  Diagnosing the minimal set of faults capable of explaining a set of given
observations, e.g., from sensor readouts, is a hard combinatorial optimization
problem usually tackled with artificial intelligence techniques. We present the
mapping of this combinatorial problem to quadratic unconstrained binary
optimization (QUBO), and the experimental results of instances embedded onto a
quantum annealing device with 509 quantum bits. Besides being the first time a
quantum approach has been proposed for problems in the advanced diagnostics
community, to the best of our knowledge this work is also the first research
utilizing the route Problem $\rightarrow$ QUBO $\rightarrow$ Direct embedding
into quantum hardware, where we are able to implement and tackle problem
instances with sizes that go beyond previously reported toy-model
proof-of-principle quantum annealing implementations; this is a significant
leap in the solution of problems via direct-embedding adiabatic quantum
optimization. We discuss some of the programmability challenges in the current
generation of the quantum device as well as a few possible ways to extend this
work to more complex arbitrary network graphs.
