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Chinese Physics Letters

Certain Properties of a Quantum Information Network Driven by External Fields

毕桥, 方锦清, 邹秦

Published Jan 1, 2006• 0 citations
OpenAlex

Abstract

有 Gaussian 隧道的结构的一个量信息网络被建议。网络拓扑的性质和信息特征被学习。在开车的灾难、指数的外部领域下面，度分发有空间、时间的特征，并且积极或否定的力量索引出现它能影响 theassortativity 系数。这是可能有用的打开一个方法到使用开车为网络介绍期望的性质的不同类型。

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有 Gaussian 隧道的结构的一个量信息网络被建议。网络拓扑的性质和信息特征被学习。在开车的灾难、指数的外部领域下面，度分发有空间、时间的特征，并且积极或否定的力量索引出现它能影响 theassortativity 系数。这是可能有用的打开一个方法到使用开车为网络介绍期望的性质的不同类型。
A quantum information network with the structure of the Gaussian channel is proposed. The network topological property and information characteristic is studied. Under the catastrophic and exponential external field driving, the degree distribution has the spatial and temporal characteristics, and the positive or negative power index appears, which can influence the assortativity coefficient. This is possibly helpful to open a way to using the different type of driving to introduce the expected properties for the network.
We propose a quantum information network with structure of the Gauss channel, and has studied this network topological property and the information characteristic under specific external field driving and different parameter, if introducing catastrophic polynomials, the degree distribution has the spatial and temporal characteristic, and appear the positive or negative exponent of power-law which can influence the assortative coefficient. This is possibly helpful to open a way using the different type of driving to introduce the expected properties for the network.
The formalism of quantum estimation theory with a specific focus on classical data postprocessing is applied to a two-level system driven by an external gyrating magnetic field. We employed both Bayesian and frequentist approaches to estimate the unknown transition frequency. In the frequentist approach, we have shown that only reducing the distance between the classical and the quantum Fisher information does not necessarily mean that the estimators as functions of the data deliver an estimate with desirable accuracy, as the classical Fisher information takes small values. We have proposed and investigated a cost function to account for the maximization of the classical Fisher information and the minimization of the aforementioned distance. Due to the nonlinearity of the probability mass function of the data on the transition frequency, the minimum variance unbiased estimator may not exist. The maximum likelihood and the maximum estimators often result in ambiguous estimates, which in certain cases can be made unambiguous upon changing the parameters of the external field. It is demonstrated that the minimum mean-square error estimator of the Bayesian statistics provides unambiguous estimates. In the Bayesian approach, we have also investigated the effects of noninformative and informative priors on the Bayesian estimates, including a uniform prior, Jeffrey's prior, and a Gaussian prior.