Quantum mean-square predictors and thermodynamics

TL;DR

This paper introduces a method to accurately estimate thermodynamic quantities like heat and work from indirect measurements, providing optimal predictions with minimal mean-squared error, applicable to stochastic and driven quantum systems.

Contribution

It presents a novel quantum mean-square predictor technique that yields true averages of thermodynamic quantities from indirect measurements, improving estimation accuracy.

Findings

Method always yields the true average of thermodynamic quantities.

Mean-squared error is proportional to the variance estimation accuracy.

Applied to energy fluctuations in driven and avoided crossing systems.

Abstract

Thermodynamic quantities, such as heat and work, are not functions of state, but rather of the process undergone by a physical system. Assessing them can therefore be difficult, since it requires probing the system at least twice. This is even more so when these quantities are to be assessed at the stochastic level. In this letter we show how to obtain optimal estimates of thermodynamic quantities solely from indirect measurement unravellings of an auxiliary system. The method always yields the true average. And the mean-squared error of the prediction is directly proportional to how well the method estimates the variance. As an application, we study energy fluctuations in a driven system, and in an avoided crossing work protocol.