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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- PossiblePossibly related (embedding) · 46%FareedKhan-dev/agentic-quantum-computing →
- LinkedLinked via arxiv author · 85%M. Doris →
“Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications”
- LinkedLinked via arxiv author · 85%S. Guo →
“Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications”
- LinkedLinked via arxiv author · 85%S. M. Koh →
“Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications”
- LinkedLinked via arxiv author · 85%L. Ritter →
“Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications”
- LinkedLinked via arxiv author · 85%A. R. Fritsch →
“Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications”
- LinkedLinked via arxiv author · 85%S. Mukherjee →
“Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications”
- LinkedLinked via arxiv author · 85%I. B. Spielman →
“Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications”