Designing Autonomous Maxwell Demon via Stochastic Resetting

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 when the resetting is always on, in which case the efficacious region of the demon being extended significantly. Moreover, a dual function region in a new phase diagram of the demon with resetting has been found. Remarkably, in this dual function region the demon with resetting can realize anomalous output of work and erasure of information on the tape simultaneously, violating the second law of thermodynamics apparently. To this question, we derive a new modified Clausius inequality to restore the second law by taking the cost of resetting into account.

16 pages, 5 figures; all comments are welcome

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Cited by in corpus (3)

• Speedups in nonequilibrium thermal relaxation: Mpemba and related effects
• Accelerating Quantum Relaxation via Temporary Reset: A Mpemba-Inspired Approach
• Universal trade-off between irreversibility and intrinsic timescale in thermal relaxation with applications to thermodynamic inference