Battery charging in collision models with Bayesian risk strategies

TL;DR

This paper introduces a collision model where quantum measurements and Bayesian decision strategies classify and process quantum batteries to optimize their ergotropy, effectively implementing an autonomous Maxwell demon with a reset mechanism.

Contribution

It develops a novel collision model integrating Bayesian decision rules for quantum battery management, advancing autonomous quantum thermodynamic control.

Findings

Successfully classifies ancillas based on ergotropy using Bayesian strategies

Implements an autonomous Maxwell demon with reset via a cold heat bath

Enhances quantum battery charging protocols with collision models

Abstract

We construct 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.