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Lin Zhang

Publications and source records attributed to Lin Zhang.

At least 433 records · Page 24Linked to original sources
arXiv2012

Remark on the coherent information saturating its upper bound

Coherent information is a useful concept in quantum information theory. It connects with other notions in data processing. In this short remark, we discuss the coherent information saturating its upper bound. A necessary and sufficient condition for this saturation is derived.

quant-ph↗
arXiv2012

Universal upper bound for the Holevo information induced by a quantum operation

Let $\cH_A\ot \cH_B$ be a bipartite system and $ρ_{AB}$ a quantum state on $\cH_A\ot \cH_B$, $ρ_A = \Ptr{B}{ρ_{AB}}$, $ρ_B = \Ptr{A}{ρ_{AB}}$. Then each quantum operation $Φ_B$ on the quantum system $\cH_B$ can induce a quantum ensemble $\set{(p_μ,ρ_{A,μ})}$ on quantum system $\cH_A$. In this paper, we show that the Holevo quantity $χ\set{(p_μ,ρ_{A,μ})}$ of the quantum ensemble $\set{(p_μ,ρ_{A,μ})}$ can be upper bounded by both subsystem entropies. By using the result, we answer partly a conjecture of Fannes, de Melo, Roga and Życzkowski.

quant-ph↗
arXiv2012

Unifying Treatment of Discord via Relative Entropy

A new form of zero-discord state via Petz's monotonicity condition on relative entropy with equality has been derived systematically. A generalization of symmetric zero-discord states is presented and the related physical implications are discussed.

quant-ph↗
arXiv2012

Logarithmic tensor category theory, V: Convergence condition for intertwining maps and the corresponding compatibility condition

This is the fifth part in a series of papers in which we introduce and develop a natural, general tensor category theory for suitable module categories for a vertex (operator) algebra. In this paper (Part V), we study products and iterates of intertwining maps and of logarithmic intertwining operators and we begin the development of our analytic approach.

math.QA↗
arXiv2012

Logarithmic tensor category theory, VI: Expansion condition, associativity of logarithmic intertwining operators, and the associativity isomorphisms

This is the sixth part in a series of papers in which we introduce and develop a natural, general tensor category theory for suitable module categories for a vertex (operator) algebra. In this paper (Part VI), we construct the appropriate natural associativity isomorphisms between triple tensor product functors. In fact, we establish a "logarithmic operator product expansion" theorem for logarithmic intertwining operators. In this part, a great deal of analytic reasoning is needed; the statements of the main theorems themselves involve convergence assertions.

math.QA↗
arXiv2012

Win-stay-lose-learn promotes cooperation in the spatial prisoner's dilemma game

Holding on to one's strategy is natural and common if the later warrants success and satisfaction. This goes against widespread simulation practices of evolutionary games, where players frequently consider changing their strategy even though their payoffs may be marginally different than those of the other players. Inspired by this observation, we introduce an aspiration-based win-stay-lose-learn strategy updating rule into the spatial prisoner's dilemma game. The rule is simple and intuitive, foreseeing strategy changes only by dissatisfied players, who then attempt to adopt the strategy of one of their nearest neighbors, while the strategies of satisfied players are not subject to change. We find that the proposed win-stay-lose-learn rule promotes the evolution of cooperation, and it does so very robustly and independently of the initial conditions. In fact, we show that even a minute initial fraction of cooperators may be sufficient to eventually secure a highly cooperative final state. In addition to extensive simulation results that support our conclusions, we also present results obtained by means of the pair approximation of the studied game. Our findings continue the success story of related win-stay strategy updating rules, and by doing so reveal new ways of resolving the prisoner's dilemma.

q-bio.PE↗
arXiv2012

Opto-mechanical measurement of micro-trap via nonlinear cavity enhanced Raman scattering spectrum

High-gain resonant nonlinear Raman scattering on trapped cold atoms within a high-fineness ring optical cavity is simply explained under a nonlinear opto-mechanical mechanism, and a proposal using it to detect frequency of micro-trap on atom chip is presented. The enhancement of scattering spectrum is due to a coherent Raman conversion between two different cavity modes mediated by collective vibrations of atoms through nonlinear opto-mechanical couplings. The physical conditions of this technique are roughly estimated on Rubidium atoms, and a simple quantum analysis as well as a multi-body semiclassical simulation on this nonlinear Raman process is conducted.

physics.atom-ph↗
arXiv2012

Minimax-Optimal Bounds for Detectors Based on Estimated Prior Probabilities

In many signal detection and classification problems, we have knowledge of the distribution under each hypothesis, but not the prior probabilities. This paper is aimed at providing theory to quantify the performance of detection via estimating prior probabilities from either labeled or unlabeled training data. The error or {\em risk} is considered as a function of the prior probabilities. We show that the risk function is locally Lipschitz in the vicinity of the true prior probabilities, and the error of detectors based on estimated prior probabilities depends on the behavior of the risk function in this locality. In general, we show that the error of detectors based on the Maximum Likelihood Estimate (MLE) of the prior probabilities converges to the Bayes error at a rate of $n^{-1/2}$, where $n$ is the number of training data. If the behavior of the risk function is more favorable, then detectors based on the MLE have errors converging to the corresponding Bayes errors at optimal rates of the form $n^{-(1+α)/2}$, where $α>0$ is a parameter governing the behavior of the risk function with a typical value $α= 1$. The limit $α\rightarrow \infty$ corresponds to a situation where the risk function is flat near the true probabilities, and thus insensitive to small errors in the MLE; in this case the error of the detector based on the MLE converges to the Bayes error exponentially fast with $n$. We show the bounds are achievable no matter given labeled or unlabeled training data and are minimax-optimal in labeled case.

cs.IT↗
arXiv2012

Solutions For A Generalized Fractional Anomalous Diffusion Equation

In this paper, we investigate the solutions for a generalized fractional diffusion equation that extends some known diffusion equations by taking a spatial time-dependent diffusion coefficient and an external force into account, which subjects to the natural boundaries and the generic initial condition. We obtain explicit analytical expressions for the probability distribution and study the relation between our solutions and those obtained within the maximum entropy principle by using the Tsallis entropy.

math-ph↗