Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
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 for cold-atom experiments. Out of the box, Q-GAIN implements classification, object detection, and physics-informed metrics for feature detection in images of atomic Bose-Einstein condensates (BECs). Q-GAIN encourages a natural, module-based workflow: starting with data loading and preprocessing, followed by ML-based feature identification, and ending with conventional analysis techniques. We demonstrate this modularit
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- Linked via arxiv authorM. Doris →
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
- Linked via arxiv authorS. Guo →
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
- Linked via arxiv authorS. M. Koh →
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
- Linked via arxiv authorL. Ritter →
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
- Linked via arxiv authorA. R. Fritsch →
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
- Linked via arxiv authorS. Mukherjee →
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
- Linked via arxiv authorI. B. Spielman →
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
- Linked via arxiv authorJ. P. Zwolak →
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications