11 citations · 21 across the 11 of their papers we have counts for
5 papers · 1 filter
Deep Learning Based Superconductivity: Prediction and Experimental Tests
Daniel Kaplan, Adam Zhang, Joanna Blawat +6
The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and comput…
Learning interactions between Rydberg atoms
Olivier Simard, Anna Dawid, Joseph Tindall +3
Quantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To…
Fast Scrambling at the Boundary
Ancel Larzul, Anirvan M. Sengupta, Antoine Georges +1
Many-body systems which saturate the quantum bound on chaos are attracting interest across a wide range of fields. Notable examples include the Sachdev-Ye-Kitaev model and its vari…
Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function
Jiawei Zang, Matija Medvidović, Dominik Kiese +3
Characterizing complex many-body phases of matter has been a central question in quantum physics for decades. Numerical methods built around approximations of the renormalization g…
Compressing the two-particle Green's function using wavelets: Theory and application to the Hubbard atom
Emin Moghadas, Nikolaus Dräger, Alessandro Toschi +7
Precise algorithms capable of providing controlled solutions in the presence of strong interactions are transforming the landscape of quantum many-body physics. Particularly exciti…