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Deep learning methods have been shown to be effective in representing ground-state wave functions of quantum many-body systems.
Ground state of liquid he 4
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Phase diagram of a frustrated quantum antiferromagnet on the honeycomb lattice: Magnetic order versus valence-bond crystal formation
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Jacob Biamonte and Ville Bergholm · 2017
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Giuseppe Carleo and Matthias Troyer · 2017
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Quantum entanglement in neural network states
Dong-Ling Deng, Xiaopeng Li, and S Das Sarma · 2017
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Efficient representation of quantum many-body states with deep neural networks
Xun Gao and Lu-Ming Duan · 2017
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Yusuke Nomura, Andrew S Darmawan, Youhei Yamaji, and Masatoshi Imada · 2017
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Hitesh J Changlani, Dmitrii Kochkov, Krishna Kumar, Bryan K Clark, and Eduardo Fradkin · 2018
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Quantum entanglement in deep learning architectures
Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua · 2019
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Backflow transformations via neural networks for quantum many-body wave functions
Di Luo and Bryan K Clark · 2019
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Lattice convolutional neural network modeling of adsorbate coverage effects
Jonathan Lym, Geun Ho Gu, Yousung Jung, and Dionisios G Vlachos · 2019
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Generalized transfer matrix states from artificial neural networks
Lorenzo Pastori, Raphael Kaubruegger, and Jan Carl Budich · 2019
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Pytorch: An imperative style, high-performance deep learning library
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Ivan Glasser, Nicola Pancotti, Moritz August, Ivan D Rodriguez, and J Ignacio Cirac · 2018
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Dirac-type nodal spin liquid revealed by machine learning
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Iterative retraining of quantum spin models using recurrent neural networks
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Deep autoregressive models for the efficient variational simulation of many-body quantum systems
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Hybrid convolutional neural network and projected entangled pair states wave functions for quantum many-particle states
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Group convolutional neural networks improve quantum state accuracy
Christopher Roth and Allan H MacDonald · 2021
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Han Zheng, Zimu Li, Junyu Liu, Sergii Strelchuk, and Risi Kondor · 2021
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Subdivision-based mesh convolution networks
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