Code(TM) i7-2720QM CPU at 2.20 GHZ and 8GB of RAM workstation, the computer exited without providing an answer due to memory insufficiency when there were 6 or more faults. Even though we studied all the cases reported in the table, and many other benchmark problems with networks of binary and quaternary trees of different sizes, we focused on the instance of 21 CBs, 16 sensors, and 6 faults since this was the first small scenario where HyDE, as an exact solver, failed to provide a diagnosis when run on a conventional workstation. For this problem, the number of logical binary variables, $n_l$, for each problem instance can be split among the 37 binary variables providing the relevant information about the 21 CBs and 16 sensors, and the remaining ancilla variables needed for the final construction of $H_{\text{QUBO}}$, as described at the end of Sec. \[subsec:mapping2qubo\].

Quantum annealing is designed to sample the low energy (low cost) solutions of the objective cost function $H_{\text{QUBO}}$, but the algorithm does it in a probabilistic manner. Therefore, the strategy for finding the solution is to run the algorithm at least a certain number of repetitions, $R_P$, large enough to find the desired optimal solution 