1 citations · 1 across the 2 of their papers we have counts for
4 papers
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
Juan AgustÃn Duque, Sergio GarcÃa Heredia, Vinicius Hernandes +4
Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are espe…
Double descent: When do neural quantum states generalize?
M. Schuyler Moss, Alev Orfi, Christopher Roth +5
Neural quantum states (NQS) provide flexible and compact wavefunction parameterizations for numerical studies of quantum many-body physics. In particular, NQS aim to circumvent the…
Learning interactions between Rydberg atoms
Olivier Simard, Anna Dawid, Joseph Tindall +3
Quantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To…
Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters
Kacper CybiÅski, James Enouen, Antoine Georges +1
Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating…