Maximal fluctuation exploitation in Gaussian information engines

Understanding the connections between information and thermodynamics has been among the most visible applications of stochastic thermodynamics. While recent theoretical advances have established that the second law of thermodynamics sets limits on information-to-energy conversion, it is currently unclear to what extent real systems can achieve the predicted theoretical limits. Using a simple model of an information engine that has recently been experimentally implemented, we explore the limits of information-to-energy conversion when an information engine's benefit is limited to output energy that can be stored. We find that restricting the engine's output in this way can limit its ability to convert information to energy. Nevertheless, a feedback control that inputs work can allow the engine to store energy at the highest achievable rate. These results sharpen our theoretical understanding of the limits of real systems that convert information to energy.

Automated summary

Understanding the relationship between information and thermodynamics is a key application of stochastic thermodynamics. It is still uncertain to what extent real systems can achieve the theoretical limits of information-to-energy conversion. Research indicates that limiting an engine's output in converting information to energy can restrict its capability, yet feedback control can enable the engine to store energy at the highest achievable rate.