5 papers
Exact Neural-Network Representations of the Motzkin States
Runde Zha, Yuntian Gu, Chaohui Fan +3
Motzkin spin chains are paradigmatic frustration-free one-dimensional quantum systems whose ground states feature exactly solvable combinatorial structures and exotic, area-law-vio…
Efficient classical simulation of two-dimensional long-range systems: Rydberg arrays and beyond
Jia-Lin Chan, Tao Xiang, Yantao Wu
In variational Monte Carlo (VMC) calculations of -site quantum systems with arbitrary all-to-all two-body interactions, evaluating the local energy generally costs . We…
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…
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…
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…