Discrete optimization in quantum computing

Details

Supervisors

Delvenne, Jean-Charles

Faculty

Ecole polytechnique de Louvain

Degree label

Master [120] : ingénieur civil en science des données, à finalité spécialisée
Master [120] : ingénieur civil en mathématiques appliquées, à finalité spécialisée

Abstract

Quantum computing has revolutionized computational paradigms, leveraging quantum mechanical principles to surpass classical computational limits. These computers make it possible to speed up many problems and classical algorithms. In this context, this thesis aims to contribute to this burgeoning field by presenting two discrete optimization algorithms tailored for quantum frameworks. The first algorithm focuses on the minimization of a discrete real-valued function, pivotal in bioinformatics, telecommunications network design, airline scheduling, circuit design, and efficient resource allocation and many others. The second algorithm addresses the weighted maximum satisfiability (Max-SAT) problem, a fundamental issue in various fields including artificial intelligence and operations research. Both algorithms demonstrate a significant quadratic speed-up over their classical counterparts, showcasing the profound potential of quantum computing in solving optimization problems.