Figure 9From: Qualifying quantum approaches for hard industrial optimization problems. A case study in the field of smart-charging of electric vehiclesThe zoom effect. From left to right, we normalized an instance from (SC1) respectively by \(R/5\), \(R/2\) and R to observe the effect on the energy landscape of \(\mathrm{QAOA}_{1}\). The closer to R, the better the zoom on the global minima. The re-weighting of a graph affects the energy landscape: it can therefore be used as leverage to either zoom on the point of interest to apply local optimization, or on the contrary it might be used to zoom out of barren plateaus to explore more interesting phase spacesBack to article page