516 citations · 688 across the 9 of their papers we have counts for
11 papers
A low-loss, 24-mode laser-written universal photonic processor in a glass-based platform
Andrea Barzaghi, Maëlle Bénéfice, Francesco Ceccarelli +10
We report the fabrication of the first 24-mode universal photonic processor (UPP) realized through femtosecond laser writing (FLW), marking the most complex UPP demonstrated to dat…
Understanding holographic error correction via unique algebras and atomic examples
Jason Pollack, Patrick Rall, Andrea Rocchetto
We introduce a fully constructive characterisation of holographic quantum error-correcting codes. That is, given a code and an erasure error we give a recipe to explicitly compute…
Statistical Limits of Supervised Quantum Learning
Carlo Ciliberto, Andrea Rocchetto, Alessandro Rudi +1
Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy. We show that if the boun…
Decomposition of Pauli groups via weak central products
Andrea Rocchetto, Francesco G. Russo
For any and odd prime power , for , and for any , we show a result of decomposition for Pauli groups $\mathcal{P}_{n,\math…
Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
Leonardo Banchi, Edward Grant, Andrea Rocchetto +1
Quantum systems interacting with an unknown environment are notoriously difficult to model, especially in presence of non-Markovian and non-perturbative effects. Here we introduce…
Approximating Hamiltonian dynamics with the Nyström method
Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto +3
Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers. We consider classical algorithms f…
Learning DNFs under product distributions via μ-biased quantum Fourier sampling
Varun Kanade, Andrea Rocchetto, Simone Severini
We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions using a quantum example oracle. The best classical algorithm (without access to…
Experimental learning of quantum states
Andrea Rocchetto, Scott Aaronson, Simone Severini +5
The number of parameters describing a quantum state is well known to grow exponentially with the number of particles. This scaling clearly limits our ability to do tomography to sy…
Learning hard quantum distributions with variational autoencoders
Andrea Rocchetto, Edward Grant, Sergii Strelchuk +2
Studying general quantum many-body systems is one of the major challenges in modern physics because it requires an amount of computational resources that scales exponentially with…
Quantum machine learning: a classical perspective
Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo +4
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classifica…
Stabiliser states are efficiently PAC-learnable
Andrea Rocchetto
The exponential scaling of the wave function is a fundamental property of quantum systems with far reaching implications in our ability to process quantum information. A problem wh…