Quantum dynamics in driven spin systems with neural-network quantum states
ORAL
Abstract
Neural-network quantum states (NQS) provide an effective variational representation of quantum states, which can be used for the study of many-body quantum systems [1]. NQS can be time-propagated using time-dependent variational Monte Carlo (tVMC) [1,2], making it possible to simulate non-equilibrium phenomena. In particular, this approach can be used to compute dynamical properties of two-dimensional spin systems [3], a setting that has proven to be challenging for established numerical techniques. In this talk, we study magnetic excitations in a driven two-dimensional Heisenberg antiferromagnet. Further, we provide benchmarks of time-dependent NQS against results obtained from exact calculations for small systems as well as results obtained using a time-dependent matrix product state (t-MPS) approach.
[1] Carleo and Troyer. Science 355, 602 (2017).
[2] Carleo, Becca, Schiró, Fabrizio. Sci. Rep. 2, 243 (2012).
[3] Fabiani and Mentink. SciPost Phys. 7, 004 (2019).
[1] Carleo and Troyer. Science 355, 602 (2017).
[2] Carleo, Becca, Schiró, Fabrizio. Sci. Rep. 2, 243 (2012).
[3] Fabiani and Mentink. SciPost Phys. 7, 004 (2019).
*We acknowledge support from Flatiron Institute, a division of the Simons Foundation.
M.S. acknowledges funding from the DFG through the Emmy Noether program (SE 2558/2-1).
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Presenters
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Damian Hofmann
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany