4 papers
Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
Cole Miles, Annabelle Bohrdt, Ruihan Wu +7
Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states. Recen…
Classifying Snapshots of the Doped Hubbard Model with Machine Learning
Annabelle Bohrdt, Christie S. Chiu, Geoffrey Ji +6
Quantum gas microscopes for ultracold atoms can provide high-resolution real-space snapshots of complex many-body systems. We implement machine learning to analyze and classify suc…
String patterns in the doped Hubbard model
Christie S. Chiu, Geoffrey Ji, Annabelle Bohrdt +6
Understanding strongly correlated quantum many-body states is one of the most difficult challenges in modern physics. For example, there remain fundamental open questions on the ph…
Implementation of a stable, high-power optical lattice for quantum gas microscopy
A. Mazurenko, S. Blatt, F. Huber +5
We describe the design and implementation of a stable high-power 1064 nm laser system to generate optical lattices for experiments with ultracold quantum gases. The system is based…