Optimal Cold Atom Thermometry Using Adaptive Bayesian Strategies
Optimal Cold Atom Thermometry Using Adaptive Bayesian Strategies
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DOI：
10.1103/prxquantum.3.040330
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发表时间：
2022-04
期刊：
影响因子：
9.7
通讯作者：
Jonas Glatthard;Jesús Rubio;Rahul Sawant;T. Hewitt;G. Barontini;L. Correa
中科院分区：
文献类型：
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作者：
Jonas Glatthard;Jesús Rubio;Rahul Sawant;T. Hewitt;G. Barontini;L. Correa
在量子技术中，对少数超冷原子系统进行精确的温度测量是至关重要的，但可能非常耗费资源。在这里，我们提出了一个自适应贝叶斯框架，大大提高了冷原子温度估计的性能。具体地说，我们处理了几个钾原子在光镊子中冷却到微开尔文范围内的释放-再捕获测温实验数据。我们证明，自适应地选择释放-重新捕获时间来最大化信息增益确实大大减少了估计收敛到fiNAL读数所需的测量数量。与传统方法不同，我们的建议系统地避免捕获和处理无信息的测量。此外，我们能够做出更可靠的估计，特别是在测量数据稀缺和有噪音的情况下。同样，由此得到的估计在渐近极限下更快地收敛到实际温度。我们的方法可以用来提高在不同实验装置上运行的许多其他技术的精度和资源效率，从而为量子测温开辟了新的途径。
Precise temperature measurements on systems of few ultracold atoms is of paramount importance in quantum technology, but can be very resource-intensive. Here, we put forward an adaptive Bayesian framework that substantially boosts the performance of cold atom temperature estimation. Specifically, we process data from release–recapture thermometry experiments on few potassium atoms cooled down to the microkelvin range in an optical tweezer. We demonstrate that adaptively choosing the release–recapture times to maximise information gain does substantially reduce the number of measurements needed for the estimate to converge to a final reading. Unlike conventional methods, our proposal systematically avoids capturing and processing uninformative measurements. Furthermore, we are able to produce much more reliable estimates, especially when the measured data are scarce and noisy. Likewise, the resulting estimates converge faster to the real temperature in the asymptotic limit. Our method can be adapted to enhance the precision and resource-efficiency of many other techniques running on different experimental setups, thus opening new avenues in quantum thermometry.