Automated extraction of capacitive coupling for quantum dot systems

Abstract

Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform. However, near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operation of QD devices. One such problem is the capacitive cross-talk between the metallic gates that define and control QD qubits. A way to compensate for the capacitive cross-talk and enable targeted control of specific QDs independent of coupling is by the use of virtual gates. Here, we demonstrate a reliable automated capacitive coupling identification method that combines machine learning with traditional fitting to take advantage of the desirable properties of each. We also show how the cross-capacitance measurement may be used for the identification of spurious QDs sometimes formed during tuning experimental devices. Our systems can autonomously flag devices with spurious dots near the operating regime, which is crucial information for reliable tuning to a regime suitable for qubit operations.

9 pages, 5 figures

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• Industrially fabricated single-electron quantum dots in Si/Si-Ge heterostructures
• Fully autonomous tuning of a spin qubit
• Autonomous Bootstrapping of Quantum Dot Devices
• Automatic detection of single-electron regime of quantum dots and definition of virtual gates using U-Net and clustering
• Bootstrapping, autonomous testing, and initialization system for Si/Si${}_x$Ge${}_{1-x}$ multi-quantum-dot devices
• QDFlow: A Python package for physics simulations of quantum dot devices