GPU-accelerated approximate kernel method for quantum machine learning

doi:10.1063/5.0108967

GPU-accelerated approximate kernel method for quantum machine learning

2022-12-06

11

OA

AI

N

Nicholas J. Browning *

F

Felix A. Faber

O

O. Anatole von Lilienfeld

DOI：10.1063/5.0108967

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We introduce Quantum Machine Learning (QML)-Lightning, a PyTorch package containing graphics processing unit (GPU)-accelerated approximate kernel models, which can yield trained models within seconds. QML-Lightning includes a cost-efficient GPU implementation of FCHL19, which together can provide energy and force predictions with competitive accuracy on a microsecond per atom timescale. Using modern GPU hardware, we report learning curves of energies and forces as well as timings as numerical evidence for select legacy benchmarks from atomistic simulation including QM9, MD-17, and 3BPA. (c) 2022 Author(s).

GPU-accelerated approximate kernel method for quantum machine learning

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