Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning

Abstract

Machine learning provides a novel avenue for the study of experimental realizations of many-body systems, and has recently been proven successful in analyzing properties of experimental data of ultracold quantum gases. We here show that deep learning succeeds in the more challenging task of modelling such an experimental data distribution. Our generative model (RUGAN) is able to produce snapshots of a doped two-dimensional Fermi-Hubbard model that are indistinguishable from previously reported experimental realizations. Importantly, it is capable of accurately generating snapshots at conditions for which it did not observe any experimental data, such as at higher doping values. On top of that, our generative model extracts relevant patterns from small-scale examples and can use these to construct new configurations at a larger size that serve as a precursor to observations at scales that are currently experimentally inaccessible. The snapshots created by our model---which come at effectively no cost---are extremely useful as they can be employed to quantitatively test new theoretical developments under conditions that have not been explored experimentally, parameterize phenomenological models, or train other, more data-intensive, machine learning methods. We provide predictions for experimental observables at unobserved conditions and benchmark these against modern theoretical frameworks. The deep learning method we develop here is broadly applicable and can be used for the efficient large-scale simulation of equilibrium and nonequilibrium physical systems.

References in corpus (8)

• Many-Body Physics with Ultracold Gases
• Ultracold atomic gases in optical lattices: mimicking condensed matter physics and beyond
• Learning phase transitions by confusion
• Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory
• Transfer learning for scalability of neural-network quantum states
• Visualizing Strange Metallic Correlations in the 2D Fermi-Hubbard Model with AI
• Single-exposure absorption imaging of ultracold atoms using deep learning
• Scalable neural networks for the efficient learning of disordered quantum systems

Cited by in corpus (3)

• Machine Learning for Quantum Matter
• Unsupervised machine learning of topological phase transitions from experimental data
• Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data