cond-mat.quant-gas · 2026-07-04 · No. 43

Quantum Gases, 2026-07-04.

1 new papers in cond-mat.quant-gas. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

1 entries
  1. 01

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

    M. Doris, S. Guo, S. M. Koh, L. Ritter, A. R. Fritsch, S. Mukherjee, I. B. Spielman, J. P. Zwolak

    cond-mat.quant-gas · cs.LG

    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...

    arxiv.org/abs/2607.02413 · PDF

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