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Renormalization is an essential technique in field-theoretic descriptions of natural phenomena, where the absence of a UV-complete description yields an abundance of divergent quantities. While the renormalization prescription has been…

High Energy Physics - Theory · Physics 2025-11-14 Brenden Bowen , Albert Farah , Spasen Chaykov , Nishant Agarwal

We present a possible approach to the study of the renormalization group (RG) flow based entirely on the information theory. The average information loss under a single step of Wilsonian RG transformation is evaluated as a conditional…

Statistical Mechanics · Physics 2011-10-13 S. M. Apenko

There is a vast body of recent literature on the reliability of communication through noisy channels, the recovery of community structures in the stochastic block model, the limiting behavior of the free entropy in spin glasses and the…

Combinatorics · Mathematics 2022-07-25 Konstantinos Panagiotou , Matija Pasch

We introduce a "renormalized entanglement entropy" which is intrinsically UV finite and is most sensitive to the degrees of freedom at the scale of the size R of the entangled region. We illustrated the power of this construction by showing…

High Energy Physics - Theory · Physics 2015-03-20 Hong Liu , Mark Mezei

We investigate the analogy between the renormalization group (RG) and deep neural networks, wherein subsequent layers of neurons are analogous to successive steps along the RG. In particular, we quantify the flow of information by…

High Energy Physics - Theory · Physics 2021-12-22 Johanna Erdmenger , Kevin T. Grosvenor , Ro Jefferson

We consider the formalism of information decomposition of target effects from multi-source interactions, i.e. the problem of defining redundant and synergistic components of the information that a set of source variables provides about a…

Statistical Mechanics · Physics 2019-04-24 Daniele Marinazzo , Leonardo Angelini , Mario Pellicoro , Sebastiano Stramaglia

The observed IR and the spectator UV particles of a regulated, cutoff quantum field theory are entangled by their interactions; hence, the IR sector can be described by the help of the density matrix only. The tree-level renormalized…

High Energy Physics - Theory · Physics 2016-02-17 S. Nagy , J. Polonyi , I. Steib

We propose a general formulation of the renormalisation group as a family of quantum channels which connect the microscopic physical world to the observable world at some scale. By endowing the set of quantum states with an operationally…

Quantum Physics · Physics 2015-08-14 Cédric Bény , Tobias J. Osborne

We explore the connection between deep learning and information theory through the paradigm of diffusion models. A diffusion model converts noise into structured data by reinstating, imperfectly, information that is erased when data was…

Machine Learning · Computer Science 2025-11-04 Akhil Premkumar

The functional renormalization group flow of a scalar field theory with quartic couplings and a sharp spatial momentum cutoff is presented in four-dimensional Minkowski space-time for the bare action by retaining the entanglement of the IR…

High Energy Physics - Theory · Physics 2022-02-16 S. Nagy , J. Polonyi

We consider optical communications with intensity modulation and direct detection affected by laser relative intensity noise (RIN). Starting from a continuous-time waveform model, we derive an equivalent discrete-time channel model. As a…

Information Theory · Computer Science 2026-05-11 Felipe Villenas , Yunus Can Gültekin , Alex Alvarado

The density matrix renormalization group (DMRG) has been extended to study quantum phase transitions on random graphs of fixed connectivity. As a relevant example, we have analysed the random Ising model in a transverse field. If the…

Disordered Systems and Neural Networks · Physics 2009-11-11 Javier Rodriguez-Laguna

Information theory, though originally developed for communications engineering, provides mathematical tools with broad applications across science. These tools characterize the fundamental limits of data compression and transmission in the…

Information Theory · Computer Science 2026-03-09 Henry Pinkard , Laura Waller

The renormalization group (RG) approach is largely responsible for the considerable success that has been achieved in developing a quantitative theory of phase transitions. Physical properties emerge from spectral properties of the…

Mathematical Physics · Physics 2015-05-14 Mei Yin

Quantum entanglement entropy has a geometric character. This is illustrated by the interpretation of Rindler space or black hole entropy as entanglement entropy. In general, one can define a "geometric entropy", associated with an event…

Quantum Physics · Physics 2007-05-23 Jose Gaite

The renormalization group (RG) is an essential technique in statistical physics and quantum field theory, which considers scale-invariant properties of physical theories and how these theories' parameters change with scaling. Deep learning…

Statistical Mechanics · Physics 2023-08-23 Kelsie Taylor

We have adjusted the Density Matrix Renormalization method to handle two dimensional systems of limited width. The key ingredient for this extension is the incorporation of symmetries in the method. The advantage of our approach is that we…

Statistical Mechanics · Physics 2009-10-30 M. S. L. du Croo de Jongh , J. M. J. van Leeuwen

We consider the approach describing glass formation in liquids as a progressive trapping in an exponentially large number of metastable states. To go beyond the mean-field setting, we provide a real-space renormalization group (RG) analysis…

Disordered Systems and Neural Networks · Physics 2012-03-15 Chiara Cammarota , Giulio Biroli , Marco Tarzia , Gilles Tarjus

In this Thesis, several results in quantum information theory are collected, most of which use entropy as the main mathematical tool. *While a direct generalization of the Shannon entropy to density matrices, the von Neumann entropy behaves…

Quantum Physics · Physics 2018-10-25 Christian Majenz

We investigate the fundamental limits of reliable communication over multi-view channels, in which the channel output is comprised of a large number of independent noisy views of a transmitted symbol. We consider first the setting of…

Information Theory · Computer Science 2024-05-14 V. Arvind Rameshwar , Nir Weinberger
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