104 citations · 277 across the 8 of their papers we have counts for
8 papers
Generative modeling with projected entangled-pair states
Tom Vieijra, Laurens Vanderstraeten, Frank Verstraete
We argue and demonstrate that projected entangled-pair states (PEPS) outperform matrix product states significantly for the task of generative modeling of datasets with an intrinsi…
Direct sampling of projected entangled-pair states
Tom Vieijra, Jutho Haegeman, Frank Verstraete +1
Variational Monte Carlo studies employing projected entangled-pair states (PEPS) have recently shown that they can provide answers on long-standing questions such as the nature of…
Many-Body Quantum States with Exact Conservation of Non-Abelian and Lattice Symmetries through Variational Monte Carlo
Tom Vieijra, Jannes Nys
Optimization of quantum states using the variational principle has recently seen an upsurge due to developments of increasingly expressive wave functions. In order to improve on th…
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz
Corneel Casert, Tom Vieijra, Stephen Whitelam +1
We use a neural network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We obtain the scaled c…
Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning
Corneel Casert, Kyle Mills, Tom Vieijra +2
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 experim…
Isospin composition of the high-momentum fluctuations in nuclei from asymptotic momentum distributions
Jan Ryckebusch, Wim Cosyn, Tom Vieijra +1
The variations of short-range correlations (SRC) across nuclei can be quantified in an approximately model-independent fashion in terms of the so-called SRC scaling factors. We pro…
Restricted Boltzmann Machines for Quantum States with Nonabelian or Anyonic Symmetries
Tom Vieijra, Corneel Casert, Jannes Nys +4
Although artificial neural networks have recently been proven to provide a promising new framework for constructing quantum many-body wave functions, the parameterization of a quan…
Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system
C. Casert, T. Vieijra, J. Nys +1
Still under debate is the question of whether machine learning is capable of going beyond black-box modeling for complex physical systems. We investigate the generalizing and inter…