Designing autonomous Maxwell's demon via stochastic resetting

Autonomous Maxwell's demon is a new type of information engine proposed by Mandal and Jarzynski [Proc. Natl. Acad. Sci. USA 109, 11641 (2012)0027-842410.1073/pnas.1204263109], which can produce work by exploiting an information tape. Here, we show how stochastic resetting can notably enhance the performance of autonomous Maxwell's demons in two ways: the speed of reaching their functional states and the range of their effective work regions. We propose some design principles for this system using stochastic resetting. Firstly, one can drive any autonomous demon system to its functional periodic steady state at a fastest pace from any initial distribution through resetting the demon for a predetermined critical time and then stopping the reset. Secondly, we can make the system achieve a new functional state with a larger effective working region by keeping the reset on. We also discover a dual-function region in a new phase diagram of the demon with resetting, where the demon can remarkably produce work and erase information on the tape at the same time, apparently breaking the second law of thermodynamics. We resolve this paradox by deriving a modified Clausius inequality that includes the cost of resetting.