Learning Coulomb Diamonds in Large Quantum Dot Arrays
This work addresses the challenge of characterizing quantum dot arrays for quantum computing applications, representing an incremental improvement in simulation-based methods.
The researchers tackled the problem of identifying charge transitions in quantum dot arrays by introducing an algorithm that learns Coulomb diamond facets using one-dimensional raster scans and regularized maximum likelihood estimation, achieving high precision in finding the majority of transitions as validated against exact simulations for smaller devices.
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.