GAnnET: A principled approach to automatically annotating charge stability diagrams

Abstract

Gate-defined semiconductor quantum-dot (QD) arrays are a promising platform for quantum computing. However, their large configuration spaces and inherent measurement noise make device tuning time consuming and difficult to scale. Although machine-learning (ML) approaches for automated tuning have proliferated rapidly, a fair comparison of methods remains limited by inconsistent metrics and a lack of standardized labeled datasets. Here, we introduce Geometry-Informed ANNotator for Electrostatic Topologies (GAnnET), a physics- and geometry-informed algorithm that automatically generates probabilistic state labels for charge stability diagrams (CSDs) of double-QD systems. GAnnET constructs local geometric models of CSD structure and combines them into a global state map with calibrated confidence, eliminating the need for manual annotation. We validate GAnnET on simulated and experimental CSDs and demonstrate consistent labeling suitable for ML training and benchmarking. By enabling reproducible annotation of experimental data, GAnnET supports community benchmarking and scalable automated QD operation.

arXiv comment14 pages, 8 figures, 4 pages SM

Cited by in corpus (2)

• Data needs and challenges for quantum dot devices automation
• QDarts: A Quantum Dot Array Transition Simulator for finding charge transitions in the presence of finite tunnel couplings, non-constant charging energies and sensor dots