Circuit fault diagnosis using quantum annealing and other spin glass solvers

In this work we present a novel approach to solving circuit fault diagnosis (CFD) problems using quantum annealers and other spin glass solvers, such as simulated annealing and parallel tempering.
The cost function we construct does not minimize the number of faults but rather the distance between real and model circuit outputs: as such it has the attractive property of processing multiple circuit input/output pairs contrary to existing schemes. By showcasing the algorithms' performance through comparison of various metrics, such as time-to-solution, we aim to offer a fresh perspective on using real world-applicative
problems in order to understand the nature of quantum annealing optimizers.