Quantum computing for solving Bayesian networks of bridges – method and recommendations

The use of quantum computing as an efficient alternative to classical computing is rapidly evolving in diverse engineering applications to solve complex numerical problems. As its application becomes increasingly prominent in the future, its use should be complemented by validation and practical recommendations. This paper provides practical recommendations regarding the accuracy and efficiency of quantum computing for solving diverse types of Bayesian networks for bridge evaluation and maintenance, including basic and fuzzy-based networks. The methodology for solving the considered networks, development of quantum circuits, and accuracy of the results (quantum computing versus simulator and classical computing) were examined. Research findings indicate the feasibility of quantum computing for solving small-scale bridge Bayesian networks (error of 6% approximately), whereas the accuracy of solutions for solving complex networks using available open-access quantum computers is compromised owing to the limited number of available attempts and the compound effect of quantum error (error up to 37% approximately). Practical recommendations were provided to practitioners and future research needs are identified.