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

TL;DR

This paper introduces a novel quantum annealing method for fault detection in graph-based systems, successfully embedding larger problem instances onto a quantum device, marking a significant advancement in quantum optimization applications.

Contribution

The work is the first to map fault diagnosis problems to QUBO and directly embed them into a quantum annealer, enabling larger problem instances than previous quantum approaches.

Findings

Successfully embedded problem instances with over 500 qubits

First quantum approach applied to advanced diagnostics problems

Demonstrated potential for extending quantum fault diagnosis to complex networks

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 $\rightarrow$ 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.