Quantum neural networks force field generation

Journal article

Oriel Kiss, Francesco Tacchino, Sofia Vallecorsa, Ivano Tavernelli
Mach. Learn.: Sci. Technol., vol. 3, 2022, p. 035004

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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.

The energy on the umbrella motion of the H3O molecule predicted with a quantum neural network.