| Quantum computing for high-energy physics: State of the art and challenges. summary of the qc4hep working group A Di Meglio, K Jansen, I Tavernelli, C Alexandrou, S Arunachalam, ... arXiv preprint arXiv:2307.03236, 2023 | 52 | 2023 |
| Unravelling physics beyond the standard model with classical and quantum anomaly detection J Schuhmacher, L Boggia, V Belis, E Puljak, M Grossi, M Pierini, ... Machine Learning: Science and Technology 4 (4), 045031, 2023 | 18* | 2023 |
| Symmetry-invariant quantum machine learning force fields INM Le, O Kiss, J Schuhmacher, I Tavernelli, F Tacchino arXiv preprint arXiv:2311.11362, 2023 | 8 | 2023 |
| Quantum generative adversarial networks for anomaly detection in high energy physics E Bermot, C Zoufal, M Grossi, J Schuhmacher, F Tacchino, S Vallecorsa, ... 2023 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2023 | 8 | 2023 |
| Extending the reach of quantum computing for materials science with machine learning potentials J Schuhmacher, G Mazzola, F Tacchino, O Dmitriyeva, T Bui, S Huang, ... AIP Advances 12 (11), 2022 | 5 | 2022 |
| Optimizing quantum classification algorithms on classical benchmark datasets M John, J Schuhmacher, P Barkoutsos, I Tavernelli, F Tacchino Entropy 25 (6), 860, 2023 | 4 | 2023 |
| Hybrid tree tensor networks for quantum simulation J Schuhmacher, M Ballarin, A Baiardi, G Magnifico, F Tacchino, ... arXiv preprint arXiv:2404.05784, 2024 | 1 | 2024 |
