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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Confinement modulation during shuttling enables dressed-state dynamical decoupling that mitigates both global and local magnetic/electric noise in hole-spin qubits.
Optimized MBE growth of 22-nm-deep Ge quantum wells achieves a peak hole mobility of 105000 cm2/Vs at 2 K, the highest reported for shallow MBE-grown samples.
citing papers explorer
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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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Suppressing spin qubit decoherence during shuttling via confinement modulation
Confinement modulation during shuttling enables dressed-state dynamical decoupling that mitigates both global and local magnetic/electric noise in hole-spin qubits.
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Growth optimization of shallow Ge quantum wells grown by molecular beam epitaxy
Optimized MBE growth of 22-nm-deep Ge quantum wells achieves a peak hole mobility of 105000 cm2/Vs at 2 K, the highest reported for shallow MBE-grown samples.