Search Results for author: Simone Severini

Found 12 papers, 0 papers with code

The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool

no code implementations29 Apr 2021 Biagini Luigi, Simone Severini

Secondly, it analyses the role of a specific and relatively new CAP measure (i. e., the Income Stabilisation Tool - IST) that is specifically aimed at stabilising farm income.

Neural Network Cost Landscapes as Quantum States

no code implementations ICLR 2019 Abdulah Fawaz, Sebastien Piat, Paul Klein, Peter Mountney, Simone Severini

We search this meta-loss landscape with the same method to simultaneously train and design a binary neural network.

Graph Cut Segmentation Methods Revisited with a Quantum Algorithm

no code implementations7 Dec 2018 Lisa Tse, Peter Mountney, Paul Klein, Simone Severini

The design and performance of computer vision algorithms are greatly influenced by the hardware on which they are implemented.

Computer Vision Semantic Segmentation

Adversarial quantum circuit learning for pure state approximation

no code implementations1 Jun 2018 Marcello Benedetti, Edward Grant, Leonard Wossnig, Simone Severini

Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data.

Quantum State Tomography

Universal discriminative quantum neural networks

no code implementations ICLR 2019 Hongxiang Chen, Leonard Wossnig, Simone Severini, Hartmut Neven, Masoud Mohseni

This circuit learns to simulates the unknown structure of a generalized quantum measurement, or Positive-Operator-Value-Measure (POVM), that is required to optimally distinguish possible distributions of quantum inputs.

Stochastic Optimization

Hierarchical quantum classifiers

no code implementations10 Apr 2018 Edward Grant, Marcello Benedetti, Shuxiang Cao, Andrew Hallam, Joshua Lockhart, Vid Stojevic, Andrew G. Green, Simone Severini

Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state.

Quantum Physics

Approximating Hamiltonian dynamics with the Nyström method

no code implementations6 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.

Learning DNFs under product distributions via μ-biased quantum Fourier sampling

no code implementations15 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.

Experimental learning of quantum states

no code implementations30 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.

Learning Theory

Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz

no code implementations ICLR 2018 Andrew Hallam, Edward Grant, Vid Stojevic, Simone Severini, Andrew G. Green

This paper demonstrates a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA).

Learning hard quantum distributions with variational autoencoders

no code implementations2 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.

Quantum machine learning: a classical perspective

no code implementations26 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.

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