Unpublished
Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher, Ivano Tavernelli, Francesco Tacchino
arXiv preprint arXiv:2311.11362, 2023 Nov
arXiv preprint arXiv:2311.11362, 2023 Nov
arXiv
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APA
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Le, I. N. M., Kiss, O., Schuhmacher, J., Tavernelli, I., & Tacchino, F. (2023, November). Symmetry-invariant quantum machine learning force fields. arXiv preprint arXiv:2311.11362.
Chicago/Turabian
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Le, Isabel Nha Minh, Oriel Kiss, Julian Schuhmacher, Ivano Tavernelli, and Francesco Tacchino. “Symmetry-Invariant Quantum Machine Learning Force Fields.” ArXiv Preprint ArXiv:2311.11362, November 2023.
MLA
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Le, Isabel Nha Minh, et al. “Symmetry-Invariant Quantum Machine Learning Force Fields.” ArXiv Preprint ArXiv:2311.11362, Nov. 2023.
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@unpublished{le2023a,
title = {Symmetry-invariant quantum machine learning force fields},
year = {2023},
month = nov,
journal = {arXiv preprint arXiv:2311.11362},
author = {Le, Isabel Nha Minh and Kiss, Oriel and Schuhmacher, Julian and Tavernelli, Ivano and Tacchino, Francesco},
month_numeric = {11}
}
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.
