3 papers
cond-mat.str-el2026
Pareto Frontier of Neural Quantum States: Scalable, Affordable, and Accurate Convolutional Backflow for Strongly Correlated Lattice Fermions
Yuntian Gu, Zeyao Han, Wenrui Li +5
Neural Quantum States (NQS) are now among the most accurate methods for studying strongly correlated many-fermion systems, outperforming existing many-body approaches for large sys…
cond-mat.str-el2026
Disentangling Tensor Network States with Deep Neural Network
Chaohui Fan, Bo Zhan, Yuntian Gu +5
We introduce Neural Tensor Network States (TNS), a variational many-body wave-function ansatz that integrates deep neural networks with tensor-network architectures. In the $ν…
cond-mat.str-el2025
Solving the Hubbard model with Neural Quantum States
Yuntian Gu, Wenrui Li, Heng Lin +9
The rapid development of neural quantum states (NQS) has established it as a promising framework for studying quantum many-body systems. In this work, by leveraging the cutting-edg…