# Can machine learning for quantum-gas experiments be explainable?

arXiv: https://arxiv.org/abs/2605.18689 · HTML: https://mlsanity.com/paper/2605.18689/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [Machine-learning enhanced dark soliton detection in Bose-Einstein condensates](https://mlsanity.com/paper/2101.05404/) (2021)
- [Single-exposure absorption imaging of ultracold atoms using deep learning](https://mlsanity.com/paper/2003.01643/) (2020)
- [Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research](https://mlsanity.com/paper/2205.09114/) (2022)

## Abstract (arXiv)

Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for classical simulation of generic quantum systems often scale exponentially with system size. Machine learning (ML) methods are already assisting in each of these areas and are poised to become transformative. Here, we focus on two specific applications of ML to cold-atom-based quantum simulators. These devices generally generate data in the form of images; we first showcase denoising of raw images and then identify solitonic waves in Bose-Einstein condensates. In both of these examples, we comment on the interplay between performance, model complexity, and interpretability.