no code implementations • 28 Jan 2020 • Carlo Ciliberto, Andrea Rocchetto, Alessandro Rudi, Leonard Wossnig
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.
no code implementations • 6 Apr 2018 • Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini
Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers.
no code implementations • 15 Feb 2018 • 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.
no code implementations • 30 Nov 2017 • Andrea Rocchetto, Scott Aaronson, Simone Severini, Gonzalo Carvacho, Davide Poderini, Iris Agresti, Marco Bentivegna, Fabio Sciarrino
The number of parameters describing a quantum state is well known to grow exponentially with the number of particles.
no code implementations • 2 Oct 2017 • Andrea Rocchetto, Edward Grant, Sergii Strelchuk, Giuseppe Carleo, Simone Severini
This suggests that the probability distributions associated to hard quantum states might have a compositional structure that can be exploited by layered neural networks.
no code implementations • 26 Jul 2017 • Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
no code implementations • 30 Apr 2017 • Andrea Rocchetto
State tomography, whose objective is to obtain a full description of a quantum system, can be analysed in the framework of computational learning theory.