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Search Publications by: Justyna Zwolak (Fed)

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Displaying 1 - 20 of 20

NIST Scientific Integrity Program Annual Report

March 22, 2024
Author(s)
Anne Andrews, Justyna Zwolak
This report summarizes the findings of the National Institute of Standards and Technology Scientific Integrity Program assessment of the program for the period between 1 June 2022 and 30 September 2023. It provides an assessment of the current state of the

Extending Explainable Boosting Machines to Scientific Image Data

November 30, 2023
Author(s)
Daniel Schug, Sai Yerramreddy, Rich Caruana, Craig Greenberg, Justyna Zwolak
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. Driven by the pressing need for interpretable models in science, we propose

Tuning Arrays with Rays: Physics-Informed Tuning of Quantum Dot Charge States

September 28, 2023
Author(s)
Joshua Ziegler, Florian Luthi, Mick Ramsey, Felix Borjans, Guoji Zheng, Justyna Zwolak
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 has been a range

Automated extraction of capacitive coupling for quantum dot systems

May 24, 2023
Author(s)
Joshua Ziegler, Florian Luthi, Mick Ramsey, Felix Borjans, Guoji Zheng, Justyna Zwolak
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

Colloquium: Advances in automation of quantum dot devices control

February 17, 2023
Author(s)
Justyna Zwolak, Jacob Taylor
Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual

Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research

December 21, 2022
Author(s)
Amilson R. Fritsch, Shangjie Guo, Sophia Koh, Ian Spielman, Justyna Zwolak
We establish a dataset of over 1.6 x 10^4 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

Network analysis approach to Likert-style surveys

September 2, 2022
Author(s)
Robert Dalka, Diana Sachmpazidi, Charles Henderson, Justyna Zwolak
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 analysis (NA) can be employed to model and evaluate the interconnectedness of

Toward Robust Autotuning of Noisy Quantum Dot Devices

February 25, 2022
Author(s)
Joshua Ziegler, Thomas McJunkin, Emily Joseph, Sandesh Kalantre, Benjamin Harpt, Donald Savage, Max Lagally, Mark Eriksson, Jacob Taylor, Justyna Zwolak
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 or otherwise low-quality data is processed by an autonomous system. In this

Theoretical Bounds on Data Requirements for the Ray-Based Classification

November 10, 2021
Author(s)
Brian Weber, Sandesh Kalantre, Thomas McJunkin, Jacob Taylor, Justyna Zwolak
The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of different geometries, a new classification framework has recently been proposed

Mapping employee networks through the NIST Interactions Survey

June 21, 2021
Author(s)
Laura Espinal, Camila Young, Justyna Zwolak
As we begin to adopt approaches to help the U.S. National Institute of Standards and Technology (NIST) become a more inclusive organization, we need a way to assess the current level of inclusivity. The extent to which individuals have access to

Machine-learning enhanced dark soliton detection in Bose-Einstein condensates

June 15, 2021
Author(s)
Shangjie Guo, Amilson R. Fritsch, Ian Spielman, Justyna Zwolak
Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well-defined case of detecting dark solitons—appearing as local density

Ray-based framework for state identification in quantum dot devices

June 7, 2021
Author(s)
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, Samuel Neyens, Evan MacQuarrie, Mark A. Eriksson, Jacob Taylor
Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement

Survey on Gender, Equity and Inclusion

March 29, 2021
Author(s)
Mary Frances Theofanos, Jasmine Evans, Justyna Zwolak, Sandra Spickard Prettyman
In the fall of 2019, the National Institute of Standards and Technology (NIST) funded threes studies to better understand equity and inclusivity. The present study represents phase three of a sequential, exploratory mixed methods study designed to provide

Auto-tuning of double dot devices it in situ with machine learning

March 31, 2020
Author(s)
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, J. P. Dodson, Evan MacQuarrie, D. E. Savage, M. G. Lagally, S N. Coppersmith, Mark A. Eriksson, Jacob Taylor
The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time- consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the \it in situ} implementation of a

Ray-based classification framework for high-dimensional data

February 3, 2020
Author(s)
Justyna Zwolak, Jacob Taylor, Sandesh Kalantre, Thomas McJunkin, Brian Weber
While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than

Machine Learning techniques for state recognition and auto-tuning in quantum dots

January 21, 2019
Author(s)
Sandesh Kalantre, Justyna Zwolak, Stephen Ragole, Xingyao Wu, Neil M. Zimmerman, Michael Stewart, Jacob Taylor
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many cases, including in