4 citations · 8 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
Classical shadows meet quantum optimal mass transport
Giacomo De Palma, Tristan Klein, Davide Pastorello
Classical shadows constitute a protocol to estimate the expectation values of a collection of M observables acting on O(1) qubits of an unknown n-qubit state with a number of measu…