AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
This work addresses the challenge of precise parameter tuning in quantum dot systems for physicists and engineers, representing an incremental improvement by applying existing neural network methods to a new domain-specific problem.
The researchers tackled the problem of tuning quantum dot simulators to achieve Majorana modes by developing a neural network model that learns from conductance maps and autotunes devices based on transport measurements, resulting in efficient generation of nontrivial zero modes with a single update step from broad initial detunings.
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.