Enhancing Optimization Techniques with Quantum-Inspired Generative Models (Part 2)

Optimization with classical, quantum-inspired, or quantum methods often displays best performance when a high degree of problem knowledge is incorporated. In typical industrial applications this domain-specific knowledge already exists. In this work we identify a method of data encoding and search space reduction in a BMW plant optimization problem. The approach is based on a problem relaxation that incorporates varying degrees of problem knowledge. We then use this method to improve upon previous no-knowledge results. In particular, we show how problem knowledge helps the quantum-inspired optimizer find better solutions.