Automated Extraction of Capacitive Coupling for Quantum Dot Systems

Abstract

Single electrons trapped on semiconductor-defined quantum dots (QDs) are a promising platform for large-scale quantum computing. The authors demonstrate a reliable, automated method to identify the capacitive coupling between QDs. The approach combines machine learning with traditional fitting, to take advantage of the desirable properties of each. Also, analysis of cross capacitance may be used for automatic identification of the spurious QDs that occasionally form during device tuning. These techniques can autonomously flag devices with spurious dots near the operating regime, which is crucial information for reliable tuning for qubit operations.