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The Q-GAIN Python package facilitates the integration of machine learning and physics-informed analysis for cold-atom experiments, specifically targeting tasks such as classification and object detection. By providing a modular workflow, Q-GAIN streamlines processes from data loading to feature identification and conventional analysis, enhancing the efficiency of research in quantum gas studies. Key results include successful implementations of handwritten digit classification, soliton detection, and quantized vortex identification, showcasing the package's versatility and effectiveness in analyzing complex physical phenomena.
Q-GAIN transforms cold-atom research by seamlessly combining machine learning with physics-informed analysis, enabling rapid and accurate feature detection in complex datasets.
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.
