Optimal Work Extraction and the Minimum Description Length Principle
L\'eo Touzo, Matteo Marsili, Neri Merhav, and \'Edgar Rold\'an

TL;DR
This paper links optimal work extraction in classical information engines to universal data compression, showing that driving the system to a critical state maximizes work and connects thermodynamics with information theory.
Contribution
It introduces a framework where the optimal non-equilibrium state is given by the maximum-likelihood distribution, connecting thermodynamics with universal coding theory.
Findings
Optimal non-equilibrium state is the maximum-likelihood distribution.
Work extracted is bounded by half the Shannon entropy of measurements.
Driving system to a critical state enhances optimal work extraction.
Abstract
We discuss work extraction from classical information engines (e.g., Szil\'ard) with -particles, partitions, and initial arbitrary non-equilibrium states. In particular, we focus on their {\em optimal} behaviour, which includes the measurement of a set of quantities with a feedback protocol that extracts the maximal average amount of work. We show that the optimal non-equilibrium state to which the engine should be driven before the measurement is given by the normalised maximum-likelihood probability distribution of a statistical model that admits as sufficient statistics. Furthermore, we show that the minimax universal code redundancy associated to this model, provides an upper bound to the work that the demon can extract on average from the cycle, in units of . We also find that, in the limit of large, the maximum average extracted…
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