A Quantum Annealing Approach for Fault Detection and Diagnosis of Graph-Based Systems

Abstract

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 $\to$ QUBO $\to$ 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.

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Cited by in corpus (4)

• Bayesian Network Structure Learning Using Quantum Annealing
• Multivariable Optimization: Quantum Annealing & Computation
• Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods
• Adiabatic Quantum Optimization for Associative Memory Recall