AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908959042961408 |
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| author | Krawczyk, Mateusz Pawłowski, Jarosław |
| author_facet | Krawczyk, Mateusz Pawłowski, Jarosław |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_02149 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes Krawczyk, Mateusz Pawłowski, Jarosław Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks Artificial Intelligence 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. |
| title | AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes |
| topic | Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2601.02149 |