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.
Numerical simulations predict that tensile or unstrained germanium heterostructures yield spin splittings over 100 times larger than compressive cases, enabling GHz Andreev spin qubits with 100 ns all-electric gates.
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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Strain engineering of Andreev spin qubits in Germanium
Numerical simulations predict that tensile or unstrained germanium heterostructures yield spin splittings over 100 times larger than compressive cases, enabling GHz Andreev spin qubits with 100 ns all-electric gates.