Search Results for author: Julian Schuhmacher

Found 2 papers, 0 papers with code

Symmetry-invariant quantum machine learning force fields

no code implementations19 Nov 2023 Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher, Ivano Tavernelli, Francesco Tacchino

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.

Atomic Forces Quantum Machine Learning

Unravelling physics beyond the standard model with classical and quantum anomaly detection

no code implementations25 Jan 2023 Julian Schuhmacher, Laura Boggia, Vasilis Belis, Ema Puljak, Michele Grossi, Maurizio Pierini, Sofia Vallecorsa, Francesco Tacchino, Panagiotis Barkoutsos, Ivano Tavernelli

Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC).

Anomaly Detection

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