Fermionic neural-network states for ab-initio electronic structure

[1]  Yichen Huang,et al.  Neural Network Representation of Tensor Network and Chiral States. , 2017, Physical review letters.

[2]  F. Noé,et al.  Deep-neural-network solution of the electronic Schrödinger equation , 2019, Nature Chemistry.

[3]  Shengyu Zhang,et al.  Unitary-coupled restricted Boltzmann machine ansatz for quantum simulations , 2019, npj Quantum Information.

[4]  David Pfau,et al.  Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks , 2019, Physical Review Research.

[5]  J. Cirac,et al.  Restricted Boltzmann machines in quantum physics , 2019, Nature Physics.

[6]  J. Carrasquilla,et al.  Neural Gutzwiller-projected variational wave functions , 2019, Physical Review B.

[7]  Guglielmo Mazzola,et al.  NetKet: A machine learning toolkit for many-body quantum systems , 2019, SoftwareX.

[8]  J. Mentink,et al.  Investigating ultrafast quantum magnetism with machine learning , 2019, SciPost Physics.

[9]  N. Regnault,et al.  Variational Neural-Network Ansatz for Steady States in Open Quantum Systems. , 2019, Physical review letters.

[10]  Alexandra Nagy,et al.  Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems. , 2019, Physical review letters.

[11]  Nobuyuki Yoshioka,et al.  Constructing neural stationary states for open quantum many-body systems , 2019, Physical Review B.

[12]  Michael J. Hartmann,et al.  Neural-Network Approach to Dissipative Quantum Many-Body Dynamics. , 2019, Physical review letters.

[13]  James D. Whitfield,et al.  Superfast encodings for fermionic quantum simulation , 2018, Physical Review Research.

[14]  Bryan K. Clark,et al.  Backflow Transformations via Neural Networks for Quantum Many-Body Wave Functions. , 2018, Physical review letters.

[15]  E Weinan,et al.  Solving many-electron Schrödinger equation using deep neural networks , 2018, J. Comput. Phys..

[16]  Amnon Shashua,et al.  Quantum Entanglement in Deep Learning Architectures. , 2018, Physical review letters.

[17]  G. Carleo,et al.  Symmetries and Many-Body Excitations with Neural-Network Quantum States. , 2018, Physical review letters.

[18]  Sabre Kais,et al.  Quantum machine learning for electronic structure calculations , 2018, Nature Communications.

[19]  T. Gasenzer,et al.  Quenches near Ising quantum criticality as a challenge for artificial neural networks , 2018, Physical Review B.

[20]  Markus Holzmann,et al.  Nonlinear Network Description for Many-Body Quantum Systems in Continuous Space. , 2017, Physical review letters.

[21]  Sandeep Sharma,et al.  PySCF: the Python‐based simulations of chemistry framework , 2017, 1701.08223.

[22]  J. Chen,et al.  Equivalence of restricted Boltzmann machines and tensor network states , 2017, 1701.04831.

[23]  Sandeep Sharma,et al.  PySCF: the Python‐based simulations of chemistry framework , 2018 .

[24]  Andrew S. Darmawan,et al.  Restricted Boltzmann machine learning for solving strongly correlated quantum systems , 2017, 1709.06475.

[25]  J. Gambetta,et al.  Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.

[26]  D. Deng,et al.  Quantum Entanglement in Neural Network States , 2017, 1701.04844.

[27]  Matthias Troyer,et al.  Solving the quantum many-body problem with artificial neural networks , 2016, Science.

[28]  Sarah E. Sofia,et al.  The Bravyi-Kitaev transformation: Properties and applications , 2015 .

[29]  P. Love,et al.  The Bravyi-Kitaev transformation for quantum computation of electronic structure. , 2012, The Journal of chemical physics.

[30]  W. Marsden I and J , 2012 .

[31]  Sandeep Sharma,et al.  The density matrix renormalization group in quantum chemistry. , 2011, Annual review of physical chemistry.

[32]  Sandro Sorella,et al.  Role of backflow correlations for the nonmagnetic phase of the t-t(') Hubbard model , 2008, 0805.1476.

[33]  D. Rocca,et al.  Weak binding between two aromatic rings: feeling the van der Waals attraction by quantum Monte Carlo methods. , 2007, The Journal of chemical physics.

[34]  M. Casula,et al.  Geminal wave functions with Jastrow correlation: A first application to atoms , 2003, cond-mat/0305169.

[35]  Shiwei Zhang,et al.  Quantum Monte Carlo method using phase-free random walks with slater determinants. , 2002, Physical review letters.

[36]  A. Kitaev,et al.  Fermionic Quantum Computation , 2000, quant-ph/0003137.

[37]  S. White,et al.  Ab initio quantum chemistry using the density matrix renormalization group , 1998, cond-mat/9808118.

[38]  S. Sorella GREEN FUNCTION MONTE CARLO WITH STOCHASTIC RECONFIGURATION , 1998, cond-mat/9803107.

[39]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[40]  V. Kisil Properties and Applications , 1994 .

[41]  White,et al.  Density matrix formulation for quantum renormalization groups. , 1992, Physical review letters.

[42]  Physical Review Letters 63 , 1989 .

[43]  James B. Anderson,et al.  A random‐walk simulation of the Schrödinger equation: H+3 , 1975 .

[44]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[45]  J. Cizek On the Correlation Problem in Atomic and Molecular Systems. Calculation of Wavefunction Components in Ursell-Type Expansion Using Quantum-Field Theoretical Methods , 1966 .

[46]  F. Coester,et al.  Short-range correlations in nuclear wave functions , 1960 .

[47]  Richard Phillips Feynman,et al.  Energy Spectrum of the Excitations in Liquid Helium , 1956 .

[48]  R. Jastrow Many-Body Problem with Strong Forces , 1955 .

[49]  E. Wigner,et al.  Über das Paulische Äquivalenzverbot , 1928 .

[50]  Journal of Chemical Physics , 1932, Nature.