@santoro_optimization_2006; @das_2008] to solve classical optimization problems could be advantageous over its classical analogue (simulated annealing [@kirkpatrick_optimization_1983]). For the case of the D-wave quantum processor, several benchmark studies [@McGeoch2013; @boixo_evidence_2014; @Ronnow2014_quantumspeedup] have recently been reported with, as yet, inconclusive results about the quantum speedup that do not rule out the possibility of finding applications or problem instances where one could take advantage of a QA algorithm. The search for scenarios where this advantage can be harnessed is still an open question [@katzgraberPRX2014; @Venturelli2014; @GoogleQuantumAI]. In QA, quantum mechanical tunneling allows for more efficient exploration of difficult potential energy landscapes such as that of classical spin-glass problems. In our implementation of the diagnosis of multiple faults, quantum fluctuations (tunneling) occurs between states representing different diagnosis candidates.

Despite the significant progress and exponential growth in the number of qubits in the commercial D-wave quantum annealer, implementing and solving practical applications in these devices is still a challenging task, as discussed in this work and other recently developed QA algorithms in other applications domains [@perdomo08; @PerdomoOrtiz2012_LPF; @planningquantum; @ogorman2014_bayesnet]. When compared to traditional computers or workstations, with billions of bits, the main bottleneck for quantum annealers is still the size of the device, which 