随机热力学的隐马尔可夫模型
Hidden Markov models for stochastic thermodynamics
摘要：
状态估计和隐马尔可夫模型的形式化可以简化和澄清存在反馈和测量误差的随机热力学的讨论。在回顾了基本的形式主义，我们用它来阐明最近的讨论相变的信息引擎，其中测量噪声作为一个控制参数的优化响应。HMM形式主义还表明，附加信息的值在中间信噪比处显示出最大值。最后，我们讨论了如何开放的信息流系统显然可以违反因果关系的HMM形式主义可以量化的性能增益，由于这种违反。
英文摘要：
The formalism of state estimation and hidden Markov models (HMMs) can simplify and clarify the discussion of stochastic thermodynamics in the presence of feedback and measurement errors. After reviewing the basic formalism, we use it to shed light on a recent discussion of phase transitions in the optimized response of an information engine, for which measurement noise serves as a control parameter. The HMM formalism also shows that the value of additional information shows a maximum at intermediate signal-to-noise ratios. Finally, we discuss how systems open to information flow can apparently violate causality; the HMM formalism can quantify the performance gains due to such violations.
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