AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes

• URL: http://arxiv.org/abs/2601.02149v1
• Date: Mon, 05 Jan 2026 14:25:49 GMT
• Title: AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
• Authors: Mateusz Krawczyk, Jarosław Pawłowski,
• Abstract summary: We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators.<n>We show that, with appropriate training, a deep vision-transformer network can efficiently memorize relation between Hamiltonian parameters and structures on conductance maps.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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