English
Related papers

Related papers: Approximate maximum likelihood decoding with $K$ m…

200 papers

We introduce Decision Tree Decoders (DTDs), which rely only on the sparsity of the binary check matrix, making them broadly applicable for decoding any quantum low-density parity-check (qLDPC) code and fault-tolerant quantum circuits. DTDs…

Quantum Physics · Physics 2025-02-25 Kai R. Ott , Bence Hetényi , Michael E. Beverland

In this work it is shown that locally repairable codes (LRCs) can be list-decoded efficiently beyond the Johnson radius for a large range of parameters by utilizing the local error-correction capabilities. The corresponding decoding radius…

Information Theory · Computer Science 2020-09-16 Lukas Holzbaur , Sven Puchinger , Antonia Wachter-Zeh

In this work we analyze the problem of linear correction of the reliability metrics (L-values) in BICM receivers. We want to find the correction factors that minimize the probability of error of a maximum likelihood decoder that uses the…

Information Theory · Computer Science 2011-12-01 Szczecinski Leszek

In this work, we develop an efficient decoding method for graph codes, a class of stabilizer quantum error-correcting codes constructed from graph states. While optimal decoding is generally NP-hard, we propose a faster decoder exploiting…

Quantum Physics · Physics 2026-02-17 Nirupam Basak , Goutam Paul

Topological quantum error correction is a milestone in the scaling roadmap of quantum computers, which targets circuits with trillions of gates that would allow running quantum algorithms for real-world problems. The square-lattice surface…

Quantum Physics · Physics 2025-02-12 César Benito , Esperanza López , Borja Peropadre , Alejandro Bermudez

Least squares (LS) fitting is one of the most fundamental techniques in science and engineering. It is used to estimate parameters from multiple noisy observations. In many problems the parameters are known a-priori to be bounded integer…

Information Theory · Computer Science 2009-01-05 Amir Leshem , Jacob Goldberger

Based on the notion of supercodes, we propose a two-phase maximum-likelihood soft-decision decoding (tpMLSD) algorithm for binary linear block codes in this work. The first phase applies the Viterbi algorithm backwardly to a trellis derived…

Information Theory · Computer Science 2014-08-07 Yunghsiang S. Han , Hung-Ta Pai , Po-Ning Chen , Ting-Yi Wu

In this paper, we propose an enhanced quasi-maximum likelihood (EQML) decoder for LDPC codes with short block lengths. After the failure of the conventional belief propagation (BP) decoding, the proposed EQML decoder selects unreliable…

Information Theory · Computer Science 2019-01-31 Peng Kang , Yixuan Xie , Lei Yang , Chen Zheng , Jinhong Yuan , Yuejun Wei

Stochastic PDE eigenvalue problems are useful models for quantifying the uncertainty in several applications from the physical sciences and engineering, e.g., structural vibration analysis, the criticality of a nuclear reactor or photonic…

Numerical Analysis · Mathematics 2022-10-07 Alexander D. Gilbert , Robert Scheichl

Link adaptation is a crucial part of many modern communications systems, allowing the system to adapt the transmission and reception strategies to changes in channel conditions. One of the fundamental components of the link adaptation…

Information Theory · Computer Science 2009-09-08 Oded Redlich , Doron Ezri , Dov Wulich

We perform an extended numerical search for practical fermion-to-qubit encodings with error correcting properties. Ideally, encodings should strike a balance between a number of the seemingly incompatible attributes, such as having a high…

Quantum Physics · Physics 2024-05-29 Fedor Simkovic , Martin Leib , Francisco Revson F. Pereira

Let G be an edge-weighted hypergraph on n vertices, m edges of size \le s, where the edges have real weights in an interval [1,W]. We show that if we can approximate a maximum weight matching in G within factor alpha in time T(n,m,W) then…

Data Structures and Algorithms · Computer Science 2011-01-12 Andrzej Lingas , Cui Di

In this paper, Sphere Decoding (SD) algorithms for Spatial Modulation (SM) are developed to reduce the computational complexity of Maximum-Likelihood (ML) detectors. Two SDs specifically designed for SM are proposed and analysed in terms of…

Information Theory · Computer Science 2013-05-31 Abdelhamid Younis , Sinan Sinanović , Marco Di Renzo , Raed Mesleh , Harald Haas

In this paper, new techniques are presented to either simplify or improve most existing upper bounds on the maximum-likelihood (ML) decoding performance of the binary linear codes over additive white Gaussian noise (AWGN) channels. Firstly,…

Information Theory · Computer Science 2015-03-19 Xiao Ma , Jia Liu , Baoming Bai

We consider the maximum vertex-weighted matching problem (MVM), in which non-negative weights are assigned to the vertices of a graph, the weight of a matching is the sum of the weights of the matched vertices, and we are required to…

Data Structures and Algorithms · Computer Science 2018-10-12 Florin Dobrian , Mahantesh Halappanavar , Alex Pothen , Ahmed Al-Herz

This paper studies the problem of reconstructing a word given several of its noisy copies. This setup is motivated by several applications, among them is reconstructing strands in DNA-based storage systems. Under this paradigm, a word is…

Information Theory · Computer Science 2020-01-17 Omer Sabary , Eitan Yaakobi , Alexander Yucovich

We study two related problems: finding a set of k vertices and minimum number of edges (kmin) and finding a graph with at least m' edges and minimum number of vertices (mvms). Goldschmidt and Hochbaum \cite{GH97} show that the mvms problem…

Data Structures and Algorithms · Computer Science 2013-11-05 Rajiv Gandhi , G. Kortsarz

Maximum distance separable (MDS) array codes constitute an important class of error-correcting codes due to their optimal distance properties and their relevance in distributed storage systems. In this paper, we investigate the construction…

Stochastic PDE eigenvalue problems often arise in the field of uncertainty quantification, whereby one seeks to quantify the uncertainty in an eigenvalue, or its eigenfunction. In this paper we present an efficient multilevel quasi-Monte…

Numerical Analysis · Mathematics 2022-10-07 Alexander D. Gilbert , Robert Scheichl

In this paper, we propose a Riemannian steepest descent method for solving a blind deconvolution problem. We prove that the proposed algorithm with an appropriate initialization will recover the exact solution with high probability when the…

Information Theory · Computer Science 2018-04-17 Wen Huang , Paul Hand