Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications

• URL: http://arxiv.org/abs/2607.02413v1
• Date: Thu, 02 Jul 2026 16:45:34 GMT
• Title: Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
• Abstract summary: We describe the quantum gas analysis and inference (Q-GAIN) Python package.<n>Q-GAIN enables rapid deployment of machine learning (ML) and physics-informed analysis techniques for cold-atom experiments.
• Score: 0.249833418215628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 modularity by configuring Q-GAIN for three ML tasks. First, we demonstrate the basic workflow of the Q-GAIN framework by implementing the standard task of classifying handwritten digits from the MNIST dataset. Then, we re-implement our earlier soliton detection (SolDet) package in the Q-GAIN framework, enabling the detection and analysis of solitonic excitations in time-of-flight data. Finally, we develop an object-detection tool that identifies quantized vortices in images of ring-shaped BECs.

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