Sander Gribling

Address: IRIF, Université de Paris (Bâtiment Sophie Germain), 8 Place Aurélie Nemours, Paris

Office: 4053

email: gribling at irif dot fr

News:

About me:

Since September 2020 I am a postdoc at IRIF, Université de Paris in the Algorithms and Complexity group.

Previously, in the academic year 2019-2020, I have been a postdoc at Centrum Wiskunde & Informatica (CWI) in the research group Networks & Optimization.

From September 2015 to September 2019, I was a PhD student in the same group. I have defended my thesis "Applications of optimization to factorization ranks and quantum information theory" at the University of Tilburg on September 30th, 2019.


[CV] (last updated: January 2022)

Research interests:

Broadly speaking, I am interested in convex optimization and quantum information theory / quantum computing. Some of my favorite topics include:

  • Quantum algorithms for classical optimization problems (e.g., SDP-solvers, convex optimization, matrix scaling)

  • Semidefinite programming and in particular its connection to polynomial optimization

  • Polynomial optimization approaches to matrix factorization ranks, quantum graph parameters, and most recently mutually unbiased bases

Employment:

  • Postdoc at IRIF

    • From 09/2020 to present, in the Algorithms and Complexity group.

  • Postdoc at CWI & QuSoft

    • From 10/2019 to 08/2020, in the Networks & Optimization group.

  • PhD student at CWI & QuSoft

    • From 09/2015 to 09/2019, in the Networks & Optimization group.

    • Advisors: Monique Laurent & Ronald de Wolf.

Publications / preprints:

  1. Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random steps.

    • with Simon Apers & Dániel Szilágyi.

  2. Mutually unbiased bases: polynomial optimization and symmetry.

    • with Sven Polak.

  3. Improved quantum lower and upper bounds for matrix scaling.

    • with Harold Nieuwboer. STACS'22.

  4. Bounding the separable rank via polynomial optimization.

    • with Monique Laurent & Andries Steenkamp. Linear Algebra and Its Applications, Volume 648, pages 1-55, September 2022.

  5. Approximate Pythagoras Numbers .

    • with Paria Abbasi, Andreas Klingler & Tim Netzer. Journal of Complexity, available online, August 2022.

  6. Improving quantum linear system solvers via a gradient descent perspective .

    • with Iordanis Kerenidis & Dániel Szilágyi.

  7. On a tracial version of Haemers bound .

    • with Li Gao & Yinan Li. AQIS'20 and Beyond IID in Information Theory'20.
      Journal version: IEEE Transactions on Information Theory, vol. 68, no. 10, pp. 6585-6604, Oct. 2022, doi:10.1109/TIT.2022.3176935.

  8. Quantum algorithms for matrix scaling and matrix balancing.

    • with Joran van Apeldoorn, Yinan Li, Harold Nieuwboer, Michael Walter & Ronald de Wolf. TQC'21. ICALP'21, conference version.

  9. The Haemers bound of noncommutative graphs.

    • with Yinan Li. QIP'20. IEEE Journal on Selected Areas in Information Theory, Volume 1, Issue 2, pp 424-431, 2020.

  10. Semidefinite programming formulations for the completely bounded norm of a tensor.

    • with Monique Laurent. QIP'19 (part of a joint submission).

  11. Simon's problem for linear functions.

    • with Joran van Apeldoorn.

  12. Convex optimization using quantum oracles.

    • with Joran van Apeldoorn, András Gilyén & Ronald de Wolf. QIP'19. The journal version appeared in Quantum.

  13. Quantum SDP-Solvers: Better upper and lower bounds.

    • with Joran van Apeldoorn, András Gilyén & Ronald de Wolf. FOCS'17, conference version. The journal version appeared in Quantum.

  14. Lower bounds on matrix factorization ranks via noncommutative polynomial optimization

    • with David de Laat & Monique Laurent. Code. Foundations of Computational Mathematics, Volume 19, Issue 5, pp 1013–1070, 2019.

  15. Bounds on entanglement dimensions and quantum graph parameters via noncommutative polynomial optimization.

    • with David de Laat & Monique Laurent. Mathematical Programming Series B, Volume 170, Issue 1, pp 5–42, 2018.

  16. Matrices with high completely positive semidefinite rank.

    • with David de Laat & Monique Laurent. Linear Algebra and Its Applications, 513, 122-148, 2017.

Education:

Theses: