基于量子传感器的磁场资源有效自适应贝叶斯跟踪
Resource-efficient adaptive Bayesian tracking of magnetic fields with a quantum sensor
摘要：
单自旋量子传感器，例如基于金刚石中的氮空位中心，提供磁场的纳米级映射。在磁场可能快速变化的应用中，总感测时间至关重要，必须最小化。贝叶斯估计和自适应实验优化可以通过减少所需的测量次数来加快传感过程。这些协议包括基于测量结果计算和更新磁场的概率分布，以及确定下一次测量的优化采集设置。然而，输入到下一次迭代的测量设置中的计算步骤必须足够快地执行，以允许实时更新。本文通过实现近似贝叶斯估计技术来解决计算速度问题，其中概率分布由高斯函数的有限和近似。考虑到只需要三个参数来完全描述高斯密度，我们发现在许多情况下，磁场概率分布可以用不到十个参数来描述，与现有方法相比，计算时间减少了10倍。对于T2* = 1微秒，仅实现了计算时间的小幅减少。然而，在这些制度，建议高斯协议优于现有的跟踪精度。
英文摘要：
Single-spin quantum sensors, for example based on nitrogen-vacancy centres in diamond, provide nanoscale mapping of magnetic fields. In applications where the magnetic field may be changing rapidly, total sensing time is crucial and must be minimised. Bayesian estimation and adaptive experiment optimisation can speed up the sensing process by reducing the number of measurements required. These protocols consist of computing and updating the probability distribution of the magnetic field based on measurement outcomes and of determining optimized acquisition settings for the next measurement. However, the computational steps feeding into the measurement settings of the next iteration must be performed quickly enough to allow for real-time updates. This article addresses the issue of computational speed by implementing an approximate Bayesian estimation technique, where probability distributions are approximated by a finite sum of Gaussian functions. Given that only three parameters are required to fully describe a Gaussian density, we find that in many cases, the magnetic field probability distribution can be described by fewer than ten parameters, achieving a reduction in computation time by factor 10 compared to existing approaches. For T2* = 1 micro second, only a small decrease in computation time is achieved. However, in these regimes, the proposed Gaussian protocol outperforms the existing one in tracking accuracy.
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