{"ID":5676796,"CreatedAt":"2026-07-03T03:29:23.032456456Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.02413","arxiv_id":"2607.02413","title":"Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications","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.","short_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 detect...","url_abs":"https://arxiv.org/abs/2607.02413","url_pdf":"https://arxiv.org/pdf/2607.02413v1","authors":"[\"M. Doris\",\"S. Guo\",\"S. M. Koh\",\"L. Ritter\",\"A. R. Fritsch\",\"S. Mukherjee\",\"I. B. Spielman\",\"J. P. Zwolak\"]","published":"2026-07-02T16:45:34Z","proceeding":"cond-mat.quant-gas","tasks":"[\"cond-mat.quant-gas\",\"cs.LG\"]","methods":"[]","has_code":false}