用机器学习提升冷原子实验图像处理，兼顾效果与可解释性。
Can machine learning for quantum-gas experiments be explainable?
- 通过机器学习对冷原子图像去噪并识别孤子波
- 模型复杂度越高，性能越强但可解释性下降
- 适合关注量子实验与AI结合的物理与计算研究者
多体原子物理几乎处处充满挑战：实验技术要求高，数据集规模庞大，而通用量子系统的经典模拟在系统规模增大时内存与计算量呈指数增长。机器学习已助力解决这些问题，并有望实现突破性进展。本文聚焦冷原子量子模拟器的两类具体应用：设备生成图像数据，首先展示原始图像去噪，随后识别玻色-爱因斯坦凝聚体中的孤子波。在这些案例中，我们探讨了性能、模型复杂度与可解释性之间的权衡关系。
原文摘要 · Abstract (English)
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.
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