贝叶斯统计最优工作的通用效率
Universal efficiency at optimal work with Bayesian statistics
摘要：
如果量子热机的每个循环的功在外部参数$a$的适当的先验分布上被平均，则功在Curzon-Ahlborn效率下变得最优。形式为$Π(a) \propto 1/a^γ$的更一般的先验在保持接近CA值的效率下产生最优功，特别是在接近平衡时，效率缩放为卡诺值的一半。这一特点是类似于最近在文献中观察到的有限时间热力学的某些模型。此外，贝叶斯定理的使用意味着用后验概率估计的工作也与经典公式密切相似。这些发现表明，先验信息的概念可以用来揭示量子系统中的热力学特征，从而指出了热力学行为和信息概念之间的新联系。
英文摘要：
If the work per cycle of a quantum heat engine is averaged over an appropriate prior distribution for an external parameter $a$, the work becomes optimal at Curzon-Ahlborn efficiency. More general priors of the form $Π(a) \propto 1/a^γ$ yield optimal work at an efficiency which stays close to CA value, in particular near equilibrium the efficiency scales as one-half of the Carnot value. This feature is analogous to the one recently observed in literature for certain models of finite-time thermodynamics. Further, the use of Bayes' theorem implies that the work estimated with posterior probabilities also bears close analogy with the classical formula. These findings suggest that the notion of prior information can be used to reveal thermodynamic features in quantum systems, thus pointing to a new connection between thermodynamic behavior and the concept of information.
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。
