QuBRA (Quantum Methods and Benchmarks for Resource Allocation)
Tim Bittner, Joshua Ammermann, Ina Schaefer
Bundesministerium für Bildung und Forschung
Infineon Technologies AG, Volkswagen AG, Leibniz Universität Hannover, TU Braunschweig, Karlsruher Institut für Technologie, Ruhr-Universität Bochum, Universität zu Köln
Quantum computing is said to have an advantage in the near future compared toclassical computing for computationally hard problems, ranging from simulation of complex materials to computing optimal solutions in operations research. In QuBRA, experts from industry and research facilities address the challenge of quantifying this quantum leap.
Production of modern hardware and technical goods is based on complex industrial processes. A key part of these processes includes the formalization and solving of combinatorial optimization problems, which are typically NP-hard (i.e., there exists no known efficient algorithm to solve these problems). Even small improvements w.r.t. resource consumption for such problems can have tremendous economic benefits.
Application of quantum computing and quantum algorithms raises hope that specific classes of these problems may be addressed more efficietly in the near future. The goal of QuBRA is to bring together research experts and experts from industry to quantify the quantum advantage for combinatorial optimization problems. In particular, configuration and scheduling problems, as applied in industry, are of paramount interest.