Simone Severini


Professor of Physics of Information

Department of Computer Science

University College London


Royal Society University Research Fellow



Office: GS3.13 (UCL map)

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

Tel: +44 (0)20 3108 7093 (Direct Dial)

Internal: 57093

Fax: +44 (0)20 7387 1397




Projects and research areas (2018)

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


UCL research groups

Intelligent Systems

UCL CS Quantum

UCL Quantum Science and Technology Institute




Current professional service

Associate Editor, Philosophical Transactions of the Royal Society A

Editorial Advisory Board, Special Matrices

Associate Editor, Journal of Complex Networks

Member, International Exchanges Committee, Royal Society

Steering Committee, TQC









Most recent papers:

1. arXiv:1808.01374 [pdf, other]

Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks

Leonardo Banchi, Edward Grant, Andrea Rocchetto, Simone Severini

Comments: 10 pages, 8 figures

Subjects: Quantum Physics (quant-ph)

2. arXiv:1808.01154 [pdf, ps, other]

Unitary equivalence between the Green's function and Schrodinger approaches for quantum graphs

Fabiano M. Andrade, Simone Severini

Comments: 5 pages, 4 figures

Subjects: Quantum Physics (quant-ph); Mathematical Physics (math-ph)

3. arXiv:1806.00463 [pdf, other]

Adversarial quantum circuit learning for pure state approximation

Marcello Benedetti, Edward Grant, Leonard Wossnig, Simone Severini

Comments: 14 pages, 6 figures. Corrected typos, updated references

Subjects: Quantum Physics (quant-ph); Machine Learning (stat.ML)

4. arXiv:1805.08654 [pdf, other]

Universal discriminative quantum neural networks

Hongxiang Chen, Leonard Wossnig, Simone Severini, Hartmut Neven, Masoud Mohseni

Comments: 19 pages, 10 figures

Subjects: Quantum Physics (quant-ph); Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML)

5. arXiv:1804.10560 [pdf, other]

Quantum Walk Search on Kronecker Graphs

Thomas G. Wong, Konstantin Wunscher, Joshua Lockhart, Simone Severini

Comments: 10 pages, 8 figures

Journal-ref: Phys. Rev. A 98, 012338 (2018)

Subjects: Quantum Physics (quant-ph); Combinatorics (math.CO)

6. arXiv:1804.03680 [pdf, other]

Hierarchical quantum classifiers

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

Subjects: Quantum Physics (quant-ph)

7. arXiv:1804.02484 [pdf, ps, other]

Approximating Hamiltonian dynamics with the Nystrom method

Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini

Comments: v2: 22 pages, fixed typos in Eq.27 and 28 + other minor changes to the presentation of the results

Subjects: Quantum Physics (quant-ph); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Machine Learning (stat.ML)

8. arXiv:1802.09844 [pdf, other]

Constructing graphs with limited resources

Danial Dervovic, Avinash Mocherla, Simone Severini

Comments: 16 pages, 1 figure, comments welcome

Subjects: Discrete Mathematics (cs.DM); Combinatorics (math.CO)

9. arXiv:1802.08227 [pdf, other]

Quantum linear systems algorithms: a primer

Danial Dervovic, Mark Herbster, Peter Mountney, Simone Severini, Nairi Usher, Leonard Wossnig

Comments: 55 pages, 5 figures, comments welcome

Subjects: Quantum Physics (quant-ph); Data Structures and Algorithms (cs.DS); Numerical Analysis (math.NA)

10. arXiv:1802.05690 [pdf, ps, other]

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

Varun Kanade, Andrea Rocchetto, Simone Severini

Comments: 17 pages; v2 minor corrections and clarifications

Subjects: Quantum Physics (quant-ph); Discrete Mathematics (cs.DM); Machine Learning (cs.LG)