为了实现航空航天发动机尾焰、燃烧、爆炸等高温温度场的非接触高精度测量，对静态干涉型高温温度场探测方法展开研究。首先，设计静态干涉型高温温度场探测系统，理论分析高温温度场测量原理，研究高温干涉信号强度最低点光程差与温度的关系；其次，针对常用温度范围及可见光面阵探测器的响应波段，设计静态干涉具Savart棱镜，结合一维扫描实现温度场成像；最后，设计光学系统，拟合获得干涉最弱光程差与温度的对应关系，并获得线性拟合公式，仿真验证温度场经过系统后到达面阵探测器的干涉信号图像。结果表明，该静态干涉型高温温度场探测方法可实现1000 K−3000 K温度的高精度探测，且在线性区域，温度测量分辨率为1.4 K，温度测量相对误差优于0.8%。本文研究为军民领域的高精度高温温度场成像提供参考。

针对目前常见广义椭偏仪变角结构多采用双电机驱动，且存在结构复杂、变角后光路存在偏离等问题，论文提出一种单电机驱动广义椭偏仪新的变角结构。该结构采用固定起偏臂，利用同步齿轮传动单电机驱动方式使载物台和检偏臂同步旋转，实现椭偏仪多角度测量。在传统卧式结构广义椭偏仪变角结构基础上，设置样品载物台和检偏臂1∶2同步传动，并通过Inventor运动仿真验证了本结构的可行性。论文提出的单电机驱动变角结构变角后不仅维持了变角前后的准直，而且其整体结构简单，极大地优化了椭偏仪变角的复杂度，在广义椭偏仪工程化进程中有比较重要的应用价值。

研究了基于波长调制的螺旋形塑料光纤（plastic optical fiber, POF）表面等离子体共振（surface plasmon resonance, SPR）折射率传感器。采用机械热压和扭曲法将塑料光纤制备成螺旋形，在螺旋形POF上通过磁控溅射蒸镀一定厚度（约50 nm）的金属薄膜来激励SPR效应，从而形成螺旋形POF-SPR传感器。通过对螺旋形POF-SPR传感器的结构进行修饰，研究不同结构参数对折射率传感特性的影响。实验结果表明：由厚度为500 μm扁平形POF扭制、螺纹数为4的螺旋形POF-SPR传感器具有较好的线性度和折射率传感特性，在折射率为1.335~1.400范围内测得的灵敏度为1 262 nm/RIU。该传感器具有成本较低、制备简单、结构稳定等优点。

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.

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.

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.