发表机构
Monash University（蒙纳士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对Schulman型逆因果模型，通过自适应测量建立记录保留的定量组合约束，推导出最坏扰动的最小值及线性改进系数，为逆因果装置设计提供精确条件。
AI 中文摘要
自适应测量检验当实验通过经典反馈连接时，逆因果模型是否保留可观测记录。我们为具有正包裹柯西传播权重的有限宽度、共面Schulman型源建立了精确且定量的组合约束。原始段组合允许显式的自适应信号见证。一个唯一确定的标量校正保留记录的延续，而具有准备记忆的探测器实现此校正同时保留源相关性。对于通过到达角耦合到源的常见、独立准备的终端探测器，精确校准迫使等质量反极原子响应。在仅设置原始反馈下，早期记录的最坏扰动的尖锐最小值为$\epsilon_\star(0)=[2\cosh(2\Gamma)]^{-1}$，其中$\Gamma$是总源路径宽度。我们推导出显式的有限误差界并证明线性小误差改进。对于$\Gamma=0.4$的四个设置，在任意非负响应测度上的均匀减少和连续多项式证书确立$\epsilon_\star(\delta)=\epsilon_\star(0)-C_{0.4}\delta+O(\delta^2)$，其中$C_{0.4}\simeq9.351091771057010$。一个正五原子探测器达到此系数。结果量化了孤立源准确性与自适应记录保留的兼容性，并为具有显式准备、记忆和控制的逆因果装置模型提供了精确的设计条件。
英文摘要
Adaptive measurements test whether a retrocausal model preserves observable records when experiments are connected by classical feedback. We establish exact and quantitative composition constraints for a finite-width, coplanar Schulman-type source with positive wrapped-Cauchy propagation weights. Raw segment composition admits an explicit adaptive signalling witness. A uniquely determined scalar correction preserves recorded continuations, and a detector with preparation memory implements this correction while retaining the source correlations. For common, independently prepared terminal detectors coupled to the source through arrival angles, exact calibration forces equal-mass antipodal atomic responses. Under setting-only raw feedback, the sharp minimum worst disturbance of an earlier record is $ε_\star(0)=[2\cosh(2Γ)]^{-1}$, where $Γ$ is the total source-path width. We derive explicit finite-error bounds and prove linear small-error improvement. For four settings at $Γ=0.4$, a uniform reduction over arbitrary nonnegative response measures and a continuous polynomial certificate establish $ε_\star(δ)=ε_\star(0)-C_{0.4}δ+O(δ^2)$, with $C_{0.4}\simeq9.351091771057010$. A positive five-atom detector attains this coefficient. The results quantify the compatibility of isolated-source accuracy and adaptive record preservation, and provide precise design conditions for retrocausal apparatus models with explicit preparation, memory, and control.
