Leo Monbroussou
Research Associate
Research Interests:
Quantum Machine Learning
Quantum Optical Computing
Machine Learning
Léo is a researcher at the Quantum Software Lab, focusing on the theory and applications of quantum machine learning, and on the interaction between quantum hardware and algorithms. He is the lead researcher on the photonic testbeds at the NQCC. He received his PhD from Sorbonne University in 2025, supervised by Prof. Elham Kashefi and Dr. Alex Grilo, and funded by Naval Group. He is a former student of the École Normale Supérieure Paris-Saclay and the Institut Polytechnique de Paris, where he received his master's in information theory and quantum technologies.
Featured Publications:
- L. Monbroussou, H. Thomas, H. Mhiri, Z. Holmes, E. Kashefi, Classical simulation and model concentration in passive linear optics
- S. Raj, B. Coyle, L. Monbroussou, A. J. Ferreira-Martins, R. M. S. Farias, E. Kashefi. “Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler–Leman Hierarchy”: https://arxiv.org/abs/2606.26873.
- H. Mhiri, H. Thomas , L. Monbroussou, U. Chabaud, Z. Holmes, E. Kashefi. “Boson sampling beyond the dilute regime: second moments and anti-concentration,” https://arxiv.org/abs/2604.14323.
- S. Thabet, L. Monbroussou, E. Z. Mamon, and J. Landman. “When Quantum and
Classical Models Disagree: Learning Beyond Minimum Norm Least Square,” npj Quantum Information, DOI: https://www.nature.com/articles/s41534-026-01217-y.
- L. Monbroussou, B. Polacchi, V. Yacoub, E. Caruccio, G. Rodari, F. Hoch, G. Carvacho, N. Spagnolo, T. Giordani, M. Bossi, A. Rajan, N. Di Giano, R. Albiero, F. Ceccarelli, R. Osellame, E. Kashefi, F. Sciarrino. “Photonic quantum convolutional neural networks with adaptive state injection ”Advanced Photonics, Vol. 7, Issue 6, 066012 (November 2025). DOI: https://doi.org/10.1117/1.AP.7.6.066012.
- L. Monbroussou, E. Z. Mamon, H. Thomas, V. Yacoub, U. Chabaud, and E. Kashefi. “Toward quantum advantage with photonic state injection,” Physical Review Research, vol. 7, p. 033051, July 2025. DOI: https://doi.org/10.1103/PhysRevResearch.7.033051
- L. Monbroussou, J. Landman, L. Wang, A. B. Grilo, and E. Kashefi. “Subspace preserving quantum convolutional neural network architectures,” Quantum Science and Technology, vol. 10, p. 025050, Mar. 2025. DOI: https://doi.org/10.1088/2058-9565/adbf43.
- H. Mhiri, L. Monbroussou, M. Herrero-Gonzalez, S. Thabet, E. Kashefi, and J. Landman. “Constrained and Vanishing Expressivity of Quantum Fourier Models,” Quantum, vol. 9, p. 1847, Sept. 2025. DOI: https://doi.org/10.22331/q-2025-09-03-1847.
- L. Monbroussou, E. Z. Mamon, J. Landman, A. B. Grilo, R. Kukla, and E. Kashefi. “Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning,” Quantum, vol. 9, p. 1745, May 2025. DOI: https://doi.org/10.22331/q-2025-05-15-1745.