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The projected ensemble -- an ensemble of pure states on a subsystem conditioned on projective measurement outcomes on its complement -- provides a finer probe of ergodicity and information structure than the reduced density matrix of the…

Quantum Physics · Physics 2026-04-22 Alan Sherry , Saptarshi Mandal , Sthitadhi Roy

In this paper we study the quantum generalisation of the skew divergence, which is a dissimilarity measure between distributions introduced by L. Lee in the context of natural language processing. We provide an in-depth study of the quantum…

Mathematical Physics · Physics 2015-06-15 Koenraad M. R. Audenaert

In this paper we establish lower bounds on information divergence from a distribution to certain important classes of distributions as Gaussian, exponential, Gamma, Poisson, geometric, and binomial. These lower bounds are tight and for…

Information Theory · Computer Science 2011-02-15 Peter Harremoës , Christophe Vignat

We define a measure of redundant information based on projections in the space of probability distributions. Redundant information between random variables is information that is shared between those variables. But in contrast to mutual…

Information Theory · Computer Science 2013-05-30 Malte Harder , Christoph Salge , Daniel Polani

Randomness in scientific estimation is generally assumed to arise from unmeasured or uncontrolled factors. However, when combining subjective probability estimates, heterogeneity stemming from people's cognitive or information diversity is…

Methodology · Statistics 2015-09-14 Ville A. Satopää , Robin Pemantle , Lyle H. Ungar

We present a generalization of the Holevo theorem by means of distances used in the definition of distinguishability of states, showing that each one leads to an alternative Holevo theorem. This result involves two quantities: the…

Quantum Physics · Physics 2020-01-29 Diego G. Bussandri , Pedro W. Lamberti

Prediction polling is an increasingly popular form of crowdsourcing in which multiple participants estimate the probability or magnitude of some future event. These estimates are then aggregated into a single forecast. Historically,…

Methodology · Statistics 2016-04-25 Ville A. Satopää , Shane T. Jensen , Robin Pemantle , Lyle H. Ungar

A Holevo measure is used to discuss how much information about a given POVM on system $a$ is present in another system $b$, and how this influences the presence or absence of information about a different POVM on $a$ in a third system $c$.…

Quantum Physics · Physics 2013-05-29 Patrick J. Coles , Li Yu , Vlad Gheorghiu , Robert B. Griffiths

Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliability of the descriptive value, one must consider…

Machine Learning · Computer Science 2007-07-13 Marcus Hutter , Marco Zaffalon

This paper introduces the induced divergence, a new quantum divergence measure that replaces the hypothesis testing divergence in position-based decoding, simplifying the analysis of quantum communication and state redistribution while…

Quantum Physics · Physics 2025-02-20 Gilad Gour

In this paper we establish lower bounds on information divergence of a distribution on the integers from a Poisson distribution. These lower bounds are tight and in the cases where a rate of convergence in the Law of Thin Numbers can be…

Probability · Mathematics 2016-01-19 Peter Harremoës , Oliver Johnson , Ioannis Kontoyiannis

Quantum theory imposes fundamental limitations to the amount of information that can be carried by any quantum system. On the one hand, Holevo bound rules out the possibility to encode more information in a quantum system than in its…

Quantum Physics · Physics 2015-07-29 Michele Dall'Arno

This paper is devoted to the mathematical study of some divergences based on the mutual information well-suited to categorical random vectors. These divergences are generalizations of the "entropy distance" and "information distance". Their…

Statistics Theory · Mathematics 2016-08-16 Jean-François Coeurjolly , Rémy Drouilhet , Jean-François Robineau

We introduce a divergence measure between data distributions based on operators in reproducing kernel Hilbert spaces defined by kernels. The empirical estimator of the divergence is computed using the eigenvalues of positive definite Gram…

Machine Learning · Computer Science 2023-05-31 Jhoan Keider Hoyos Osorio , Oscar Skean , Austin J. Brockmeier , Luis Gonzalo Sanchez Giraldo

Sandwiched (quantum) $\alpha$-R\'enyi divergence has been recently defined in the independent works of Wilde et al. (arXiv:1306.1586) and M\"uller-Lennert et al (arXiv:1306.3142v1). This new quantum divergence has already found applications…

Quantum Physics · Physics 2013-12-16 Salman Beigi

Randomness in scientific estimation is generally assumed to arise from unmeasured or uncontrolled factors. However, when combining subjective probability estimates, heterogeneity stemming from people's cognitive or information diversity is…

Methodology · Statistics 2015-05-28 Ville Satopää , Robin Pemantle , Lyle Ungar

We develop a novel application of hybrid information divergences to analyze uncertainty in steady-state subsurface flow problems. These hybrid information divergences are non-intrusive, goal-oriented uncertainty quantification tools that…

Probability · Mathematics 2019-07-05 Eric Joseph Hall , Markos A. Katsoulakis

Holevo information is an upper bound for the accessible classical information of an ensemble of quantum states. In this work, we use Holevo information to investigate the ensemble theory interpretation of quantum gravity. We study the…

High Energy Physics - Theory · Physics 2022-03-02 Xiao-Liang Qi , Zhou Shangnan , Zhenbin Yang

Quantum information, though not precisely defined, is a fundamental concept of quantum information theory which predicts many fascinating phenomena and provides new physical resources. A basic problem is to recognize the features of quantum…

Quantum Physics · Physics 2007-06-13 M. Horodecki , P. Horodecki , R. Horodecki , M. Piani

This paper focuses on parameter estimation and introduces a new method for lower bounding the Bayesian risk. The method allows for the use of virtually \emph{any} information measure, including R\'enyi's $\alpha$, $\varphi$-Divergences, and…

Information Theory · Computer Science 2023-03-27 Amedeo Roberto Esposito , Adrien Vandenbroucque , Michael Gastpar
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