2 citations · 2 across the 5 of their papers we have counts for
3 papers · 1 filter
Lazy training of quantum physics informed neural networks
Anderson Melchor Hernandez, Giacomo De Palma
We study the gradient-flow training dynamics of quantum physics-informed neural networks (QPINNs) for the solution of second-order elliptic partial differential equations with Diri…
Efficient classical computation of the neural tangent kernel of quantum neural networks
Anderson Melchor Hernandez, Davide Pastorello, Giacomo De Palma
We propose an efficient classical algorithm to estimate the Neural Tangent Kernel (NTK) associated with a broad class of quantum neural networks. These networks consist of arbitrar…
Quantitative convergence of trained quantum neural networks to a Gaussian process
Anderson Melchor Hernandez, Filippo Girardi, Davide Pastorello +1
We study quantum neural networks where the generated function is the expectation value of the sum of single-qubit observables across all qubits. In [Girardi \emph{et al.}, arXiv:24…