当基于轨迹的界失效时：带噪声反馈下的信息热力学
When trajectory-based bounds fail: information thermodynamics under noisy feedback
浏览论文内容
中文总结 AI 辅助
本文针对带噪声反馈的欠阻尼反馈控制系统，实验与理论对比三种信息论界，揭示轨迹依赖界的局限性，为信息热力学提供统一视角。
中文摘要 AI 辅助
信息引擎利用反馈从热涨落中提取功，通过信息论界拓展了热力学第二定律。尽管已提出多种此类界，但它们在实际条件（测量含噪声、反馈存在时间关联）下的相对性能仍不明确。本文针对欠阻尼反馈控制系统（具有马尔可夫测量但非马尔可夫控制序列），从实验和理论两方面研究该问题，对比了由转移熵、不可用信息及马尔可夫互信息导出的三种代表性界，发现三者均非普适最优。测量噪声会优先影响依赖详细轨迹统计的信息度量，而对基于瞬时关联的量影响较小；因此，依赖轨迹的界会快速退化，在宽测量噪声范围内，马尔可夫互信息界会变得比不可用信息界更紧。本研究揭示了实际场景中依赖详细轨迹统计的信息论描述的普遍局限性，为超越理想化反馈协议的信息热力学提供了统一视角。
英文摘要
Information engines exploit feedback to extract work from thermal fluctuations, extending the second law of thermodynamics through information-theoretic bounds. While several such bounds have been proposed, their relative performance under realistic conditions---where measurements are noisy and feedback is temporally correlated---remains largely unclear. Here, we experimentally and theoretically investigate this problem in an underdamped feedback-controlled system with Markovian measurements but non-Markovian control sequences. We compare three representative bounds derived from transfer entropy, unavailable information, and Markovian mutual information, and find that none is universally optimal. Instead, measurement noise preferentially affects information measures that rely on detailed trajectory statistics, while leaving quantities based on instantaneous correlations comparatively robust. As a consequence, trajectory-dependent bounds deteriorate rapidly, giving rise to a crossover in which the Markovian mutual-information bound becomes tighter than the unavailable-information bound over a broad range of measurement noise. Our results reveal a general limitation of information-theoretic descriptions that rely on detailed trajectory statistics in realistic settings and provide a unified perspective on information thermodynamics beyond idealised feedback protocols.
