APA (7th ed.) Citation

Wu, Y., Guo, C., Fan, Y., Zhou, P., & Shang, H. (2023, November 11). NNQS-Transformer: An Efficient and Scalable Neural Network Quantum States Approach for Ab Initio Quantum Chemistry. International Conference for High Performance Computing, Networking, Storage and Analysis (Online), 1-14. https://doi.org/10.1145/3581784.3607061

Chicago Style (17th ed.) Citation

Wu, Yangjun, Chu Guo, Yi Fan, Pengyu Zhou, and Honghui Shang. "NNQS-Transformer: An Efficient and Scalable Neural Network Quantum States Approach for Ab Initio Quantum Chemistry." International Conference for High Performance Computing, Networking, Storage and Analysis (Online) 11 Nov. 2023: 1-14. https://doi.org/10.1145/3581784.3607061.

MLA (9th ed.) Citation

Wu, Yangjun, et al. "NNQS-Transformer: An Efficient and Scalable Neural Network Quantum States Approach for Ab Initio Quantum Chemistry." International Conference for High Performance Computing, Networking, Storage and Analysis (Online), 11 Nov. 2023, pp. 1-14, https://doi.org/10.1145/3581784.3607061.