Simone Severini


Department of Computer Science and
Department of Physics & Astronomy
University College London
Gower Street, WC1E 6BT
London, United Kingdom
Email: simoseve@gmail.com
Skype: simoneseverini
Phone: +44 (0)777 8519716






Academic history: PhD from Bristol University, in the then newly created Quantum Computation and Information Group, advised by Richard Jozsa (whose advisor was Roger Penrose). Visiting student at UC Berkeley (and MSRI). Studied Chemistry at the University of Siena and Philosophy (Logic) at the University of Florence.

Employment history: URF and previously Newton International Fellow at UCL. Post-doctoral research fellow of the Institute for Quantum Computing and the Department of Combinatorics & Optimization at the University of Waterloo (mentored by Michele Mosca). Post-doctoral research assistant in the Department of Computer Science and the Department of Mathematics at the University of York. Long term visitor at CRI, CWI, MIT, Nankai, NUS and RISC-Linz. Invited participant in various semesters at international visitor research centres including The Isaac Newton Institute for Mathematical Sciences (3 programmes) and The Institute Henri Poincaré.

Research: arXiv, MathSciNet, ZBMATH Database, INSPIRE, CiteSeerX, MPRA, PubMed, and IRIS.

Links at UCL: Atomic, Molecular, Optical and Positron Physics Group (AMOPP), UCL Quantum Information Initiative, CoMPLEX (CoMPLEX Research Group), UCL Institute of Origins, Complex Networks Interest Group, London Center for Nanotechnology.

Written works



  1. T. Fritz, A. B. Sainz, R. Augusiak, J. B. Brask, R. Chaves, A. Leverrier, and A. Acin, Local orthogonality: a multipartite principle for correlations, arXiv:1210.3018 [quant-ph];

  2. J. Henson, Quantum Contextuality from a Simple Principle, arXiv:1210.5978 [quant-ph];

  3. S. Abramsky, A. Brandenburger, The sheaf-theoretic structure of non-locality and contextuality, New Journal of Physics 13 (2011) 113036. arXiv:1102.0264 [quant-ph];

  4. L. Heaney, A. Cabello, M. F. Santos, V. Vedral, Extreme nonlocality with one photon, New Journal of Physics, 13 (2011) 053054. arXiv:0911.0770 [quant-ph];

  5. X. Kong, M. Shi, F. Shi, P. Wang, P. Huang, Q. Zhang, C. Ju, C. Duan, S. Yu, J. Dum, An experimental test of the non-classicality of quantum mechanics using an unmovable and indivisible system, arXiv:1210.0961 [quant-ph];

  6. R. Hermens, The Problem of Contextuality and the Impossibility of Experimental Metaphysics Thereof, Stud. Hist. Phil. Mod. Phys., 42:214–225, 2011. arXiv:1012.3052 [quant-ph];

  7. H. Sharma and R Srikanth, No-signaling from Gleason non-contextuality and the Tensor Product Structure, arXiv:1202.1804 [quant-ph];

  8. M. Araújo, M. T. Quintino, C. Budroni, M. T. Cunha, A. Cabello, Complete characterization of the n-cycle noncontextual polytope, arXiv:1206.3212v2 [quant-ph];

  9. I. Bengtsson, Gleason, Kochen-Specker, and a competition that never was, arXiv:1210.0436v1 [quant-ph];

  10. A. Cabello, L. E. Danielsen, A. J. Lopez-Tarrida, J. R. Portillo, Quantum social networks, arXiv:1112.0617v2 [physics.soc-ph];

  11. P. Kurzynski, A. Soeda, J. Thompson, D. Kaszlikowski, Bosonic bunching reveals strong contextual behaviour, arXiv:1211.6907v1 [quant-ph];

  12. V. D'Ambrosio, I. Herbauts, E. Amselem, E. Nagali, M. Bourennane, F. Sciarrino, A. Cabello, Experimental implementation of a Kochen-Specker set of quantum tests, arXiv:1209.1836 [quant-ph];

  13. T. Vidick, S. Wehner, Does ignorance of the whole imply ignorance of the parts? -- Large violations of non-contextuality in quantum theory, Phys. Rev. Lett. 107, 030402 (2011). arXiv:1011.6448v2 [quant-ph];

  14. A. Cabello, Twin inequality for fully contextual quantum correlations, arXiv:1209.0112v1 [quant-ph];

  15. Andre O. Ranchin, Progress in Quantum Fondations, MRes Dissertation, Department of Physics, Imperial College London, 2012; T. Fritz, R. Chaves, Entropic Inequalities and Marginal Problems, arXiv:1112.4788v2 [cs.IT];

  16. A. Cabello, Simple explanation of the quantum violation of a fundamental inequality, Phys. Rev. Lett. 110 (2013) 060402. arXiv:1210.2988 [quant-ph];

  17. A. Cabello, Twin inequalities for fully contextual correlations, arXiv:1209.0112 [quant-ph];

  18. M. Sadiq, P. Badziag, M. Bourennane, A. Cabello, Bell inequalities for the simplest exclusivity graph, arXiv:1106.4754 [quant-ph]

  19. A. Cabello, The contextual computer, in A Computable Universe, edited by H. Zenil (World Scientific, Singapore, 2013), Chap. 31, pp. 595-604.

Meetings


Videos:

Quantum information and graphs

In 2008, at the Perimeter Institute for Theoretical Physics, we organized a conference titled Quantum Information and Graph Theory: emerging connections. Below is an incomplete list of references on this topic (Oct 2011):


Graph states:

  • R. Raussendorf, D.E. Browne, H.J. Briegel, Measurement-based quantum computation with cluster states, Phys. Rev. A 68, 022312 (2003). arXiv:quant-ph/0301052v2

  • M. Hein, W. Dür, J. Eisert, R. Raussendorf, M. Van den Nest, H.-J. Briegel, Entanglement in Graph States and its Applications, Proceedings of the International School of Physics "Enrico Fermi" on "Quantum Computers, Algorithms and Chaos", Varenna, Italy, July, 2005. arXiv:quant-ph/0602096v1

State transfer on spin systems:

  • M. Christandl, N. Datta, T. C. Dorlas, A. Ekert, A. Kay, A. J. Landahl, Perfect Transfer of Arbitrary States in Quantum Spin Networks, Phys. Rev. A 71, 032312 (2005). arXiv:quant-ph/0411020v2

  • C. Godsil, State Transfer on Graphs, 2011. arXiv:1102.4898v2 [math.CO]

Quantum expanders:

  • A. Ben-Aroya, A. Ta-Shma, Quantum expanders and the quantum entropy difference problem, 2007. arXiv:quant-ph/0702129v3

  • A. W. Harrow, R. A. Low, Efficient Quantum Tensor Product Expanders and k-designs, Proceedings of RANDOM 2009, LNCS, 5687:548-561, 2009. arXiv:0811.2597v3 [quant-ph]

Quantum walks:

Quantum graphs:

Graphs of unitary matrices:

Complexity metrics:

Isomorphism (via encoding):

  • K. Audenaert, C. D. Godsil, G. F. Royle, T. Rudolph, Symmetric squares of graphs, J. Comb. Theory, Ser. B 97(1): 74-90 (2007). arXiv:math/0507251v1 [math.CO]

  • J. K. Gamble, M. Friesen, D. Zhou, R. Joynt, S. N. Coppersmith, Two-particle quantum walks applied to the graph isomorphism problem. Phys. Rev. A, 81(5):052313, 2010. arXiv:1002.3003v1 [quant-ph]

Network coding:

  • M. Hayashi, K. Iwama, H. Nishimura, R. Raymond, and S. Yamashita. Quantum network coding. In STACS 2007, volume 4393 of Lecture Notes in Computer Science, pages 610–621, 2007. arXiv:quant-ph/0601088v2

  • D. Leung, J. Oppenheim, and A.Winter. Quantum network communication — the butterfly and beyond. IEEE Transactions on Information Theory, 56(7):3478–3490, 2010. arXiv:quant-ph/0608223v5

Complex networks:

  • S. Perseguers, M. Lewenstein, A. Acín, J. I. Cirac, Quantum complex networks, Nature Physics 6, 539 - 543 (2010). arXiv:0907.3283v1 [quant-ph]

Graphs as channels:

  • T. S. Cubitt, D. Leung, W. Matthews, A. Winter, Improving zero-error classical communication with entanglement, Phys. Rev. Lett., 104(23):230503, 2010. arXiv:0911.5300v2 [quant-ph]

  • D. Leung, L. Mancinska, W. Matthews, M. Ozols, A. Roy, Entanglement can increase asymptotic rates of zero-error classical communication over classical channels, 2010. arXiv:1009.1195v2 [quant-ph]

Quantum colouring:

  • C. Godsil, M. W. Newman, Coloring an Orthogonality Graph, SIAM J. Discrete Math. 22(2): 683-692 (2008). arXiv:math/0509151v1 [math.CO]

  • J. Fukawa, Hi. Imai, F. Le Gall, Quantum Coloring Games via Symmetric SAT Games. Presented as a long talk at the 11th Asian Quantum Information Science Conference (AQIS 2011).

Background independent models of quantum gravity based on time-dependent graphs:

  • A. Hamma, F. Markopoulou, Background independent condensed matter models for quantum gravity, New J. Phys. 13:095006, 2011. arXiv:1011.5754v1 [gr-qc]

  • F. Caravelli, F. Markopoulou, Properties of quantum graphity at low temperature, Phys. Rev. D 84 024002, 2011. arXiv:1008.1340v3 [gr-qc]

Memoranda (2012)

  1. R. Penrose, On the nature of quantum geometry, in Magic Without Magic, ed. J. Klauder, Freeman, San Francisco, 1972, pp. 333-354.

  2. N. Linial, Z. Luria, An upper bound on the number of high dimensional permutations, arXiv:1106.0649v1 [math.CO]

  3. A. Ashikhmin, A. Robert Calderbank, W. Kewlin, Multidimensional Second Order Reed-Muller Codes as Grassmannian Packings, ISIT 2006, Seattle, USA, July 9 14, 2006.

  4. P. Diaconis and J. Salzman, Projection pursuit for discrete data. IMS Collections Probability and Statistics: Essays in Honor of David A. Freedman, Vol. 2 (2008) 265–288. arXiv:0805.3043v1 [math.ST]

  5. B. J. Frey and D. Dueck, Clustering by Passing Messages Between Data Points, Science 315, 972–976, February 2007. citeseerx. Software.

  6. D. Jakobson, S. D.Miller, I. Rivin, Z. Rudnick, Eigenvalue spacings for regular graphs, IMA vol. 109 (Emerging applications of number theory, Minneapolis, MN 1996). arXiv:hep-th/0310002v1

  7. S. M. Pincus, Approximate entropy as a measure of system complexity, PNAS March 15, 1991 vol. 88 no. 6 2297-2301. (See our work arXiv:1201.0045v1 [cond-mat.dis-nn].)

  8. H. R. Kleinberg, and A. Lehman, On the capacity of information networks, IEEE Transactions on Information Theory, 52(6):2345–2364, 2006.

  9. N. Goldenfeld, L. P. Kadanoff, Simple lessons from complexity, Science 2 April 1999: Vol. 284 no. 5411 pp. 87-89.

  10. G. Tkacik and A M. Walczak, Information transmission in gene regulatory networks: a review, J. Phys.: Condens. Matter 23 (2011) 153102 (31pp). arXiv:1101.4240v1 [physics.bio-ph]

  11. E. Carlstein. Non-parametric change point estimation, The Annals of Statistics, Vol. 16, No. 1. (1988), pp. 188-197.

  12. S. Janson, D. E. Knuth, T. Łuczak, B. Pittel, The birth of the giant component, Random Structures Algorithms 4 (1993), no. 3, 231-358. arXiv:math/9310236v1 [math.PR]

  13. D. Deutsch, Physics, Philosophy and Quantum Technology (talk PDF), The Sixth International Conference on Quantum Communication, Measurement and Computing, in Proceedings of the Sixth International Conference on Quantum Communication, Measurement and Computing, Shapiro, J.H. and Hirota, O., Eds. (Rinton Press, Princeton, NJ. 2003).

  14. E. A. Rietman, R. L. Karp, and J. A Tuszynski, Review and application of group theory to molecular systems biology, Theoretical Biology and Medical Modelling 2011, 8:21.

  15. L. A. Zager, G. C. Verghese, Graph similarity scoring and matching, Applied Mathematics Letters, 21:1(2008), 86-94.

  16. M. M. Wilde, From Classical to Quantum Shannon Theory, 2011. arXiv:1106.1445v2 [quant-ph]

  17. N. Alon, C. Avin, M. Koucky, G. Kozma, Z. Lotker, M. R. Tuttle, Many Random Walks Are Faster Than One, Combinatorics, Probability and Computing (2011), 20 : pp 481-502. arXiv:0705.0467v2 [math.PR]

  18. P. Expert, T. Evans, V. D. Blondel, R. Lambiotte, Beyond Space For Spatial Networks, PNAS 2011 108 (19) 7663-7668.

  19. Planar Separator Theorem: D. A. Spielman, S.-H. Teng (1996), Disk packings and planar separators, Proc. 12th ACM Symposium on Computational Geometry (SCG '96), pp. 349–358; D. A. Spielman, S.-H. Teng (2007), Spectral partitioning works: Planar graphs and finite element meshes, Linear Algebra and its Applications 421 (2–3): 284–305,

  20. J. H. Conway, A. J. Jones, Trigonometric Diophantine equations (On vanishing sums of roots of unity), Acta Arith. 30 (1976), no. 3, 229--240.

  21. B. Poonen, M. Rubinstein, The Number of Intersection Points Made by the Diagonals of a Regular Polygon, SIAM Journal on Discrete Mathematics 11 (1998), no. 1, 135-156. arXiv:math/9508209v3 [math.MG]

  22. P. Bourgade, J. P. Keating, Quantum chaos, random matrix theory, and the Riemann zeta-function, Seminaire Poincare, XIV (2010) 115-153.

  23. C. W. J. Granger, Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37:3 (1969), 424–438.

  24. Y.-A. Kim, S. Wuchty, T. M. Przytycka, Identifying causal genes and dysregulated pathways in complex disease, PLoS Comput Biol 7(3): e1001095.

  25. T. Ideker, N. J. Krogan, Differential network biology, Molecular Systems Biology 8:565.

  26. T. Manke, L. Demetrius, and M. Vingron, An entropic characterization of protein interaction networks and cellular robustness, J. R. Soc. Interface. 2006 December 22; 3(11): 843–850.

  27. A. E. Motter, Cascade control and defense in complex networks, Phys. Rev. Lett. 93, 098701 (2004).

  28. T. Schreiber, Meauring information transfer, Phys. Rev. Lett. 85, 461 (2000).

  29. H. G. Tanner, On the controllability of nearest neighbor interconnections, Decision and Control, 2004. CDC. 43rd IEEE Conference on.

  30. D. Ruelle, Is our mathematics natural: The case of equilibrium statistical mechanics, Bull. Amer. Math. Soc. (N.S.) Volume 19, Number 1 (1988).

  31. J. Paris, J. and L. Harrington, A Mathematical Incompleteness in Peano Arithmetic. In Handbook for Mathematical Logic (Ed. J. Barwise). Amsterdam, Netherlands: North-Holland, 1977.

  32. M. Warmuth, A Bayes Rule for Density Matrices, In Y. Weiss, B. Schölkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18, pages 1457–1464. MIT Press, Cambridge, MA, 2005.

  33. M. K. Warmuth, D. Kuzmin, Bayesian Generalized Probability Calculus for Density Matrices, In Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence, 2006.

  34. S. Boyd, P. Diaconis, and L. Xiao, Fastest Mixing Markov Chain on a Graph, SIAM Review, 46(4):667-689, 2004.

  35. K. P. Murphy, An introduction to graphical models, 2001.

  36. J. Gunawardena, Six Lectures on System Biology (Cambridge 2011).

  37. Hedetniemi conjecture.

  38. J. Cooper, Graph Theory Study Guide.

  39. Rotor Router Model by Jim Propp. The model involves a bug moving along a directed graph.

  40. Igraph is a free software package for creating and manipulating undirected and directed graphs. Cytoscape is an open source software platform for visualizing complex networks. CFinder is a free software for finding and visualizing overlapping dense groups of nodes in networks, based on the Clique Percolation Method (CPM) of Palla et. al., Nature 435, 814-818 (2005).

  41. S. Severini, Mathoverflow: Rewiring graphs.

  42. KONECT is the Koblenz Network Collection. KONECT is a project to collect large network datasets of all types in order to perform research in the area of network mining, collected by the Institute of Web Science and Technologies of the University of Koblenz–Landau. (7/2/12)

  43. Mark Newman Network Data.

  44. E. D. Kolaczyk, (2009), Statistical Analysis of Network Data: Methods and Models. Springer, New York.

  45. Kappalanguage: A rule-based language for modeling protein interaction networks.

  46. Two interesting topics in Markov chains analysis: P. Doyle, Kemeny constant: C. D. Meyer, Stochastic Complementation.

  47. Smart Cities: World Economic Forum, Urban Sustainability. Companies in Smart Cities.

  48. Subhash A. Khot, Nisheeth K. Vishnoi, The Unique Games Conjecture, Integrality Gap for Cut Problems and Embeddability of Negative Type Metrics into l_1, 2005.

  49. A. Kavruk, V. I. Paulsen, I. G. Todorov, M. Tomforde, Tensor products of operator systems. J. Funct. Anal. 261 (2011), no. 2, 267–299.

  50. M.V. Berry and J.P. Keating The Riemann zeros and eigenvalue asymptotics, SIAM Review, 41, No. 2 (1999), 236-266.

  51. A. Ferrante, M. Pavon, Matrix completion à la Dempster by the principle of parsimony, IEEE Trans. Inform. Theory 57 (2011), no. 6, 3925-3931.

  52. Information System on Graph Classes and their Inclusions.

  53. Yvo Desmedt, Yongge Wang, Perfectly Secure Message Transmission Revisited, IEEE Trans. Inform. Theory 54 (2008), 25820-2595. arXiv:cs/0208041v1 [cs.CR]

  54. Asu Ozdaglar: Learning and dynamics on networks, Acemoglu, Daron, Munther Dahleh, Ilan Lobel, and Asuman Ozdaglar (2008), Bayesian learning in social networks, LIDS Working Paper 2780, Massachusetts nstitute of Technology.

  55. M. Agrawal, Axiomatic / Postulatory Quantum Mechanics, in Fundamental Physics in Nano-Structured Materials and Devices (Stanford University, 2008).

  56. R. Diestel, Graph Theory, Electronic Edition 2010.

  57. Measuring Worth, historical data on important economic aggregates, with particular emphasis on nominal measures.

  58. Hà Quang Minh, Reproducing Kernel Hilbert Spaces and Learning Problems on the Hypercube , preprint, 2012.

  59. Network Workbench: A Large-Scale Network Analysis, Modeling and Visualization Toolkit for Biomedical, Social Science and Physics Research.

  60. Gephi The Open Graph Viz Platform.

  61. Measuring complexity (by S. Lloyd): 1. How hard is it to describe? Entropy; Algorithmic Complexity or Algorithmic Information Content; Minimum Description Length; Fisher Information; Renyi Entropy; Code Length (prefix-free, Huffman, Shannon-Fano, error-correcting, Hamming); Chernoff Information; Dimension; Fractal Dimension; Lempel-Ziv Complexity. 2. How hard is it to create? Time Computational Complexity; Space Computational Complexity; Information-Based Complexity; Logical Depth; Thermodynamic Depth; Cost; Crypticity. 3. What is its degree of organization? Metric Entropy; Fractal Dimension; Excess Entropy; Stochastic Complexity; Sophistication; Effective Measure Complexity; True Measure Complexity; Topological epsilon-machine size; Conditional Information; Conditional Algorithmic Information Content; Schema length; Ideal Complexity; Hierarchical Complexity; Tree subgraph diversity; Homogeneous Complexity; Grammatical Complexity. Algorithmic Mutual Information; Channel Capacity; Correlation; Stored Information; Organization.

  62. S. Bhamidi, R. Rajagopal, S. Roch, Network Delay Inference from Additive Metrics. arXiv:math/0604367v2 [math.PR]

  63. R. Castro, M. Coates, G. Liang, R. Nowak and B. Yu, Network Tomography: Recent Developments, Statistical Science, Vol. 19, No. 3, 499–517, 2004.

  64. GraphCrunch2 is a software tool for network analysis, modelling and alignment.

  65. L. Lovász, Very large graphs, Current Developments in Mathematics 2008 (eds. D. Jerison, B. Mazur, T. Mrowka, W. Schmid, R. Stanley, and S. T. Yau), International Press, Somerville, MA (2009), 67-128.

  66. A. Clauset, C. R. Shalizi, M. E. J. Newman, Power law distribution of empirical data, SIAM Review 51, 661-703 (2009).

  67. Blonstein, D. Fahmy, H., and Grbavec, A, (1996), Issues in the practical use of graph rewriting.

  68. Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data.

  69. NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

  70. Pajek, program for large networks analysis.

  71. Guess, the graph exploration system.

  72. Pegasus, open-source, graph-mining system with massive scalability.

  73. NetLogo is a multi-agent programmable modeling environment.

  74. Massey Tutorial on Zero-error Information Theory, Information Theory Winter School, La Colle Sur Loup, March 2007.

  75. D. Zelazo and M. Mesbahi, Graph-theoretic methods for networked dynamic systems: Heterogeneity and H2 Performance, in Efficient Modeling and Control of Large-Scale Systems (J. Mohammadpour and K.M. Grigoriadis, eds.), New York, New York: Springer, pp. 219-249.

  76. B. Chazelle, The Convergence of Bird Flocking, arXiv:0905.4241v1 [cs.CG]

  77. Arora, S., Rao, S., and Vazirani, U. 2009., Expander flows, geometric embeddings and graph partitioning, J. ACM 56, 2, Article 5 (April 2009).

  78. G. Szabo, G. Fath, Evolutionary games on graphs, Physics Reports 446 (4-6), 97-217 (2007).

  79. L. Vinet, A. Zhedanov, Dual -1 Hahn polynomials and perfect state transfer, arXiv:1110.6477v3 [math-ph]

  80. Some nice conjectures in algebraic combinatorics by Tewodros Amdeberhan (8 August 2012).

  81. T. Bu, N. Duffield, F. Lo Presti, D. Towsley, Network Tomography on General Topology, Proc. of ACM SIGMETRICS 2002.

  82. NetEvo is a computing framework and collection of end-user tools designed to allow researchers to investigate evolutionary aspects of dynamical complex networks.

  83. P. Malacaria, F. Smeraldi, The Thermodynamics of Confidentiality, CSF 2012: 280-290, 2011.

  84. D. Aharonov, A. Ta-Shma, Adiabatic Quantum State Generation and Statistical Zero Knowledge, arXiv:quant-ph/0301023v2.

  85. B. Sinaimeri, Structures of Diversity, PhD Thesis, Sapienza University, Roma, 2009.

  86. B. D. McKay, Hadamard equivalence via graph isomorphism, Discrete Math. 27 (1979), no. 2, 213–214.

  87. P. Schweitzer, Problems of Unknown Complexity, PhD Thesis, Universitat des Saarlandes, Saarbrucken, 2009.

  88. G. Haggard, D. J. Pearce, and G. Royle, Computing Tutte Polynomials, ACM Trans. Math. Softw. 37, 3, Article 24 (September 2010), 17 pages.

  89. M. Fürer, On the power of combinatorial and spectral invariants, Linear Algebra Appl. 432 (2010), no. 9, 2373–2380.

  90. Network Coding Bibliography

  91. A. C. Kalloniatis, From incoherence to synchronicity in the network Kuramoto model, Phys. Rev. E (3) 82 (2010), no. 6, 066202.

  92. M. P. Frank, Physical Limits of Computing, IEEE Computing in Science & Engineering magazine, May/June 2002.

  93. K. Tsuda, Machine learning with quantum relative entropy, International Workshop on Statistical-Mechanical Informatics 2008 (IW-SMI 2008).

  94. L. H. Kauffman, Biologic, 2002.

  95. L. G. Valiant, Evolvability, ECCC TR06-120.

  96. A beautiful bibliography on the Physics of Algorithms (Los Alamos National Laboratory).

  97. Y. Colin de Verdiere, Spectral Graph Theory (Spectres de Graphes).


Memoranda (2013)

  1. A. Dutta, U. Divakaranb , D. Senc , B. K Chakrabartid , T. F. Rosenbaume, and G. Aeppli, Quantum phase transitions in transverse field spin models: From Statistical Physics to Quantum Information, arXiv:1012.0653 [cond-mat.stat-mech]

  2. M. Laurent, Semidefinite Optimization (great lectures)

  3. Some of my favourite conjectures: Hadamard conjecture; Hedetniemi conjecture; Rosenfeld conjecture: the Shannon capacity of the complement of the Clebsch graph is 16^(1/3).

  4. J. C. Colburn, The combinatorics of network reliability, Oxford Univ. Press, 1987.

  5. DBPedia.

  6. Writelatex is good.

  7. A good lecture on the Ising model (Frank Krauss).

  8. S. Kauffman, L. Smolin, Combinatorial dynamics in quantum gravity. arXiv:hep-th/9809161

  9. Richard Feynman's lectures (on enhanced video player platform).

  10. Cook D. J. and Holder L. B., Mining Graph Data, John Wiley & Sons, 2007.

  11. Sperner Capacities, 1993.

  12. J. P. Keating and N. C. Snaith (2003), Random matrices and L-functions, J. Phys. A 36, pp. 2859-2881.

Current neighbourhood

Past neighbourhood

  • Zhihao Ma, Academic Visitor, Department of Mathematics, Shanghai Jiao Tong University.

  • Andrea Casaccino, PhD in Information Engineering, University of Siena / MIT, Quantum computation and communication with qubit systems, co-supervised with Enrico Martinelli; next at General Electric Energy.

  • Bobby Bentham, MRes Project CP3, CoMPLEX, Perturbations of Networks and the Transition to Cancer.

  • Thomas Wyatt, MRes Project CP3, CoMPLEX, Closed Walks in Biological Networks.

  • Tim Lucas, MRes Project CP2, CoMPLEX, Subgraphs in Cancer Research.

  • Opponent at the Disputas for the Ph.d.-grad of Ramji Rahaman, Study of nonlocal correlations and entanglement in the context of quantum information Processing, The Faculty of Mathematics and Natural Sciences, University of Bergen.

  • Sophie Atkinson, MRes Project CP2, CoMPLEX, Applying networks and graphs theory to breast cancer genomics.

  • Alessandro Cosentino, MSci in Computer Science, University of Pisa; On some combinatorial properties of graph states, co-supervised with Anna Bernasconi; next PhD student at the University of Waterloo.

  • Tommaso Gagliardoni, MSci in Mathematics, University of Perugia, Classical simulation of quantum circuits, co-supervised with Marco Baioletti; next PhD student at the Centre for Advanced Security Research Darmstadt.

Professional service