非经典光学梳:超短脉冲光的五模量子挤迫态生成与应用

0 下载量 109 浏览量 更新于2024-08-27 收藏 436KB PDF 举报
本文主要探讨了在量子计算的连续变量领域中,非经典光学频率梳(nonclassical optical frequency combs)的重要作用。作者通过一项创新性研究,利用一种双峰型I同步泵浦的光学参量振荡器(degenerate type-I synchronously pumped optical parametric oscillator, OPO)成功生成了时域多模的非经典频梳态(generation of temporal multimode squeezed states)。这些频梳态是针对飞秒脉冲光(femtosecond pulsed light)的,特别关注了前五个模式的量子挤迫现象(squeezing of the leading five temporal modes)。 实验结果显示,这些模式的光谱重叠特性表明,它们具有在现实世界量子信息应用中的潜力。通过直接观测,研究人员能够验证和量化这些模式的非经典性质,这对于量子通信、量子信息处理以及量子精密测量等领域具有重要意义。非经典频率梳的实现不仅有助于提升光子源的量子性能,而且对于构建高效的量子网络和量子计算机平台至关重要。 文章的作者包括Chihua Zhou(周驰华)、Changchun Zhang(张长春)、Hongbo Liu(刘宏波)、Kui Liu(刘奎)(通讯作者:liukui@sxu.edu.cn)、Hengxin Sun(孙恒信)和Jiangrui Gao(郜江瑞),他们来自中国山西大学的量子光学与量子光学设备国家重点实验室和极端光学协同创新中心,他们的研究成果发表于2017年,被赋予了光学科学分类号270.6570、190.7110和270.5585,doi:10.3788/COL201715.092。 这项工作为开发和利用非经典光学频率梳在量子信息处理中的应用迈出了重要一步,展示了其在提高量子通信的可靠性和效率方面可能带来的革命性进展。未来的研究将着重于优化技术,扩大光谱覆盖范围,并探索如何在实际量子通信系统中集成这些非经典光场。

Please revise the paper:Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.

2023-02-18 上传