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20182025
most citedRandom deep neural networks are biased towards simple functions

30 citations · 87 across the 7 of their papers we have counts for

collaborators

7 papers

quant-ph2025

Statistical Characterization of Entanglement Degradation Under Markovian Noise in Composite Quantum Systems

Nunzia Cerrato, Sauro Succi, Giacomo De Palma +1

Understanding how noise degrades entanglement is crucial for the development of reliable quantum technologies. While the Markovian approximation simplifies the analysis of noise, i…

quant-ph2024★ 2 cited

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…

quant-ph2024★ 1 cited

The multimode conditional quantum Entropy Power Inequality and the squashed entanglement of the multimode extreme bosonic Gaussian channels

Alessandro Falco, Giacomo De Palma

We prove the multimode conditional quantum Entropy Power Inequality for bosonic quantum systems. This inequality determines the minimum conditional von Neumann entropy of the outpu…

quant-ph2021★ 2 cited

Hamiltonian singular value transformation and inverse block encoding

Seth Lloyd, Bobak T. Kiani, David R. M. Arvidsson-Shukur +5

The quantum singular value transformation is a powerful quantum algorithm that allows one to apply a polynomial transformation to the singular values of a matrix that is embedded a…

quant-ph2020★ 24 cited

Quantum algorithm for nonlinear differential equations

Seth Lloyd, Giacomo De Palma, Can Gokler +5

Quantum computers are known to provide an exponential advantage over classical computers for the solution of linear differential equations in high-dimensional spaces. Here, we pres…

stat.ML2018★ 30 cited

Random deep neural networks are biased towards simple functions

Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd

We prove that the binary classifiers of bit strings generated by random wide deep neural networks with ReLU activation function are biased towards simple functions. The simplicity…

math-ph2018★ 28 cited

Gaussian optimizers for entropic inequalities in quantum information

Giacomo De Palma, Dario Trevisan, Vittorio Giovannetti +1

We survey the state of the art for the proof of the quantum Gaussian optimizer conjectures of quantum information theory. These fundamental conjectures state that quantum Gaussian…