IEEE International Conference on Quantum Computing and Engineering (QCE)

Quantum Bayesian Inference for Rail Infrastructure Condition Assessment

Dengimowei D. Alabintei, Nii Attoh-Okine

Published Aug 30, 2025• 0 citations

Abstract

Bayesian networks (BNs) are increasingly used for modeling complex infrastructure systems under uncertainty because of their ability to enable probabilistic reasoning. In this work, the potential of Quantum Bayesian networks (QBNs) to infer infrastructure deterioration in a rail system was explored. QBNs are quantum versions of classical BNs that utilize quantum circuits and algorithms to encode prior and conditional dependencies among variables. In this case study, a hierarchical rail condition system comprising substructure and superstructure components such as ballast, subgrade, rails, and ties, along with environmental factors like temperature and moisture, is described using a BN. In particular, conditional relationships are defined using classical conditional probability tables and mapped onto a parameterized quantum circuit with the aid of quantum gates. The QBN was executed on IBM's quantum simulator, and inference is performed through repeated quantum sampling. The QBN was evaluated by comparing its marginal, joint, and conditional probability estimates against those obtained from an equivalent classical BN implemented using exact inference. Our results, visualized through probability distributions and bitstring statistics, show a high degree of consistency between quantum and classical models, with low variance across multiple runs. This study demonstrates the feasibility of using QBNs for interpretable and scalable infrastructure assessment.

DOI: 10.1109/qce65121.2025.10390

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