Designing Autonomous Maxwell Demon via Stochastic Resetting
Ruicheng Bao, Zhiyu Cao, Jiming Zheng, and Zhonghuai Hou

TL;DR
This paper introduces a stochastic resetting mechanism to enhance the performance of autonomous Maxwell demons, enabling faster state attainment, extended functional regions, and simultaneous work output and information erasure, while addressing thermodynamic law violations.
Contribution
It proposes a novel resetting-based design principle for autonomous Maxwell demons, improving their efficiency and revealing a new phase diagram with dual functions.
Findings
Resetting drives the demon to its steady state faster.
Extended functional region with continuous resetting.
Discovery of a dual function phase where work and erasure coexist.
Abstract
Autonomous Maxwell demon is a new type of information engine proposed by Mandal and Jarzynski, which can produce work by exploiting an information tape. Here, we show that a stochastic resetting mechanism can be used to improve the performance of autonomous Maxwell demons notably. Generally, the performance is composed of two important features, the time cost for an autonomous demon to reach its functional state and its efficacious working region in its functional state. Here, we provide a set of design principles for the system, which are capable of improving the two important features. On the one hand, one can drive any autonomous demon system to its functional periodic steady state at a fastest pace for any initial distribution through resetting the demon for a predetermined critical time and closing the reset after that. On the other hand, the system can reach a new functional state…
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Taxonomy
TopicsAdvanced Thermodynamics and Statistical Mechanics · Diffusion and Search Dynamics · nanoparticles nucleation surface interactions
