LIP6 - QI Team
LIP6 - QI Team
  • Research
  • Open positions
  • People
  • News
  • Publications
  • Contact
  • Intranet
  • English
    Français
Léo Monbroussou

Léo Monbroussou

My research focuses on several key areas, including the design and analysis of NISQ models such as Subspace-preserving quantum circuits. I also explore the expressivity and trainability of variational quantum algorithms, and particularly the resulting Fourier models. My efforts extend to specific hardware, including photonic platforms.

Personal page here

Latest

  • Quantum Machine Learning for Industrial Applications
  • Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
  • Subspace preserving quantum convolutional neural network architectures
  • Constrained and Vanishing Expressivity of Quantum Fourier Models
  • Subspace Preserving Quantum Convolutional Neural Network Architectures
  • Towards quantum advantage with photonic state injection
  • Subspace Preserving Quantum Convolutional Neural Network Architectures
  • Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning

Terms

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.

Cite
Copy Download