Tyler J. Kovach

Tyler J. Kovach, Daniel Schug, Zach D. Merino et al.

As spin-based quantum systems scale, their setup and control complexity increase sharply. In semiconductor quantum dot (QD) experiments, device-to-device variability, heterogeneous control-electronics stacks, and differing operational modalities make it difficult to reuse characterization, calibration, and control logic across laboratories. We present FAlCon, an open-source software ecosystem for portable, automated characterization and tuning measurement workflows. FAlCon provides (i) a lightweight domain-specific language for expressing state-based tuning logic in a hardware-agnostic form; (ii) specialized transmittable libraries of physics-informed QD data structures (``tuning vernacula''); and (iii) extensible libraries of shared measurement protocols enabling an interoperable lab-agnostic measurement stack. By separating algorithm intent from instrument realization, while preserving traceability and supporting typed scripting, FAlCon enables researchers and engineers to exchange, adapt, and deploy characterization and autotuning routines across heterogeneous QD setups. The framework supports all users, ranging from end users running prebuilt algorithms with custom initial conditions to developers extending instrumentation support and contributing new tuning strategies. Although the present release targets QD experiments, other qubit modalities and scientific experiments could reuse FAlCon's modular abstractions by providing new tuning data types and instrument control templates.

Justyna P. Zwolak, Jacob M. Taylor, Reed W. Andrews et al.

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving 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. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts.

Daniel Schug, Tyler J. Kovach, M. A. Wolfe et al.

The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. This work demonstrates the feasibility and advantages of applying explainable machine learning techniques to the analysis of quantum dot measurements, paving the way for further advances in automated and transparent QD device tuning. Many of the measurements acquired during the tuning process come in the form of images that need to be properly analyzed to guide the subsequent tuning steps. By design, features present in such images capture certain behaviors or states of the measured QD devices. When considered carefully, such features can aid the control and calibration of QD devices. An important example of such images are so-called $\textit{triangle plots}$, which visually represent current flow and reveal characteristics important for QD device calibration. While image-based classification tools, such as convolutional neural networks (CNNs), can be used to verify whether a given measurement is $\textit{good}$ and thus warrants the initiation of the next phase of tuning, they do not provide any insights into how the device should be adjusted in the case of $\textit{bad}$ images. This is because CNNs sacrifice prediction and model intelligibility for high accuracy. To ameliorate this trade-off, a recent study introduced an image vectorization approach that relies on the Gabor wavelet transform (Schug $\textit{et al.}$ 2024 $\textit{Proc. XAI4Sci: Explainable Machine Learning for Sciences Workshop (AAAI 2024) (Vancouver, Canada)}$ pp 1-6). Here we propose an alternative vectorization method that involves mathematical modeling of synthetic triangles to mimic the experimental data. Using explainable boosting machines, we show that this new method offers superior explainability of model prediction without sacrificing accuracy.

Tyler J. Kovach, Daniel Schug, M. A. Wolfe et al.

Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. However, the increasing complexity of modern QD devices makes calibration and control -- particularly at elevated temperatures -- a bottleneck to progress, highlighting the need for robust and scalable autonomous solutions. A major hurdle arises from trapped charges within the oxide layers, which induce random offset voltage shifts on gate electrodes, with a standard deviation of approximately 83~\si{\milli\volt} of variation within state-of-the-art present-day devices. Efficient characterization and tuning of large arrays of QD qubits depend on choices of automated protocols. Here, we introduce a physically intuitive framework for a bootstrapping, autonomous testing, and initialization system (BATIS) designed to streamline QD device evaluation and calibration. BATIS navigates high-dimensional gate voltage spaces, automating essential steps such as leakage testing, formation of all current channels, and gate characterization in the presence of trapped charges. For forming the current channels, BATIS follows a non-standard approach that requires a single set of measurements regardless of the number of channels. Demonstrated at $1.3$~\si{\kelvin} on a quad-QD Si/Si$_x$Ge$_{1-x}$ device, BATIS eliminates the need for deep cryogenic environments during initial device diagnostics, significantly enhancing scalability and reducing setup times. By requiring only minimal prior knowledge of the device architecture, BATIS represents a platform-agnostic solution, adaptable to various QD systems, which bridges a critical gap in QD autotuning.