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Information ratchets exploiting spatially structured information reservoirs

Spinney, Richard E., Prokopenko, Mikhail, Chu, Dominique (2018) Information ratchets exploiting spatially structured information reservoirs. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 98 (2). Article Number 022124. ISSN 2470-0045. (doi:10.1103/PhysRevE.98.022124) (KAR id:67362)

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Fully mechanized Maxwell's demons, also called information ratchets, are an important conceptual link between computation, information theory, and statistical physics. They exploit low-entropy information reservoirs to extract work from a heat reservoir. Previous models of such demons have either ignored the cost of delivering bits to the demon from the information reservoir or assumed random access or infinite-dimensional information reservoirs to avoid such an issue. In this work we account for this cost when exploiting information reservoirs with physical structure and show that the dimensionality of the reservoir has a significant impact on the performance and phase diagram of the demon. We find that for conventional one-dimensional tapes the scope for work extraction is greatly reduced. An expression for the net-extracted work by demons exploring information reservoirs by means of biased random walks on d-dimensional, \(\mathbb Z^d\), information reservoirs is presented. Furthermore, we derive exact probabilities of recurrence in these systems, generalizing previously known results. We find that the demon is characterized by two critical dimensions. First, to extract work at zero bias the dimensionality of the information reservoir must be larger than d=2, corresponding to the dimensions where a simple random walker is transient. Second, for integer dimensions d>4 the unbiased random walk optimizes work extraction corresponding to the dimensions where a simple random walker is strongly transient.

Item Type: Article
DOI/Identification number: 10.1103/PhysRevE.98.022124
Uncontrolled keywords: information theory, energetics of computation
Subjects: Q Science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Dominique Chu
Date Deposited: 19 Jun 2018 10:08 UTC
Last Modified: 16 Feb 2021 13:55 UTC
Resource URI: (The current URI for this page, for reference purposes)
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