提出了一种弱耦合领结型椭圆芯应力保偏少模光纤(PM-FMF)，通过使用高折射率纤芯，所提出的光纤可在1505～1585 nm波段下，支持32个独立的本征模式。椭圆纤芯和领结型应力区的引入，有效地分离了相邻的本征模式。采用有限元法对领结型椭圆芯应力PM-FMF的纤芯及领结型应力区的结构参数进行优化。评估了光纤参数对模式数量、模式间的最小有效折射率差、模态双折射、应力双折射以及弯曲损耗的影响。此外还分析了该光纤的带宽性能，包括模式间的有效折射率、有效折射率差、差分模式时延(DMD)。经数据分析，在1505～1585 nm波段下，该光纤支持的32个本征模式是完全分离的，相邻模式之间的最小有效折射率差大于1.295×10⁻⁴。所提出的弱耦合保偏少模光纤能够提高传输容量，在本征模式复用传输中具有潜在的应用前景。

We experimentally demonstrate a method for detection of entanglement via construction of entanglement witnesses from a limited fixed set of local measurements (M). Such a method does not require a priori knowledge about the form of the entanglement witnesses. It is suitable for a scenario where a full state tomography is not available, but the only resource is a limited set of M. We demonstrate the method on pure two-qubit entangled states and mixed two-qubit entangled states, which emerge from photonic implementation of controllable quantum noisy channels. The states we select are motivated by realistic experimental conditions, and we confirm it works well for both cases. Furthermore, possible generalizations to higher-dimensional bipartite systems have been considered, which can potentially detect both decomposable and indecomposable entanglement witnesses. Our experimental results show perfect validity of the method, which indicates that even a limited set of local measurements can be used for quick entanglement detection and further provide a practical test bed for experiments with entanglement witnesses.

红外探测器在**侦查、遥感、通信、精确制导和航空航天等领域发挥着关键作用，受到世界各国长期关注，具有重要的研究价值和应用前景。微纳结构与传统半导体探测器集成后能够有效提高光子耦合效率和等效光程，突破传统体材料的吸收极限，提高光电器件的量子效率并降低器件的暗电流，为高性能红外探测器的研究提供了全新的技术手段。文中围绕近年来各种不同类型的微纳结构增强型红外探测器的研究展开综述。首先，介绍了微纳结构增强型红外探测器的基本原理，根据微纳结构的材料和功能不同，进行了分类和对比；其次，分别从介质型、表面金属型和三维等离子腔型等方面对微纳结构在红外探测器上的研究进展进行了阐述；最后，对基于微纳结构增强型红外探器的发展趋势进行了总结和展望。

Quantum machine learning aspires to overcome intractability that currently limits its applicability to practical applications. However, quantum machine learning itself is limited by low effective dimensions achievable in state-of-the-art experiments. Here, we demonstrate highly successful classifications of real-life images using photonic qubits, combining a quantum tensor-network representation of hand-written digits and entanglement-based optimization. Specifically, we focus on binary classification for hand-written zeroes and ones, whose features are cast into the tensor-network representation, further reduced by optimization based on entanglement entropy and encoded into two-qubit photonic states. We then demonstrate image classification with a high success rate exceeding 98%, through successive gate operations and projective measurements. Although we work with photons, our approach is amenable to other physical realizations such as nitrogen-vacancy centers, nuclear spins, and trapped ions, and our scheme can be scaled to efficient multi-qubit encodings of features in the tensor-product representation, thereby setting the stage for quantum-enhanced multi-class classification.

针对现有算法卫星姿态确定中模型参数估计不准确，系统存在外界干扰下稳定性差和跟踪精度不足的问题，提出一种自适应无迹卡尔曼滤波算法，对卫星三轴姿态进行估计。首先分析了陀螺和星敏组合定姿的工作原理，然后推导了以误差四元数为状态变量的卫星姿态运动学方程。滤波过程中，该算法引入自适应矩阵，对量测噪声协方差矩阵进行调整；依据滤波发散判别准则，对系统噪声协方差矩阵进行自适应修正，抑制滤波过程中可能的发散情形，获得了良好的自适应性能。实验结果表明，在参数估计不准确时，自适应无迹卡尔曼滤波相比鲁棒自适应UKF算法，三轴估计精度的均方根误差（RMSE）分别提升了30.0%，34.1%，22.4%。该算法基本满足卫星姿态确定的高精度、强鲁棒性等要求。

Quantum walks, a counterpart of classical random walks, have many applications due to their neoteric features. Since they were first proposed, quantum walks have been explored in many fields theoretically and have also been demonstrated experimentally in various physical systems. In this paper, we review the experimental realizations of discrete-time quantum walks in photonic systems with different physical structures, such as bulk optics and time-multiplexed framework. Then, some typical applications using quantum walks are introduced. Finally, the advantages and disadvantages of these physical systems are discussed.