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Related papers: Simulating causal collapse models

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We propose a novel coarse graining tensor renormalization group method based on the higher-order singular value decomposition. This method provides an accurate but low computational cost technique for studying both classical and quantum…

Statistical Mechanics · Physics 2015-03-19 Z. Y. Xie , J. Chen , M. P. Qin , J. W. Zhu , L. P. Yang , T. Xiang

Theories including a collapse mechanism have been presented various years ago. They are based on a modification of standard quantum mechanics in which nonlinear and stochastic terms are added to the evolution equation. Their principal…

Quantum Physics · Physics 2014-01-17 G. C. Ghirardi , R. Romano

We develop a coarse grained (CG) approach for efficiently simulating calcium dynamics in the endoplasmic reticulum membrane based on a fine stochastic lattice gas model. By grouping neighboring microscopic sites together into CG cells and…

Chemical Physics · Physics 2015-06-15 Chuansheng Shen , Hanshuang Chen

Cold atoms have become a powerful platform for quantum-simulating lattice gauge theories in higher spatial dimensions. However, such realizations have been restricted to the lowest possible truncations of the gauge field, which limit the…

Multiscale simulations facilitate the efficient exploration of large spatiotemporal scales in chemical and physical systems, yet particle-based simulations become prohibitively expensive at time and length scales beyond the molecular level.…

Chemical Physics · Physics 2026-02-25 Jaehyeok Jin , Yining Han , Gregory A. Voth

Causal Dynamical Triangulations is a non-perturbative quantum gravity model, defined with a lattice cut-off. The model can be viewed as defined with a proper time but with no reference to any three-dimensional spatial background geometry.…

High Energy Physics - Theory · Physics 2019-06-26 Jan Ambjørn , Zbigniew Drogosz , Jakub Gizbert-Studnicki , Andrzej Görlich , Jerzy Jurkiewicz

After coarse-graining a complex system, the dynamics of its macro-state may exhibit more pronounced causal effects than those of its micro-state. This phenomenon, known as causal emergence, is quantified by the indicator of effective…

Information Theory · Computer Science 2025-02-13 Kaiwei Liu , Bing Yuan , Jiang Zhang

The idea that in dynamical wave function collapse models the wave function is superfluous is investigated. Evidence is presented for the conjecture that, in a model of a field theory on a 1+1 lightcone lattice, knowing the field…

Quantum Physics · Physics 2009-11-10 Fay Dowker , Isabelle Herbauts

Simulating thimble regularization of lattice field theory can be tricky when more than one thimble is to be taken into account. A couple of years ago we proposed a solution for this problem. More recently this solution proved to be…

High Energy Physics - Lattice · Physics 2017-10-20 Francesco Di Renzo

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…

Some basic questions about the hydrodynamical approach to relativistic heavy ion collisions are discussed aiming to clarify how far we can go with such an approach to extract useful information on the properties and dynamics of the QCD…

High Energy Physics - Phenomenology · Physics 2015-06-11 Ph. Mota , T. Kodama , J. Takahashi , R. Derradi de Souza

Lattice gauge theories describe fundamental phenomena in nature, but calculating their real-time dynamics on classical computers is notoriously difficult. In a recent publication [Nature 534, 516 (2016)], we proposed and experimentally…

A 2D contact dynamics model is proposed as a microscopic description of a collapsing suspension/soil to capture the essential physical processes underlying the dynamics of generation and collapse of the system. Our physical model is…

Soft Condensed Matter · Physics 2009-11-05 D. Kadau , J. S. Andrade , H. J. Herrmann

Gauge fields coupled to dynamical matter are ubiquitous in many disciplines of physics, ranging from particle to condensed matter physics, but their implementation in large-scale quantum simulators remains challenging. Here we propose a…

Numerical simulations have become an important tool to understand and predict non-perturbative phenomena in particle physics. In this article we attempt to present a general overview over the field. First, the basic concepts of lattice…

High Energy Physics - Lattice · Physics 2010-12-17 F. Karsch , E. Laermann

We study the quantum simulation of Z2 lattice gauge theory in 2+1 dimensions. The dual variable formulation, the so-called Wegner duality, is utilized for reducing redundant gauge degrees of freedom. The problem of artificial charge…

High Energy Physics - Lattice · Physics 2021-02-02 Arata Yamamoto

We introduce a variational ansatz based on Gaussian states for (1+1)-dimensional lattice gauge models. To this end we identify a set of unitary transformations which decouple the gauge degrees of freedom from the matter fields. Using our…

High Energy Physics - Lattice · Physics 2022-09-21 P. Sala , T. Shi , S. Kühn , M. C. Bañuls , E. Demler , J. I. Cirac

The causal dynamical triangulations approach aims to construct a quantum theory of gravity as the continuum limit of a lattice-regularized model of dynamical geometry. A renormalization group scheme--in concert with finite size scaling…

General Relativity and Quantum Cosmology · Physics 2016-03-09 Joshua H. Cooperman

Two coarse-grained models which capture some universal characteristics of stripe forming systems are stud- ied. At high temperatures, the structure factors of both models attain their maxima on a circle in reciprocal space, as a consequence…

Statistical Mechanics · Physics 2012-12-18 Alejandro Mendoza-Coto , Daniel A. Stariolo

A vast amount of expert and domain knowledge is captured by causal structural priors, yet there has been little research on testing such priors for generalization and data synthesis purposes. We propose a novel model architecture, Causal…

Machine Learning · Computer Science 2022-11-08 Jeffrey Jiang , Omead Pooladzandi , Sunay Bhat , Gregory Pottie