2 citations · 2 across the 2 of their papers we have counts for
3 papers
cond-mat.mes-hall2022★ 2 cited
Bayesian autotuning of Hubbard model quantum simulators
Ludmila Szulakowska, Jun Dai
Spins in gated semiconductor quantum dots (QDs) are a promising platform for Hubbard model simulation inaccessible to computation. Precise control of the tunnel couplings by tuning…
physics.chem-ph2020
Machine-learning-corrected quantum dynamics calculations
A. Jasinski, J. Montaner, R. C. Forrey +6
Quantum scattering calculations for all but low-dimensional systems at low energies must rely on approximations. All approximations introduce errors. The impact of these errors is…
physics.chem-ph2019
Interpolation and extrapolation of global potential energy surfaces for polyatomic systems by Gaussian processes with composite kernels
Jun Dai, Roman V. Krems
Gaussian process regression has recently emerged as a powerful, system-agnostic tool for building global potential energy surfaces (PES) of polyatomic molecules. While the accuracy…