Explainable Classification Techniques for Quantum Dot Device Measurements

In the physical sciences, there is an increased need for robust feature representations of image data: image acquisition, in the generalized sense of two-dimensional data, is now widespread across a large number of fields, including quantum information science, which we consider here. While traditional image features are widely utilized in such cases, their use is rapidly being supplanted by Neural Network-based techniques that often sacrifice explainability in exchange for high accuracy. To ameliorate this trade-off, we propose a synthetic data-based technique that results in explainable features. We show, using Explainable Boosting Machines (EBMs), that this method offers superior explainability without sacrificing accuracy. Specifically, we show that there is a meaningful benefit to this technique in the context of quantum dot tuning, where human intervention is necessary at the current stage of development.

Data Needs and Challenges of Quantum Dot Devices Automation: Workshop Report

Gate-defined quantum dots are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. In this report, we outline current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present ideas put forward by the quantum dot community on how to overcome them.

QDA$^2$: A principled approach to automatically annotating charge stability diagrams

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.

Extending Explainable Boosting Machines to Scientific Image Data

As the deployment of computer vision technology becomes increasingly common in applications of consequence such as medicine or science, the need for explanations of the system output has become a focus of great concern. Unfortunately, many state-of-the-art computer vision models are opaque, making their use challenging from an explanation standpoint, and current approaches to explaining these opaque models have stark limitations and have been the subject of serious criticism. In contrast, Explainable Boosting Machines (EBMs) are a class of models that are easy to interpret and achieve performance on par with the very best-performing models, however, to date EBMs have been limited solely to tabular data. Driven by the pressing need for interpretable models in science, we propose the use of EBMs for scientific image data. Inspired by an important application underpinning the development of quantum technologies, we apply EBMs to cold-atom soliton image data, and, in doing so, demonstrate EBMs for image data for the first time. To tabularize the image data we employ Gabor Wavelet Transform-based techniques that preserve the spatial structure of the data. We show that our approach provides better explanations than other state-of-the-art explainability methods for images.

Automated extraction of capacitive coupling for quantum dot systems

Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform, however, near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operation of QD devices. One such problem is the capacitive cross-talk between the metallic gates that define and control QD qubits. A way to compensate for the capacitive cross-talk and enable targeted control of specific QDs independent of coupling is by the use of virtual gates. Here, we demonstrate a reliable automated capacitive coupling identification method that combines machine learning with traditional fitting to take advantage of the desirable properties of each. We also show how the cross-capacitance measurement may be used for the identification of spurious QDs sometimes formed during tuning experimental devices. Our systems can autonomously flag devices with spurious dots near the operating regime which is crucial information for reliable tuning to a regime suitable for qubit operations.

Tuning arrays with rays: Physics-informed tuning of quantum dot charge states

Quantum computers based on gate-defined quantum dots (QDs) are expected to scale. However, as the number of qubits increases, the burden of manually calibrating these systems becomes unreasonable and autonomous tuning must be used. There have been a range of recent demonstrations of automated tuning of various QD parameters such as coarse gate ranges, global state topology (e.g. single QD, double QD), charge, and tunnel coupling with a variety of methods. Here, we demonstrate an intuitive, reliable, and data-efficient set of tools for automated global state and charge tuning in a framework deemed physics-informed tuning (PIT). The first module of PIT is an action-based algorithm that combines a machine learning (ML) classifier with physics knowledge to navigate to a target global state. The second module uses a series of one-dimensional measurements to tune to a target charge state by first emptying the QDs of charge, followed by calibrating capacitive couplings, and navigating to the target charge state. The success rate for the action-based tuning consistently surpasses $95~\%$ on both simulated and experimental data suitable for off-line testing. The success rate for charge setting is comparable when testing with simulated data, at $95.5(5.4)~\%$, and only slightly worse for off-line experimental tests, with an average of $89.7(17.4)~\%$ (median $97.5~\%$). It's noteworthy that the high performance is demonstrated both on data from samples fabricated in an academic cleanroom as well as on an industrial 300 mm process line, further underlining the device-agnosticity of PIT. Together, these tests on a range of simulated and experimental devices demonstrate the effectiveness and robustness of PIT.

Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research

We establish a dataset of over $1.6\times10^4$ experimental images of Bose-Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About 33 % of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physics-informed ML data analysis framework -- consisting of a convolutional-neural-network-based classifier and object detector as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.

Colloquium: Advances in automation of quantum dot devices control

Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubits systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. The mapping of requisite dot locations and charges to gate voltages presents a challenging classical control problem. With an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In recent years, there has been a considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, we present a comprehensive overview of the recent progress in the automation of QD device control, with a particular emphasis on silicon- and GaAs-based QDs formed in two-dimensional electron gases. Combining physics-based modeling with modern numerical optimization and ML has proven quite effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds tremendous potential in advancing semiconductor and other platforms for quantum computing.

Combining Machine Learning with Physics: A Framework for Tracking and Sorting Multiple Dark Solitons

In ultracold atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated sub-categories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a kink soliton. Our trained implementation of this framework -- SolDet -- is publicly available as an open-source python package. SolDet is broadly applicable to feature identification in cold atom images when trained on a suitable user-provided dataset.

Toward Robust Autotuning of Noisy Quantum Dot Devices

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a ``gatekeeper'' system, ensuring that only reliable data is processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.1(7) % when tested on experimental data. We then validate the functionality of the data quality control module by showing the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.