Bayesian information engine that optimally exploits noisy measurements

TL;DR

This paper reports the experimental realization of a Bayesian information engine using an optically trapped bead, demonstrating optimal energy extraction from noisy measurements and revealing a phase transition in performance.

Contribution

It introduces a Bayesian feedback strategy for an information engine that maintains optimal energy extraction despite measurement noise, highlighting a phase transition phenomenon.

Findings

Bayesian estimation improves energy extraction under noise.

Engine performance peaks at a critical signal-to-noise ratio.

Phase transition occurs when feedback quality drops below a threshold.

Abstract

We have experimentally realized an information engine consisting of an optically trapped, heavy bead in water. The device raises the trap center after a favorable "up" thermal fluctuation, thereby increasing the bead's average gravitational potential energy. In the presence of measurement noise, poor feedback decisions degrade its performance; below a critical signal-to-noise ratio, the engine shows a phase transition and cannot store any gravitational energy. However, using Bayesian estimates of the bead's position to make feedback decisions can extract gravitational energy at all measurement noise strengths and has maximum performance benefit at the critical signal-to-noise ratio.