Journal article
Oriel Kiss, Francesco Tacchino, Sofia Vallecorsa, Ivano Tavernelli
Mach. Learn.: Sci. Technol., vol. 3, 2022, p. 035004
Mach. Learn.: Sci. Technol., vol. 3, 2022, p. 035004
Paper
Demos
Cite
Cite
APA
Click to copy
Kiss, O., Tacchino, F., Vallecorsa, S., & Tavernelli, I. (2022). Quantum neural networks force field generation. Mach. Learn.: Sci. Technol., 3, 035004. https://doi.org/10.1088/2632-2153/ac7d3c
Chicago/Turabian
Click to copy
Kiss, Oriel, Francesco Tacchino, Sofia Vallecorsa, and Ivano Tavernelli. “Quantum Neural Networks Force Field Generation.” Mach. Learn.: Sci. Technol. 3 (2022): 035004.
MLA
Click to copy
Kiss, Oriel, et al. “Quantum Neural Networks Force Field Generation.” Mach. Learn.: Sci. Technol., vol. 3, 2022, p. 035004, doi:10.1088/2632-2153/ac7d3c.
BibTeX Click to copy
@article{kiss2022a,
title = {Quantum neural networks force field generation},
year = {2022},
journal = {Mach. Learn.: Sci. Technol.},
pages = {035004},
volume = {3},
doi = {10.1088/2632-2153/ac7d3c},
author = {Kiss, Oriel and Tacchino, Francesco and Vallecorsa, Sofia and Tavernelli, Ivano}
}
We establish a direct connection between classical and quantum solutions for learning neural network potentials. To this end, we design a quantum neural network architecture and apply it successfully to different molecules of growing complexity. The quantum models exhibit larger effective dimension with respect to classical counterparts and can reach competitive performances, thus pointing towards potential quantum advantages in natural science applications via quantum machine learning.
