2 papers
cond-mat.str-el2024
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…
cond-mat.str-el2024
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…