The extraction of work from hidden degrees of freedom

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 history of the system 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 the extraction of work 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 through backflow from hidden bath degrees of freedom. We show that this information backflow alters the 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.

Authors

Institutions

Publication Details

Journal
Nature Physics
Published
2026-10-16
DOI
https://doi.org/10.1038/s41567-026-03442-w
Primary Topic
Advanced Thermodynamics and Statistical Mechanics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The extraction of work from hidden degrees of freedom

Clemens Bechinger, Sarah A. M. Loos, Muruga Lokesh, Félix Ginot
Nature Physics
Advanced Thermodynamics and Statistical Mechanics
article

The extraction of work from hidden degrees of freedom

Clemens Bechinger, Sarah A. M. Loos, Muruga Lokesh, Félix Ginot
article en

Abstract

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 history of the system 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 the extraction of work 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 through backflow from hidden bath degrees of freedom. We show that this information backflow alters the 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.

Nature Physics
University of Konstanz (DE), Max Planck Institute for Dynamics and Self-Organization (DE)
Openalex Percentile: Top 13%
Advanced Thermodynamics and Statistical Mechanics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.