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Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and…

Machine Learning · Statistics 2013-01-11 Alex Kulesza , Ben Taskar

Recently, diffusion probabilistic models (DPMs) have achieved promising results in diverse generative tasks. A typical DPM framework includes a forward process that gradually diffuses the data distribution and a reverse process that…

Machine Learning · Computer Science 2023-10-31 Tianyu Pang , Cheng Lu , Chao Du , Min Lin , Shuicheng Yan , Zhijie Deng

Score-based diffusion models, which generate new data by learning to reverse a diffusion process that perturbs data from the target distribution into noise, have achieved remarkable success across various generative tasks. Despite their…

Machine Learning · Computer Science 2025-01-23 Gen Li , Yuling Yan

Optimal transport (OT) defines a powerful framework to compare probability distributions in a geometrically faithful way. However, the practical impact of OT is still limited because of its computational burden. We propose a new class of…

Optimization and Control · Mathematics 2016-05-30 Genevay Aude , Marco Cuturi , Gabriel Peyré , Francis Bach

In this paper, we study a matricial version of the Byrnes-Georgiou-Lindquist generalized moment problem with complexity constraint. We introduce a new metric on multivariable spectral densities induced by the family of their spectral…

Optimization and Control · Mathematics 2007-05-23 A. Ferrante , M. Pavon , F. Ramponi

Ideal density-functional approximations (DFAs) should account for dynamic, static, and nondynamic correlation. While common DFAs struggle with the latter two, the Ziegler-Rauk-Baerends-Daul multiplet sum method (MSM) provides a pragmatic…

Chemical Physics · Physics 2026-05-29 Mark E Casida , Abraham Ponra , Gadzikano Munyuki , Bharathi Natarajan

A novel perturbative method, proposed by Panda {\it et al.} [1] to solve the Helmholtz equation in two dimensions, is extended to three dimensions for general boundary surfaces. Although a few numerical works are available in the literature…

Mathematical Physics · Physics 2016-06-21 Subhasis Panda , S. Pratik Khastgir

To obtain strong convergence rates of numerical schemes, an overwhelming majority of existing works impose a global monotonicity condition on coefficients of SDEs. Nevertheless, there are still many SDEs from applications that do not have…

Numerical Analysis · Mathematics 2025-04-03 Lei Dai , Xiaojie Wang

We demonstrate how to parallelize the density matrix renormalization group (DMRG) algorithm in real space through a straightforward modification of serial DMRG. This makes it possible to apply at least an order of magnitude more…

Strongly Correlated Electrons · Physics 2013-04-25 E. M. Stoudenmire , Steven R. White

We study an ensemble of random matrices (the Rosenzweig-Porter model) which, in contrast to the standard Gaussian ensemble, is not invariant under changes of basis. We show that a rather complete understanding of its level correlations can…

Mesoscale and Nanoscale Physics · Physics 2009-10-30 Alexander Altland , Martin Janssen , Boris Shapiro

We consider the relation between three different approaches to defining quantum states across several times and locations: the pseudo-density matrix (PDM), the process matrix, and the multiple-time state approaches. Previous studies have…

Quantum Physics · Physics 2024-03-14 Xiangjing Liu , Zhian Jia , Yixian Qiu , Fei Li , Oscar Dahlsten

In this paper, we present a general framework to solve a fundamental problem in Random Matrix Theory (RMT), i.e., the problem of describing the joint distribution of eigenvalues of the sum $\bsA+\bsB$ of two independent random Hermitian…

Quantum Physics · Physics 2019-11-14 Lin Zhang , Yixin Jiang , Junde Wu

The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as…

Machine Learning · Statistics 2014-11-05 Yordan P. Raykov , Alexis Boukouvalas , Max A. Little

Traditional random-walk particle-tracking (PT) models of advection and dispersion do not track entropy, because particle masses remain constant. Newer mass-transfer particle tracking (MTPT) models have the ability to do so because masses of…

Computational Physics · Physics 2019-05-16 David A. Benson , Stephen Pankavich , Michael Schmidt , Guillem Sole-Mari

Pseudorandom states (PRSs) are state ensembles that cannot be efficiently distinguished from Haar random states. However, the definition of PRSs has been limited to pure states and lacks robustness against noise. Here, we introduce…

Quantum Physics · Physics 2025-05-26 Nikhil Bansal , Wai-Keong Mok , Kishor Bharti , Dax Enshan Koh , Tobias Haug

We adapt the one parameter scaling theory (OPT) to the context of quantum chaos. As a result we propose a more precise characterization of the universality classes associated to Wigner-Dyson and Poisson statistics which takes into account…

Disordered Systems and Neural Networks · Physics 2009-11-13 Antonio M. Garcia-Garcia , Jiao Wang

We classify one-dimensional symmetry-protected topological (SPT) phases protected by dipole symmetries. A dipole symmetry comprises two sets of symmetry generators: charge and dipole operators, which together form a non-trivial algebra with…

Strongly Correlated Electrons · Physics 2023-11-10 Ho Tat Lam

Computing the electronic structure of incommensurate materials is a central challenge in condensed matter physics, requiring efficient ways to approximate spectral quantities such as the density of states (DoS). In this paper, we…

Mathematical Physics · Physics 2025-12-09 Eric Cancès , Daniel Massatt , Long Meng , Étienne Polack , Xue Quan

Multi-configurational wave functions are known to describe electronic structure across a Born-Oppenheimer surface qualitatively correct. However, for quantitative reaction energies, dynamical correlation originating from the many…

Chemical Physics · Physics 2020-04-16 Christopher J. Stein , Markus Reiher

Quantum trajectories and superoperator algorithms implemented within the matrix product state (MPS) framework are powerful tools to simulate the real-time dynamics of open dissipative quantum systems. As for the unitary case, the reachable…

Quantum Gases · Physics 2014-11-19 Lars Bonnes , Andreas M. Läuchli
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