178 citations · 976 across the 49 of their papers we have counts for
50 papers
Spectroscopy With Intelligent Feature Tracking for automated characterization of excited-state structure in semiconductor quantum dot devices
A. R. Huffman, Daniel Schug, Merritt Losert +6
The characterization of excited-state structure in semiconductor quantum dot (QD) devices is an important component of tuning them for spin-qubit operation. We present Spectroscopy…
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
M. Doris, S. Guo, S. M. Koh +5
Here we describe the quantum gas analysis and inference (Q-GAIN) Python package, which enables rapid deployment of machine learning (ML) and physics-informed analysis techniques fo…
Overcoming Configuration Bottleneck: Modular Pathways to Stable Semiconductor Spin-Qubit Arrays
Justyna P. Zwolak, Anthony Sigillito
Over the past decade, semiconductor spin qubits have progressed from few-qubit demonstrations towards larger-scale devices fabricated in increasingly reproducible academic and indu…
Can machine learning for quantum-gas experiments be explainable?
I. B. Spielman amd J. P. Zwolak
Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for cla…
FAlCon: A unified framework for algorithmic control of quantum dot devices
Tyler J. Kovach, Daniel Schug, Zach D. Merino +3
As spin-based quantum systems scale, their setup and control complexity increase sharply. In semiconductor quantum dot (QD) experiments, device-to-device variability, heterogeneous…
Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures
Merritt P. R. Losert, Dario Denora, Barnaby van Straaten +6
As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device characterization and data analysis tools become critical. Th…
Towards autonomous time-calibration of large quantum-dot devices: Detection, real-time feedback, and noise spectroscopy
Anantha S. Rao, Barnaby van Straaten, Valentin John +8
The performance and scalability of semiconductor quantum-dot (QD) qubits are limited by electrostatic drift and charge noise that shift operating points and destabilize qubit param…
Benchmarking machine learning models for multi-class state recognition in double quantum dot data
Valeria Díaz Moreno, Ryan P Khalili, Daniel Schug +2
Semiconductor quantum dots (QDs) are a leading platform for scalable quantum processors. However, scaling to large arrays requires reliable, automated tuning strategies for devices…
Nondestructive characterization of laser-cooled atoms using machine learning
G. De Sousa, M. Doris, D. D'Amato +3
We develop machine learning techniques for estimating physical properties of laser-cooled potassium-39 atoms in a magneto-optical trap using only the scattered light -- i.e., fluor…
QDFlow: A Python package for physics simulations of quantum dot devices
Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler +2
Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, repre…
Report on reproducibility in condensed matter physics
A. Akrap, D. Bordelon, S. Chatterjee +20
We present recommendations to improve reproducibility and replicability in condensed matter physics. This area of physics has consistently produced both fundamental insights into t…
Bootstrapping, autonomous testing, and initialization system for Si/SiGe multi-quantum-dot devices
Tyler J. Kovach, Daniel Schug, M. A. Wolfe +7
Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. However, the increasing complexity of modern QD devices makes calibratio…
Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays
Anantha S. Rao, Donovan Buterakos, Barnaby van Straaten +8
Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of s…
Autonomous Bootstrapping of Quantum Dot Devices
Anton Zubchenko, Danielle Middlebrooks, Torbjørn Rasmussen +4
Semiconductor quantum dots (QDs) are a promising platform for multiple different qubit implementations, all of which are voltage controlled by programmable gate electrodes. However…
Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning
Daniel Schug, Tyler J. Kovach, M. A. Wolfe +6
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 demo…
QDA: A principled approach to automatically annotating charge stability diagrams
Brian Weber, Justyna P. Zwolak
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…
Data needs and challenges for quantum dot devices automation
Justyna P. Zwolak, Jacob M. Taylor, Reed W. Andrews +17
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,…
Network analysis of graduate program support structures through experiences of various demographic groups
Robert P. Dalka, Justyna P. Zwolak
Physics graduate studies are substantial efforts on the part of individual students, departments, and institutions of higher education. Understanding the factors that lead to stude…
Extending Explainable Boosting Machines to Scientific Image Data
Daniel Schug, Sai Yerramreddy, Rich Caruana +2
As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern.…
Automated extraction of capacitive coupling for quantum dot systems
Joshua Ziegler, Florian Luthi, Mick Ramsey +3
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 acco…
Understanding the development of interest and self-efficacy in active-learning undergraduate physics courses
Remy Dou, Eric Brewe, Geoff Potvin +2
Modeling Instruction (MI), an active-learning introductory physics curriculum, has been shown to improve student academic success. Peer-to-peer interactions play a salient role in…
Tuning arrays with rays: Physics-informed tuning of quantum dot charge states
Joshua Ziegler, Florian Luthi, Mick Ramsey +3
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 becom…
Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research
Amilson R. Fritsch, Shangjie Guo, Sophia M. Koh +2
We establish a dataset of over experimental images of Bose--Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body phys…
Network analysis approach to Likert-style surveys
Robert P. Dalka, Diana Sachmpazidi, Charles Henderson +1
Likert-style surveys are a widely used research instrument to assess respondents' preferences, beliefs, or experiences. In this paper, we propose and demonstrate how network analys…