3 citations · 4 across the 5 of their papers we have counts for
6 papers
TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
Kacper Cybiński, Björn van Zwol, James Enouen +5
Detecting phases of matter in general relies on identifying the correct order parameter - a task that remains notoriously difficult for unknown transitions and traditionally is gui…
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
Juan Agustín Duque, Sergio García 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 Cybiński, 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…
Deconstructing the Goldilocks Zone of Neural Network Initialization
Artem Vysogorets, Anna Dawid, Julia Kempe
The second-order properties of the training loss have a massive impact on the optimization dynamics of deep learning models. Fort & Scherlis (2019) discovered that a large excess o…