4 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
Mean-field limit from general mixtures of experts to quantum neural networks
Anderson Melchor Hernandez, Davide Pastorello, Giacomo De Palma
In this work, we study the asymptotic behavior of Mixture of Experts (MoE) trained via gradient flow on supervised learning problems. Our main result establishes the propagation of…
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…
Quantum concentration inequalities and equivalence of the thermodynamical ensembles: an optimal mass transport approach
Giacomo De Palma, Davide Pastorello
We prove new concentration inequalities for quantum spin systems which apply to any local observable measured on any product state or on any state with exponentially decaying corre…
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…