most citedTrained quantum neural networks are Gaussian processes

3 citations · 5 across the 5 of their papers we have counts for

collaborators

6 papers

quant-ph2026

Generalised quantum Stein's lemma more robust than ever

Filippo Girardi, Kuan-Yi Lee, Masahito Hayashi +1

The generalised quantum Stein's lemma is a key result in quantum hypothesis testing, and connects this fundamental primitive of quantum information processing with quantum resource…

quant-ph2026

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…

quant-ph2026

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…

quant-ph2026

Quantum channel tomography: optimal bounds and a Heisenberg-to-classical phase transition

Kean Chen, Filippo Girardi, Aadil Oufkir +2

How many black-box queries to a quantum channel are needed to learn its full classical description? This question lies at the heart of quantum channel tomography (also known as qua…

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★ 3 cited

Trained quantum neural networks are Gaussian processes

Filippo Girardi, Giacomo De Palma

We study quantum neural networks made by parametric one-qubit gates and fixed two-qubit gates in the limit of infinite width, where the generated function is the expectation value…