Oriel Kiss - Symmetry-invariant quantum machine learning force fields

Journal article

Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher, Ivano Tavernelli, Francesco Tacchino
New Journal of Physics, vol. 27, 2025 Nov, p. 023015

DOI: 10.1088/1367-2630/adad0c

arXiv Demos Paper

Cite

APA Click to copy
Le, I. N. M., Kiss, O., Schuhmacher, J., Tavernelli, I., & Tacchino, F. (2025). Symmetry-invariant quantum machine learning force fields. New Journal of Physics, 27, 023015. https://doi.org/10.1088/1367-2630/adad0c

Chicago/Turabian Click to copy
Le, Isabel Nha Minh, Oriel Kiss, Julian Schuhmacher, Ivano Tavernelli, and Francesco Tacchino. “Symmetry-Invariant Quantum Machine Learning Force Fields.” New Journal of Physics 27 (November 2025): 023015.

MLA Click to copy
Le, Isabel Nha Minh, et al. “Symmetry-Invariant Quantum Machine Learning Force Fields.” New Journal of Physics, vol. 27, Nov. 2025, p. 023015, doi:10.1088/1367-2630/adad0c.

BibTeX Click to copy

@article{le2025a,
  title = {Symmetry-invariant quantum machine learning force fields},
  year = {2025},
  month = nov,
  journal = {New Journal of Physics},
  pages = {023015},
  volume = {27},
  doi = {10.1088/1367-2630/adad0c},
  author = {Le, Isabel Nha Minh and Kiss, Oriel and Schuhmacher, Julian and Tavernelli, Ivano and Tacchino, Francesco},
  month_numeric = {11}
}

[Picture]

Equivariant Quantum neural network for learning the energy potential surface of di- and tri-atomic molecules.

We design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries. We find that our invariant quantum learning models outperform their more generic counterparts on individual molecules of growing complexity. Furthermore, we study a water dimer as a minimal example of a system with multiple components, showcasing the versatility of our proposed approach and opening the way towards larger simulations. Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning, and that chemical systems represent, in fact, an interesting and rich playground for the development and application of advanced quantum machine learning tools.