Retentive neural quantum states: efficient ansätze for ab initio quantum chemistry

Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum problems. A significant advancement toward improving the...

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書誌詳細

出版年:	Machine Learning: Science and Technology
主要な著者:	Oliver Knitter, Dan Zhao, James Stokes, Martin Ganahl, Stefan Leichenauer, Shravan Veerapaneni
フォーマット:	論文
言語:	英語
出版事項:	IOP Publishing 2025-01-01
主題:	machine learning quantum chemistry neural quantum states AI for science RetNets variational neural annealing
オンライン･アクセス:	https://doi.org/10.1088/2632-2153/adcb88