QDA$^2$: A principled approach to automatically annotating charge
  stability diagrams
Brian Weber Justyna P. Zwolak
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.