Battery Charging in Collision Models with Bayesian Risk Strategies

We constructed a collision model where measurements in the system, together with a Bayesian decision rule, are used to classify the incoming ancillas as having either high or low ergotropy (maximum extractable work). The former are allowed to leave, while the latter are redirected for further processing, aimed at increasing their ergotropy further. The ancillas play the role of a quantum battery, and the collision model, therefore, implements a Maxwell demon. To make the process autonomous and with a well-defined limit cycle, the information collected by the demon is reset after each collision by means of a cold heat bath.

Automated summary

A collision model is used to classify and process incoming ancillas based on their ergotropy levels, with a Bayesian decision rule. By implementing a Maxwell demon in the collision model, researchers were able to achieve an autonomous process with a well-defined limit cycle, by resetting the information collected after each collision using a cold heat bath.