Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator

2025-09-17

AI Summary

Addressing challenges in quantum simulator experiments—including strong measurement noise, sparse observables, and limited prior knowledge of microscopic models—this work introduces an unsupervised learning framework based on variational autoencoders (VAEs) to extract minimal latent representations of many-body systems from noisy interference-image snapshots. Our approach pioneers the use of generative modeling for robust, physically interpretable latent-variable extraction under experimental noise. Applied to one-dimensional Bose gases, it successfully resolves both equilibrium states and quench-induced nonequilibrium dynamics: it constructs a low-dimensional latent space strongly correlated with experimental control parameters, identifies soliton freezing, and uncovers anomalous dynamical behavior beyond conventional two-body correlations. This constitutes the first data-driven, interpretable machine learning probe specifically designed for sparse, noisy quantum many-body experiments—enabling model-agnostic physical insight without reliance on theoretical priors or dense measurement data.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Representation Learning for Vision
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• Extracting physical insights from noisy quantum simulator data
• Learning minimal latent representations from experimental measurements
• Uncovering non-equilibrium dynamics beyond conventional correlation methods

Innovation

• Variational autoencoder analyzes quantum simulator snapshots
• Learns minimal latent representation from unsupervised training
• Extracts interpretable variables from noisy experimental data