相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Bridging the Gap between Deep Learning and Frustrated Quantum Spin System for Extreme-Scale Simulations on New Generation of Sunway Supercomputer
Mingfan Li et al.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS (2022)
Helping restricted Boltzmann machines with quantum-state representation by restoring symmetry
Yusuke Nomura
JOURNAL OF PHYSICS-CONDENSED MATTER (2021)
Broken-Symmetry Ground States of the Heisenberg Model on the Pyrochlore Lattice
Nikita Astrakhantsev et al.
PHYSICAL REVIEW X (2021)
Possible Inversion Symmetry Breaking in the S=1/2 Pyrochlore Heisenberg Magnet
Imre Hagymasi et al.
PHYSICAL REVIEW LETTERS (2021)
Hybrid convolutional neural network and projected entangled pair states wave functions for quantum many-particle states
Xiao Liang et al.
PHYSICAL REVIEW B (2021)
Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems
Or Sharir et al.
PHYSICAL REVIEW LETTERS (2020)
Restricted Boltzmann Machines for Quantum States with Non-Abelian or Anyonic Symmetries
Tom Vieijra et al.
PHYSICAL REVIEW LETTERS (2020)
Generalization properties of neural network approximations to frustrated magnet ground states
Tom Westerhout et al.
NATURE COMMUNICATIONS (2020)
Neural network wave functions and the sign problem
Attila Szabo et al.
PHYSICAL REVIEW RESEARCH (2020)
Deep learning-enhanced variational Monte Carlo method for quantum many-body physics
Li Yang et al.
PHYSICAL REVIEW RESEARCH (2020)
Quantum and Classical Phases of the Pyrochlore Heisenberg Model with Competing Interactions
Yasir Iqbal et al.
PHYSICAL REVIEW X (2019)
Two-dimensional frustrated J1-J2 model studied with neural network quantum states
Kenny Choo et al.
PHYSICAL REVIEW B (2019)
Neural Gutzwiller-projected variational wave functions
Francesco Ferrari et al.
PHYSICAL REVIEW B (2019)
Neural-network quantum state tomography
Giacomo Torlai et al.
NATURE PHYSICS (2018)
Approximating quantum many-body wave functions using artificial neural networks
Zi Cai et al.
PHYSICAL REVIEW B (2018)
Solving frustrated quantum many-particle models with convolutional neural networks
Xiao Liang et al.
PHYSICAL REVIEW B (2018)
Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo et al.
SCIENCE (2017)
Restricted Boltzmann machine learning for solving strongly correlated quantum systems
Yusuke Nomura et al.
PHYSICAL REVIEW B (2017)
Plaquette Ordered Phase and Quantum Phase Diagram in the Spin-1/2 J1-J2 Square Heisenberg Model
Shou-Shu Gong et al.
PHYSICAL REVIEW LETTERS (2014)
The density-matrix renormalization group in the age of matrix product states
Ulrich Schollwoeck
ANNALS OF PHYSICS (2011)
Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems
F. Verstraete et al.
ADVANCES IN PHYSICS (2008)
Variational Monte Carlo Method Combined with Quantum-Number Projection and Multi-Variable Optimization
Daisuke Tahara et al.
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (2008)
Weak binding between two aromatic rings: Feeling the van der Waals attraction by quantum Monte Carlo methods
Sandro Sorella et al.
JOURNAL OF CHEMICAL PHYSICS (2007)
Computational complexity and fundamental limitations to fermionic quantum monte carlo simulations
M Troyer et al.
PHYSICAL REVIEW LETTERS (2005)
分享论文
