Welcome to the QuantHEP project website
QuantHEP – Quantum Computing Solutions for High-Energy Physics is a research project whose key goal is to develop quantum algorithms as a solution to the increasingly challenging, and soon intractable, problem of analysing and simulating events from large particle-physics experiments.
Project QuantHEP bring together researchers from the Physics of Information and Quantum Technologies Group at CeFEMA, IST-ID & PQI in Portugal, from the National Institute for Nuclear Physics in Italy, and from the Quantum Computing Group of the University of Latvia.
QuantHEP is funded through QuantERA, the European cofund programme in Quantum Technologies.
News
QuantHEP Seminar, Brian Swingle
8 March 2023, online
QuantHEP Conference
25-27 September 2023, Bari, Italy
D. Magano, A. Kumar, M. Kālis, A. Locāns, A. Glos, S. Pratapsi, G. Quinta, M. Dimitrijevs, A. Rivošs, P. Bargassa, J. Seixas, A. Ambainis, Y. Omar, Quantum speedup for track reconstruction in particle accelerators, Physical Review D 105, 076012 (2022). Get PDF
@article{d. magano 2022quantum,
title={Quantum speedup for track reconstruction in particle accelerators},
author={D. Magano and A. Kumar and M. Kālis and A. Locāns and A. Glos and S. Pratapsi and G. Quinta and M. Dimitrijevs and A. Rivošs and P. Bargassa and J. Seixas and A. Ambainis and Y. Omar},
journal={Physical Review D},
year={2022},
pages={105, 076012}
}A. Delgado, K. E. Hamilton, P. Date, J. Vlimant, D. Magano, Y. Omar, P. Bargassa, A. Francis, A. Gianelle, L. Sestini, D. Lucchesi, D. Zuliani, D. Nicotra, J. Vries, D. Dibenedetto, M. L. Martinez, E. Rodrigues, C. V. Sierra, S. Vallecorsa, J. Thaler, C. Bravo-Prieto, S. Y. Chang, J. Lazar, C. A. Argüelles, Quantum Computing for Data Analysis in High-Energy Physics, arXiv:2203.08805 (2022). Get PDF
@article{a. delgado2022quantum,
title={Quantum Computing for Data Analysis in High-Energy Physics},
author={A. Delgado, K. E. Hamilton, P. Date, J. Vlimant, D. Magano, Y. Omar, P. Bargassa, A. Francis, A. Gianelle, L. Sestini, D. Lucchesi, D. Zuliani, D. Nicotra, J. Vries, D. Dibenedetto, M. L. Martinez, E. Rodrigues, C. V. Sierra, S. Vallecorsa, J. Thaler, C. Bravo-Prieto, S. Y. Chang, J. Lazar, C. A. Argüelles },
journal={arXiv},
year={2022},
pages={2203.08805 }
}