Deterministic quantum key distribution based on Gaussian-modulated EPR correlations

This paper proposes a deterministic quantum key distribution scheme based on Gaussian-modulated continuous variable EPR correlations. This scheme can implement fast and efficient key distribution. The security is guaranteed by continuous variable EPR entanglement correlations produced by nondegenerate optical parametric amplifier. For general beam splitter eavesdropping strategy, the secret information rate ΔI = I（α, β）-I（α, ε） is calculated in view of Shannon information theory. Finally the security analysis is presented.

Creation of Multiple Subwavelength Focal Spot Segments Using Phase Modulated Radially Polarized Multi Gaussian Beam

Based on the vector diffraction theory, the effect of complex phase filters on intensity distribution of a radially polarized multi Gaussian beam in the focal region of high NA lens is theoretically investigated. It is observed that a properly designed multi belt complex phase filter can generate subwavelength novel focal patterns including splitting of focal spots and generation of multiple focal spot segments such as eight, six and four focal spots along the optical axis are obtained. We expect that such an investigation is useful for optical manipulation and material processing, multiple high refractive index particle trapping technologies.

Tight Focusing Properties of Phase Modulated Radially Polarized Laguerre Bessel Gaussian Beam

We propose a new approach for generating a multiple focal spot segment of subwavelength size, by tight focusing of a phase modulated radially polarized Laguerre Bessel Gaussian beam. The focusing properties are investigated theoretically by .vector diffraction theory. We observe that the focal segment with multiple focal structures is separated with different axial distances and a super long dark channel can be generated by properly tuning the phase of the incident radially polarized Laguerre Bessel Gaussian beam. We presume that such multiple focal patterns and high intense beam may find applications in atom optics, optical manipulations and multiple optical trapping.

A complementary integrated Transformer network for hyperspectral image classification

In the past,convolutional neural network(CNN)has become one of the most popular deep learning frameworks,and has been widely used in Hyperspectral image classification tasks.Convolution(Conv)in CNN uses filter weights to extract features in local receiving domain,and the weight parameters are shared globally,which more focus on the highfrequency information of the image.Different from Conv,Transformer can obtain the long‐term dependence between long‐distance features through modelling,and adaptively focus on different regions.In addition,Transformer is considered as a low‐pass filter,which more focuses on the low‐frequency information of the image.Considering the complementary characteristics of Conv and Transformer,the two modes can be integrated for full feature extraction.In addition,the most important image features correspond to the discrimination region,while the secondary image features represent important but easily ignored regions,which are also conducive to the classification of HSIs.In this study,a complementary integrated Transformer network(CITNet)for hyperspectral image classification is proposed.Firstly,three‐dimensional convolution(Conv3D)and two‐dimensional convolution(Conv2D)are utilised to extract the shallow semantic information of the image.In order to enhance the secondary features,a channel Gaussian modulation attention module is proposed,which is embedded between Conv3D and Conv2D.This module can not only enhance secondary features,but suppress the most important and least important features.Then,considering the different and complementary characteristics of Conv and Transformer,a complementary integrated Transformer module is designed.Finally,through a large number of experiments,this study evaluates the classification performance of CITNet and several state‐of‐the‐art networks on five common datasets.The experimental results show that compared with these classification networks,CITNet can provide better classification performance.

Capacity Scaling Limits and New Advancements in Optical Transmission Systems

Optical transmission technologies have gone through several generations of development.Spectral efficiency has significant ly improved,and industry has begun to search for an answer to a basic question：What are the fundamental linear and nonlin ear signal channel limitations of the Shannon theory when there is no compensation in an optical fiber transmission system？Next-generation technologies should exceed the 100G transmis sion capability of coherent systems in order to approach the Shannon limit.Spectral efficiency first needs to be improved be fore overall transmission capability can be improved.The means to improve spectral efficiency include more complex modulation formats and channel encoding/decoding algorithms,prefiltering with multisymbol detection,optical OFDM and Ny quist WDM multicarrier technologies,and nonlinearity compen sation.With further optimization,these technologies will most likely be incorporated into beyond-100G optical transport sys tems to meet bandwidth demand.