Welcome and Introduction

• Vít Vondrák

Atos Quantum – From learning to solving practical use cases

• Sabine Keravel

Quantum Computing is no longer a theoretical-physicists-only playground. Numerous use cases have been identified, which will benefit from a quantum advantage in the coming years. Discover how Atos guides end-users through this journey.

Quantum machine learning: will it work?

• Łukasz Pawela

During the talk we will explore the possibilities for levaraging quantum computing in machine learning tasks. The realm of quantum computation forces us to focus not only on learning with classical data. We can venture beyond, to the field of quantum data, which seems to greatly enhance our learning power. During the presentation we will explore, when this statement might hold.

Introduction to quantum variational algorithms

• Piotr Gawron

Variational quantum algorithms exploit the capacity of existing Near Intermediate Scale Quantum computers to calculate expectation values of functions defined by quantum states, unitary operations and quantum observables. There exists a variety of applications of such algorithms solving problems in combinatorial optimization, simulation of physical systems and machine learning.

Variational Quantum Algorithms: Origin, Potentials and Problems

• Frank Leymann

Today’s quantum computers are noisy, i.e. only algorithms that spent a short amount of time can be successfully executed. This can be achieved by hybrid algorithms that split their work between a classical computer and a CPU. In this talk we focus on the often used subcategory of variational quantum algorithms, describe their principles and sketch the workhorses VQE and QAOA. The promising technique of warm starting algorithms and their origin are described. Problems and potentials are scratched to show lots of research opportunities in this domain.