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Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. As the complexity of QD devices grows, manual tuning becomes increasingly infeasible, necessitating robust and scalable autotuning solutions. Tuning large arrays of QD qubits depends on efficient choices of automated protocols. Here, we introduce 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 and gate characterization. For forming the current channels, BATIS follows a non-standard approach that requires a single measurement regardless of the number of channels. Demonstrated at 1.3 K 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., Comment: 10 pages, 3 figures

Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly granular silicon pixel detectors with billions of readout channels. With event rates as high as 40 MHz, these detectors will generate petabytes of data per second. To enable discovery within strict bandwidth and latency constraints, future trackers must be capable of fast, power efficient, and radiation hard data-reduction at the source. We are developing a radiation hard readout integrated circuit (ROIC) in 28nm CMOS with on-chip machine learning (ML) for future intelligent pixel detectors. We will show track parameter predictions using a neural network within a single layer of silicon and hardware tests on the first tape-outs produced with TSMC. Preliminary results indicate that reading out featurized clusters from particles above a modest momentum threshold could enable using pixel information at 40 MHz., Comment: Contribution to the 42nd International Conference on High Energy Physics (ICHEP)

As we approach this Labor Day, the Pennsylvania economy is growing steadily. Working families are sharing in prosperity in a more sustained way than at any point since 1980--although many families still struggle to make ends meet and, in our polarized nation, a big partisan divide exists in perceptions of whether the economy is better than four years ago. A central reason for the economy's objectively strong recent performance: federal policies during and since the outbreak of COVID-19 have quickly restored the 2013-2019 trajectory of steady job growth, low unemployment, and rising wages. Even inflation--which spiked in the pandemic--has fallen below 3%. The critical question before the country now: will policies going forward lock-in shared prosperity sufficiently for working families to "feel it"? Or will policies in 2025 and beyond restore an economy like that of most of the three-plus decades after 1980, which mostly benefited a thin slice of the very rich?

"The State of Working Pennsylvania 2022" centered on the continued recovery from the COVID-19 recession, highlighting that Pennsylvania was at a policy crossroads: would political leaders embrace policies to strengthen the individual and collective worker power evident a year ago? Or would austerity and anti-worker policies after the 2022 elections undercut the possibility of a more sustained period of shared prosperity, a "New Deal for a New Economy?" In the past year, policymakers have not made a decisive choice between these two policy options. In part because the 2022 elections led to divided government. Executive authority at both the federal and state level remains in the hands of policymakers supportive of workers and labor unions, which makes possible small steps to enhance workers' power. But divided legislative branches rule out big changes in state and federal laws. On the upside, there have not yet been a reversal of the 2021-22 federal investments in climate, infrastructure, and innovation now sustaining a strong economy. On the downside, a federal or state minimum wage increase has not yet passed and there is zero chance of enacting a major pro-worker labor law reform (i.e., Protecting the Right to Organize, or PRO Act) federally before 2025. In this context of divided government and a President and Governor generally supportive of workers, a Pennsylvania job market favorable to workers has been sustained and stabilized, the latter in part because inflation--as expected--has quickly fallen to around 3%. Some readers may see the policy recommendations in this report as too ambitious and far-reaching. The authors disagree--they are only as forceful and creative as required to bring an end to nearly a half century of rising inequality. If readers agree that there is need to restore shared prosperity and share the authors' view that doing so would lay the foundation for social unity and a team effort to address climate change, then readers should embrace these policy recommendations as the best way to achieve these vital goals. [For the 2022 report, see ED624091.]

Meeting students' basic food and nutrition requirements is an essential role of public schools and critical to student learning. Yet as with other support services in schools -- such as transportation and janitorial services -- school boards or school administrators who are worried about the bottom line may turn to outside companies to manage or replace in-house district school food programs. In 2004, about 70% of Pennsylvania school districts self-operated their food service programs. But by 2019, that number dropped to 47%, as more districts chose management companies to run their school food programs. This report relies on a mix of rigorous statistical analysis and interview-based qualitative research and finds that contracting out food services -- their management or the entire operation -- does not yield the financial benefits school districts are promised by contractors.

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., Comment: 20 pages, 5 figures, abbreviated version published in Proceedings of the XAI4Sci: Explainable machine learning for sciences workshop at AAAI 2024, (Vancouver, Canada)

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., Comment: A meeting report from a workshop held at the National Institute of Standards and Technology, Gaithersburg, MD

Optical illumination of quantum-dot qubit devices at cryogenic temperatures, while not well studied, is often used to recover operating conditions after undesired shocking events or charge injection. Here, we demonstrate systematic threshold voltage shifts in a dopant-free, Si/SiGe field effect transistor using a near infrared (780 nm) laser diode. We find that illumination under an applied gate voltage can be used to set a specific, stable, and reproducible threshold voltage that, over a wide range in gate bias, is equal to that gate bias. Outside this range, the threshold voltage can still be tuned, although the resulting threshold voltage is no longer equal to the applied gate bias during illumination. We present a simple and intuitive model that provides a mechanism for the tunability in gate bias. The model presented also explains why cryogenic illumination is successful at resetting quantum dot qubit devices after undesired charging events., Comment: 8 pages, 6 figures

The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information of the charged particle passing through from pixel-cluster properties. This detector technology immediately improves the situation for offline tracking, but any major improvements in physics reach are unrealized since they are dominated by lowest-level hardware trigger acceptance. We will demonstrate track angle and hit position prediction, including errors, using a mixture density network within a single layer of silicon as well as the progress towards and status of implementing the neural network in hardware on both FPGAs and ASICs., Comment: 6 pages, 3 figures, submitted to Neural Information Processing Systems 2023 (NeurIPS)

We study how a Bayesian decision maker (DM) learns about the biases of novel information sources to predict a random state. Absent frictions, the DM uses familiar sources as yardsticks to accurately discern the biases of novel sources. We derive the distortion of the DM's long-run prediction when he holds misspecified beliefs about the biases of several familiar sources. The distortion aggregates misspecifications across familiar sources independently of the number and nature of the novel sources the DM learns about. This has implications for labor market discrimination, media bias, and project finance and oversight.

We propose a generalization of Quantal Response Equilibrium (QRE) built on a simple premise: some actions are more focal than others. In our model, which we call the Focal Quantal Response Equilibrium (Focal QRE), each player plays a stochastic version of Nash equilibrium as in the QRE, but some strategies are focal and thus are chosen relatively more frequently than other strategies after accounting for expected utilities. The Focal QRE is able to systemically account for various forms of bounded rationality of players, especially regret-aversion, salience, or limited consideration. The Focal QRE is also useful to explain observed heterogeneity of bounded rationality of players across different games. We show that regret-based focal sets perform relatively well at predicting strategies that are chosen more frequently relative to their expected utilities.

We introduce and characterize inertial updating of beliefs. Under inertial updating, a decision maker (DM) chooses a belief that minimizes the subjective distance between their prior belief and the set of beliefs consistent with the observed event. Importantly, by varying the subjective notion of distance, inertial updating provides a unifying framework that nests three different types of belief updating: (i) Bayesian updating, (ii) non-Bayesian updating rules, and (iii) updating rules for events with zero probability, including the conditional probability system (CPS) of Myerson (1986a,b). We demonstrate that our model is behaviorally equivalent to the Hypothesis Testing model (HT) of Ortoleva (2012), clarifying the connection between HT and CPS. We apply our model to a persuasion game.