Automated extraction of capacitive coupling for quantum dot systems

TL;DR

This paper presents an automated method combining machine learning and traditional fitting to accurately identify capacitive coupling in quantum dot systems, aiding in device tuning and detection of spurious quantum dots.

Contribution

It introduces a reliable automated approach for measuring capacitive cross-talk and identifying spurious quantum dots, enhancing quantum dot device tuning processes.

Findings

Effective machine learning and fitting combination for capacitive coupling measurement

Automated detection of spurious quantum dots during device tuning

System can flag devices with undesirable quantum dots autonomously

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.