12 papers
Quantum Shannon theory made robust: a tale of three protocols for almost i.i.d. sources
Filippo Girardi, Nilanjana Datta, Giacomo De Palma +1
The asymptotic rates of information-theoretic protocols - including error exponents, data-compression rates, and channel capacities - are traditionally derived under the idealised…
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…
New approaches to almost i.i.d. information theory
Filippo Girardi, Giacomo De Palma, Ludovico Lami
Independent and identically distributed (i.i.d.) states are ubiquitous in quantum information theory. However, in a practical setting, the i.i.d. assumption is too stringent, and p…
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…
Convex combinations of bosonic pure-loss channels
Giuseppe Catalano, Marco Fanizza, Francesco Anna Mele +2
The pure-loss channel is a fundamental model for describing noise in bosonic quantum platforms. It is characterised by a single parameter, the transmissivity, which quantifies the…
Exponential concentration of fluctuations in mean-field boson dynamics
Matias Gabriel Ginzburg, Simone Rademacher, Giacomo De Palma
We study the mean-field dynamics of a system of interacting bosons starting from an initially condensated state. For a broad class of mean-field Hamiltonians, including models…