Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures
This work addresses the need for scalable characterization tools in quantum computing, specifically for spin qubits, but it is incremental as it applies existing methods to automate a specific task in a niche domain.
The researchers tackled the problem of manually interpreting charge stability diagrams for quantum dot devices, which is time-consuming and error-prone, by developing an automated protocol that integrates machine learning and image processing to extract capacitive properties, demonstrating it on experimental data from single- and bilayer heterostructures.
As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device characterization and data analysis tools become critical. The orientation and spacing of transition lines in a charge stability diagram (CSD) contain a fingerprint of a QD device's capacitive environment, making these measurements useful tools for device characterization. However, manually interpreting these features is time-consuming, error-prone, and impractical at scale. Here, we present an automated protocol for extracting underlying capacitive properties from CSDs. Our method integrates machine learning, image processing, and object detection to identify and track charge transitions across large datasets without manual labeling. We demonstrate this method using experimentally measured data from a strained-germanium single-quantum-well (planar) and a strained-germanium double-quantum-well (bilayer) QD device. Unlike for planar QD devices, CSDs in bilayer germanium heterostructure exhibit a larger set of transitions, including interlayer tunneling and distinct loading lines for the vertically stacked QDs, making them a powerful testbed for automation methods. By analyzing the properties of many CSDs, we can statistically estimate physically relevant quantities, like relative lever arms and capacitive couplings. Thus, our protocol enables rapid extraction of useful, nontrivial information about QD devices.