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Daniel Schug

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Articles

Bootstrapping, autonomous testing, and initialization system for Si/Si$_x$Ge$_1-x}$ multi-quantum-dot devices

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By Tyler J. Kovach, Daniel Schug, Michael Wolfe, Evan MacQuarrie
| nist.gov
Tyler Kovach, Daniel Schug, Michael Wolfe, Evan MacQuarrie, Patrick Walsh, Jared Benson, Mark Friesen, Mark Eriksson, 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.
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[2511.22451] Benchmarking machine learning models for multi-class state recognition in double duantum dot data

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By Valeria Cynthia Diaz, Valeria Guzmán Díaz, Daniel Schug, Patrick Walsh, Ryan P Khalili
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (624) Issue with article?

Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning

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By Daniel Schug, Tyler J. Kovach, Michael Wolfe, Jared Benson
| nist.gov
Daniel Schug, Tyler Kovach, Michael Wolfe, Jared Benson, Sanghyeok Park, J. P. Dodson, Joelle Corrigan, Mark Eriksson, The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and 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.
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