Supervised ML trained on field- and bias-dependent conductance extracts the q-vector of arbitrary spin-spiral magnets in 2D moiré systems.
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A flexible optimization framework is introduced to suppress in-plane g-tensor components in SiGe-Ge-SiGe quantum wells by tuning silicon concentration, enabling gapless single-spin qubit encoding.
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Hamiltonian learning for spin-spiral moir\'e magnets from electronic magnetotransport
Supervised ML trained on field- and bias-dependent conductance extracts the q-vector of arbitrary spin-spiral magnets in 2D moiré systems.
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g-tensor Optimization in Ge/SiGe Quantum Dots
A flexible optimization framework is introduced to suppress in-plane g-tensor components in SiGe-Ge-SiGe quantum wells by tuning silicon concentration, enabling gapless single-spin qubit encoding.