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We consider the quantum decoding problem. It consists in recovering a codeword given a superposition of noisy versions of this codeword. By measuring the superposition, we get back to the classical decoding problem. It appears for the first…

Quantum Physics · Physics 2026-02-05 Agathe Blanvillain , André Chailloux , Jean-Pierre Tillich

Network information theory is the study of communication problems involving multiple senders, multiple receivers and intermediate relay stations. The purpose of this thesis is to extend the main ideas of classical network information theory…

Quantum Physics · Physics 2012-08-22 Ivan Savov

Guided by critical systems found in nature we develop a novel mechanism consisting of inhomogeneous polynomial regularisation via which we can induce scale invariance in deep learning systems. Technically, we map our deep learning (DL)…

Statistical Mechanics · Physics 2017-06-01 Dan Oprisa , Peter Toth

The inherent uncertainty of communication channels implies that any coding scheme has a non-zero probability of failing to correct errors, making retransmission mechanisms essential. To ensure message reliability and integrity, a dual-layer…

Information Theory · Computer Science 2025-11-20 Chaofeng Guan , Gaojun Luo , Lan Luo , Yangyang Fei , Hong Wang

I describe a new method for smoothing a one-dimensional curve in Euclidian space with an arbitrary number of dimensions. The basic idea is borrowed from renormalization group theory which previously was applied to biological macromolecules.…

Numerical Analysis · Computer Science 2018-06-06 Anna Sinelnikova

We review some of our recent results concerning the relationship between the Real-Space Renormalization Group method and Quantum Groups. We show this relation by applying real-space RG methods to study two quantum group invariant…

High Energy Physics - Theory · Physics 2016-11-03 Miguel A. Martin-Delgado , German Sierra

Renormalization group has enjoyed successes in other areas of statistical physics. However, its application to turbulence faces several technical difficulties, which have had to be circumvented by uncontrolled approximations. Indeed, in…

Fluid Dynamics · Physics 2007-05-23 David McComb , Jaek-Jin Yang , Alistair Young , Luc Machiels

We present a recently-developed renormalization group scheme, the functional renormalization group (fRG), as a many-particle method suited to account for the two-particle interactions between the electrons in complex quantum dot geometries.…

Strongly Correlated Electrons · Physics 2007-05-23 C. Karrasch

Inspired by the superblock method of White, we introduce a simple modification of the standard Renormalization Group (RG) technique for the study of quantum lattice systems. Our method which takes into account the effect of Boundary…

Statistical Mechanics · Physics 2009-10-28 A. Langari , V. Karimipour

It is argued that cluster methods provide a viable alternative to Wilson's momentum shell integration technique at the early stage of renormalization in the field-theoretic models with strongly coupled fields because these methods allow for…

Statistical Mechanics · Physics 2016-06-23 V. I. Tokar

We clarify the notion of Wilsonian renormalization group (RG) invariance in supersymmetric gauge theories, which states that the low-energy physics can be kept fixed when one changes the ultraviolet cutoff, provided appropriate changes are…

High Energy Physics - Theory · Physics 2016-09-06 Nima Arkani-Hamed , Hitoshi Murayama

Topological quantum error-correcting codes are defined by geometrically local checks on a two-dimensional lattice of quantum bits (qubits), making them particularly well suited for fault-tolerant quantum information processing. Here, we…

Quantum Physics · Physics 2012-02-16 Guillaume Duclos-Cianci , David Poulin

Group synchronization is a fundamental task involving the recovery of group elements from pairwise measurements. For orthogonal group synchronization, the most common approach reformulates the problem as a constrained nonconvex optimization…

Machine Learning · Statistics 2026-04-10 Haiyang Peng , Deren Han , Xin Chen , Meng Huang

We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalisation group (SDRG) method. The system consists of…

Disordered Systems and Neural Networks · Physics 2026-03-06 A. Ustyuzhanin , J. Vahedi , S. Kettemann

We propose a new interpretation of measures of information and disorder by connecting these concepts to group theory in a new way. Entropy and group theory are connected here by their common relation to sets of permutations. A combinatorial…

Information Theory · Computer Science 2019-11-25 David J. Galas

The similarity renormalization group (SRG) is based on unitary transformations that suppress off-diagonal matrix elements, forcing the hamiltonian towards a band-diagonal form. A simple SRG transformation applied to nucleon-nucleon…

Nuclear Theory · Physics 2008-11-26 S. K. Bogner , R. J. Furnstahl , R. J. Perry

We set up the Functional Renormalisation Group formalism for Tensorial Group Field Theory in full generality. We then apply it to a rank-3 model over U(1) x U(1) x U(1), endowed with a linear kinetic term and nonlocal interactions. The…

High Energy Physics - Theory · Physics 2015-07-09 Dario Benedetti , Joseph Ben Geloun , Daniele Oriti

The renormalization group (RG) is used in order to obtain the RG improved effective potential in curved spacetime. This potential is explicitly calculated for the Yukawa model and for scalar electrodynamics, i.e. theories with several…

High Energy Physics - Theory · Physics 2009-09-17 E. Elizalde , S. D. Odintsov

We present in this work an exact renormalization group (RG) treatment of a one-dimensional $p$-wave superconductor. The model proposed by Kitaev consists of a chain of spinless fermions with a $p$-wave gap. It is a paradigmatic model of…

Superconductivity · Physics 2014-04-15 Mucio A. Continentino , Fernanda Deus , Heron Caldas

The renormalization group equations for large-scale structure (RG-LSS) describe how the bias and stochastic (noise) parameters -- both of matter and biased tracers such as galaxies -- evolve as a function of the cutoff $\Lambda$ of the…

Cosmology and Nongalactic Astrophysics · Physics 2024-10-10 Henrique Rubira , Fabian Schmidt