Uncertainty Relations Based on Modified Wigner-Yanase-Dyson Skew Information

• URL: http://arxiv.org/abs/2004.11644v1
• Date: Fri, 24 Apr 2020 10:30:53 GMT
• Title: Uncertainty Relations Based on Modified Wigner-Yanase-Dyson Skew Information
• Authors: Zhaoqi Wu, Lin Zhang, Jianhui Wang, Xianqing Li-Jost, Shao-Ming Fei
• Abstract summary: Uncertainty relation is a core issue in quantum mechanics and quantum information theory. We introduce modified generalized Wigner-Yanase-Dyson skew information and modified weighted generalizedWigner-Yanase-Dyson skew information.
• Score: 4.857933738254887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty relation is a core issue in quantum mechanics and quantum information theory. We introduce modified generalized Wigner-Yanase-Dyson (MGWYD) skew information and modified weighted generalizedWigner-Yanase-Dyson (MWGWYD) skew information, and establish new uncertainty relations in terms of the MGWYD skew information and MWGWYD skew information.

Related papers

• On the Properties and Estimation of Pointwise Mutual Information Profiles [49.877314063833296]
  The pointwise mutual information profile, or simply profile, is the distribution of pointwise mutual information for a given pair of random variables. We introduce a novel family of distributions, Bend and Mix Models, for which the profile can be accurately estimated using Monte Carlo methods.
  arXiv  Detail & Related papers  (2023-10-16T10:02:24Z)
• Wigner-Yanase skew information-based uncertainty relations for quantum channels [2.1320960069210484]
  Wigner-Yanase skew information stands for the uncertainty about the information on the values of observables not commuting with the conserved quantity. We present tight uncertainty relations in both product and summation forms for two quantum channels based on the Wigner-Yanase skew information.
  arXiv  Detail & Related papers  (2023-06-11T06:39:30Z)
• Uncertainty-guided Source-free Domain Adaptation [77.3844160723014]
  Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation.
  arXiv  Detail & Related papers  (2022-08-16T08:03:30Z)
• Gacs-Korner Common Information Variational Autoencoder [102.89011295243334]
  We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables. We demonstrate that our formulation allows us to learn semantically meaningful common and unique factors of variation even on high-dimensional data such as images and videos.
  arXiv  Detail & Related papers  (2022-05-24T17:47:26Z)
• Generalized Wigner-Yanase Skew Information and the Affiliated Inequality [0.0]
  A family of skew information quantities is obtained, in which the well-known Wigner-Yanase skew information and quantum Fisher information stand as special cases. We find in this work an exact skew information inequality for qubit system, which may regard as the information counterpart of the uncertainty relation.
  arXiv  Detail & Related papers  (2022-05-14T07:01:19Z)
• A note on uncertainty relations of metric-adjusted skew information [10.196893054623969]
  Uncertainty principle is one of the fundamental features of quantum mechanics. We study uncertainty relations based on metric-adjusted skew information for finite quantum observables.
  arXiv  Detail & Related papers  (2022-03-02T13:57:43Z)
• Non-Linear Spectral Dimensionality Reduction Under Uncertainty [107.01839211235583]
  We propose a new dimensionality reduction framework, called NGEU, which leverages uncertainty information and directly extends several traditional approaches. We show that the proposed NGEU formulation exhibits a global closed-form solution, and we analyze, based on the Rademacher complexity, how the underlying uncertainties theoretically affect the generalization ability of the framework.
  arXiv  Detail & Related papers  (2022-02-09T19:01:33Z)
• A Bayesian Framework for Information-Theoretic Probing [51.98576673620385]
  We argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. This paper proposes a new framework to measure what we term Bayesian mutual information.
  arXiv  Detail & Related papers  (2021-09-08T18:08:36Z)
• Uncertainty and complementarity relations based on generalized skew information [0.0]
  Uncertainty relations and complementarity relations are core issues in quantum mechanics and quantum information theory. We derive several uncertainty and complementarity relations with respect to mutually unbiased measurements.
  arXiv  Detail & Related papers  (2021-08-05T04:13:45Z)
• BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
  We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
  arXiv  Detail & Related papers  (2021-06-07T10:14:18Z)
• Coherence and complementarity based on modified generalized skew information [3.7298088649201353]
  We introduce modified generalized Wigner-Yanase-Dyson (MG WYD) skew information and modified weighted generalized Wigner-Yanase-Dyson (MWGWYD) skew information. By revisiting state-channel interaction based on MG WYD skew information, a family of coherence measures with respect to quantum channels is proposed.
  arXiv  Detail & Related papers  (2020-04-24T08:00:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.