454 citations · 3.6k across the 53 of their papers we have counts for
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Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T)
Michael S. Chen, Joonho Lee, Hong-Zhou Ye +3
Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we explo…
Twenty Years of Auxiliary-Field Quantum Monte Carlo in Quantum Chemistry: An Overview and Assessment on Main Group Chemistry and Bond-Breaking
Joonho Lee, Hung Q. Pham, David R. Reichman
In this work, we present an overview of the phaseless auxiliary-field quantum Monte Carlo (ph- AFQMC) approach from a computational quantum chemistry perspective, and present a num…
ipie: A Python-based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUs
Fionn D. Malone, Ankit Mahajan, James S. Spencer +1
We report the development of a python-based auxiliary-field quantum Monte Carlo (AFQMC) program, ipie, with preliminary timing benchmarks and new AFQMC results on the isomerization…
Is there evidence for exponential quantum advantage in quantum chemistry?
Seunghoon Lee, Joonho Lee, Huanchen Zhai +15
The idea to use quantum mechanical devices to simulate other quantum systems is commonly ascribed to Feynman. Since the original suggestion, concrete proposals have appeared for si…
Response to "Exponential challenges in unbiasing quantum Monte Carlo algorithms with quantum computers"
Joonho Lee, David R. Reichman, Ryan Babbush +4
A recent preprint by Mazzola and Carleo numerically investigates exponential challenges that can arise for the QC-QMC algorithm introduced in our work, "Unbiasing fermionic quantum…
Matchgate Shadows for Fermionic Quantum Simulation
Kianna Wan, William J. Huggins, Joonho Lee +1
"Classical shadows" are estimators of an unknown quantum state, constructed from suitably distributed random measurements on copies of that state [Nature Physics 16, 1050-1057]. He…