NNQS-Transformer: An Efficient and Scalable Neural Network Quantum States Approach for Ab Initio Quantum Chemistry

Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We develop a high-performance NNQS method for ab initio electronic structure calculati...

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Vydané v:	International Conference for High Performance Computing, Networking, Storage and Analysis (Online) s. 1 - 14
Hlavní autori:	Wu, Yangjun, Guo, Chu, Fan, Yi, Zhou, Pengyu, Shang, Honghui
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	ACM 11.11.2023
Predmet:	Artificial neural networks autoregressive sampling Costs Graphics processing units High performance computing many-body Schrödinger equation neural network quantum state Quantum chemistry Scalability Technological innovation transformer based architecture
ISSN:	2167-4337
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