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The numerical simulation of dynamical phenomena in interacting quantum systems is a notoriously hard problem. Although a number of promising numerical methods exist, they often have limited applicability due to the growth of entanglement or…

Quantum Physics · Physics 2021-09-08 Stefano De Nicola

We review some connections between quantum information and statistical mechanics. We focus on three sets of results for classical spin models. First, we show that the partition function of all classical spin models (including models in…

Quantum Physics · Physics 2013-12-23 Gemma De las Cuevas

Although not as wide, and popular, as that of quantum mechanics, the investigation of fundamental aspects of statistical mechanics constitutes an important research field in the building of modern physics. Besides the interest for itself,…

Statistical Mechanics · Physics 2025-05-23 Marco Baldovin , Giacomo Gradenigo , Angelo Vulpiani , Nino Zanghì

Facilitated or kinetically constrained spin models (KCSM) are a class of interacting particle systems reversible w.r.t. to a simple product measure. Each dynamical variable (spin) is re-sampled from its equilibrium distribution only if the…

Probability · Mathematics 2012-10-04 Nicoletta Cancrini , Fabio Martinelli , Cyril Roberto , Cristina Toninelli

In soft matter systems the local displacement field can be accessed directly by video microscopy enabling one to compute local strain fields and hence the elastic moduli using a coarse-graining procedure. We study this process for a simple…

Soft Condensed Matter · Physics 2015-05-14 K. Franzrahe , P. Nielaba , S. Sengupta

The theory of spin models intersects with condensed matter physics, complex systems, graph theory, combinatorial optimization, computational complexity and neural networks. Many ensuing applications rely on the fact that complicated spin…

Mathematical Physics · Physics 2024-08-02 Tobias Reinhart , Benjamin Engel , Gemma De les Coves

As a complementary tool to laboratory experiments, discrete numerical simulation, applied to granular materials, provides valuable information on the grain and contact scale microstructure, thereby enabling one to better understand the…

Classical Physics · Physics 2009-01-23 Jean-Noël Roux , François Chevoir

We first define the coarse-graining of probability measures in terms of stochastic kernels. We define when a probability measure is part of another probability measure and say that two probability measures coexist if they are both parts of…

Quantum Physics · Physics 2022-10-14 Stan Gudder

Understanding the large-scale physics is crucial for the spin foam approach to quantum gravity. We tackle this challenge from a statistical physics perspective using simplified, yet feature-rich models. In particular, this allows us to…

General Relativity and Quantum Cosmology · Physics 2015-06-03 Bianca Dittrich , Frank C Eckert

An efficient Monte Carlo algorithm for the simulation of spin models with long-range interactions is discussed. Its central feature is that the number of operations required to flip a spin is independent of the number of interactions…

Statistical Mechanics · Physics 2007-05-23 Erik Luijten

Two very basic constructions involving experimental procedures are the formation of coarse-grained versions of experiments, and the formation of branching sequential experiments. The latter allow for the conditioning of states on the…

Quantum Physics · Physics 2024-10-14 Alex Wilce

Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in multiscale modeling, provided that information between the…

Computational Physics · Physics 2024-07-23 Jun Zhang , Xiaohan Lin , Weinan E , Yi Qin Gao

Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it…

Stochastic modelling of complex systems plays an essential, yet often computationally intensive role across the quantitative sciences. Recent advances in quantum information processing have elucidated the potential for quantum simulators to…

Quantum Physics · Physics 2021-06-22 Thomas J. Elliott

We propose a new holonomy formulation for spin foams, which naturally extends the theory space of lattice gauge theories. This allows current spin foam models to be defined on arbitrary two-complexes as well as to generalize current spin…

General Relativity and Quantum Cosmology · Physics 2013-03-14 Benjamin Bahr , Bianca Dittrich , Frank Hellmann , Wojciech Kaminski

Stochastic kinetic models are often used to describe complex biological processes. Typically these models are analytically intractable and have unknown parameters which need to be estimated from observed data. Ideally we would have…

Computation · Statistics 2018-03-13 Richard J. Boys , Holly F. Ainsworth , Colin S. Gillespie

The evolution of local spin polarization in finite systems involves interference phenomena that give rise to {\bf quantum dynamical echoes }and non-ergodic behavior. We predict the conditions to observe these echoes by exploiting the NMR…

Condensed Matter · Physics 2009-10-28 Horacio M. Pastawski , Patricia R. Levstein , Gonzalo Usaj

In this talk we review our theoretical understanding of spin glasses paying a particular attention to the basic physical ideas. We introduce the replica method and we describe its probabilistic consequences (we stress the recently…

Disordered Systems and Neural Networks · Physics 2007-05-23 Giorgio Parisi

Constrained Hamiltonian dynamics of a quantum system of nonlinear oscillators is used to provide the mathematical formulation of a coarse-grained description of the quantum system. It is seen that the evolution of the coarse-grained system…

Quantum Physics · Physics 2015-05-28 Milan Radonjić , Slobodan Prvanović , Nikola Burić

We give a survey of the foundations of statistical queries and their many applications to other areas. We introduce the model, give the main definitions, and we explore the fundamental theory statistical queries and how how it connects to…

Machine Learning · Computer Science 2020-05-15 Lev Reyzin
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