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.

References in corpus (6)

• Scalable gate architecture for densely packed semiconductor spin qubits
• An electrostatically defined serial triple quantum dot charged with few electrons
• Loading a quantum-dot based "Qubyte" register
• Ray-based framework for state identification in quantum dot devices
• Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays
• Theoretical bounds on data requirements for the ray-based classification