Relating an entanglement measure with statistical correlators for
two-qudit mixed states using only a pair of complementary observables
- URL: http://arxiv.org/abs/2201.06188v1
- Date: Mon, 17 Jan 2022 02:58:36 GMT
- Title: Relating an entanglement measure with statistical correlators for
two-qudit mixed states using only a pair of complementary observables
- Authors: Simanraj Sadana, Som Kanjilal, Dipankar Home, Urbasi Sinha
- Abstract summary: We focus on characterizing entanglement of high dimensional bipartite states using various statistical correlators for two-qudit mixed states.
relations linking Negativity with the statistical correlators have been derived for such Horodecki states in the domain of distillable entanglement.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We focus on characterizing entanglement of high dimensional bipartite states
using various statistical correlators for two-qudit mixed states. The salient
results obtained are as follows: (a) A scheme for determining the entanglement
measure given by Negativity is explored by analytically relating it to the
widely used statistical correlators viz. mutual predictability, mutual
information and Pearson Correlation coefficient for different types of
bipartite arbitrary dimensional mixed states. Importantly, this is demonstrated
using only a pair of complementary observables pertaining to the mutually
unbiased bases. (b) The relations thus derived provide the separability bounds
for detecting entanglement obtained for a fixed choice of the complementary
observables, while the bounds per se are state-dependent. Such bounds are
compared with the earlier suggested separability bounds. (c) We also show how
these statistical correlators can enable distinguishing between the separable,
distillable and bound entanglement domains of the one-parameter Horodecki
two-qutrit states. Further, the relations linking Negativity with the
statistical correlators have been derived for such Horodecki states in the
domain of distillable entanglement. Thus, this entanglement characterisation
scheme based on statistical correlators and harnessing complementarity of the
obsevables opens up a potentially rich direction of study which is applicable
for both distillable and bound entangled states.
Related papers
- Consistent Estimation of a Class of Distances Between Covariance Matrices [7.291687946822539]
We are interested in the family of distances that can be expressed as sums of traces of functions that are separately applied to each covariance matrix.
A statistical analysis of the behavior of this class of distance estimators has also been conducted.
We present a central limit theorem that establishes the Gaussianity of these estimators and provides closed form expressions for the corresponding means and variances.
arXiv Detail & Related papers (2024-09-18T07:36:25Z) - Operational quantification of simultaneous correlations in complementary bases of two-qubit states via one-sided semi-device-independent steering [0.0]
For two-qubit states, we study the relationships between the quantification of one-sided semi-device-independent steerability and information-theoretic quantification of simultaneous correlations in mutually unbiased bases.
We invoke quantum steering ellipsoid formalism to shed intuitions on our operational characterization of simultaneous correlations in complementary bases of two-qubit states that we consider.
arXiv Detail & Related papers (2024-07-16T14:17:59Z) - Tracing quantum correlations back to collective interferences [0.04096453902709291]
We investigate nonclassical correlations between two quantum systems in terms of quantum interferences between collective states of the two systems.
We show that the relation between probability currents and correlations can be represented by continuous conditional (quasi)probability currents through the interferometer.
Our results help to explain the meaning of nonlocal correlations in quantum mechanics, and support Feynman's claim that interference is the origin of all quantum phenomena.
arXiv Detail & Related papers (2024-01-30T06:15:53Z) - Applications of flow models to the generation of correlated lattice QCD ensembles [69.18453821764075]
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters.
This work demonstrates how these correlations can be exploited for variance reduction in the computation of observables.
arXiv Detail & Related papers (2024-01-19T18:33:52Z) - Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse
Actions, Interventions and Sparse Temporal Dependencies [58.179981892921056]
This work introduces a novel principle for disentanglement we call mechanism sparsity regularization.
We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors.
We show that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
arXiv Detail & Related papers (2024-01-10T02:38:21Z) - Exact asymptotics of long-range quantum correlations in a nonequilibrium steady state [0.0]
We analytically study the scaling of quantum correlation measures on a one-dimensional containing a noninteracting impurity.
We derive the exact form of the subleading logarithmic corrections to the extensive terms of correlation measures.
This echoes the case of equilibrium states, where such logarithmic terms may convey universal information about the physical system.
arXiv Detail & Related papers (2023-10-25T18:00:48Z) - Statistical Correlators and Tripartite Entanglement [0.07812210699650153]
We show that two measures can be empirically determined for the two important classes of tripartite entangled states.
Such a formulated scheme would provide for the first time the means to exactly quantify tripartite entanglement.
arXiv Detail & Related papers (2023-08-30T18:00:07Z) - Statistical Efficiency of Score Matching: The View from Isoperimetry [96.65637602827942]
We show a tight connection between statistical efficiency of score matching and the isoperimetric properties of the distribution being estimated.
We formalize these results both in the sample regime and in the finite regime.
arXiv Detail & Related papers (2022-10-03T06:09:01Z) - Data-Driven Influence Functions for Optimization-Based Causal Inference [105.5385525290466]
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing.
We study the case where probability distributions are not known a priori but need to be estimated from data.
arXiv Detail & Related papers (2022-08-29T16:16:22Z) - On Disentangled Representations Learned From Correlated Data [59.41587388303554]
We bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data.
We show that systematically induced correlations in the dataset are being learned and reflected in the latent representations.
We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.
arXiv Detail & Related papers (2020-06-14T12:47:34Z) - Nonparametric Score Estimators [49.42469547970041]
Estimating the score from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of probabilistic models.
We provide a unifying view of these estimators under the framework of regularized nonparametric regression.
We propose score estimators based on iterative regularization that enjoy computational benefits from curl-free kernels and fast convergence.
arXiv Detail & Related papers (2020-05-20T15:01:03Z)
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.