Generalized Continuous Maxwell Demons

TL;DR

This paper introduces a family of generalized Maxwell Demon models operating on idealized devices, analyzing their efficiency, fluctuations, and the role of temporal correlations in optimizing information-to-energy conversion.

Contribution

It extends Maxwell Demon models to a generalized family, deriving cycle distributions and analyzing efficiency, including finite-time effects and correlations.

Findings

Efficiency at maximum power is highest for continuous protocols in rare event regimes.

Finite-time correlations enhance information-to-work conversion efficiency.

GCMD models outperform single-measurement Szilard in thermodynamic efficiency.

Abstract

We introduce a family of Generalized Continuous Maxwell Demons (GCMDs) operating on idealized single-bit equilibrium devices that combine the single-measurement Szilard and the repeated measurements of the Continuous Maxwell Demon protocols. We derive the cycle distributions for extracted work, information-content, and time and compute the power and information-to-work efficiency fluctuations for the different models. We show that the efficiency at maximum power is maximal for an opportunistic protocol of continuous type in the dynamical regime dominated by rare events. We also extend the analysis to finite-time work extracting protocols by mapping them to a three-state GCMD. We show that dynamical finite-time correlations in this model increase the information-to-work conversion efficiency, underlining the role of temporal correlations in optimizing information-to-energy conversion. The effect of finite-time work extraction and demon memory resetting is also analyzed. We conclude that GCMD models are thermodynamically more efficient than the single-measurement Szilard and preferred for describing biological processes in an information-redundant world.