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Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems.
Zur quantentheorie der molekeln
Max Born and Robert Oppenheimer · 1927
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Short-range correlations in nuclear wave functions
Fritz Coester and Hermann Kümmel · 1960
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über das paulische äquivalenzverbot
Pascual Jordan and Eugene Paul Wigner · 1993
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Monte Carlo methods in ab initio quantum chemistry
Brian L Hammond, William A Lester, and Peter James Reynolds · 1994
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Advances in quantum chemistry
C David Sherrill and HF Schaefer III · 1999
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Modeling high-dimensional discrete data with multi-layer neural networks
Yoshua Bengio and Samy Bengio · 2000
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Fermionic quantum computation
Sergey B Bravyi and Alexei Yu Kitaev · 2002
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Computational complexity and fundamental limitations to fermionic quantum monte carlo simulations
Matthias Troyer and Uwe-Jens Wiese · 2005
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Coupled-cluster theory in quantum chemistry
Rodney J Bartlett and Monika Musiał · 2007
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On the quantitative analysis of deep belief networks
Ruslan Salakhutdinov and Iain Murray · 2008
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Pubchem: a public information system for analyzing bioactivities of small molecules
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The neural autoregressive distribution estimator
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S Langhoff · 2012
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Made: Masked autoencoder for distribution estimation
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Adam: A method for stochastic optimization
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J Dally · 2017
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Fermionic neural-network states for ab-initio electronic structure
Kenny Choo, Antonio Mezzacapo, and Giuseppe Carleo · 2020
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Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, and Amnon Shashua · 2020
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Flowket: an open-source library based on tensorflow for running variational monte-carlo simulations on gpus
Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, and Amnon Shashua · 2020
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Recurrent neural network wave functions
Mohamed Hibat-Allah, Martin Ganahl, Lauren E Hayward, Roger G Melko, and Juan Carrasquilla · 2020
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Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, koray kavukcuoglu, Oriol Vinyals, and Alex Graves · 2016
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Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo and Matthias Troyer · 2017
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Openfermion: the electronic structure package for quantum computers
Jarrod R McClean, Nicholas C Rubin, Kevin J Sung, Ian D Kivlichan, Xavier Bonet-Monroig, Yudong Cao, Chengyu Dai, E Schuyler Fried, Craig Gidney, Brendan Gimby, et al · 2020
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Overcoming barriers to scalability in variational quantum monte carlo
Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, and Shravan Veerapaneni · 2021
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Autoregressive neural-network wavefunctions for ab initio quantum chemistry
Thomas D Barrett, Aleksei Malyshev, and AI Lvovsky · 2022
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