Learning Coulomb Diamonds in Large Quantum Dot Arrays

TL;DR

This paper presents an algorithm that efficiently identifies Coulomb diamond facets in quantum dot arrays using raster scans and maximum likelihood estimation, aiding in device characterization.

Contribution

The novel algorithm enables accurate detection of Coulomb diamond transitions in large quantum dot arrays with minimal measurements.

Findings

Successfully identifies Coulomb diamond facets with high precision

Validates the algorithm using simulated data from the constant-interaction model

Can determine transition points and gate voltages for charge states

Abstract

We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on one-dimensional raster scans (rays) to learn a model of the device using regularized maximum likelihood estimation. This allows us to determine, for a given charge state of the device, which transitions exist and what the compensated gate voltages for these are. For smaller devices the simulator can also be used to compute the exact boundaries of the Coulomb diamonds, which we use to assess that our algorithm correctly finds the vast majority of transitions with high precision.