In the modern financial industry system, the structure of products has become more and more complex, and the bottleneck constraint of classical computing power has already restricted the development of the financial industry. Here, we present a photonic chip that implements the unary approach to European option pricing, in combination with the quantum amplitude estimation algorithm, to achieve quadratic speedup compared to classical Monte Carlo methods. The circuit consists of three modules: one loading the distribution of asset prices, one computing the expected payoff, and a third performing the quantum amplitude estimation algorithm to introduce speedups. In the distribution module, a generative adversarial network is embedded for efficient learning and loading of asset distributions, which precisely captures market trends. This work is a step forward in the development of specialized photonic processors for applications in finance, with the potential to improve the efficiency and quality of financial services.

Gas sensors have a wide variety of applications. Among various existing gas sensing technologies, optical gas sensors have outstanding advantages. The development of the Internet of Things and consumer electronics has put stringent requirements on miniaturized gas sensing technology. Here, we demonstrate a chip-scale silicon substrate-integrated hollow waveguide (Si-iHWG) to serve as an optical channel and gas cell in an optical gas sensor. It is fabricated through silicon wafer etching and wafer bonding. The Si-iHWG chip is further assembled with an off-chip light source and detector to build a fully functional compact nondispersive infrared (NDIR) CO2 sensor. The chip size is 10mm×9mm, and the dimension of the sensor excluding the microcontroller board is 50mm×25mm×16mm. This chip solution with compactness, versatility, robustness, and low cost provides a cost-effective platform for miniaturized optical sensing applications ranging from air quality monitoring to consumer electronics.

为了在多云天气下实现天空偏振定向，提出了一种利用改进的U-Net深度神经网络结合偏振度阈值进行云分割的方法，并基于瑞利散射理论获取太阳方向与体轴夹角实现天空偏振定向。采用包含3个残差结构的Respath模块将U-Net神经网络的编码区与解码区更好的融合，并在下采样时添加MultiBlock模块，使输入图像的特征更好地被学习。将神经网络预测的二值分割图与设置偏振度阈值为0.1的二值分割图进行或运算得到能够较好地分割云和蓝天的二值图。最后，将该二值图作为mask模板去除偏振方位角图中异常像素点，使保留的像素点符合瑞利散射理论，进而获取太阳方向与体轴的夹角。多云天气下的实验表明，该方法获取载体坐标系下太阳方位角的均方根误差为0.42°，能够满足实际导航需求。