Articles
M. Trenti, L. Sestini, A. Gianelle, D. Zuliani, T. Felser, D. Lucchesi, S. Montangero, Quantum-inspired Machine Learning on high-energy physics data, arXiv:2004.13747 (2020). Get PDF
Trenti M., Sestini L., Gianelle A., Zuliani D., Felser T., Lucchesi D., Montagero S.. Quantum-inspired Machine Learning on high-energy physics data. arXiv: 2004.13747, 2020.
Get BibTeX
@article{m.2020quantum,
title={Quantum-inspired Machine Learning on high-energy physics data},
author={M., Trenti and L., Sestini and A., Gianelle and D., Zuliani and T., Felser and D., Lucchesi and S., Montagero },
journal={arXiv},
year={2020},
pages={2004.13747}
}G. Magnifico, M. Dalmonte, P. Facchi, S. Pascazio, F.V. Pepe, E. Ercolessi, Real Time Dynamics and Confinement in the Zn Schwinger-Weyl lattice model for 1+1 QED, arXiv:1909.04821 (2019). Get PDF
Magnifico G., Dalmonte M., Facchi P., Pascazio S., Pepe F.V., Ercolessi E.. Real Time Dynamics and Confinement in the Zn Schwinger-Weyl lattice model for 1+1 QED. arXiv: 1909.04821, 2019.
Get BibTeX
@article{g.2019real,
title={Real Time Dynamics and Confinement in the Zn Schwinger-Weyl lattice model for 1+1 QED},
author={G., Magnifico and M., Dalmonte and P., Facchi and S., Pascazio and F.V., Pepe and E., Ercolessi},
journal={arXiv},
year={2019},
pages={1909.04821}
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