Q-GAIN让冷原子实验的机器学习分析更简单，支持图像分类、物体检测和物理约束分析。
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
- 模块化设计：从数据加载到机器学习识别再到传统分析，流程清晰。
- 可直接用于冷原子图像分析，实现手写数字分类、孤子与涡旋检测。
- 适合从事冷原子实验与机器学习交叉研究的科研人员使用。
本文介绍量子气体分析与推断（Q-GAIN）Python工具包，支持冷原子实验中快速部署机器学习（ML）与物理信息分析技术。Q-GAIN开箱即用，提供玻色-爱因斯坦凝聚体（BEC）图像中的特征检测分类、目标检测及物理信息度量方法。该工具包倡导模块化工作流：从数据加载与预处理，到基于机器学习的特征识别，最终衔接传统分析手段。我们通过三个任务展示其灵活性：首先在MNIST数据集上实现手写数字分类；其次将此前的孤子检测（SolDet）工具重构至Q-GAIN框架，实现时间飞行图像中孤子激发的检测与分析；最后开发出一种目标检测工具，用于识别环形BEC图像中的量子涡旋。
原文摘要 · Abstract (English)
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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