  Quantum computing has the potential for disruptive change in many sectors of
industry, especially in materials science and optimization. In this paper, we
describe how the Turbine Balancing Problem can be solved with quantum
computing, which is the NP-hard optimization problem of analytically balancing
rotor blades in a single plane as found in turbine assembly. Small yet relevant
instances occur in industry, which makes the problem interesting for early
quantum computing benchmarks. We model it as a Quadratic Unconstrained Binary
Optimization problem and compare the performance of a classical rule-based
heuristic and D-Wave Systems' Quantum Annealer Advantage_system4.1. In this
case study, we use real-world as well as synthetic datasets and observe that
the quantum hardware significantly improves an actively used heuristic's
solution for small-scale problem instances with bare disk imbalance in terms of
solution quality. Motivated by this performance gain, we subsequently design a
quantum-inspired classical heuristic based on simulated annealing that achieves
extremely good results on all given problem instances, essentially solving the
optimization problem sufficiently well for all considered datasets, according
to industrial requirements.
