Q-GAIN帮你快速上手冷原子实验的机器学习分析，能直接检测孤子和量子涡旋，比从头写省事多了。
Q-GAIN是一个专为冷原子实验设计的Python包，集成了分类、目标检测和物理信息度量功能，可直接分析玻色-爱因斯坦凝聚（BEC）图像。通过模块化工作流，用户可依次完成数据加载、ML特征识别和传统分析。演示任务包括：MNIST手写数字分类（标准基准）、基于SolDet的孤子检测（时间飞行数据），以及识别环形BEC中的量子涡旋。
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 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.