Fig. 5: Performance of the noise-aware RL agent.
The agent finds n = 9, k = 1 codes and encoding circuits, simultaneously for different levels of noise bias cZ, with single-qubit fidelity pI = 0.9. In panels a,b,c, green represents the agent that was post-selected among all trained agents for performing best at minimizing the weighted Knill-Laflamme sum, averaged over all cZ values. Orange refers to the agent minimizing the failure probability, averaged over cZ. a Weighted Knill-Laflamme sum as a function of the noise bias parameter cZ (best agent: green line). b Failure probability as a function of the noise bias parameter cZ (best agent: orange line) (c) Smallest undetected effective weight (effective code distance is the integer part) as a function of the noise bias parameter cZ. While there is almost a perfect overlap between both best agents until cZ = 1.1, the situation changes afterwards, leading at cZ = 2 to a de = 5 code (green) or a de = 4 code (orange) that perform equally well in terms of the failure probability, as seen in b. d Evaluation of the failure-probability of the best-performing agent (orange in the other panels) for larger values of pI (smaller errors) than the ones it was trained on.
