AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes

TL;DR

This paper introduces a neural network model that autonomously tunes quantum dot devices to realize Majorana modes by learning from conductance data and iteratively optimizing Hamiltonian parameters.

Contribution

It presents a physics-informed, unsupervised deep learning approach for efficient, autonomous tuning of quantum dot systems toward topological phases with Majorana modes.

Findings

Deep vision-transformer network memorizes Hamiltonian-conductance relations.

Single update step can induce zero modes from broad initial detunings.

Iterative tuning broadens the parameter space where Majorana modes are achieved.

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.