Sparse autoencoders applied to Neural Quantum States extract unsupervised features correlating with and causally steering physical observables such as order parameters while preserving variational energy.
When can classical neural networks represent quantum states?
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
years
2026 5representative citing papers
WF-Bench is a new benchmark for neural network wavefunctions that matches them to diverse quantum many-body targets and derives empirical scaling laws for representability based on system size and model parameters like determinant count and depth.
Dilated RNN wave functions induce power-law correlations for the critical 1D transverse-field Ising model and the Cluster state, unlike the exponential decay of conventional RNN ansatze.
Variational optimization of quantum ground states represented as SIC-POVM outcome probabilities using GRU autoregressive networks, tested on 1D Ising and Heisenberg models up to L=128.
A review of how quantum information science is expected to provide new tools and insights for nuclear and high-energy physics phenomenology and quantum simulations.
citing papers explorer
No citing papers match the current filters.