115 citations · 791 across the 57 of their papers we have counts for
57 papers
An Application of Quantum Machine Learning on Quantum Correlated Systems: Quantum Convolutional Neural Network as a Classifier for Many-Body Wavefunctions from the Quantum Variational Eigensolver
Nathaniel Wrobel, Anshumitra Baul, Juana Moreno +1
Machine learning has been applied on a wide variety of models, from classical statistical mechanics to quantum strongly correlated systems for the identification of phase transitio…
Nonequilibrium DMFT+CPA for Correlated Disordered Systems
Eric Dohner, Hanna Terletska, Ka-Ming Tam +2
We present a solution for the nonequilibrium dynamics of an interacting disordered system. The approach adapts the combination of the equilibrium dynamical mean field theory (DMFT)…
Application of the Variational Autoencoder to Detect the Critical Points of the Anisotropic Ising Model
Anshumitra Baul, Nicholas Walker, Juana Moreno +1
We generalize the previous study on the application of variational autoencoders to the two-dimensional Ising model to a system with anisotropy. Due to the self-duality property of…
Real Space Quantum Cluster Formulation for the Typical Medium Theory of Anderson Localization
Ka-Ming Tam, Hanna Terletska, Tom Berlijn +2
We develop a real space cluster extension of the typical medium theory (cluster-TMT) to study Anderson localization. By construction, the cluster-TMT approach is formally equivalen…
Non-local corrections to the typical medium theory of Anderson localization
H. Terletska, A. Moilanen, K. -M. Tam +6
We use the recently developed finite cluster typical medium approach to study the Anderson localization transition in three dimensions. Applying our method to the box and binary al…
Beyond Quantum Cluster Theories: Multiscale Approaches for Strongly Correlated Systems
Herbert F Fotso, Ka-Ming Tam, Juana Moreno
The degrees of freedom that confer to strongly correlated systems their many intriguing properties also render them fairly intractable through typical perturbative treatments. For…