Optimization problems lie at the heart of many important scientific, technological, and industrial applications. Tasks such as scheduling, resource allocation, network design, and combinatorial search are often computationally hard, with the required resources growing rapidly with problem size. Quantum computing has emerged as a promising new approach for tackling such problems, with several quantum hardware platforms already available today, including quantum annealers, neutral-atom devices, and gate-based quantum computers. A central challenge is now to determine under which conditions these devices can offer a meaningful advantage over state-of-the-art classical methods.
The development of quantum computing has already demonstrated advantages for carefully designed artificial tasks. The next major step is to assess its potential for practical quantum advantage, where quantum devices solve optimization problems of real interest that are difficult to treat efficiently with conventional high-performance computing. In the near term, one of the most promising directions is quantum optimization: the design, implementation, and benchmarking of quantum and hybrid quantum-classical algorithms for hard optimization problems such as Max-Cut, QUBO, and related combinatorial models.
The objective of this diploma or master’s project is to study how optimization algorithms can be implemented and benchmarked on programmable quantum devices and compared against modern classical solvers. The work will involve investigating quantum annealing, hybrid quantum-classical workflows, and gate-based quantum optimization methods, with particular focus on performance, solution quality, scalability, and robustness to noise. The student will gain insight into how different quantum platforms behave in practice and how their capabilities compare to established classical optimization approaches.
Depending on the student’s background and interests, the project may include the formulation of optimization problems in forms suitable for quantum hardware, implementation of benchmarking pipelines, analysis of real-device and simulator results, and comparison with classical methods such as simulated annealing and other heuristic or exact optimization techniques. The project offers an opportunity to work at the interface of quantum computing, computational physics, and optimization, and to contribute to the broader effort of identifying useful near-term applications of programmable quantum devices.