7 papers
Clifford disentanglers for entanglement reduction in molecular electronic structure simulations
Longfei Chang, Zibo Wu, Yunzhi Li +5
Entanglement is a key bottleneck limiting the efficiency of tensor-network and quantum simulations of molecular electronic structures. Here, we systematically assess and extend Cli…
Spin-adapted neural network backflow for symmetry-preserving simulations of strongly correlated electrons
Yunzhi Li, Zibo Wu, Bohan Zhang +2
Strongly correlated molecules often contain dense manifolds of low-lying spin states, making total-spin symmetry essential for predictive electronic-structure theory. Neural-networ…
A unified diagrammatic formulation of single-reference and multi-reference random phase approximations: the particle-hole and particle-particle channels
Yuqi Wang, Wei-Hai Fang, Zhendong Li
A diagrammatic multi-reference generalization of many-body perturbation theory was recently introduced [J. Phys. Chem. Lett., 2025, 16, 3047]. This framework allows us to extend si…
Hybrid tensor network and neural network quantum states for quantum chemistry
Zibo Wu, Bohan Zhang, Wei-Hai Fang +1
Neural network quantum states (NQS) have emerged as a powerful and flexible framework for addressing quantum many-body problems. While successful for model Hamiltonians, their appl…
Quantum-assisted variational Monte Carlo
Longfei Chang, Zhendong Li, Wei-Hai Fang
Solving the ground state of quantum many-body systems remains a fundamental challenge in physics and chemistry. Recent advancements in quantum hardware have opened new avenues for…
Generalized many-body perturbation theory for the electron correlation energy: multi-reference random phase approximation via diagrammatic resummation
Yuqi Wang, Wei-Hai Fang, Zhendong Li
Many-body perturbation theory (MBPT) based on Green's functions and Feynman diagrams provides a fundamental theoretical framework for various \emph{ab initio} computational approac…