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2,006 results on '"Liu, Liqiang"'

62. Search for Light Dark Matter-Electron Scatterings in the PandaX-II Experiment

Author

Cheng, Chen, Xie, Pengwei, Abdukerim, Abdusalam, Chen, Wei, Chen, Xun, Chen, Yunhua, Cui, Xiangyi, Fan, Yingjie, Fang, Deqing, Fu, Changbo, Fu, Mengting, Geng, Lisheng, Giboni, Karl, Gu, Linhui, Guo, Xuyuan, Han, Ke, He, Changda, Huang, Di, Huang, Yan, Huang, Yanlin, Huang, Zhou, Ji, Xiangdong, Ju, Yonglin, Li, Shuaijie, Lin, Qing, Liu, Huaxuan, Liu, Jianglai, Liu, Liqiang, Lu, Xiaoying, Ma, Wenbo, Ma, Yugang, Mao, Yajun, Meng, Yue, Shaheed, Nasir, Ni, Kaixiang, Ning, Jinhua, Ning, Xuyang, Ren, Xiangxiang, Shang, Changsong, Shen, Guofang, Si, Lin, Tan, Andi, Wang, Anqing, Wang, Hongwei, Wang, Meng, Wang, Qiuhong, Wang, Siguang, Wang, Wei, Wang, Xiuli, Wang, Zhou, Wu, Mengmeng, Wu, Shiyong, Wu, Weihao, Xia, Jingkai, Xiao, Mengjiao, Xiao, Xiang, Yan, Binbin, Yang, Jijun, Yang, Yong, Yu, Chunxu, Yuan, Jumin, Yuan, Ying, Zeng, Xinning, Zhang, Dan, Zhang, Tao, Zhao, Li, Zheng, Qibin, Zhou, Jifang, Zhou, Ning, and Zhou, Xiaopeng

Subjects
High Energy Physics - Experiment
Abstract

We report constraints on light dark matter through its interactions with shell electrons in the PandaX-II liquid xenon detector with a total 46.9 tonne$\cdot$day exposure. To effectively search for these very low energy electron recoils, ionization-only signals are selected from the data. 1821 candidates are identified within ionization signal range between 50 to 75 photoelectrons, corresponding to a mean electronic recoil energy from 0.08 to 0.15 keV. The 90% C.L. exclusion limit on the scattering cross section between the dark matter and electron is calculated based on Poisson statistics. Under the assumption of point interaction, we provide the world's most stringent limit within the dark matter mass range from 15 to 30 $\rm MeV/c^2$, with the corresponding cross section from $2.5\times10^{-37}$ to $3.1\times10^{-38}$ cm$^2$., Comment: 6 pages, 5 figures, 2 tables

Published
2021
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65. Ultimate precision of multi-parameter quantum magnetometry under the parallel scheme

Author

Hou, Zhibo, Chen, Hongzhen, Liu, Liqiang, Zhang, Zhao, Xiang, Guo-Yong, Li, Chuan-Feng, Guo, Guang-Can, and Yuan, Haidong

Subjects
Quantum Physics
Abstract

The precise measurement of a magnetic field is one of the most fundamental and important tasks in quantum metrology. Although extensive studies on quantum magnetometry have been carried out over past decades, the ultimate precision that can be achieved for the estimation of all three components of a magnetic field with entangled probe states under the parallel scheme remains unknown. Here we present the ultimate lower bound for the sum of arbitrarily weighted variances in the estimation of all three components of a magnetic field under the parallel scheme and show that this lower bound can be achieved for sufficiently large N. The optimal entangled probe state that achieves the ultimate precision is also explicitly constructed. The obtained precision sets the ultimate limit for the multi-parameter quantum magnetometry under the parallel scheme, which is of fundamental interest and importance in quantum metrology. Our approach also provides a way to characterize the tradeoff among the precisions of multiple parameters that arise from the constraints on the probe states., Comment: 20 pages. Comments are welcome

Published
2020
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80. Generalizable control for quantum parameter estimation through reinforcement learning

Author

Xu, Han, Li, Junning, Liu, Liqiang, Wang, Yu, Yuan, Haidong, and Wang, Xin

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Measurement and estimation of parameters are essential for science and engineering, where one of the main quests is to find systematic schemes that can achieve high precision. While conventional schemes for quantum parameter estimation focus on the optimization of the probe states and measurements, it has been recently realized that control during the evolution can significantly improve the precision. The identification of optimal controls, however, is often computationally demanding, as typically the optimal controls depend on the value of the parameter which then needs to be re-calculated after the update of the estimation in each iteration. Here we show that reinforcement learning provides an efficient way to identify the controls that can be employed to improve the precision. We also demonstrate that reinforcement learning is highly generalizable, namely the neural network trained under one particular value of the parameter can work for different values within a broad range. These desired features make reinforcement learning an efficient alternative to conventional optimal quantum control methods., Comment: 7+7 pages, 6+3 figures

Published
2019
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