What we are interested in
Exploring the multifaceted aspects of quantum computation and quantum information theory. The goal is to the develop application of new algorithms and quantum-inspired methods for solving problems in various fields such as dynamical systems, Bell nonlocality, and supervised learning. Our research encompasses a wide range of topics and utilizes a variety of mathematical tools, including but not limited to: tensor decompositions and tensor-based algorithms, convex optimization methods, operator theory (in particular transfer operators), numerical methods for partial differential equations, data-driven and kernel-based techniques.
Through our work, we aim to advance knowledge and understanding in the field of quantum science and technology by developing mathematically profound methods which exploit the potential of quantum mechanical concepts.
🧑🎓 Members
steuer (at) zib.de
stengl (at) zib.de
knebel (at) zib.de
designolle (at) zib.de
liu (at) zib.de
nguyen (at) zib.de
ramos (at) zib.de
🔬 Projects
We will apply a Frank-Wolfe-based approach for separability certification and entanglement detection of multipartite quantum states. The method will be further exploited to derive entanglement witnesses in the case, often encountered in experiments, of incomplete characterisation of the quantum state.
Computational devices and quantum mechanics revolutionized the 20th century. Quantum computation merges these fields to solve optimization problems faster than classical computers. This project aims to develop new quantum algorithms for general-purpose and specific applications, including optimization, mixed-integer programs, and uses in machine learning, logistics, big data, and physics. We will explore quantum dynamic programming, graph sparsification, and QAOA-type algorithms for small quantum computers.
This project will explore to what extent near-term quantum computing may potentially provide better approximations to combinatorial optimization problems, bringing together expertise from quantum computing and applied mathematics. It is a collaborative effort within the Einstein Research Unit on Quantum Devices, involving the Freie Universität Berlin (FU Berlin), the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), and the Zuse Institute Berlin (ZIB).
Quantum computing has the potential to occupy an important position in the field of computing in the long term. In the short to medium term, however, we can expect the emergence of so-called "Noisy Intermediate-Scale Quantum algorithms" and "Quantum Approximate Optimization Algorithms" in an initial phase. These algorithms can often solve a special subclass of problems approximately and very quickly using quantum computing and are significantly easier to implement technically. In this pilot project, a workflow will be tested, ranging from the design of hybrid quantum-classical algorithms to their simulation using quantum simulators on HPC hardware.
💬 Talks and posters
Conference and workshop talks
- Jul 2024
- Bell and Grothendieck Meet Frank-Wolfe by Sébastien Designolle
25th International Symposium on Mathematical Programming (ISMP), Montréal - Feb 2024
- Fredholm Integral Equations for the Training of Shallow Neural Networks by Patrick Gelß
7th SIAM conference on uncertainty quantification (SIAM UQ) - Sep 2023
- Fredholm Integral Equations for the Training of Shallow Neural Networks by Patrick Gelß
7th ZIB-IMI-ISM-NUS-RIKEN-MODAL Workshop, Berlin - Jun 2023
- Fredholm Integral Equations for the Training of Shallow Neural Networks by Patrick Gelß
workshop on quantum computation and optimization, Berlin - Jun 2023
- Improved Local Models and New Bell Inequalities by Sébastien Designolle
workshop on quantum computation and optimization, Berlin - Mar 2023
- Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms by Sébastien Designolle
15th annual meeting photonic devices, Berlin - Sep 2022
- Low-rank Tensor Decompositions of Quantum Circuits by Patrick Gelß
6th ZIB-IMI-ISM-NUS-RIKEN-MODAL Workshop, Tokyo / Fukuoka - May 2022
- Low-rank Tensor Decompositions of Quantum Circuits by Patrick Gelß
3rd workshop on quantum algorithms and applications, Brussels
Research seminar talks
- Dec 2024
- What Are the Most Incompatible Quantum Measurements? by Sébastien Designolle
QINFO seminar, Lyon - Nov 2024
- Bell and Grothendieck Meet Frank-Wolfe by Sébastien Designolle
ICFO seminar, Castelldefels - Nov 2024
- Bell and Grothendieck Meet Frank-Wolfe by Sébastien Designolle
Valladolid seminar, Valladolid - Nov 2024
- What Are the Most Incompatible Quantum Measurements? by Sébastien Designolle
IT seminar, Lisbon - Oct 2024
- Bell and Grothendieck Meet Frank-Wolfe by Sébastien Designolle
quantum information & quantum computing working group seminar, Warsaw - Nov 2023
- Frank-Wolfe Algorithms for Bell Nonlocality by Sébastien Designolle
IRIF seminar, Paris - Nov 2023
- Frank-Wolfe Algorithms for Bell Nonlocality by Sébastien Designolle
QAT seminar, Paris - Nov 2023
- Frank-Wolfe Algorithms for Bell Nonlocality by Sébastien Designolle
SIERRA seminar, Paris - Nov 2023
- Frank-Wolfe Algorithms for Bell Nonlocality by Sébastien Designolle
PhiQus seminar, Palaiseau - Nov 2023
- Frank-Wolfe Algorithms for Bell Nonlocality by Sébastien Designolle
LIP6 seminar, Paris - Oct 2023
- Frank-Wolfe Algorithms for Bell Nonlocality by Sébastien Designolle
QINFO seminar, Lyon - Jul 2023
- Fredholm Integral Equations for the Training of Shallow Neural Networks by Patrick Gelß
IBM NOIP seminar, Berlin - Jun 2023
- Fredholm Integral Equations for the Training of Shallow Neural Networks by Patrick Gelß
DESY CQTA seminar, Zeuthen - Mar 2023
- Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms by Sébastien Designolle
JQIT seminar, Krakow - Feb 2023
- Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms by Sébastien Designolle
Atomki seminar, Debrecen - Feb 2023
- Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms by Sébastien Designolle
IQOQI seminar, Vienna - Oct 2022
- The Tensor-train Format and Its Applications by Patrick Gelß
Tensor Learning Team Seminar - Aug 2022
- Semidefinite Programming and Incompatibility of Quantum Measurements by Sébastien Designolle
AISST seminar, Berlin
📝 Publications and preprints
- Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D. J., Elmegreen, B. G., Franco, N., Fratini, F., Fuller, B., Gacon, J., Gonciulea, C., Gribling, S., Gupta, S., Hadfield, S., Heese, R., … Zoufal, C. (2024). Challenges and Opportunities in Quantum Optimization. Nature Reviews Physics.
DOI: https://doi.org/10.1038/s42254-024-00770-9
[arXiv]
[BibTeX]
- Designolle, S., Vértesi, T., and Pokutta, S. (2024). Better Bounds on Grothendieck Constants of Finite Orders.
[arXiv]
[BibTeX]
- Gelß, P., Issagali, A., and Kornhuber, R. (2024). Fredholm Integral Equations for Function Approximation and the Training of Neural Networks. SIAM Journal on Mathematics of Data Science.
DOI: 10.1137/23M156642X
[URL]
[arXiv]
[BibTeX]
- Stengl, S.-M., Gelß, P., Klus, S., and Pokutta, S. (2024). Existence and Uniqueness of Solutions of the Koopman–von Neumann Equation on Bounded Domains. Journal of Physics A: Mathematical and Theoretical.
DOI: 10.1088/1751-8121/ad6f7d
[URL]
[arXiv]
[BibTeX]
- Stengl, S.-M. (2024). An Alternative Formulation of the Quantum Phase Estimation Using Projection-based Tensor Decompositions. Quantum Information Processing, 23.
DOI: 10.1007/s11128-024-04347-4
[URL]
[arXiv]
[BibTeX]
- Designolle, S., Vértesi, T., and Pokutta, S. (2024). Symmetric Multipartite Bell Inequalities Via Frank-Wolfe Algorithms. Physics Review A.
[arXiv]
[BibTeX]
- Liu, Y.-C., and Shang, J. (2024). Beating the Optimal Verification of Entangled States Via Collective Strategies.
[arXiv]
[BibTeX]
- Designolle, S., Iommazzo, G., Besançon, M., Knebel, S., Gelß, P., and Pokutta, S. (2023). Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms. Physical Review Research, 5(4).
DOI: 10.1103/PhysRevResearch.5.043059
[arXiv]
[slides]
[code]
[BibTeX]
- Gelß, P., Klein, R., Matera, S., and Schmidt, B. (2023). Quantum Dynamics of Coupled Excitons and Phonons in Chain-like Systems: Tensor Train Approaches and Higher-order Propagators.
[arXiv]
[BibTeX]
- Klus, S., and Gelß, P. (2023). Continuous Optimization Methods for the Graph Isomorphism Problem.
[arXiv]
[BibTeX]
- Riedel, J., Gelß, P., Klein, R., and Schmidt, B. (2023). WaveTrain: A Python Package for Numerical Quantum Mechanics of Chain-like Systems Based on Tensor Trains. The Journal of Chemical Physics, 158(16), 164801.
DOI: 10.1063/5.0147314
[URL]
[arXiv]
[BibTeX]
- Gelß, P., Klein, R., Matera, S., and Schmidt, B. (2022). Solving the Time-independent Schrödinger Equation for Chains of Coupled Excitons and Phonons Using Tensor Trains. The Journal of Chemical Physics, 156, 024109.
DOI: 10.1063/5.0074948
[URL]
[arXiv]
[BibTeX]
- Gelß, P., Klus, S., Knebel, S., Shakibaei, Z., and Pokutta, S. (2022). Low-rank Tensor Decompositions of Quantum Circuits.
[arXiv]
[BibTeX]
- Nüske, F., Gelß, P., Klus, S., and Clementi, C. (2021). Tensor-based Computation of Metastable and Coherent Sets. Physica D: Nonlinear Phenomena, 427, 133018.
DOI: 10.1016/j.physd.2021.133018
[URL]
[arXiv]
[BibTeX]
- Gelß, P., Klus, S., Schuster, I., and Schütte, C. (2021). Feature Space Approximation for Kernel-based Supervised Learning. Knowledge-Based Systems, 221, 106935.
DOI: 10.1016/j.knosys.2021.106935
[URL]
[arXiv]
[BibTeX]
- Klus, S., Gelß, P., Nüske, F., and Noé, F. (2021). Symmetric and Antisymmetric Kernels for Machine Learning Problems in Quantum Physics and Chemistry. Machine Learning: Science and Technology, 2(4), 18958.
DOI: 10.1088/2632-2153/ac14ad
[URL]
[arXiv]
[BibTeX]