DOI: 10.1103/PhysRevX.8.011006

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• Title: Neural-Network Quantum States, String-Bond States, and Chiral Topological States
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• Summary (optional): Show some equivalence between some types of RBM and some special TNS states. Also using VMC with RBM ansatz to tackle a ground state of chiral spin liquid(SR optimization).
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Neural Network as Wavefunctions

RBM

such work is divided into two classes: one is to use RBM or DBM as wavefunction anstaz, and the other one is to construct counterpart by RBM analytically due to its structure suiting physics very much. For example, one can construct ground state of some exact model by RBM wavefunction, one can also show equivalence between various types of RBM and various types of TNS.

• Restricted Boltzmann Machines for Quantum States with Nonabelian or Anyonic Symmetries
• Neural network representation of tensor network and chiral states some exact construction
• Chiral topological phases from artificial neural networks
• Efficient Representation of Topologically Ordered States with Restricted Boltzmann Machines exact construct
• An Efficient Algorithmic Way to Construct Boltzmann Machine Representations for Arbitrary Stabilizer Code
• Constructing exact representations of quantum many-body systems with deep neural networks DBM
• Machine learning topological states
• Quantum Entanglement in Neural Network States
• Equivalence of restricted Boltzmann machines and tensor network states
• Symmetries and Many-Body Excitations with Neural-Network Quantum States both ANN and RBM, excited states, also an approach to utilize Abelian symmetry in general form supplement parallel tempering method in MCMC described in this supplemental material
• Restricted Boltzmann machine learning for solving strongly correlated quantum systems Appendix C1 SR method 终极炼丹术。。。
• Ground state phase diagram of the one-dimensional Bose-Hubbard model from restricted Boltzmann machines
• Multi-Layer Restricted Boltzmann Machine Representation of 1D Quantum Many-Body Wave Functions exact construction

Others

• Approximating quantum many-body wave-functions using artificial neural networks
• Solving the Bose–Hubbard Model with Machine Learning
• Deep Learning-Enhanced Variational Monte Carlo Method for Quantum Many-Body Physics
• Solving frustrated quantum many-particle models with convolutional neural networks
• Neural-network quantum states at finite temperature