Learning Coulomb Diamonds in Large Quantum Dot Arrays

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.

Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)

“…To determine the systems virtual voltages, [98] applied CIM simulated and experimental CSD data of a 2x2 QD array to train and validate regression models for the extraction of the gradients from a Hough transformation [98]. A purely theoretical approach to CIM simulated data [99] tries to find the most probable convex polytope of Coulomb diamonds in QD measurements by learning a device model using regularized maximum likelihood estimation and one-dimensional raster scans (rays) only. [100] studied the effects of involved quantum parameters on CSDs of a serial triple QD and confirmed their global features by the similarity between transport measurements and CIM-based simulations.…”