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Researcher
Tom Vieijra
- Keywords:many-body physics
- Disciplines:Quantum physics not elsewhere classified, Computational physics, Quantum information, computation and communication
Affiliations
- Department of Physics and astronomy (Department)
Member
From1 Sep 2018 → 25 Sep 2022
Projects
1 - 1 of 1
- Forging connections between machine learning techniques and strongly correlated physical systemsFrom1 Oct 2018 → 15 Sep 2022Funding: FWO fellowships
Publications
1 - 8 of 8
- Artificial neural networks and tensor networks in Variational Monte Carlo(2022)
Authors: Tom Vieijra
- Direct sampling of projected entangled-pair states(2021)
Authors: Tom Vieijra, Jutho Haegeman, Frank Verstraete, Laurens Vanderstraeten
- Optical lattice experiments at unobserved conditions with generative adversarial deep learning(2021)
Authors: Corneel Casert, Kyle Mills, Tom Vieijra, Jan Ryckebusch, Isaac Tamblyn
- Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz(2021)
Authors: Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn
- Many-body quantum states with exact conservation of non-Abelian and lattice symmetries through variational Monte Carlo(2021)
Authors: Tom Vieijra, Jannes Nys
- Restricted Boltzmann machines for quantum states with non-abelian or anyonic symmetries(2020)
Authors: Tom Vieijra, Corneel Casert, Jannes Nys, Wesley De Neve, Jutho Haegeman, Jan Ryckebusch, Frank Verstraete
- Isospin composition of the high-momentum fluctuations in nuclei from asymptotic momentum distributions(2019)
Authors: Jan Ryckebusch, Wim Cosyn, Tom Vieijra, Corneel Casert
- Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system(2019)
Authors: Corneel Casert, Tom Vieijra, Jannes Nys, Jan Ryckebusch