Extracting work from hidden degrees of freedom

TL;DR

This paper demonstrates experimentally that environmental memory in non-Markovian systems can be harnessed as a thermodynamic resource, enabling enhanced work extraction beyond traditional Markovian assumptions.

Contribution

It introduces a novel measurement protocol to extract work from hidden bath degrees of freedom in non-Markovian environments, showing how memory effects can be exploited as a thermodynamic resource.

Findings

Work extraction exceeds energy stored in observable degrees of freedom.

Environmental memory can be quantified independently of initial conditions.

Non-Markovian dynamics can improve the efficiency of information engines.

Abstract

Thermodynamics establishes that information acquired through measurement can be converted into work, as exemplified by Maxwell's demon and Szilard engines. Most experimental realizations of information engines, however, implicitly assume Markovian environments, in which information exchanged with the surroundings is irreversibly lost. Many physical systems instead exhibit environmental memory, with hidden degrees of freedom retaining correlations with the system's past and giving rise to non Markovian dynamics. Whether and how such concealed memory can be harnessed as a thermodynamic resource has remained an open question. Here we experimentally demonstrate work extraction from environmental memory. Using time resolved measurements on an optically trapped Brownian particle in equilibrium, we implement a time delayed double measurement protocol that retrieves information via backflow from hidden bath degrees of freedom. We show that this information backflow alters relaxation dynamics, can be quantified independently of initial state effects, and when appropriately exploited enhances work extraction. Notably, we identify regimes in which the extracted work exceeds the energy stored in the observable degree of freedom alone. Our results establish environmental memory as an experimentally accessible thermodynamic resource and reveal how non Markovian dynamics can be systematically explored to improve the performance of information engines operating in time-correlated environments.