From Hype to Placement: Where Quantum Computing Can Support Industrial Optimization

Quantum computing is transitioning from research to industrial deployment. However, assessing its practical value requires going beyond device-level metrics. The performance of industrial applications with algorithms combining classical and quantum components cannot be inferred from hardware benchmarks alone. Application-driven benchmarking on realistic use cases is needed to evaluate end-to-end workflow performance.

Together with our BenchQC project partner Fraunhofer IKS, we present a concrete optimization use case with high relevance to industrial production across sectors from automotive to mechanical engineering: the Assembly Line Balancing Problem. Here, production steps in a facility must be assigned to a minimal number of stations while ensuring that all tasks can be completed within the available time per station and in the correct order. Using this example, we identify promising hybrid workflows combining classical and quantum subroutines, and investigate where they can provide a meaningful advantage.

This enables a reliable and reproducible foundation for measuring quantum computing benefits. The assignment of production tasks is modeled as an assembly line balancing problem, and a series of benchmarking instances with increasing numbers of tasks is defined. To this end, hybrid solutions combining classical decomposition and specialized quantum subroutines are implemented and executed. Finally, application-specific metrics such as solution quality and time-to-solution reveal the performance of the hybrid algorithms, putting clear numbers to the key question: Where and how can quantum computing provide measurable value in real-world optimization problems?