QDA$^2$: A principled approach to automatically annotating charge stability diagrams
This work addresses the lack of standardized datasets for benchmarking tuning methods in quantum dot systems, which is a bottleneck for researchers in quantum computing, though it is an incremental step as it focuses on data annotation rather than full automation.
The paper tackles the problem of automating the interpretation of charge stability diagrams for quantum dot devices, which is critical for scalable quantum computing, by introducing QD auto-annotator, a classical algorithm that uses geometric principles to label experimental data, enabling the creation of a public repository for standardized benchmarking.
Gate-defined semiconductor quantum dot (QD) arrays are a promising platform for quantum computing. However, presently, the large configuration spaces and inherent noise make tuning of QD devices a nontrivial task and with the increasing number of QD qubits, the human-driven experimental control becomes unfeasible. Recently, researchers working with QD systems have begun putting considerable effort into automating device control, with a particular focus on machine-learning-driven methods. Yet, the reported performance statistics vary substantially in both the meaning and the type of devices used for testing. While systematic benchmarking of the proposed tuning methods is necessary for developing reliable and scalable tuning approaches, the lack of openly available standardized datasets of experimental data makes such testing impossible. The QD auto-annotator -- a classical algorithm for automatic interpretation and labeling of experimentally acquired data -- is a critical step toward rectifying this. QD auto-annotator leverages the principles of geometry to produce state labels for experimental double-QD charge stability diagrams and is a first step towards building a large public repository of labeled QD data.