Machine classification for probe based quantum thermometry

Understand

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.