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We present algorithms to compute the Smith Normal Form of matrices over two families of local rings. The algorithms use the \emph{black-box} model which is suitable for sparse and structured matrices. The algorithms depend on a number of…

Symbolic Computation · Computer Science 2012-05-01 Mustafa Elsheikh , Mark Giesbrecht , Andy Novocin , B. David Saunders

A recently proposed renormalization group approach to dimensional crossover in quasi-one-dimensional quantum antiferromagnets is improved and then shown to give identical results, in some cases, to those obtained earlier.

Condensed Matter · Physics 2008-11-26 Ian Affleck , Bertrand I. Halperin

We present a binary code for spinors and Clifford multiplication using non-negative integers and their binary expressions, which can be easily implemented in computer programs for explicit calculations. As applications, we present explicit…

Differential Geometry · Mathematics 2019-09-27 Gerardo Arizmendi , Rafael Herrera

We study the renormalization of (softly) broken supersymmetric theories at the one loop level in detail. We perform this analysis in a superspace approach in which the supersymmetry breaking interactions are parameterized using spurion…

High Energy Physics - Theory · Physics 2008-11-26 Stefan Groot Nibbelink , Tino S. Nyawelo

We present SymForce, a library for fast symbolic computation, code generation, and nonlinear optimization for robotics applications like computer vision, motion planning, and controls. SymForce combines the development speed and flexibility…

We present a tool for batch generation of input scripts and multi-type Fourier analysis from simulation results for the micromagnetic software MuMax3. The introduction of graphical user interface and parameter-sweeping functionality…

Signal Processing · Electrical Eng. & Systems 2019-10-02 Zhiwei Ren , Lichuan Jin , Tianlong Wen , Yulong Liao , Xiaoli Tang , Huaiwu Zhang , Zhiyong Zhong

Quantum computing is expected to provide new promising approaches for solving the most challenging problems in material science, communication, search, machine learning and other domains. However, due to the decoherence and gate…

Quantum Physics · Physics 2020-04-22 Oleg M. Sotnikov , Vladimir V. Mazurenko

We propose the use of low bit-depth Sigma-Delta and distributed noise-shaping methods for quantizing the Random Fourier features (RFFs) associated with shift-invariant kernels. We prove that our quantized RFFs -- even in the case of $1$-bit…

Machine Learning · Computer Science 2022-04-14 Jinjie Zhang , Harish Kannan , Alexander Cloninger , Rayan Saab

Probabilistic machine learning models are distinguished by their ability to integrate prior knowledge of noise statistics, smoothness parameters, and training data uncertainty. A common approach involves modeling data with Gaussian…

Computation · Statistics 2025-07-31 Cristian A. Galvis-Florez , Ahmad Farooq , Simo Särkkä

The nonuniform fast Fourier transform (NUFFT) generalizes the FFT to off-grid data. Its many applications include image reconstruction, data analysis, and the numerical solution of differential equations. We present FINUFFT, an efficient…

Numerical Analysis · Mathematics 2019-04-10 Alex H. Barnett , Jeremy F. Magland , Ludvig af Klinteberg

Simulating fermionic systems on a quantum computer requires representing fermionic states using qubits. The complexity of many simulation algorithms depends on the complexity of implementing rotations generated by fermionic…

Quantum Physics · Physics 2024-10-08 Joseph Carolan , Luke Schaeffer

In this paper a deterministic sparse Fourier transform algorithm is presented which breaks the quadratic-in-sparsity runtime bottleneck for a large class of periodic functions exhibiting structured frequency support. These functions…

Numerical Analysis · Mathematics 2017-11-21 Sina Bittens , Ruochuan Zhang , Mark A. Iwen

As state of the art neural networks (NNs) continue to grow in size, their resource-efficient implementation becomes ever more important. In this paper, we introduce a compression scheme that reduces the number of computations required for…

Machine Learning · Computer Science 2025-04-25 Hans Rosenberger , Rodrigo Fischer , Johanna S. Fröhlich , Ali Bereyhi , Ralf R. Müller

Although the functional renormalization group (fRG) is by now a well-established method for investigating correlated electron systems, it is still undergoing significant technical and conceptual improvements. In particular, the motivation…

Strongly Correlated Electrons · Physics 2018-05-09 G. A. H. Schober , J. Ehrlich , T. Reckling , C. Honerkamp

Most research in quantum computing today is performed against simulations of quantum computers rather than true quantum computers. Simulating a quantum computer entails implementing all of the unitary operators corresponding to the quantum…

Quantum Physics · Physics 2021-08-17 Maksim Levental

Simulation of stabilizer circuits is a well-studied problem in quantum information processing, with a number of highly optimized algorithms available. Yet, we argue that further improvements can arise from the theoretical structure of…

The study of quantum circuit simulation using classical computers is a key research topic that helps define the boundary of verifiable quantum advantage, solve quantum many-body problems, and inform development of quantum hardware and…

Quantum Physics · Physics 2026-02-05 Benjamin N. Miller , Peter K. Elgee , Jason R. Pruitt , Kevin C. Cox

This article presents some aspects and experience in the use of algebraic manipulation software applied to general relativity. Some years ago certain results were reported using computer algebra platforms, but the growing popularity of…

Computational Physics · Physics 2022-06-01 Víctor Medina

We demonstrate a fast spin-s spherical harmonic transform algorithm, which is flexible and exact for band-limited functions. In contrast to previous work, where spin transforms are computed independently, our algorithm permits the…

Instrumentation and Methods for Astrophysics · Physics 2010-07-22 K. M. Huffenberger , B. D. Wandelt

The conceptual framework provided by the functional Renormalization Group (fRG) has become a formidable tool to study correlated electron systems on lattices which, in turn, provided great insights to our understanding of complex many-body…

Computational Engineering, Finance, and Science · Computer Science 2016-11-02 Julian Lichtenstein , Jan Winkelmann , David Sánchez de la Peña , Toni Vidović , Edoardo Di Napoli