Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
For experimental physicists working with cold-atom systems, Q-GAIN provides a modular, out-of-the-box toolkit to integrate ML into their analysis pipeline, though it is an incremental tool rather than a novel method.
Q-GAIN is a Python package that enables rapid deployment of machine learning and physics-informed analysis for cold-atom experiments, demonstrating classification, object detection, and physics-informed metrics for feature detection in Bose-Einstein condensate images. It was tested on MNIST digit classification, soliton detection, and vortex identification in ring-shaped BECs.
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.