Maxwell Demon from a Quantum Bayesian Networks Perspective

TL;DR

This paper introduces the conditional ageing inequality (CAIN), a new thermodynamic inequality, and applies quantum Bayesian networks to analyze Maxwell demon processes across classical and quantum systems with correlations.

Contribution

It proposes the CAIN as a generalization of the Second Law for non-equilibrium systems and employs quantum Bayesian networks to analyze Maxwell demon scenarios.

Findings

CAIN extends thermodynamic inequalities to non-equilibrium.

Quantum Bayesian networks provide new insights into Maxwell demon processes.

Analysis covers classical and quantum systems with correlations.

Abstract

We propose a new inequality that we call the conditional ageing inequality (CAIN). The CAIN is a slight generalization to non-equilibrium situations of the Second Law of thermodynamics. The goal of this paper is to study the consequences of the CAIN. We use the CAIN to discuss Maxwell demon processes (i.e., thermodynamic processes with feedback.) In particular, we apply the CAIN to four cases of the Szilard engine: for a classical or a quantum system with either one or two correlated particles. Besides proposing this new inequality that we call the CAIN, another novel feature of this paper is that we use quantum Bayesian networks for our analysis of Maxwell demon processes.