147 citations · 346 across the 6 of their papers we have counts for
6 papers
Can neural quantum states learn volume-law ground states?
Giacomo Passetti, Damian Hofmann, Pit Neitemeier +3
We study whether neural quantum states based on multi-layer feed-forward networks can find ground states which exhibit volume-law entanglement entropy. As a testbed, we employ the…
Witnessing nonequilibrium entanglement dynamics in a strongly correlated fermionic chain
Denitsa R. Baykusheva, Mona H. Kalthoff, Damian Hofmann +4
Many-body entanglement in condensed matter systems can be diagnosed from equilibrium response functions through the use of entanglement witnesses and operator-specific quantum boun…
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
Filippo Vicentini, Damian Hofmann, Attila Szabó +8
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms…
Role of stochastic noise and generalization error in the time propagation of neural-network quantum states
Damian Hofmann, Giammarco Fabiani, Johan H. Mentink +2
Neural-network quantum states (NQS) have been shown to be a suitable variational ansatz to simulate out-of-equilibrium dynamics in two-dimensional systems using time-dependent vari…
Resonant laser excitation and time-domain imaging of chiral topological polariton edge states
Damian Hofmann, Michael A. Sentef
We investigate the dynamics of chiral edge states in topological polariton systems under laser driving. Using a model system comprised of topolgically trivial excitons and photons…
NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
Giuseppe Carleo, Kenny Choo, Damian Hofmann +15
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and…