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 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.
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.