Path integral calculations of vacancies in solid Helium. - Bryan K. Clark; David M. Ceperley (Comput. Phys. Commun., 2008)

Improved Scaling for Quantum Monte Carlo on Insulators. - Kapil Ahuja; Bryan K. Clark; Eric de Sturler; David M. Ceperley; Jeongnim Kim (SIAM J. Sci. Comput., 2011)

Distributed-memory DMRG via sparse and dense parallel tensor contractions. - Ryan Levy; Edgar Solomonik; Bryan K. Clark (SC, 2020)

Gauge equivariant neural networks for quantum lattice gauge theories. - Di Luo; Giuseppe Carleo; Bryan K. Clark; James Stokes (CoRR, 2020)

Gauge Invariant Autoregressive Neural Networks for Quantum Lattice Models. - Di Luo; Zhuo Chen; Kaiwen Hu; Zhizhen Zhao 001; Vera Mikyoung Hur; Bryan K. Clark (CoRR, 2021)

Spacetime Neural Network for High Dimensional Quantum Dynamics. - Jiangran Wang; Zhuo Chen; Di Luo; Zhizhen Zhao 001; Vera Mikyoung Hur; Bryan K. Clark (CoRR, 2021)

Learning ground states of quantum Hamiltonians with graph networks. - Dmitrii Kochkov; Tobias Pfaff; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Bryan K. Clark (CoRR, 2021)

Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation. - Di Luo; Shunyue Yuan; James Stokes; Bryan K. Clark (CoRR, 2022)

Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions. - Zhuo Chen; Di Luo; Kaiwen Hu; Bryan K. Clark (CoRR, 2022)

Using CycleGANs to Generate Realistic STEM Images for Machine Learning. - Abid Khan; Chia-Hao Lee 002; Pinshane Y. Huang; Bryan K. Clark (CoRR, 2023)

Nearly-frustration-free ground state preparation. - Matthew Thibodeau; Bryan K. Clark (Quantum, 2023)

Transforming the Hybrid Cloud for Emerging AI Workloads. - Deming Chen; Alaa Youssef; Ruchi Pendse; André Schleife; Bryan K. Clark; Hendrik F. Hamann; Jingrui He; Teodoro Laino; Lav R. Varshney; Yuxiong Wang; Avirup Sil; Reyhaneh Jabbarvand; et al. (CoRR, 2024)