用自编码器从量子模拟器数据中提取物理核心变量。
Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator
- 用无监督变分自编码器学习系统最小隐空间表示。
- 隐空间能准确反映系统平衡控制参数，揭示冻结孤子等非平衡现象。
- 适合研究噪声大、观测少的量子多体系统，助力数据驱动发现。
模拟量子处理器可实现经典计算无法企及的多体动力学。然而，实验数据中的测量噪声、可观测量有限以及对微观模型认知不全，常阻碍物理洞见的提取。本文提出一种基于变分自编码器（VAE）的机器学习方法，分析隧穿耦合一维玻色气体的干涉测量数据，其对应正弦-戈登量子场论。该模型在无监督条件下训练，学习到一个与系统平衡控制参数强相关的最小隐空间表示。应用于非平衡过程时，隐空间揭示了快速冷却后冻结的孤子特征，以及传统关联方法无法捕捉的异常淬火后动力学。结果表明，生成模型可直接从噪声大、稀疏的实验数据中提取具有物理解释的变量，为量子模拟器中的平衡与非平衡物理提供互补探测手段。更广泛地，本工作凸显了机器学习对经典场论方法的补充作用，为量子多体系统的可扩展、数据驱动发现开辟了新路径。
原文摘要 · Abstract (English)
Analog quantum simulators provide access to many-body dynamics beyond the reach of classical computation. However, extracting physical insights from experimental data is often hindered by measurement noise, limited observables, and incomplete knowledge of the underlying microscopic model. Here, we develop a machine learning approach based on a variational autoencoder (VAE) to analyze interference measurements of tunnel-coupled one-dimensional Bose gases, which realize the sine-Gordon quantum field theory. Trained in an unsupervised manner, the VAE learns a minimal latent representation that strongly correlates with the equilibrium control parameter of the system. Applied to non-equilibrium protocols, the latent space uncovers signatures of frozen-in solitons following rapid cooling, and reveals anomalous post-quench dynamics not captured by conventional correlation-based methods. These results demonstrate that generative models can extract physically interpretable variables directly from noisy and sparse experimental data, providing complementary probes of equilibrium and non-equilibrium physics in quantum simulators. More broadly, our work highlights how machine learning can supplement established field-theoretical techniques, paving the way for scalable, data-driven discovery in quantum many-body systems.
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