Machine classification for probe based quantum thermometry
Quantum Physics
2022-02-23 v2 Statistical Mechanics
Abstract
We consider probe-based quantum thermometry and show that machine classification can provide model-independent estimation with quantifiable error assessment. Our approach is based on the k-nearest-neighbor algorithm. The machine is trained using data from either computer simulations or a calibration experiment. This yields a predictor which can be used to estimate the temperature from new observations. The algorithm is highly flexible and works with any kind of probe observable. It also allows to incorporate experimental errors, as well as uncertainties about experimental parameters. We illustrate our method with an impurity thermometer in a Bose-gas, as well as in the estimation of the thermal phonon number in the Rabi model.
Cite
@article{arxiv.2107.04555,
title = {Machine classification for probe based quantum thermometry},
author = {Fabrício S. Luiz and A. de Oliveira Junior and Felipe F. Fanchini and Gabriel T. Landi},
journal= {arXiv preprint arXiv:2107.04555},
year = {2022}
}