Microscopic treatment of the hybrid segment in mesoscopic Kitaev chains shows that Andreev bound state parity crossings define optimal sweet spots for localized Majoranas with large gaps.
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cond-mat.mes-hall 2years
2026 2verdicts
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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.
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Optimal Majoranas in Mesoscopic Kitaev Chains
Microscopic treatment of the hybrid segment in mesoscopic Kitaev chains shows that Andreev bound state parity crossings define optimal sweet spots for localized Majoranas with large gaps.
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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.