5 citations · 9 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
Feedback Induced Magnetic Phases in Binary Bose-Einstein Condensates
Hilary M. Hurst, Shangjie Guo, I. B. Spielman
Weak measurement in tandem with real-time feedback control is a new route toward engineering novel non-equilibrium quantum matter. Here we develop a theoretical toolbox for quantum…