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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cond-mat.mes-hall 2years
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A vision-transformer neural network trained unsupervised on synthetic conductance data proposes Hamiltonian parameter updates that drive quantum dot chains into the topological phase with Majorana modes, often succeeding in a single step.
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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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AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
A vision-transformer neural network trained unsupervised on synthetic conductance data proposes Hamiltonian parameter updates that drive quantum dot chains into the topological phase with Majorana modes, often succeeding in a single step.