Simone Severini

Professor of Physics of Information

Royal Society University Research Fellow

Department of Computer Science

University College London 

 

Mail: Gower Street 66-72, WC1E 6EA London, UK

Email: s.severini@ucl.ac.uk


 

I am currently a member of the following UCL research groups: Intelligent Systems, UCL CS Quantum, UCL Quantum Science and Technology Institute, CoMPLEX.

 

My (2018) projects are Quantum Computing, Information, and Algebras of Operators, Semantic Information Pursuit for Multimodal Data Analysis, Contextuality as a resource in quantum computation, Distributed Information: Theory, Analysis and Applications, Learning and Classical Simulation of Quantum States and Dynamics, Prosperity Partnership in Quantum Software for Modeling and Simulation

 

My papers are in arXiv (mostly in quant-ph), MathSciNet (mostly discrete maths), PubMed (for papers in computational biology), IRIS (the UCL research portal)

 

Here is a selection of recent works:

 

D. Temko, I. Tomlinson, S. Severini, B. Schuster-Boeckler, T. Graham, The effects of mutational process and selection on driver mutations across cancer types, Nature Communications, 9, Article number: 1857 (2018)

 

A. Rocchetto, E. Grant, S. Strelchuk, G. Carleo, S. Severini, Learning hard quantum distributions with variational autoencoders, npj Quantum Information, volume 4, Article number: 28 (2018)

 

S. Severini, Graph Parameters and Physical Correlations: from Shannon to Connes, via Lovasz and Tsirelson, ERCIM News, Special theme: Quantum Computing, January 2018

 

C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, A. Rocchetto, S. Severini, L. Wossnig, Quantum machine learning: a classical perspective, Proc. R. Soc. A 2018 474 20170551.

 

C. R. S. Banerji, M. Panamarova, H. Hebaishi, R. B. White, F. Relaix, S. Severini, P. S. Zammit, PAX7 target genes are globally repressed in facioscapulohumeral muscular dystrophy skeletal muscle, Nature Communications, 8, Article number: 2152 (2017)

 

A. Atserias, P. Kolaitis, S. Severini, Generalized satisfiability problems via operator assignments, FCT2017. Best Paper Award

 

L. Mancinska, D. E. Roberson, R. Samal, S. Severini, A. Varvitsiotis, Quantum and non-signalling graph isomorphisms, Journal of Combinatorial Theory, Series B, 2018. Conference version, ICALP2017

 

R. Duan, S. Severini, A. Winter, On zero-error communication via quantum channels in the presence of noiseless feedback, February 2015, IEEE Trans. Inf. Theory, 62:9 (2016), 5260-5277.

 

C. Godsil, D. Roberson, R. Samal, S. Severini, Sabidussi versus Hedetniemi for three variations of the chromatic number, Combinatorica, August 2016, Volume 36, Issue 4, pp 395-415.

 

V. I. Paulsen, S. Severini, D. Stahlke, I. G. Todorov, A. Winter, Estimating quantum chromatic numbers, Journal of Functional Analysis, 270 (2016), pp. 2188-2222.

 

Preprints 2018:

 

Leonardo Banchi, Edward Grant, Andrea Rocchetto, Simone Severini, Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks

 

Fabiano M. Andrade, Simone Severini, Unitary equivalence between the Green's function and Schrodinger approaches for quantum graphs

 

Marcello Benedetti, Edward Grant, Leonard Wossnig, Simone Severini, Adversarial quantum circuit learning for pure state approximation

 

Hongxiang Chen, Leonard Wossnig, Simone Severini, Hartmut Neven, Masoud Mohseni, Universal discriminative quantum neural networks

 

Thomas G. Wong, Konstantin Wunscher, Joshua Lockhart, Simone Severini, Quantum Walk Search on Kronecker Graphs

 

Edward Grant, Marcello Benedetti, Shuxiang Cao, Andrew Hallam, Joshua Lockhart, Vid Stojevic, Andrew G. Green, Simone Severini, Hierarchical quantum classifiers

 

Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini, Approximating Hamiltonian dynamics with the Nystrom method

 

Danial Dervovic, Mark Herbster, Peter Mountney, Simone Severini, Nairi Usher, Leonard Wossnig, Quantum linear systems algorithms: a primer

 

Varun Kanade, Andrea Rocchetto, Simone Severini, Learning DNFs under product distributions via μ-biased quantum Fourier sampling

 

References