Quantum Inference for Reliability Assessment

The increased availability of specialized quantum hardware has made possible the exploration of the field by the general scientific community. Enterprises such as IBM, Google, and Microsoft have launched or will launch shortly cloud quantum computing services, which will accelerate forward the development of the field even further. It is, therefore, necessary to prepare the RAMS community to harness the advantages that quantum computing may offer to our discipline. In this paper, we present a proposed application for efficient probabilistic inference in Bayesian networks using gate-based quantum computing and a quantum algorithm called Amplitude Amplification. The proposed methodology is exemplified on a non-trivial Bayesian network, composed of nodes representing discrete probability distributions. To the best of our knowledge, this is the first attempt to use Bayesian networks in combination with Amplitude Amplification for the risk and reliability assessment context. The results indicate that the usage of quantum computing can effectively increase the efficiency of the sampling process, decreasing the number of samples required to obtain estimates of similar quality when compared to a traditional Monte Carlo sampling approach.