Measurement-feedback formalism meets information reservoirs

TL;DR

This paper unifies two thermodynamics of information formalisms by deriving a stronger second-law-like inequality for information reservoirs and showing their equivalence through a bipartite system model.

Contribution

It derives a new inequality applying measurement-feedback formalism to information reservoirs, unifying the two approaches and improving bounds on work extraction.

Findings

Derived a stronger second-law-like inequality for information reservoirs.

Established the equivalence of the Mandal-Jarzynski model with a bipartite measurement-feedback system.

Provided a unified theoretical framework for thermodynamics of information.

Abstract

There have been two distinct formalisms of thermodynamics of information: One is the measurement-feedback formalism, which concerns bipartite systems with measurement and feedback processes, and the other is the information reservoir formalism, which considers bit sequences as a thermodynamic fuel. In this paper, we derive a second-law-like inequality by applying the measurement-feedback formalism to information reservoirs, which provides a stronger bound of extractable work than any other known inequality in the same setup. In addition, we demonstrate that the Mandal-Jarzynski model, which is a prominent model of the information reservoir formalism, is equivalent to a model obtained by the contraction of a bipartite system with autonomous measurement and feedback. Our results provide a unified view on the measurement-feedback and the information-reservoir formalisms.