{"ID":2822595,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.02149","arxiv_id":"2601.02149","title":"AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes","abstract":"We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this knowledge to autotune these devices - based on transport measurements - toward obtaining Majorana modes in the structure. The model is trained in an unsupervised manner on synthetic data in the form of conductance maps, using a physics-informed loss that incorporates key properties of Majorana zero modes. We show that, with appropriate training, a deep vision-transformer network can efficiently memorize relation between Hamiltonian parameters and structures on conductance maps and use it to propose parameters update for a quantum dot chain that drive the system toward topological phase. Starting from a broad range of initial detunings in parameter space, a single update step is sufficient to generate nontrivial zero modes. Moreover, by enabling an iterative tuning procedure - where the system acquires updated conductance maps at each step - we demonstrate that the method can address a much larger region of the parameter space.","short_abstract":"We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this knowledge to autotune these devices - based on transport measurements - toward obtaining Majorana modes in the structure. The model is trained in an unsupervised manner on synthet...","url_abs":"https://arxiv.org/abs/2601.02149","url_pdf":"https://arxiv.org/pdf/2601.02149v3","authors":"[\"Mateusz Krawczyk\",\"Jarosław Pawłowski\"]","published":"2026-01-05T14:25:49Z","proceeding":"cond-mat.mes-hall","tasks":"[\"cond-mat.mes-hall\",\"cond-mat.dis-nn\",\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}