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  • 标题:Fitting magnetic field gradient with Heisenberg-scaling accuracy
  • 本地全文:下载
  • 作者:Yong-Liang Zhang ; Huan Wang ; Li Jing
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2014
  • 卷号:4
  • DOI:10.1038/srep07390
  • 出版社:Springer Nature
  • 摘要:The linear function is possibly the simplest and the most used relation appearing in various areas of our world. A linear relation can be generally determined by the least square linear fitting (LSLF) method using several measured quantities depending on variables. This happens for such as detecting the gradient of a magnetic field. Here, we propose a quantum fitting scheme to estimate the magnetic field gradient with N-atom spins preparing in W state. Our scheme combines the quantum multi-parameter estimation and the least square linear fitting method to achieve the quantum Cramér-Rao bound (QCRB). We show that the estimated quantity achieves the Heisenberg-scaling accuracy. Our scheme of quantum metrology combined with data fitting provides a new method in fast high precision measurements.
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