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arXiv 2402.17860cond-mat.stat-mech
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信息引擎的性能极限

Performance limits of information engines

Johan du Buisson, David A. Sivak, John Bechhoefer

AI总结:

本文综述了胶体信息引擎的性能极限,指出测量噪声会导致相变,而贝叶斯算法可消除相变并提升性能,非平衡环境中的额外力涨落能将性能提升数个数量级。

摘要:

我们回顾了近期关于一种胶体信息引擎的研究,该引擎由水中的微粒和光阱构成。通过利用有利的涨落,微粒在没有任何明显外部做功的情况下被向上棘轮式驱动。此前关于此类引擎的诸多工作旨在表明,计入信息处理成本可以使观测到的运动与热力学第二定律相一致。相比之下,我们聚焦于限制此类引擎性能的因素,通过分别优化向上速度、重力自由能提取速率或轨迹跟踪能力来展开研究。随后,我们考虑了会降低引擎性能的测量噪声。在反馈算法中简单直接地使用含噪测量会导致在有限信噪比处发生相变:在相变点以下,引擎停止运转。一种更复杂的“贝叶斯”算法消除了相变并提升了性能。最后,在具有额外力涨落的非平衡环境中运行信息引擎,可以将性能提升数个数量级,甚至达到提取的能量超过运行信息处理所需能量的程度。在此类环境中,信息引擎的自主实现完全可以由热浴的额外能量驱动。

英文摘要:

We review recent studies of a colloidal information engine that consists of a bead in water and held by an optical trap. The bead is ratcheted upward without any apparent external work, by taking advantage of favorable thermal fluctuations. Much of the previous work on such engines aimed to show that accounting for information-processing costs can reconcile the observed motion with the second law of thermodynamics. By contrast, we focus on the factors that limit the performance of such engines by optimizing variously the upward velocity, rate of gravitational free-energy extraction, or ability to track a trajectory. We then consider measurement noise, which degrades engine performance. A naive use of noisy measurements in the feedback algorithm leads to a phase transition at finite signal-to-noise ratio: below the transition, the engine no longer functions. A more sophisticated, `Bayesian' algorithm eliminates the phase transition and improves performance. Finally, operating the information engine in a nonequilibrium environment with extra force fluctuations can enhance the performance by orders of magnitude, even to the point where the energy extracted exceeds that needed to run the information processing. Autonomous implementations of an information engine in such environments could be powered entirely by the additional energy of the bath.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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