6 citations · 7 across the 5 of their papers we have counts for
5 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…
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…
From Simulation to Experiment: Benchmarking Machine-Learning Models for Quantum-Dot State Recognition
Valeria Díaz Moreno, Ryan P Khalili, Daniel Schug +3
Machine-learning models for quantum-dot state recognition are commonly trained using synthetic data, where large labeled datasets can be generated efficiently. However, strong perf…
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…
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…