2023/04/27 by Muthumanimaran Vetrivelan, Vetrivelan, Muthumanimaran, Abhisek Panda +3 · 1 citation
Materials Science · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #FOS: Physical sciences #Machine Learning in Materials Science #Quantum Physics (quant-ph) #Quantum many-body systems
paper · pdf · doi:10.48550/arxiv.2304.14022
openalex publication_date 2023/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Thermodynamically consistent measurements can either preserve statistics (unbiased) or preserve marginal states (non-invasive) but not both. Here we show the existence of metrological tasks which unequally favor each of the aforementioned measurement types. We consider two different metrology tasks, namely weak value amplification technique and repeated metrology without resetting. We observe that unbiased measurement is better than non-invasive measurement for the former and the converse is true for the latter. We provide finite temperature simulations of transmon sensors which estimate how much cooling, a resource for realistic measurements, is required to perform these metrology tasks.