Extractable information capacity in sequential measurements metrology
Extractable information capacity in sequential measurements metrology
复制标题
连续测量计量中的可提取信息容量
DOI：
10.1103/physrevresearch.5.043273
复制
发表时间：
2023
影响因子：
4.2
通讯作者：
A. Bayat
中科院分区：
文献类型：
--
作者：
Yaoling Yang;V. Montenegro;A. Bayat
量子传感的传统公式是基于这样的假设，即在每次测量之后，探针被重置到其初始状态。在一种非常独特的方法中，也可以采用顺序测量方案，其中避免了耗时的重置。在这种情况下，每个测量结果实际上来自不同的探针，但与其他数据样本相关。找到一个适当的描述顺序测量感测的精度是非常具有挑战性的，因为它需要分析长序列与指数大的结果。在这里，我们开发了一个递归公式和一个有效的蒙特-卡罗方法来计算Fisher信息，作为一个指数的传感精度，为任意长度的连续测量。我们的研究结果表明，Fisher信息最初的尺度与测量的数量非线性，然后渐近饱和的线性缩放。这种转换，从根本上限制了有关的参数的可提取的信息，是直接链接到有限的内存探头时，经历多个连续的测量。在此基础上，我们建立了一个品质因数来确定最佳测量序列长度，并在三个不同的物理系统中对结果进行了验证。
The conventional formulation of quantum sensing is based on the assumption that the probe is reset to its initial state after each measurement. In a very distinct approach, one can also pursue a sequential measurement scheme in which time-consuming resetting is avoided. In this situation, every measurement outcome effectively comes from a different probe, yet correlated with other data samples. Finding a proper description for the precision of sequential measurement sensing is very challenging as it requires the analysis of long sequences with exponentially large outcomes. Here, we develop a recursive formula and an efficient Monte-Carlo approach to calculate the Fisher information, as a figure of merit for sensing precision, for arbitrary lengths of sequential measurements. Our results show that Fisher information initially scales non-linearly with the number of measurements and then asymptotically saturates to linear scaling. Such transition, which fundamentally constrains the extractable information about the parameter of interest, is directly linked to the finite memory of the probe when undergoes multiple sequential measurements. Based on these, we establish a figure of merit to determine the optimal measurement sequence length and exemplify our results in three different physical systems.
影响因子：
16.6
作者：
Zhou S;Zhang M;Preskill J;Jiang L
通讯作者：
Jiang L
影响因子：
19.6
作者：
D. Ding;Zongkai Liu;B. Shi;Guangtao Guo;K. Mølmer;C. Adams
通讯作者：
D. Ding;Zongkai Liu;B. Shi;Guangtao Guo;K. Mølmer;C. Adams
影响因子：
64.8
作者：
Marciniak, Christian D.;Feldker, Thomas;Monz, Thomas
通讯作者：
Monz, Thomas