Vision-transformer neural networks trained on simulated charge stability diagrams from a disordered generalized Hubbard model predict SOC-induced spin-flip tunneling amplitudes with R² ≈ 0.94 even when other parameters are unknown.
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Decoupled weight decay proportional to gamma squared yields stable weight and gradient norms under the steady-state assumption that updates are independent of weights.
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Predicting spin-orbit coupling in hole spin qubit arrays with vision-transformer-based neural networks on a generalized Hubbard model
Vision-transformer neural networks trained on simulated charge stability diagrams from a disordered generalized Hubbard model predict SOC-induced spin-flip tunneling amplitudes with R² ≈ 0.94 even when other parameters are unknown.
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Correction of Decoupled Weight Decay
Decoupled weight decay proportional to gamma squared yields stable weight and gradient norms under the steady-state assumption that updates are independent of weights.
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