0.442) -= (8, 0.025) += (8, 0.025) (15, 0.451) -= (15, 0.025) += (15, 0.025) ]{}; plot coordinates[ (1, 0.379) -= (1, 0.021) += (1, 0.021) (8, 0.433) -= (8, 0.014) += (8, 0.014) (15, 0.440) -= (15, 0.011) += (15, 0.011) ]{}; plot coordinates[ (1, 0.384) -= (1, 0.026) += (1, 0.026) (8, 0.436) -= (8, 0.031) += (8, 0.031) (15, 0.445) -= (15, 0.027) += (15, 0.027) ]{}; plot coordinates[ (1, 0.366) -= (1, 0.030) += (1, 0.030) (8, 0.444) -= (8, 0.044) += (8, 0.044) (15, 0.447) -= (15, 0.040) += (15, 0.040) ]{};

What is clear from the results on reverse annealing is that (i) a good guess for the optimal $J_F$ parameter is accurately predicted by the value obtained for forward annealing, and (ii) the optimal pause location is consistently later in the anneal for larger $\rho$. The first observation considerably simplifies the parameter setting strategy when considering operational scenarios with a new unknown instance, while the second observation is consistent with recent results and interpretation of the annealing pause tested on native Chimera problems [@marshall2018pausing].

#### Results

Figure \[fig:FQAvsRQA\] displays the TTS results for the GA algorithm, the forward QA solver and the reverse 