67 citations · 290 across the 29 of their papers we have counts for
6 papers · 1 filter
Colloquium: Advances in automation of quantum dot devices control
Justyna P. Zwolak, Jacob M. 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 se…
Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
Shangjie Guo, Sophia M. Koh, Amilson R. Fritsch +2
In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is parti…
Toward Robust Autotuning of Noisy Quantum Dot Devices
Joshua Ziegler, Thomas McJunkin, E. S. Joseph +7
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…
Theoretical bounds on data requirements for the ray-based classification
Brian J. Weber, Sandesh S. Kalantre, Thomas McJunkin +2
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 diff…
Ray-based framework for state identification in quantum dot devices
Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre +4
Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexi…
Machine-learning enhanced dark soliton detection in Bose-Einstein condensates
Shangjie Guo, Amilson R. Fritsch, Craig Greenberg +2
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…