An optimization framework for high-efficiency quantum Feshbach engines in trapped BECs is developed using variational dynamics and Nelson's stochastic quantization to minimize cost functionals for protocol duration versus physical constraints.
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2026 2verdicts
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A U-Net-based ML pipeline reconstructs the complete phase field and quantized vortex charges in 2D Bose-Einstein condensates from density snapshots alone, using synthetic training data from projected Gross-Pitaevskii simulations.
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Optimal Quantum Feshbach Engines
An optimization framework for high-efficiency quantum Feshbach engines in trapped BECs is developed using variational dynamics and Nelson's stochastic quantization to minimize cost functionals for protocol duration versus physical constraints.
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Machine Learning Phase Field Reconstruction in a Bose-Einstein Condensate
A U-Net-based ML pipeline reconstructs the complete phase field and quantized vortex charges in 2D Bose-Einstein condensates from density snapshots alone, using synthetic training data from projected Gross-Pitaevskii simulations.