LIP6 - Équipe QI
LIP6 - Équipe QI
  • Recherche
  • Candidatures
  • Équipe
  • Actualités
  • Publications
  • Séminaires
  • Contact
  • Intranet
  • Français
    English

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

Récents

  • Quantum Machine Learning for Industrial Applications
  • Quantum Machine Learning for Industrial Applications
  • Toward quantum advantage with photonic state injection
  • Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
  • Subspace preserving quantum convolutional neural network architectures
  • 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

Conditions d'utilisations

Publié avec Hugo Blox Builder — le générateur libre de site web gratuit permettant aux créateurs de s’épanouir.

Citation
Copier Télécharger