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This research applies surrogate modeling and inverse optimization to efficiently screen transport protocols for Bose-Einstein condensates on atom chips, significantly reducing the computational cost of evaluating complex Gross-Pitaevskii dynamics. By validating low-cost Gaussian phase-space models against full 3D simulations, the study demonstrates a robust workflow for achieving high-fidelity quantum state delivery with minimal sloshing.
Surrogate-assisted three-dimensional Gross--Pitaevskii characterization of Bose--Einstein condensate transport on an atom chip
arXiv:2609.13762v1 Announce Type: cross Abstract: 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.
Abstract: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.
Quantum Gases (cond-mat.quant-gas); Quantum Physics (quant-ph)
arXiv:2609.13762 [cond-mat.quant-gas]
(or arXiv:2609.13762v1 [cond-mat.quant-gas] for this version)
https://doi.org/10.48550/arXiv.2609.13762 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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