Machine classification for probe based quantum thermometry

TL;DR

This paper introduces a machine learning approach using k-nearest-neighbor classification for model-independent quantum thermometry, capable of handling experimental errors and uncertainties, demonstrated on impurity thermometers and phonon number estimation.

Contribution

It presents a flexible, model-independent quantum thermometry method based on machine classification, adaptable to various probes and experimental conditions.

Findings

Effective temperature estimation with machine learning

Handles experimental errors and uncertainties

Validated on impurity and phonon thermometry

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.