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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cond-mat.mes-hall 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Neural networks trained on local 3x3 tensor-network charge-stability data can predict on-site disorder with high accuracy (R²>0.99) for the central dot in larger 5x5 disordered Hubbard model arrays, enabling scalable tuning of quantum dot spin qubits.
citing papers explorer
-
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.
-
Large Scale Optimization of Disordered Hubbard Models through Tensor and Neural Networks
Neural networks trained on local 3x3 tensor-network charge-stability data can predict on-site disorder with high accuracy (R²>0.99) for the central dot in larger 5x5 disordered Hubbard model arrays, enabling scalable tuning of quantum dot spin qubits.