Solving the Turbine Balancing Problem using Quantum Annealing

TL;DR

This paper demonstrates that quantum annealing can effectively solve small instances of the NP-hard Turbine Balancing Problem, outperforming classical heuristics and inspiring a highly effective quantum-inspired classical approach.

Contribution

It models the turbine balancing problem as a QUBO, compares quantum and classical methods, and introduces a quantum-inspired heuristic achieving industrially relevant solutions.

Findings

Quantum hardware improves solution quality for small instances.

Quantum-inspired heuristic performs well across all datasets.

Quantum annealing shows promise for industrial optimization tasks.

Abstract

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.