---
title: "Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.02413v1"
permalink: "https://feed.astralor.com/items/itm_2dad98e3d1c09a144020342d"
published: "2026-07-02T16:45:34.000Z"
score: 56
channel: "tech"
tags: ["Python","Q-GAIN","Cold Atom","Machine Learning","Physics-informed","Bose-Einstein Condensate"]
---

# Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications

## At a glance

Q-GAIN is a Python package designed for cold-atom experiments, integrating machine learning and physics-informed analysis techniques. It supports classification, object detection, and physics-informed metrics for feature detection, especially in images of atomic Bose-Einstein condensates (BECs). The package encourages a modular workflow from data loading and preprocessing to ML-based feature identification and conventional analysis. The paper demonstrates its functionality through three tasks: MNIST classification, soliton detection (SolDet), and quantized vortex detection in ring-shaped BECs.

## Key points

- Q-GAIN implements classification, object detection, and physics-informed feature detection for cold-atom experiments.
- Modular workflow: data loading → ML-based feature identification → conventional analysis.
- Validated through three case studies: MNIST classification, soliton detection, and vortex detection.
- Refactors the existing SolDet package and extends new capabilities.

## Full text

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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