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To quantify the entanglement is one of the most important topics in quantum entanglement theory. In [arXiv: 2006.12408], the authors proposed a method to build a measure from the orginal domain to a larger one. Here we apply that method to…

Quantum Physics · Physics 2021-04-21 Xian Shi , Lin Chen

Ever since entanglement was identified as a computational and cryptographic resource, effort has been made to find an efficient way to tell whether a given density matrix represents an unentangled, or separable, state. Essentially, this is…

Data Structures and Algorithms · Computer Science 2007-05-23 Lawrence M. Ioannou

In the present study the software CrackComput, based on the Xfem and Xcrack libraries has been used for three problems- to experiment on the convergence properties of the method applied to elasto-statics crack problems, comparison of stress…

Computational Physics · Physics 2013-08-26 Dibakar Datta

This software performs the combination of $m$ correlated estimates of $n$ physics observables ($m\ge n$) using the Best Linear Unbiased Estimate (BLUE) method. It is implemented as a C++ class, to be used within the ROOT analysis package.…

Data Analysis, Statistics and Probability · Physics 2020-04-14 Richard Nisius

This article introduces PnCP, a MATLAB toolbox for constructing positive maps which are not completely positive. We survey optimization and sum of squares relaxation techniques to find the most numerically efficient methods for this…

Optimization and Control · Mathematics 2020-01-07 Abhishek Bhardwaj

To determine whether a given multipartite quantum state is separable with respect to some partition we construct a family of entanglement measures R_m. This is done utilizing generalized concurrences as building blocks which are defined by…

Quantum Physics · Physics 2010-08-06 Tsubasa Ichikawa , Marcus Huber , Philipp Krammer , Beatrix C. Hiesmayr

A numerical tool relying on sharp Immersed Boundary Method (IBM) is developed for the analysis of aerospace applications. The method, which is conceived for application using segregated solvers relying on implicit time discretization, uses…

Computational Engineering, Finance, and Science · Computer Science 2025-02-25 M. A. Chemak , E. Constant , M. Meldi

Distributed, large-scale quantum computing will need architectures that combine matter-based qubits with photonic links, but today's software stacks target either gate-based chips or linear-optical devices in isolation. We introduce Optyx,…

Quantifying coherence and entanglement is extremely important in quantum information processing. Here, we present numerical and analytical results for the geometric measure of coherence, and also present numerical results for the geometric…

Quantum Physics · Physics 2020-08-27 Zhou Zhang , Yue Dai , Yuli Dong , Chengjie Zhang

The quantification of quantum entanglement is a central issue in quantum information theory. Recently, Gao \emph{et al}. ( \href{http://dx.doi.org/10.1103/PhysRevLett.112.180501}{Phys. Rev. Lett. \textbf{112}, 180501 (2014)}) pointed out…

Quantum Physics · Physics 2021-05-11 Xianfei Qi , Ting Gao , Fengli Yan

Exploratory landscape analysis and fitness landscape analysis in general have been pivotal in facilitating problem understanding, algorithm design and endeavors such as automated algorithm selection and configuration. These techniques have…

Neural and Evolutionary Computing · Computer Science 2024-02-27 Raphael Patrick Prager , Heike Trautmann

We introduce ISO-Bench, a benchmark for coding agents to test their capabilities on real-world inference optimization tasks. These tasks were taken from vLLM and SGLang, two of the most popular LLM serving frameworks. Each task provides an…

Machine Learning · Computer Science 2026-02-24 Ayush Nangia , Shikhar Mishra , Aman Gokrani , Paras Chopra

Covariance matrices are a useful tool to investigate correlations and entanglement in quantum systems. They are widely used in continuous variable systems, but recently also for finite dimensional systems powerful entanglement criteria in…

Quantum Physics · Physics 2010-04-22 Oleg Gittsovich , Otfried Gühne

A linear program with linear complementarity constraints (LPCC) requires the minimization of a linear objective over a set of linear constraints together with additional linear complementarity constraints. This class has emerged as a…

Optimization and Control · Mathematics 2018-02-09 Bin Yu , John E. Mitchell , Jong-Shi Pang

This article focuses on the finite volume method (FVM) as an instrument tool to deal with the non-linear collisional-induced breakage equation (CBE) that arises in the particulate process. Notably, we consider the non-conservative…

Numerical Analysis · Mathematics 2024-11-27 Sanjiv Kumar Bariwal , Rajesh Kumar

The prohibitive cost of evaluating Large Language Models (LLMs) necessitates efficient alternatives to full-scale benchmarking. Prevalent approaches address this by identifying a small coreset of items to approximate full-benchmark…

Artificial Intelligence · Computer Science 2026-02-04 Yueqi Zhang , Jin Hu , Shaoxiong Feng , Peiwen Yuan , Xinglin Wang , Yiwei Li , Jiayi Shi , Chuyi Tan , Ji Zhang , Boyuan Pan , Yao Hu , Kan Li

We search a simplest and minimal way to determine whether a given quantum system is entangled or separable. For this end, we propose binary correlation measurements in which restricted knowledge of only zero or non-zero correlations is…

Quantum Physics · Physics 2021-06-29 Toru Ohira

Quantization has become one of the most effective methodologies to compress LLMs into smaller size. However, the existing quantization solutions still show limitations of either non-negligible accuracy drop or low system efficiency. In this…

Machine Learning · Computer Science 2026-04-23 Zhen Zheng , Xiaonan Song , Chuanjie Liu

The current work presents a novel COllaborative Open-source Lattice Boltzmann Method framework, so-called CooLBM. The computational framework is developed for the simulation of single and multi-component multi-phase problems, along with a…

Computational Physics · Physics 2025-02-19 R. Alamian , A. K. Nayak , M. S. Shadloo

We study the convergence rate of Bregman gradient methods for convex optimization in the space of measures on a $d$-dimensional manifold. Under basic regularity assumptions, we show that the suboptimality gap at iteration $k$ is in…

Optimization and Control · Mathematics 2023-03-15 Lénaïc Chizat
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