The NEASQC project is organising a series of webinars that are not a simple overview of the NISQ Quantum Computing use cases investigated by our project, but a platform to learn from and exchange with the best experts in the fields covered by NEASQC. Our Work Package 5 prepared a programme of four weekly webinars between 5 and 24 November, dedicated to Machine learning and optimization methods. Each week, a distinguished guest speaker will share their insights on the day’s topic, and NEASQC experts will then explain how NEASQC is addressing the issue through a specific use case.
On 24 November from 17:30 to 19:00 CET, we will address Quantum optimisation algorithms for mesh segmentation
Agenda
- 17:30-18:10 Guest speaker: Dr Martin Leib, Team Lead of the Quantum Algorithm and Application Team at IQM
Introduction to the Quantum Approximate Optimisation Algorithm - 18:10-18:15 Technical questions
- 18:15 – 18:35 Mesh Generation From Practice to Theory (Dr Jeanne Pellerin, TotalEnergies)
- 18:35 – 19: 00 Open discussion and expert Q&A session
Moderator
Dr Vedran Dunjko, Leader of the Work Package 5 (Machine Learning & Optimisation) in the NEASQC project, Assistant Professor at Leiden University
Speakers
Dr Martin Leib, Team Lead of the Quantum Algorithm and Application Team at IQM
Introduction to the Quantum Approximate Optimisation Algorithm
Abstract: In this talk I will provide a thorough introduction into the Quantum Approximate Optimisation Algorithm (QAOA). Based on the notion of a reverse causal cone we will examine the generic QAOA phenomena of concentration of results as well as concentration of parameters and show how these results can be used for parameter setting strategies as well as numerical proofs of average performance. We will further learn about the fruitful connection between quantum optimal control theory and QAOA. Finally, if time permits, we will delve into criticism and improvements on the vanilla QAOA scheme.
Dr Jeanne Pellerin, R&D Project Manager Computational Geometry & Meshing at TotalEnergies
Mesh Generation From Practice to Theory
Abstract: Meshes are ubiquitous in computer science, they define the geometry of the virtual representations of objects and permit to study the physical properties of their real world counterparts. In engineering, meshes have a key role in most scientific computation methods and are a prerequisite to reliable and efficient numerical simulations. Unstructured meshes allow size, orientation and shape variations, that are necessary for some numerical methods. The objective of my research work is to develop algorithms to generate unstructured meshes in three dimensions, a topic that combine computational geometry, computational physics and computer science challenges. In this talk, I will describe the challenges of working on with 3D models in practice, then I will focus on algorithms for 3D mesh generation. This work led me to study the underlying theoretical questions of computational geometry, the last part of the talk will focus on my contributions to the enumeration of the combinatorial subdivisions of polyhedra into tetrahedra or hexahedra
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