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…
physics.chem-ph2025
NNQS-AFQMC: Neural network quantum states enhanced fermionic quantum Monte Carlo
Zhi-Yu Xiao, Bowen Kan, Huan Ma +2
We introduce an efficient approach to implement neural network quantum states (NNQS) as trial wavefunctions in auxiliary-field quantum Monte Carlo (AFQMC). NNQS are a recently deve…
cond-mat.str-el2025
Implementing advanced trial wave functions in fermion quantum Monte Carlo via stochastic sampling
Zhi-Yu Xiao, Zixiang Lu, Yixiao Chen +2
We introduce an efficient approach to implement correlated many-body trial wave functions in auxiliary-field quantum Monte Carlo (AFQMC). To control the sign/phase problem in AFQMC…