Automated extraction of capacitive coupling for quantum dot systems
This addresses a specific bottleneck in tuning and operating quantum dot devices for quantum computing, offering an incremental improvement by automating a previously manual or less reliable process.
The paper tackled the problem of capacitive cross-talk between gates in quantum dot systems, which hinders targeted control, by developing an automated method combining machine learning and traditional fitting to reliably extract capacitive couplings and identify spurious quantum dots, enabling autonomous flagging of devices with issues near the operating regime.
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.