Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs

Documenti analoghi

• Quantitative Universal Approximation for Noisy Quantum Neural Networks
  di: Gonon, Lukas, et al.
  Pubblicazione: (2026)
• Greedy feature selection: Classifier-dependent feature selection via greedy methods
  di: Camattari, Fabiana, et al.
  Pubblicazione: (2024)
• Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms
  di: Ali, Alejandro Mata, et al.
  Pubblicazione: (2023)
• Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data
  di: Kratsios, Anastasis, et al.
  Pubblicazione: (2025)
• Hybrid quantum tensor networks for aeroelastic applications
  di: Hickmann, M. Lautaro, et al.
  Pubblicazione: (2025)