Non-Gaussianity of quantum states is a very important source for quantum information technology and can be quantified by using the known squared Hilbert–Schmidt distance recently introduced by Genoni et al.(Phys. Rev...Non-Gaussianity of quantum states is a very important source for quantum information technology and can be quantified by using the known squared Hilbert–Schmidt distance recently introduced by Genoni et al.(Phys. Rev. A 78 042327(2007)). It is, however, shown that such a measure has many imperfects such as the lack of the swapping symmetry and the ineffectiveness evaluation of even Schr?dinger-cat-like states with small amplitudes. To deal with these difficulties, we propose an improved measure of non-Gaussianity for quantum states and discuss its properties in detail. We then exploit this improved measure to evaluate the non-Gaussianities of some relevant single-mode non-Gaussian states and multi-mode non-Gaussian entangled states. These results show that our measure is reliable. We also introduce a modified measure for Gaussianity following Mandilara and Cerf(Phys. Rev. A 86 030102(R)(2012)) and establish a conservation relation of non-Gaussianity and Gaussianity of a quantum state.展开更多
Recent studies show that quantum non-Gaussian states or using non-Gaussian operations can improve entanglement distillation, quantum swapping, teleportation, and cloning. In this work, employing a strategy of non-Gaus...Recent studies show that quantum non-Gaussian states or using non-Gaussian operations can improve entanglement distillation, quantum swapping, teleportation, and cloning. In this work, employing a strategy of non-Gaussian operations（namely subtracting and adding a single photon）, we propose a scheme to generate non-Gaussian quantum states named single-photon-added and-subtracted coherent（SPASC） superposition states by implementing Bell measurements, and then investigate the corresponding nonclassical features. By squeezed the input field, we demonstrate that robustness of nonGaussianity can be improved. Controllable phase space distribution offers the possibility to approximately generate a displaced coherent superposition states（DCSS）. The fidelity can reach up to F ≥ 0.98 and F ≥ 0.90 for size of amplitude z = 1.53 and 2.36, respectively.展开更多
Aims Measures of plot-to-plot phylogenetic dissimilarity and beta diversity are providing a powerful tool for understanding the complex ecolog-ical and evolutionary mechanisms that drive community assembly.Methods Her...Aims Measures of plot-to-plot phylogenetic dissimilarity and beta diversity are providing a powerful tool for understanding the complex ecolog-ical and evolutionary mechanisms that drive community assembly.Methods Here,we review the properties of some previously published dis-similarity measures that are based on minimum or average phylo-genetic dissimilarity between species in different plots.Important Findings We first show that some of these measures violate the basic condi-tion that for two identical plots the measures take the value zero.They also violate the condition that the dissimilarity between two identical plots should always be lower than that between two differ-ent plots.Such erratic behavior renders these measures unsuitable for measuring plot-to-plot phylogenetic dissimilarity.We next pro-pose a new measure that satisfies these conditions,thus providing a more reasonable way for measuring phylogenetic dissimilarity.展开更多
针对传统分割算法难以实现高分辨率多光谱图像分割的问题,本文提出一种利用高斯混合模型的多光谱图像模糊聚类分割算法。该算法采用高斯混合模型定义像素对类属的非相似性测度,由于该算法具有高精度拟合数据统计分布能力,故可以有效剔...针对传统分割算法难以实现高分辨率多光谱图像分割的问题,本文提出一种利用高斯混合模型的多光谱图像模糊聚类分割算法。该算法采用高斯混合模型定义像素对类属的非相似性测度,由于该算法具有高精度拟合数据统计分布能力,故可以有效剔除噪声对分割结果的影响。同时,引入隐马尔科夫随机场(Hidden Markov Random Field,HMRF)定义邻域作用的先验概率,并将其作为各高斯分量权值以及KL(Kullback-Leibler)信息中控制聚类尺度的参数,从而增强了算法对复杂场景遥感图像的鲁棒性,进一步提高了算法的分割精度。对模拟图像和高分辨多光谱图像分割结果进行了定性定量分析。实验结果表明:模拟图像的总精度达96.8%以上。这验证了本文算法在分割高分辨率多光谱图像时具有保留细节信息的能力,而且也证实了算法的有效性和可行性。该算法能够实现高分辨率多光谱图像的精确分割。展开更多
密度峰值聚类(clustering by fast search and find of density peaks,简称DPC)是一种基于局部密度和相对距离属性快速寻找聚类中心的有效算法.DPC通过决策图寻找密度峰值作为聚类中心,不需要提前指定类簇数,并可以得到任意形状的簇聚类...密度峰值聚类(clustering by fast search and find of density peaks,简称DPC)是一种基于局部密度和相对距离属性快速寻找聚类中心的有效算法.DPC通过决策图寻找密度峰值作为聚类中心,不需要提前指定类簇数,并可以得到任意形状的簇聚类.但局部密度和相对距离的计算都只是简单依赖基于距离度量的相似度矩阵,所以在复杂数据上DPC聚类结果不尽如人意,特别是当数据分布不均匀、数据维度较高时.另外,DPC算法中局部密度的计算没有统一的度量,根据不同的数据集需要选择不同的度量方式.第三,截断距离dc的度量只考虑数据的全局分布,忽略了数据的局部信息,所以dc的改变会影响聚类的结果,尤其是在小样本数据集上.针对这些弊端,提出一种基于不相似性度量优化的密度峰值聚类算法(optimized density peaks clustering algorithm based on dissimilarity measure,简称DDPC),引入基于块的不相似性度量方法计算相似度矩阵,并基于新的相似度矩阵计算样本的K近邻信息,然后基于样本的K近邻信息重新定义局部密度的度量方法.经典数据集的实验结果表明,基于不相似性度量优化的密度峰值聚类算法优于DPC的优化算法FKNN-DPC和DPC-KNN,可以在密度不均匀以及维度较高的数据集上得到满意的结果;同时统一了局部密度的度量方式,避免了传统DPC算法中截断距离dc对聚类结果的影响.展开更多
基金the Natural Science Foundation of Hunan Province of China (Grant No. 2021JJ30535)the Research Foundation for Young Teachers from the Education Department of Hunan Province of China (Grant No. 20B460)。
文摘Non-Gaussianity of quantum states is a very important source for quantum information technology and can be quantified by using the known squared Hilbert–Schmidt distance recently introduced by Genoni et al.(Phys. Rev. A 78 042327(2007)). It is, however, shown that such a measure has many imperfects such as the lack of the swapping symmetry and the ineffectiveness evaluation of even Schr?dinger-cat-like states with small amplitudes. To deal with these difficulties, we propose an improved measure of non-Gaussianity for quantum states and discuss its properties in detail. We then exploit this improved measure to evaluate the non-Gaussianities of some relevant single-mode non-Gaussian states and multi-mode non-Gaussian entangled states. These results show that our measure is reliable. We also introduce a modified measure for Gaussianity following Mandilara and Cerf(Phys. Rev. A 86 030102(R)(2012)) and establish a conservation relation of non-Gaussianity and Gaussianity of a quantum state.
基金supported by the National Natural Science Foundation of China(Grant Nos.61203061 and 61074052)the Outstanding Young Talent Foundation of Anhui Province,China(Grant No.2012SQRL040)the Natural Science Foundation of Anhui Province,China(Grant No.KJ2012Z035)
文摘Recent studies show that quantum non-Gaussian states or using non-Gaussian operations can improve entanglement distillation, quantum swapping, teleportation, and cloning. In this work, employing a strategy of non-Gaussian operations（namely subtracting and adding a single photon）, we propose a scheme to generate non-Gaussian quantum states named single-photon-added and-subtracted coherent（SPASC） superposition states by implementing Bell measurements, and then investigate the corresponding nonclassical features. By squeezed the input field, we demonstrate that robustness of nonGaussianity can be improved. Controllable phase space distribution offers the possibility to approximately generate a displaced coherent superposition states（DCSS）. The fidelity can reach up to F ≥ 0.98 and F ≥ 0.90 for size of amplitude z = 1.53 and 2.36, respectively.
文摘Aims Measures of plot-to-plot phylogenetic dissimilarity and beta diversity are providing a powerful tool for understanding the complex ecolog-ical and evolutionary mechanisms that drive community assembly.Methods Here,we review the properties of some previously published dis-similarity measures that are based on minimum or average phylo-genetic dissimilarity between species in different plots.Important Findings We first show that some of these measures violate the basic condi-tion that for two identical plots the measures take the value zero.They also violate the condition that the dissimilarity between two identical plots should always be lower than that between two differ-ent plots.Such erratic behavior renders these measures unsuitable for measuring plot-to-plot phylogenetic dissimilarity.We next pro-pose a new measure that satisfies these conditions,thus providing a more reasonable way for measuring phylogenetic dissimilarity.
文摘针对传统分割算法难以实现高分辨率多光谱图像分割的问题,本文提出一种利用高斯混合模型的多光谱图像模糊聚类分割算法。该算法采用高斯混合模型定义像素对类属的非相似性测度,由于该算法具有高精度拟合数据统计分布能力,故可以有效剔除噪声对分割结果的影响。同时,引入隐马尔科夫随机场(Hidden Markov Random Field,HMRF)定义邻域作用的先验概率,并将其作为各高斯分量权值以及KL(Kullback-Leibler)信息中控制聚类尺度的参数,从而增强了算法对复杂场景遥感图像的鲁棒性,进一步提高了算法的分割精度。对模拟图像和高分辨多光谱图像分割结果进行了定性定量分析。实验结果表明:模拟图像的总精度达96.8%以上。这验证了本文算法在分割高分辨率多光谱图像时具有保留细节信息的能力,而且也证实了算法的有效性和可行性。该算法能够实现高分辨率多光谱图像的精确分割。
文摘密度峰值聚类(clustering by fast search and find of density peaks,简称DPC)是一种基于局部密度和相对距离属性快速寻找聚类中心的有效算法.DPC通过决策图寻找密度峰值作为聚类中心,不需要提前指定类簇数,并可以得到任意形状的簇聚类.但局部密度和相对距离的计算都只是简单依赖基于距离度量的相似度矩阵,所以在复杂数据上DPC聚类结果不尽如人意,特别是当数据分布不均匀、数据维度较高时.另外,DPC算法中局部密度的计算没有统一的度量,根据不同的数据集需要选择不同的度量方式.第三,截断距离dc的度量只考虑数据的全局分布,忽略了数据的局部信息,所以dc的改变会影响聚类的结果,尤其是在小样本数据集上.针对这些弊端,提出一种基于不相似性度量优化的密度峰值聚类算法(optimized density peaks clustering algorithm based on dissimilarity measure,简称DDPC),引入基于块的不相似性度量方法计算相似度矩阵,并基于新的相似度矩阵计算样本的K近邻信息,然后基于样本的K近邻信息重新定义局部密度的度量方法.经典数据集的实验结果表明,基于不相似性度量优化的密度峰值聚类算法优于DPC的优化算法FKNN-DPC和DPC-KNN,可以在密度不均匀以及维度较高的数据集上得到满意的结果;同时统一了局部密度的度量方式,避免了传统DPC算法中截断距离dc对聚类结果的影响.
