimensional condensed matter is realised in increasingly diverse forms that are accessible to experiment and of potential technological value. The properties of these systems are influenced by many length scales and reflect both generic…

Statistical Mechanics · Physics 2011-07-15 Andrea Taroni , Steven T. Bramwell , Peter C. W. Holdsworth

Higher dimensional space-time models provide us an alternative interpretation of nature, and give us different dynamical aspects than the traditional four-dimensional space-time models. Motivated by such recent interests, especially for…

General Relativity and Quantum Cosmology · Physics 2009-11-10 Hisa-aki Shinkai , Gen Yoneda

A notorious problem in mathematics and physics is to create a solvable model for random sequential adsorption of non-overlapping congruent spheres in the $d$-dimensional Euclidean space with $d\geq 2$. Spheres arrive sequentially at…

Probability · Mathematics 2019-01-25 Souvik Dhara , Johan S. H. van Leeuwaarden , Debankur Mukherjee

Focusing particularly on one-qubit and two-qubit systems, I explain how the quantum state of a system of n qubits can be expressed as a real function--a generalized Wigner function--on a discrete 2^n x 2^n phase space. The phase space is…

Quantum Physics · Physics 2007-05-23 William K. Wootters

Motivated by a range of biological applications related to the transport of molecules in cells, we present a modular framework to treat first-passage problems for diffusion in partitioned spaces. The spatial domains can differ with respect…

Statistical Mechanics · Physics 2021-04-28 Daniela Frömberg , Felix Höfling

It is notorious that quantum mechanics cannot predict well-defined values for all physical quantities. Less well-known, however, is the fact that quantum mechanics is unable to furnish -- without additional assumptions -- probabilistic…

Quantum Physics · Physics 2025-09-24 Matheus V. Scherer , Alexandre D. Ribeiro , Renato M. Angelo

In this paper we develop a very general class of bivariate discrete distributions. The basic idea is very simple. The marginals are obtained by taking the random geometric sum of a baseline distribution function. The proposed class of…

Methodology · Statistics 2018-05-22 Debasis Kundu

For a quantum-mechanically spread-out particle we investigate a method for determining its arrival time at a specific location. The procedure is based on the emission of a first photon from a two-level system moving into a laser-illuminated…

Quantum Physics · Physics 2009-11-07 J. A. Damborenea , I. L. Egusquiza , G. C. Hegerfeldt , J. G. Muga

We derive a minimalist but powerful deterministic denoising-diffusion model. While denoising diffusion has shown great success in many domains, its underlying theory remains largely inaccessible to non-expert users. Indeed, an understanding…

Graphics · Computer Science 2023-05-08 Eric Heitz , Laurent Belcour , Thomas Chambon

Under the separability assumption on the augmented density, a distribution function can be always constructed for a spherical population with the specified density and anisotropy profile. Then, a question arises, under what conditions the…

Mathematical Physics · Physics 2012-01-31 J. An

In the framework of Gibbs statistical theory, the issue of the distribution of particle sizes forming the statistical system and the moments of this distribution are considered. This task is relevant for a wide variety of applications. The…

Statistical Mechanics · Physics 2019-10-14 V. V. Ryazanov

Probabilistically creating n perfect clones from m copies for one of N priori known quantum states with minimum failure probability is a long-standing problem. We provide a rigorous proof for the geometric approach to this probabilistic…

Quantum Physics · Physics 2019-05-07 Haixin Liu , Heng Fan

The quantum O(2) model in 2+1 dimensions is studied by simulating the 3d O(2) model near criticality. Finite densities are introduced by a non-zero chemical potential mu, and the worm algorithm is used to circumvent the sign problem. The…

High Energy Physics - Lattice · Physics 2013-11-04 Kurt Langfeld

In this paper, we analyze the decision version of the NK landscape model from the perspective of threshold phenomena and phase transitions under two random distributions, the uniform probability model and the fixed ratio model. For the…

Artificial Intelligence · Computer Science 2011-06-24 J. Culberson , Y. Gao

We study goodness-of-fit of discrete distributions in the distributed setting, where samples are divided between multiple users who can only release a limited amount of information about their samples due to various information constraints.…

Data Structures and Algorithms · Computer Science 2019-07-23 Jayadev Acharya , Clément L. Canonne , Yanjun Han , Ziteng Sun , Himanshu Tyagi

A factor-graph representation of quantum-mechanical probabilities (involving any number of measurements) is proposed. Unlike standard statistical models, the proposed representation uses auxiliary variables (state variables) that are not…

Information Theory · Computer Science 2017-06-13 Hans-Andrea Loeliger , Pascal O. Vontobel

The de Finetti representation theorem for continuous variable quantum system is first developed to approximate an N-partite continuous variable quantum state with a convex combination of independent and identical subsystems, which requires…

Quantum Physics · Physics 2016-09-28 Murphy Yuezhen Niu

Time dependence for barrier penetration is considered in the phase space. An asymptotic phase-space propagator for nonrelativistic scattering on a one - dimensional barrier is constructed. The propagator has a form universal for various…

Quantum Physics · Physics 2009-10-30 M. S. Marinov , Bilha Segev

In this paper two hypotheses are developed. The first hypothesis is the existence of random phenomena/experiments in which the events cannot generally be assigned a definite probability but that nevertheless admit a class of nearly certain…

Quantum Physics · Physics 2023-02-24 Bruno Galvan

Comparing probability distributions is a core challenge across the natural, social, and computational sciences. Existing methods, such as Maximum Mean Discrepancy (MMD), struggle in high-dimensional and non-compact domains. Here we…

Machine Learning · Statistics 2025-09-09 Logan S. McCarty