Bavarian Quantum Computing eXchange
Where Quantum Computing and Supercomputing Meet
Approximating Constraint Optimization Problems with a novel QAOA Ansatz
Solving combinatorial optimization problems effectively is an important research topic in numerous fields. While the classical solutions for most of these problems remain to be infeasible, there are several quantum methods which are aimed to either find or approximate an optimal solution.
In this work, a novel quantum circuit ansatz is proposed, that is specifically aimed to solve combinatorial optimization problems with constraints and has a better efficiency compared to the state-of-the-art methods.
Mr. Burak Mete
Burak Mete is a recent member of LRZ's Quantum Computing Team. He completed his master's degree in computer science at TUM in 2022. He was a member of the Quantum Computing Chair at TUM for 2 years, where he worked mainly with QML, variational algorithms, optimization, and tensor networks, and also taught as a tutor in the Introduction to Quantum Computing course for one year. Prior to his affiliation with quantum computing, he also had experience applying machine learning/deep learning methods to develop behavior-based robots.
