![[Pasted image 20230725201901.png]] Today, there are several inefficiencies in the steel manufacturing process on a controller, process & system level. These inefficiencies **drive higher costs, create unnecessary waste/emissions & lower quality outputs.** However, overcoming these inefficiencies today with existing solutions however is challenging because of the complexity from: - dependencies between controller units, - interconnectedness of the processes, - the large problem scale driven by the number/granularity of time domains, - combinatorial explosion in the potential parameter configurations explorable. The opportunity is to develop a quantum enhanced approach that can produce feasible solutions of optimal parameter settings given the real-world constraints of the machines/controllers and the existing process. **Potential Solutions Components:** 1. Time Series Prediction powered by a Hybrid Quantum Model - Higher accuracy predictions with less data points 2. An Ensemble Model powered by a Black Box Optimiser - Navigate complex optimization landscapes, helping find great start points and escaping local minima #quantum