AI 中文总结
本文提出代理辅助工作流，结合逆优化和GPE模拟，高效筛选并精确表征原子芯片上BEC输运，实现高保真度输运。
AI 中文摘要
我们开发了一种代理辅助工作流程，用于在多线原子芯片上毫米尺度输运$^{87}$Rb玻色-爱因斯坦凝聚体（BEC）。基于梯度的逆优化生成了42个电流时间表，涵盖七个正则化强度和六个输运持续时间。一个低成本的Gaussian相空间模型通过三个原子数下的端点重叠对这些时间表进行排序。一个Thomas-Fermi标度（Ermakov）模型估计云团尺寸，用于构建特定案例的移动网格。随后，我们使用三维Gross--Pitaevskii方程（GPE）对选定的时间表进行详细评估。在$N=10^3$和$T=0.5$ s时，代理模型预测轴向质心（COM）偏移大于更长持续时间的情况。GPE证实了这一预测，并揭示了持续到输送结束的晃动，端点保真度$F_{3D}=0.9244$。对于所有五个较长的输运，代理模型预测排名最高时间表的端点重叠接近单位，使用这些时间表的GPE传播给出$F_{3D}>0.9997$。在$N=10^4$和$T=2.0$ s时，代理选择的时间表同样给出$F_{3D}=0.999932$。这些结果表明，代理估计为候选筛选提供了有用的低成本基础，而GPE传播则解析了输运动力学和交付的凝聚体状态。
英文摘要
We develop a surrogate-assisted workflow for millimeter-scale transport of a $^{87}$Rb Bose--Einstein condensate (BEC) on a multi-wire atom chip. Gradient-based inverse optimization generates 42 current schedules spanning seven regularization strengths and six transport durations. A low-cost Gaussian phase-space model ranks these schedules by endpoint overlap at three atom numbers. A Thomas--Fermi scaling (Ermakov) model estimates cloud dimensions for case-specific moving-grid construction. We then evaluate selected schedules in detail with the three-dimensional Gross--Pitaevskii equation (GPE). At $N=10^3$ and $T=0.5$ s, the surrogate predicts a larger axial center-of-mass (COM) excursion than at longer durations. The GPE confirms this prediction and reveals sloshing that persists through delivery, with an endpoint fidelity $F_{3D}=0.9244$. For all five longer transports, the surrogate predicts near-unit endpoint overlap for the top-ranked schedules, and GPE propagation using these schedules gives $F_{3D}>0.9997$. At $N=10^4$ and $T=2.0$ s, the surrogate-selected schedule similarly gives $F_{3D}=0.999932$. These results demonstrate that the surrogate estimates provide a useful low-cost basis for candidate screening, while GPE propagation resolves the transport dynamics and the delivered condensate state.
Comments41 pages, 16 figures, 10 tables
