Research Summary
My main focus is on quantum algorithms, mainly (but not only) for healthcare and life sciences applications. With my collaborators I explore:
- Quantum generative models such as quantum generative adversarial networks, to enhance expressivity and trainability for drug discovery applications and data augmentation;
- Quantum simulations for NMR spectroscopy, both with quantum artificial intelligence and quantum (Hamiltonian) simulations;
- Quantum reservoir computing techniques, in particular for time series forecasting such as for wildfire prediction;
- Quantum optimization algorithms such as quantum approximate optimization algorithms (QAOA) and its variants, as well as quantum annealing approaches (applications for molecular docking, transport network, production scheduling, etc.).
A key aspect of our investigations is also to run our new algorithmic designs on actual quantum computing systems such as IBM superconducting-qubit computers or IonQ trapped-ion quantum computers, in order to compare performances and benchmark the end-user applications.
Selected Publications
. (2024). Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies. In arXiv. https://doi.org/10.48550/arXiv.2405.04401
. (2024). Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies. In arXiv. https://doi.org/10.48550/arXiv.2405.04401
, Duhr, Claude, Mistlberger, Bernhard, & Szafron, Robert. (2022). Inclusive production cross sections at N3LO. Journal of High Energy Physics, 2022(12). https://doi.org/10.1007/jhep12(2022)066
, Duhr, Claude, Mistlberger, Bernhard, & Szafron, Robert. (2022). Inclusive production cross sections at N3LO. Journal of High Energy Physics, 2022(12). https://doi.org/10.1007/jhep12(2022)066
Bravo-Prieto, Carlos, , Cè, Marco, Francis, Anthony, Grabowska, Dorota M., & Carrazza, Stefano. (2022). Style-based quantum generative adversarial networks for Monte Carlo events. Quantum, 6. https://doi.org/10.22331/q-2022-08-17-777
Bravo-Prieto, Carlos, , Cè, Marco, Francis, Anthony, Grabowska, Dorota M., & Carrazza, Stefano. (2022). Style-based quantum generative adversarial networks for Monte Carlo events. Quantum, 6. https://doi.org/10.22331/q-2022-08-17-777
, Campanario, F., Glaus, S., Mühlleitner, M., Spira, M., & Streicher, J. (2019). Gluon fusion into Higgs pairs at NLO QCD and the top mass scheme. European Physical Journal C, 79(6). https://doi.org/10.1140/epjc/s10052-019-6973-3
, Campanario, F., Glaus, S., Mühlleitner, M., Spira, M., & Streicher, J. (2019). Gluon fusion into Higgs pairs at NLO QCD and the top mass scheme. European Physical Journal C, 79(6). https://doi.org/10.1140/epjc/s10052-019-6973-3
, Djouadi, A., Gröber, R., Mühlleitner, M.M., Quevillon, J., & Spira, M. (2013). The measurement of the Higgs self-coupling at the LHC: theoretical status. Journal of High Energy Physics, 4. https://doi.org/10.1007/JHEP04(2013)151
, Djouadi, A., Gröber, R., Mühlleitner, M.M., Quevillon, J., & Spira, M. (2013). The measurement of the Higgs self-coupling at the LHC: theoretical status. Journal of High Energy Physics, 4. https://doi.org/10.1007/JHEP04(2013)151