We believe that these results represent a significant leap in the solution of problems via direct-embedding quantum optimization.

In Sec. \[sec:quantumDMF\] we describe all the necessary steps involved in implementing a hard computational problem into the quantum machine, starting from the mapping to a QUBO, and the subsequent embedding in the quantum hardware. In Sec. \[sec:ResultsDiscussion\] we present and discuss the results of our preliminary experimental runs on the quantum annealer. Finally, in Sec. \[sec:conclusion\] we provide some concluding remarks and an outlook on the application of quantum algorithms in system health management.

Quantum annealing approach for the diagnosis of multiple faults problem {#sec:quantumDMF}
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Our approach to the problem is to use the D-Wave quantum computer and exploit quantum fluctuations, i.e., quantum tunneling, to more efficiently explore the search space. The theoretical challenge is to efficiently map the hard computational problem of interest (e.g., diagnosis of multiple faults) to a quadratic pseudoboolean objective function (classical spin-glass Hamiltonian in physics jargon) to be minimized; such a mapping requires that the number of qubits scales polynomially with the size of the problem (number of electrical components which are potentially faulty). Solving arbitrary problem instances requires a programmable quantum device to implement 