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We consider probe-based quantum thermometry and show that machineclassification can provide model-independent estimation with quantifiable errorassessment. Our approach is based on the k-nearest-neighbor algorithm. Themachine is trained using data from either computer simulations or a calibrationexperiment. This yields a predictor which can be used to estimate thetemperature from new observations. The algorithm is highly flexible and workswith any kind of probe observable. It also allows to incorporate experimentalerrors, as well as uncertainties about experimental parameters. We illustrateour method with an impurity thermometer in a Bose-gas, as well as in theestimation of the thermal phonon number in the Rabi model.

Machine classification for probe based quantum thermometry