发表机构
University of Gdańsk（格但斯克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用非平衡终点的粗粒化热力学信息构建最大熵态，从精确涨落关系族中选取物理成员，为Jarzynski估计器提供统计控制，在Bose-Hubbard系统中降低采样成本并揭示信息-采样权衡。
AI 中文摘要
Jarzynski等式从非平衡功轨迹中恢复平衡自由能差，但其指数平均可能因稀有轨迹携带大权重而收敛极慢。我们表明，在非平衡终点测得的粗粒化热力学信息可以减少这种采样负担，同时保持相同的微观轨迹和相同的自由能目标。终点平均能量和粗粒化空间记录定义了一个最大熵态，并从精确的涨落关系族中选取一个成员。该物理选取的成员为普通Jarzynski估计器提供了精确的统计控制，即使最终能量与保留记录不对易。在有限Bose-Hubbard系统中，该控制显著降低了有限置信度采样成本，而对精确终点匹配的小幅受控偏离则产生平方根的信息-采样权衡。
英文摘要
Jarzynski's equality recovers an equilibrium free-energy difference from nonequilibrium work trajectories, but its exponential average can converge very slowly because rare trajectories carry large weight. We show that coarse thermodynamic information measured at the nonequilibrium endpoint can reduce this sampling burden while keeping the same microscopic trajectories and the same free-energy target. The endpoint mean energy and a coarse spatial record define a maximum-entropy state and select one member of an exact family of fluctuation relations. That physically selected member provides an exact statistical control for the ordinary Jarzynski estimator, even when the final energy and retained record do not commute. In a finite Bose--Hubbard system, this control substantially lowers the finite-confidence sampling cost, while a small controlled departure from exact endpoint matching produces a square-root information--sampling tradeoff.
