Supervised ML trained on field- and bias-dependent conductance extracts the q-vector of arbitrary spin-spiral magnets in 2D moiré systems.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cond-mat.mes-hall 3years
2026 3verdicts
UNVERDICTED 3roles
method 1polarities
use method 1representative citing papers
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.
citing papers explorer
-
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.
-
AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
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.
-
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.