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Thermodynamics of complexity and pattern manipulation.
Garner, Andrew J P; Thompson, Jayne; Vedral, Vlatko; Gu, Mile.
Afiliación
  • Garner AJP; Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543, Singapore.
  • Thompson J; Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
  • Vedral V; Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543, Singapore.
  • Gu M; Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543, Singapore.
Phys Rev E ; 95(4-1): 042140, 2017 Apr.
Article en En | MEDLINE | ID: mdl-28505845
Many organisms capitalize on their ability to predict the environment to maximize available free energy and reinvest this energy to create new complex structures. This functionality relies on the manipulation of patterns-temporally ordered sequences of data. Here, we propose a framework to describe pattern manipulators-devices that convert thermodynamic work to patterns or vice versa-and use them to build a "pattern engine" that facilitates a thermodynamic cycle of pattern creation and consumption. We show that the least heat dissipation is achieved by the provably simplest devices, the ones that exhibit desired operational behavior while maintaining the least internal memory. We derive the ultimate limits of this heat dissipation and show that it is generally nonzero and connected with the pattern's intrinsic crypticity-a complexity theoretic quantity that captures the puzzling difference between the amount of information the pattern's past behavior reveals about its future and the amount one needs to communicate about this past to optimally predict the future.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Phys Rev E Año: 2017 Tipo del documento: Article País de afiliación: Singapur Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Phys Rev E Año: 2017 Tipo del documento: Article País de afiliación: Singapur Pais de publicación: Estados Unidos