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This article presents a theory for constructing hierarchical networks in such a way that the networks are guaranteed to be provably scale covariant. We first present a general sufficiency argument for obtaining scale covariance, which holds…

Computer Vision and Pattern Recognition · Computer Science 2024-09-20 Tony Lindeberg

We study the distribution of a fully connected neural network with random Gaussian weights and biases in which the hidden layer widths are proportional to a large constant $n$. Under mild assumptions on the non-linearity, we obtain…

Machine Learning · Computer Science 2024-06-18 Stefano Favaro , Boris Hanin , Domenico Marinucci , Ivan Nourdin , Giovanni Peccati

We expand a set of notions recently introduced providing the general setting for a universal representation of the quantum structure on which quantum information stands. The dynamical evolution process associated with generic quantum…

Quantum Physics · Physics 2009-11-10 Annalisa Marzuoli , Mario Rasetti

We summarize basic features of quantum field theories with discrete symmetry $\mathbb{Q}/\mathbb{Z}$ (possibly higher form, global or gauged). The classification of representations and anomalies is quite rich and involves the ring of…

High Energy Physics - Theory · Physics 2023-03-01 Pavel Putrov

This is a nontechnical introduction to recent work on quantum gravity using ideas from higher-dimensional algebra. We argue that reconciling general relativity with the Standard Model requires a `background-free quantum theory with local…

General Relativity and Quantum Cosmology · Physics 2015-02-13 John C. Baez

In the context of growing networks, we introduce a simple dynamical model that unifies the generic features of real networks: scale-free distribution of degree and the small world effect. While the average shortest path length increases…

Condensed Matter · Physics 2009-11-07 Konstantin Klemm , Victor M. Eguiluz

Dimensionality is one of the most important properties of complex physical systems. However, only recently this concept has been considered in the context of complex networks. In this paper we further develop the previously introduced…

Physics and Society · Physics 2013-08-19 Filipi Nascimento Silva , Luciano da Fontoura Costa

Quantum geometry is a differential geometry based on quantum mechanics. It is related to various transport and optical properties in condensed matter physics. The Zeeman quantum geometry is a generalization of quantum geometry including the…

Mesoscale and Nanoscale Physics · Physics 2026-03-16 Motohiko Ezawa

Spin networks, essentially labeled graphs, are ``good quantum numbers'' for the quantum theory of geometry. These structures encompass a diverse range of techniques which may be used in the quantum mechanics of finite dimensional systems,…

General Relativity and Quantum Cosmology · Physics 2009-10-31 Seth A. Major

We address the "inverse problem" for discrete geometry, which consists in determining whether, given a discrete structure of a type that does not in general imply geometrical information or even a topology, one can associate with it a…

General Relativity and Quantum Cosmology · Physics 2010-04-30 Luca Bombelli , Alejandro Corichi , Oliver Winkler

A new paradigm of quantum computing, namely, soft quantum computing, is proposed for nonclassical computation using real world quantum systems with naturally occurring environment-induced decoherence and dissipation. As a specific example…

Quantum Physics · Physics 2018-10-12 Zeng-Bing Chen

Just as for non-abelian gauge theories at strong coupling, discrete lattice methods are a natural tool in the study of non-perturbative quantum gravity. They have to reflect the fact that the geometric degrees of freedom are dynamical, and…

High Energy Physics - Theory · Physics 2009-10-31 R. Loll

The space of states and operators for a large class of background independent theories of quantum spacetime dynamics is defined. The SU(2) spin networks of quantum general relativity are replaced by labelled compact two-dimensional…

General Relativity and Quantum Cosmology · Physics 2016-08-25 Fotini Markopoulou , Lee Smolin

Multiple scales coexist in complex networks. However, the small world property makes them strongly entangled. This turns the elucidation of length scales and symmetries a defiant challenge. Here, we define a geometric renormalization group…

Disordered Systems and Neural Networks · Physics 2018-07-04 Guillermo García-Pérez , Marián Boguñá , M. Ángeles Serrano

Convolutional neural networks are ubiquitous in Machine Learning applications for solving a variety of problems. They however can not be used in their native form when the domain of the data is commonly encountered manifolds such as the…

Computer Vision and Pattern Recognition · Computer Science 2018-08-08 Rudrasis Chakraborty , Monami Banerjee , Baba C. Vemuri

Non-pertrubative quantum gravity formulated as a unitary four-dimensional theory suggests that certain amount of non-locality, such as infinite-derivative operators, can be present in the action, in both cases of Analytic Infinite…

High Energy Physics - Theory · Physics 2024-12-04 Alexey S. Koshelev , Chenxuan Li , Anna Tokareva

This paper introduces the Neural Differential Manifold (NDM), a novel neural network architecture that explicitly incorporates geometric structure into its fundamental design. Departing from conventional Euclidean parameter spaces, the NDM…

Machine Learning · Computer Science 2025-10-30 Di Zhang

A general noncommutative-geometric theory of principal bundles is presented. Quantum groups play the role of structure groups. General quantum spaces play the role of base manifolds. A differential calculus on quantum principal bundles is…

q-alg · Mathematics 2008-02-03 Mico Durdevic

We analytically explore the scaling properties of a general class of nested subgraphs in complex networks, which includes the $K$-core and the $K$-scaffold, among others. We name such class of subgraphs $K$-nested subgraphs due to the fact…

Disordered Systems and Neural Networks · Physics 2009-11-13 Bernat Corominas-Murtra , José F. F. Mendes , Ricard V. Solé

Quantum computing is a new computational paradigm that promises applications in several fields, including machine learning. In the last decade, deep learning, and in particular Convolutional neural networks (CNN), have become essential for…

Quantum Physics · Physics 2021-06-14 Iordanis Kerenidis , Jonas Landman , Anupam Prakash