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In this survey the possible approaches to the description of the evolution of states of quantum many-particle systems by means of the possible modifications of the density operator which kernel known as density matrix are considered. In…

Mathematical Physics · Physics 2020-01-14 V. I. Gerasimenko

We present an exact derivation of a process in which a microscopic measured system interacts with heat-bath and pointer modes of a measuring device, via a coupling involving a general Hermitian operator $\Lambda$ of the system. In the limit…

Mesoscale and Nanoscale Physics · Physics 2016-08-31 Dima Mozyrsky , Vladimir Privman

We compute averages of products and ratios of characteristic polynomials associated with Orthogonal, Unitary, and Symplectic Ensembles of Random Matrix Theory. The pfaffian/determinantal formulas for these averages are obtained, and the…

Mathematical Physics · Physics 2007-05-23 A. Borodin , E. Strahov

We compute the distribution of the purity for random density matrices (i.e.random mixed states) in a large quantum system, distributed according to the Bures measure. The full distribution of the purity is computed using a mapping to random…

Statistical Mechanics · Physics 2015-05-30 Gaëtan Borot , Céline Nadal

In general, the state of a quantum system represented by density operator and its determination is a fundamental problem in quantum mechanics. A method of direct measurement of matrix element of density operator of a single two dimensional…

Quantum Physics · Physics 2022-02-16 Yusuf Turek

Fairness concerns are increasingly critical as machine learning models are deployed in high-stakes applications. While existing fairness-aware methods typically intervene at the model level, they often suffer from high computational costs,…

Machine Learning · Computer Science 2025-11-11 Yixuan Zhang , Jiabin Luo , Zhenggang Wang , Feng Zhou , Quyu Kong

Thermodynamically consistent fractional Burgers constitutive models for viscoelastic media, divided into two classes according to model behavior in stress relaxation and creep tests near the initial time instant, are coupled with the…

Classical Physics · Physics 2019-12-03 Ljubica Oparnica , Dušan Zorica , Aleksandar Okuka

Uncertainty relation is not only of fundamental importance to quantum mechanics, but also crucial to the quantum information technology. Recently, majorization formulation of uncertainty relations (MURs) have been widely studied, ranging…

We derive on the basis of Bayes' theorem a simple but general expression for the retrodicted premeasurement state associated with the result of any measurement. The retrodictive density operator is the normalised probability operator…

Quantum Physics · Physics 2015-06-26 Stephen M. Barnett , David T. Pegg , John Jeffers

We study the local statistics of orthogonal polynomial ensembles near a hard edge, subject to a multiplicative deformation of the measure. Probabilistically, this deformation corresponds to a position-dependent conditional thinning of the…

Mathematical Physics · Physics 2026-02-23 Leslie Molag , Guilherme L. F. Silva , Lun Zhang

A coarse-grained quantum operator technique is used along with the formalism of Bohmian mechanics endowed with stochastic character at the quantum level in order to address some central issues in the quantum theory of measurement. A…

Quantum Physics · Physics 2007-05-23 Jose Ignacio Usera

We derive a Bayesian framework for incorporating selection effects into population analyses. We allow for both measurement uncertainty in individual measurements and, crucially, for selection biases on the population of measurements, and…

Data Analysis, Statistics and Probability · Physics 2019-04-10 Ilya Mandel , Will M. Farr , Jonathan R. Gair

We provide a decision-theoretic framework for dealing with uncertainty in quantum mechanics. This uncertainty is two-fold: on the one hand there may be uncertainty about the state the quantum system is in, and on the other hand, as is…

Quantum Physics · Physics 2026-05-01 Keano De Vos , Gert de Cooman , Alexander Erreygers , Jasper De Bock

Model-based clustering is widely-used in a variety of application areas. However, fundamental concerns remain about robustness. In particular, results can be sensitive to the choice of kernel representing the within-cluster data density.…

Machine Learning · Statistics 2019-06-27 Leo L Duan , David B Dunson

In this article we study in detail a family of random matrix ensembles which are obtained from random permutations matrices (chosen at random according to the Ewens measure of parameter $\theta>0$) by replacing the entries equal to one by…

Probability · Mathematics 2010-05-05 Joseph Najnudel , Ashkan Nikeghbali

A physics-informed machine learning model, in the form of a multi-output Gaussian process, is formulated using the Euler-Bernoulli beam equation. Given appropriate datasets, the model can be used to regress the analytical value of the…

Machine Learning · Statistics 2023-08-08 Gledson Rodrigo Tondo , Sebastian Rau , Igor Kavrakov , Guido Morgenthal

We reprove the well known fact that the energy distance defines a metric on the space of Borel probability measures on a Hilbert space with finite first moment by a new approach, by analyzing the behavior of the Gaussian kernel on Hilbert…

Functional Analysis · Mathematics 2021-02-02 Jean Carlo Guella

We study the mathematical properties of a model of cell division structured by two variables, the size and the size increment, in the case of a linear growth rate and a self-similar fragmentation kernel. We first show that one can construct…

Analysis of PDEs · Mathematics 2019-03-13 Pierre Gabriel , Hugo Martin

A quantum trajectory describes the evolution of a quantum system undergoing indirect measurement. In the discrete-time setting, the state of the system is updated by applying Kraus operators according to the measurement results. From an…

Quantum Physics · Physics 2022-04-04 Maël Bompais , Nina H. Amini , Clément Pellegrini

Parameter ensembles or sets of random effects constitute one of the cornerstones of modern statistical practice. This is especially the case in Bayesian hierarchical models, where several decision theoretic frameworks can be deployed. The…

Statistics Theory · Mathematics 2015-03-19 Cedric E. Ginestet
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