scholarly journals Tripartite entropic uncertainty relation under phase decoherence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
R. A. Abdelghany ◽  
A.-B. A. Mohamed ◽  
M. Tammam ◽  
Watson Kuo ◽  
H. Eleuch
Keyword(s):  
Lower Bound ◽  
Uncertainty Relation ◽  
Nearest Neighbors ◽  
The Other ◽  
Uncertainty In Measurement ◽  
Entropic Uncertainty Relation ◽  
Specific Choice ◽  
Phase Decoherence ◽  
Time Invariant ◽  
Incompatible Observables

AbstractWe formulate the tripartite entropic uncertainty relation and predict its lower bound in a three-qubit Heisenberg XXZ spin chain when measuring an arbitrary pair of incompatible observables on one qubit while the other two are served as quantum memories. Our study reveals that the entanglement between the nearest neighbors plays an important role in reducing the uncertainty in measurement outcomes. In addition we have shown that the Dolatkhah’s lower bound (Phys Rev A 102(5):052227, 2020) is tighter than that of Ming (Phys Rev A 102(01):012206, 2020) and their dynamics under phase decoherence depends on the choice of the observable pair. In the absence of phase decoherence, Ming’s lower bound is time-invariant regardless the chosen observable pair, while Dolatkhah’s lower bound is perfectly identical with the tripartite uncertainty with a specific choice of pair.

scholarly journals Entropic uncertainty relation under correlated dephasing channels

2018 ◽  
Vol 96 (7) ◽  
pp. 700-704 ◽  
Author(s):  
Göktuğ Karpat
Keyword(s):  
Quantum Mechanics ◽  
Standard Deviation ◽  
Lower Bound ◽  
Quantum System ◽  
Quantum Channel ◽  
Uncertainty Relation ◽  
Open Quantum System ◽  
Uncertainty Relations ◽  
Entropic Uncertainty Relation ◽  
Incompatible Observables

Uncertainty relations are a characteristic trait of quantum mechanics. Even though the traditional uncertainty relations are expressed in terms of the standard deviation of two observables, there exists another class of such relations based on entropic measures. Here we investigate the memory-assisted entropic uncertainty relation in an open quantum system scenario. We study the dynamics of the entropic uncertainty and its lower bound, related to two incompatible observables, when the system is affected by noise, which can be described by a correlated Pauli channel. In particular, we demonstrate how the entropic uncertainty for these two incompatible observables can be reduced as the correlations in the quantum channel grow stronger.

Witnessing entanglement between two atoms in dissipative cavities by the entropic uncertainty relation

2016 ◽  
Vol 94 (11) ◽  
pp. 1142-1147 ◽  
Author(s):  
Hong-Mei Zou ◽  
Mao-Fa Fang
Keyword(s):  
Quantum Information ◽  
Lower Bound ◽  
Uncertainty Relation ◽  
Quantum Information Processing ◽  
Equation Approach ◽  
Time Zone ◽  
Entanglement Witness ◽  
Entropic Uncertainty Relation ◽  
Cavity Coupling ◽  
Master Equation Approach

Based on the entropic uncertainty relation in the presence of quantum memory, the entanglement witness of two atoms in dissipative cavities is investigated by using the time-convolutionless master-equation approach. We discuss in detail the influences of the non-Markovian effect and the atom–cavity coupling on the lower bound of the entropic uncertainty relation and entanglement witness. The results show that, with the coupling increasing, the number of the time zone witnessed will increase so that the entanglement can be repeatedly witnessed. Enhancing the non-Markovian effect can add the number of the time zone witnessed and lengthen the time of entanglement witness. The results can be applied in quantum measurement, entanglement detecting, quantum cryptography task, and quantum information processing.

scholarly journals Optimality of entropic uncertainty relations

2015 ◽  
Vol 13 (06) ◽  
pp. 1550045 ◽  
Author(s):  
Kais Abdelkhalek ◽  
René Schwonnek ◽  
Hans Maassen ◽  
Fabian Furrer ◽  
Jörg Duhme ◽  
...  
Keyword(s):  
Lower Bound ◽  
Numerical Investigation ◽  
Uncertainty Relation ◽  
Small Dimension ◽  
Uncertainty Relations ◽  
Complete Understanding ◽  
Entropic Uncertainty Relations ◽  
Entropic Uncertainty Relation ◽  
Analytical Results ◽  
Optimal Lower Bound

The entropic uncertainty relation proven by Maassen and Uffink for arbitrary pairs of two observables is known to be nonoptimal. Here, we call an uncertainty relation optimal, if the lower bound can be attained for any value of either of the corresponding uncertainties. In this work, we establish optimal uncertainty relations by characterizing the optimal lower bound in scenarios similar to the Maassen–Uffink type. We disprove a conjecture by Englert et al. and generalize various previous results. However, we are still far from a complete understanding and, based on numerical investigation and analytical results in small dimension, we present a number of conjectures.

scholarly journals A Stronger Multi-observable Uncertainty Relation

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Qiu-Cheng Song ◽  
Jun-Li Li ◽  
Guang-Xiong Peng ◽  
Cong-Feng Qiao
Keyword(s):  
Quantum Mechanics ◽  
Lower Bound ◽  
Uncertainty Relation ◽  
Quantum States ◽  
Simple Generalization ◽  
Spin Equation ◽  
Incompatible Observables

Abstract Uncertainty relation lies at the heart of quantum mechanics, characterizing the incompatibility of non-commuting observables in the preparation of quantum states. An important question is how to improve the lower bound of uncertainty relation. Here we present a variance-based sum uncertainty relation for N incompatible observables stronger than the simple generalization of an existing uncertainty relation for two observables. Further comparisons of our uncertainty relation with other related ones for spin-"Equation missing" and spin-1 particles indicate that the obtained uncertainty relation gives a better lower bound.

scholarly journals Density Guarantee on Finding Multiple Subgraphs and Subtensors

2021 ◽  
Vol 15 (5) ◽  
pp. 1-32
Author(s):  
Quang-huy Duong ◽  
Heri Ramampiaro ◽  
Kjetil Nørvåg ◽  
Thu-lan Dam
Keyword(s):  
Lower Bound ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Exact Methods ◽  
Practical Solution ◽  
Novel Approach ◽  
Wide Range ◽  
Real World Datasets ◽  
Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

Lower Bounds to the Eigenvalues in One-Dimensional Problems by a Shift in the Weight Function

1970 ◽  
Vol 37 (2) ◽  
pp. 267-270 ◽  
Author(s):  
D. Pnueli
Keyword(s):  
Lower Bound ◽  
Weight Function ◽  
Lower Bounds ◽  
Variational Formulation ◽  
Ritz Method ◽  
The Other ◽  
One Dimensional ◽  
Systematic Shift ◽  
The One ◽  
Detailed Computation

A method is presented to obtain both upper and lower bound to eigenvalues when a variational formulation of the problem exists. The method consists of a systematic shift in the weight function. A detailed procedure is offered for one-dimensional problems, which makes improvement of the bounds possible, and which involves the same order of detailed computation as the Rayleigh-Ritz method. The main contribution of this method is that it yields the “other bound;” i.e., the one which cannot be obtained by the Rayleigh-Ritz method.

A Model of Secure Functioning of Computer Systems

2021 ◽  
Vol 12 (3) ◽  
pp. 150-156
Author(s):  
A. V. Galatenko ◽  
◽  
V. A. Kuzovikhina ◽  
Keyword(s):  
Lower Bound ◽  
Computer System ◽  
Finite Automaton ◽  
Nonnegative Integer ◽  
The Other ◽  
Shannon Function ◽  
Security Breaches ◽  
System Functioning ◽  
The Cost ◽  
The Given

We propose an automata model of computer system security. A system is represented by a finite automaton with states partitioned into two subsets: "secure" and "insecure". System functioning is secure if the number of consecutive insecure states is not greater than some nonnegative integer k. This definition allows one to formally reflect responsiveness to security breaches. The number of all input sequences that preserve security for the given value of k is referred to as a k-secure language. We prove that if a language is k-secure for some natural and automaton V, then it is also k-secure for any 0 < k < k and some automaton V = V (k). Reduction of the value of k is performed at the cost of amplification of the number of states. On the other hand, for any non-negative integer k there exists a k-secure language that is not k"-secure for any natural k" > k. The problem of reconstruction of a k-secure language using a conditional experiment is split into two subcases. If the cardinality of an input alphabet is bound by some constant, then the order of Shannon function of experiment complexity is the same for al k; otherwise there emerges a lower bound of the order nk.

DS-kNN

2021 ◽  
Vol 15 (2) ◽  
pp. 131-144
Author(s):  
Redha Taguelmimt ◽  
Rachid Beghdad
Keyword(s):  
Intrusion Detection ◽  
False Positive ◽  
Detection Rate ◽  
Nearest Neighbors ◽  
The Other ◽  
Intrusion Detection Systems ◽  
K Nearest Neighbors ◽  
Detection Systems ◽  
Knn Classifier ◽  
Better Than

On one hand, there are many proposed intrusion detection systems (IDSs) in the literature. On the other hand, many studies try to deduce the important features that can best detect attacks. This paper presents a new and an easy-to-implement approach to intrusion detection, named distance sum-based k-nearest neighbors (DS-kNN), which is an improved version of k-NN classifier. Given a data sample to classify, DS-kNN computes the distance sum of the k-nearest neighbors of the data sample in each of the possible classes of the dataset. Then, the data sample is assigned to the class having the smallest sum. The experimental results show that the DS-kNN classifier performs better than the original k-NN algorithm in terms of accuracy, detection rate, false positive, and attacks classification. The authors mainly compare DS-kNN to CANN, but also to SVM, S-NDAE, and DBN. The obtained results also show that the approach is very competitive.

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