Automated reconstruction of bound states in bilayer graphene quantum dots

TL;DR

This paper presents an algorithm that accurately reconstructs and characterizes quantum states in bilayer graphene quantum dots using experimental data, aiding the development of graphene-based qubits.

Contribution

It introduces a Hamiltonian-guided random search method for robustly identifying quantum states from measurement data in bilayer graphene quantum dots.

Findings

Successfully applied to simulated data

Effectively used on experimental data

Accurately extracts model parameters

Abstract

Bilayer graphene is a nanomaterial that allows for well-defined, separated quantum states to be defined by electrostatic gating and, therefore, provides an attractive platform to construct tunable quantum dots. When a magnetic field perpendicular to the graphene layers is applied, the graphene valley degeneracy is lifted, and splitting of the energy levels of the dot is observed. Given the experimental ability to engineer this energy valley splitting, bilayer graphene quantum dots have a great potential for hosting robust qubits. Although bilayer graphene quantum dots have been recently realized in experiments, it is critically important to devise robust methods that can identify the observed quantum states from accessible measurement data. Here, we develop an efficient algorithm for extracting the model parameters needed to characterize the states of a bilayer graphene quantum dot completely. We introduce a Hamiltonian-guided random search method and demonstrate robust identification of quantum states on both simulated and experimental data.