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Automated extraction of capacitive coupling for quantum dot systems



Joshua Ziegler, Florian Luthi, Mick Ramsey, Felix Borjans, Guoji Zheng, Justyna Zwolak


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.
Physical Review Applied


machine learning, quantum dots, autonomous control, virtual gates


Ziegler, J. , Luthi, F. , Ramsey, M. , Borjans, F. , Zheng, G. and Zwolak, J. (2023), Automated extraction of capacitive coupling for quantum dot systems, Physical Review Applied, [online],, (Accessed May 24, 2024)


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Created May 24, 2023, Updated May 25, 2023