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

Condensed Matter > Quantum Gases

arXiv:2607.02413v1 (cond-mat) [Submitted on 2 Jul 2026]

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

Authors:M. Doris, S. Guo, S. M. Koh, L. Ritter, A. R. Fritsch, S. Mukherjee, I. B. Spielman, J. P. Zwolak View a PDF of the paper titled Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications, by M. Doris and 7 other authors View PDF HTML (experimental)

Abstract: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.

Comments:	Submission to SciPost, 20 pages with 4 figures
Subjects:	Quantum Gases (cond-mat.quant-gas); Machine Learning (cs.LG)
Cite as:	arXiv:2607.02413 [cond-mat.quant-gas]
	(or arXiv:2607.02413v1 [cond-mat.quant-gas] for this version)
	https://doi.org/10.48550/arXiv.2607.02413 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Michael Doris [view email]
[v1] Thu, 2 Jul 2026 16:45:34 UTC (306 KB)
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