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Quantum entanglement is one of the core features of quantum theory. While it is typically revealed by measurements along carefully chosen directions, here we review different methods based on so-called random or randomized measurements.…

Quantum Physics · Physics 2020-11-24 Lukas Knips

We study the loss of entanglement of bipartite state subjected to discarding or measurement of one qubit. Examining the behavior of different entanglement measures, we find that entanglement of formation, entanglement cost, and logarithmic…

Quantum Physics · Physics 2009-11-10 Karol Horodecki , Michal Horodecki , Pawel Horodecki , Jonathan Oppenheim

We exactly evaluate a number of multipartite entanglement measures for a class of graph states, including d-dimensional cluster states (d = 1,2,3), the Greenberger-Horne-Zeilinger states, and some related mixed states. The entanglement…

Quantum Physics · Physics 2007-07-10 Damian Markham , Akimasa Miyake , Shashank Virmani

Given an arbitrary statistical theory, different from quantum mechanics, how to decide which are the nonclassical correlations? We present a formal framework which allows for a definition of nonclassical correlations in such theories,…

Quantum Physics · Physics 2016-11-26 F. Holik , C. Massri , A. Plastino

We can only perform a finite rounds of measurements in protocols with local operations and classical communication (LOCC). In this paper, we propose a set of product states, which require infinite rounds of measurements in order to…

Quantum Physics · Physics 2019-02-27 Mao-Sheng Li , Yan-Ling Wang

Classifications organize entities into categories that identify similarities within a category and discern dissimilarities among categories, and they powerfully classify information in support of analysis. We propose a new classification…

Optimization and Control · Mathematics 2022-09-05 Casey Garner , Allen Holder

Noise Contrastive Estimation (NCE) is a powerful parameter estimation method for log-linear models, which avoids calculation of the partition function or its derivatives at each training step, a computationally demanding step in many cases.…

Computation and Language · Computer Science 2018-09-07 Zhuang Ma , Michael Collins

Circle packing is widely used in visualization due to its aesthetic appeal and simplicity, particularly in tasks where the spatial arrangement and relationships between data are of interest, such as understanding proximity relationships…

Human-Computer Interaction · Computer Science 2026-02-03 Duan Li , Jun Yuan , Xinyuan Guo , Xiting Wang , Yang Liu , Weikai Yang , Shixia Liu

The characterization of quantum correlations, being stronger than classical, yet weaker than those appearing in non-signaling models, still poses many riddles. In this work we show that the extent of binary correlations in a general class…

Quantum Physics · Physics 2018-07-17 Avishy Carmi , Eliahu Cohen

Nonlocality as a fundamental aspect of quantum mechanics is witnessed by violation of Bell inequality or its variants, for which all relevant studies assume some correlations exhibited by local realistic theories. The strategy of Bell's…

Quantum Physics · Physics 2020-12-04 Boya Xie , Sheng Feng

Motivated by the increasing ability of experimentalists to perform detector tomography, we consider how to incorporate the imperfections and restrictions of available measurements directly into the quantification of entanglement. Exploiting…

Quantum Physics · Physics 2012-10-31 Sebastian Meznaric

We develop a novel method in classifying the multipartite entanglement state of $2\times N\times N$ under stochastic local operation and classical communication. In this method, all inequivalent classes of true entangled state can be…

Quantum Physics · Physics 2010-01-21 Shuo Cheng , Junli Li , Cong-Feng Qiao

Entanglement measures quantify nonclassical correlations present in a quantum system, but can be extremely difficult to calculate, even more so, when information on its state is limited. Here, we consider broad families of entanglement…

Quantum Physics · Physics 2021-07-07 Matteo Fadel , Ayaka Usui , Marcus Huber , Nicolai Friis , Giuseppe Vitagliano

Ensemble learning serves as a straightforward way to improve the performance of almost any machine learning algorithm. Existing deep ensemble methods usually naively train many different models and then aggregate their predictions. This is…

Computer Vision and Pattern Recognition · Computer Science 2022-12-15 Le Zhang , Qibin Hou , Yun Liu , Jia-Wang Bian , Xun Xu , Joey Tianyi Zhou , Ce Zhu

The classification of multi-class microarray datasets is a hard task because of the small samples size in each class and the heavy overlaps among classes. To effectively solve these problems, we propose novel Error Correcting Output Code…

Machine Learning · Computer Science 2018-07-10 Mengxin Sun , Kunhong Liu , Qingqi Hong , Beizhan Wang

Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the inherent structure of clusterings. We model the relation…

Machine Learning · Computer Science 2012-09-05 Qiaoliang Xiang , Qi Mao , Kian Ming Chai , Hai Leong Chieu , Ivor Tsang , Zhendong Zhao

Employing a recently proposed separability criterion we develop analytical lower bounds for the concurrence and for the entanglement of formation of bipartite quantum systems. The separability criterion is based on a nondecomposable…

Quantum Physics · Physics 2007-05-23 Heinz-Peter Breuer

Nonlocality exhibited by ensembles of composite quantum states, wherein local operations and classical communication (LOCC) yield suboptimal discrimination probabilities compared to global strategies, is one of the striking nonclassical…

Quantum Physics · Physics 2025-12-02 Samrat Sen

Ordinal classification problems, where labels exhibit a natural order, are prevalent in high-stakes fields such as medicine and finance. Accurate uncertainty quantification, including the decomposition into aleatoric (inherent variability)…

Machine Learning · Computer Science 2025-07-02 Stefan Haas , Eyke Hüllermeier

Distance correlation coefficient (DCC) can be used to identify new associations and correlations between multiple variables. The distance correlation coefficient applies to variables of any dimension, can be used to determine smaller sets…

Statistical Finance · Quantitative Finance 2023-01-13 J. E. Salgado-Hernández , Manan Vyas