Garner Andrew J P, Thompson Jayne, Vedral Vlatko, Gu Mile
Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543, Singapore.
Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
Phys Rev E. 2017 Apr;95(4-1):042140. doi: 10.1103/PhysRevE.95.042140. Epub 2017 Apr 24.
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.
许多生物体利用它们预测环境的能力来最大化可用自由能量，并将这些能量重新投入以创造新的复杂结构。这种功能依赖于对模式——按时间顺序排列的数据序列——的操纵。在此，我们提出一个框架来描述模式操纵器——将热力学功转换为模式或反之亦然的设备——并使用它们构建一个“模式引擎”，该引擎促进模式创建和消耗的热力学循环。我们表明，通过可证明最简单的设备可实现最小热耗散，这些设备在保持最少内部记忆的同时展现出期望的操作行为。我们推导出这种热耗散的极限，并表明它通常非零，且与模式的内在隐秘性相关——这是一个复杂性理论量，它捕捉了模式过去行为所揭示的关于其未来的信息量与为最优预测未来而需要传达的关于该过去的信息量之间令人困惑的差异。
