Computational complexity of the Rydberg blockade in two dimensions. - Hannes Pichler; Sheng-Tao Wang; Leo Zhou; Soonwon Choi; Mikhail D. Lukin (CoRR, 2018)

Transfer synthetic over-sampling for class-imbalance learning with limited minority class data. - Xu-Ying Liu; Sheng-Tao Wang; Min-Ling Zhang (Frontiers Comput. Sci., 2019)

Enhancing Generative Models via Quantum Correlations. - Xun Gao; Eric R. Anschütz; Sheng-Tao Wang; J. Ignacio Cirac; Mikhail D. Lukin (CoRR, 2021)

Quantum optimization with arbitrary connectivity using Rydberg atom arrays. - Minh-Thi Nguyen; Jin-Guo Liu; Jonathan Wurtz; Mikhail D. Lukin; Sheng-Tao Wang; Hannes Pichler (CoRR, 2022)

Computing Solution Space Properties of Combinatorial Optimization Problems Via Generic Tensor Networks. - Jin-Guo Liu; Xun Gao; Madelyn Cain; Mikhail D. Lukin; Sheng-Tao Wang (SIAM J. Sci. Comput., 2023)

GNN-Based Performance Prediction of Quantum Optimization of Maximum Independent Set. - Atefeh Sohrabizadeh; Wan-Hsuan Lin; Daniel Bochen Tan; Madelyn Cain; Sheng-Tao Wang; Mikhail D. Lukin; Jason Cong (ICCAD, 2024)

Digital-analog quantum learning on Rydberg atom arrays. - Jonathan Z. Lu; Lucy Jiao; Kristina Wolinski; Milan Kornjaca; Hong-Ye Hu; Sergio Cantu; Fangli Liu; Susanne F. Yelin; Sheng-Tao Wang (CoRR, 2024)

Non-native Quantum Generative Optimization with Adversarial Autoencoders. - Blake Wilson; Jonathan Wurtz; Vahagn Mkhitaryan; Michael B. Wallace; Sheng-Tao Wang; Sabre Kais; Vladimir M. Shalaev; Alexandra Boltasseva (CoRR, 2024)

Resource Analysis of Low-Overhead Transversal Architectures for Reconfigurable Atom Arrays. - Hengyun Zhou; Casey Duckering; Chen Zhao; Dolev Bluvstein; Madelyn Cain; Aleksander Kubica; Sheng-Tao Wang; Mikhail D. Lukin (ISCA, 2025)