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.
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.
Abstract Tuning of gate-defined semiconductor quantum dots (QDs) is a major bottleneck for scaling spin-qubit technologies. We present a deep learning driven, semantic-segmentation pipeline that performs charge auto-tuning by locating transition lines in full charge stability diagrams (CSDs) and returns gate voltage targets for the single charge regime. We assemble and manually annotate a large, heterogeneous dataset of 1015 experimental CSDs measured from fully depleted silicon-on-insulator (FD-SOI) QD devices, spanning nine design geometries, multiple wafers, and fabrication runs. A U-Net style convolutional neural network with a MobileNetV2 encoder is trained and validated through five-fold group cross-validation. Our model achieves an overall offline tuning success of 80.0% in locating the single-charge regime, with peak performance exceeding 88% for some designs. We analyze dominant failure modes and propose targeted mitigations. Finally, wide-range diagram segmentation also naturally enables scalable physics-based feature extraction that can feed back to fabrication and design workflows and outline a roadmap for real-time integration in a cryogenic wafer prober. Overall, our results show that neural network based wide-diagram segmentation is a practical step toward automated, high-throughput charge tuning for silicon QD qubits.
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.
Scaling semiconductor quantum dot arrays toward fault-tolerant quantum computing requires efficient tuneup of spin qubits, a process that depends on the analysis of charge stability maps (CSMs) and remains largely manual. While machine learning has been widely applied to CSM analysis in reservoir-coupled devices, automated tuning in the increasingly important isolated-mode regime has received limited attention. In isolated-mode CSMs, charge transitions appear as near-vertical lines, making them well suited to compact, task-specific models. We present two convolutional neural networks with fewer than one million parameters, trained on CSMs collected from 32 silicon metal-oxide-semiconductor (SiMOS) double-quantum-dot devices measured at approximately 1 K using an automated cryogenic probing system. Sixteen devices were used for training and sixteen were held out to evaluate cross-device generalization against hand-labeled ground truth. CSMClassifier identifies charge instability and sensor artifacts, achieving 94% macro-averaged accuracy across three quality classes on 2,407 held-out images. ChargeLineNet localizes charge-transition lines and determines electron occupancy, achieving 95.3% exact line-count accuracy on 1,131 held-out images. Combined into a single pipeline, the models correctly determine electron occupancy for 93.8% of clean held-out images. Pre-training on synthetic images substantially improves label efficiency. Fine-tuning the pre-trained model on limited experimental data maintains over 90% accuracy, whereas training from scratch degrades significantly under the same conditions. Together, the two models occupy only 6.5 MB and process images in less than 60 ms on standard laboratory hardware, demonstrating a practical path toward scalable, automated characterization and tuneup of quantum-dot devices.
Efficiently characterizing quantum dot (QD) devices is a critical bottleneck when scaling quantum processors based on confined spins. Measuring high-resolution charge stability diagrams (or CSDs, data maps which crucially define the occupation of QDs) is time-consuming, particularly in emerging architectures where CSDs must be acquired with remote sensors that cannot probe the charge of the relevant dots directly. In this work, we present a generative approach to accelerate acquisition by reconstructing full CSDs from sparse measurements, using a conditional diffusion model. We evaluate our approach using two experimentally motivated masking strategies: uniform grid-based sampling, and line-cut sweeps. Our lightweight architecture, trained on approximately 9,000 examples, successfully reconstructs CSDs, maintaining key physically important features such as charge transition lines, from as little as 4\% of the total measured data. We compare the approach to interpolation methods, which fail when the task involves reconstructing large unmeasured regions. Our results demonstrate that generative models can significantly reduce the characterization overhead for quantum devices, and provides a robust path towards an experimental implementation.
Quantum dots (QDs) must be tuned precisely to provide a suitable basis for quantum computation. A scalable platform for quantum computing can only be achieved by fully automating the tuning process. One crucial step is to trap the appropriate number of electrons in the QDs, typically accomplished by analyzing charge stability diagrams (CSDs). Training and testing automation algorithms require large amounts of data, which can be either measured and manually labeled in an experiment or simulated. This article introduces a new approach to the realistic simulation of such measurements. Our flexible framework enables the simulation of ideal CSD data complemented with appropriate sensor responses and distortions. We suggest using this simulation to benchmark published algorithms. Also, we encourage the extension by custom models and parameter sets to drive the development of robust technology-independent algorithms.
Reliable detection of charge transitions in charge stability diagrams (CSDs) is a key requirement for the full automation of quantum dot device control. Performing this task directly at the cryogenic stage reduces data transfer and supports scalability. To provide the large labeled datasets required for developing and evaluating detection methods, we introduced SimCATS [1], a simulator that generates realistic CSDs including sensor responses and distortions. We optimize both traditional and machine-learning-based detection methods using simulated data and benchmark them on simulated and experimental measurements from GaAs and SiGe qubit devices. We also investigate the potential of model compression and find its performance closely tied to task complexity, which can be alleviated by sensor dot compensation. In fact, we find that sensor compensation allows machine-learning approaches to be reduced in size by up to two orders of magnitude while maintaining, or even improving, detection quality. Together with high-quality measurements, this enables robust and scalable (ray-based) charge transition detection. Finally, we estimate the cryogenic power budget for applying this approach to large-scale systems with up to one million qubits. [1] F. Hader et al., "Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)", IEEE Transactions on Quantum Engineering, DOI: 10.1109/TQE.2024.3445967 (2024).