Automated Defect Localization in Embedded Systems via Quantum-Inspired Bayesian Network Inference

**Abstract:** This paper proposes a novel approach to automated defect localization in embedded systems, leveraging a quantum-inspired Bayesian network (QBN) for efficient probabilistic reasoning. Current defect localization techniques often struggle with the complexity and scale of modern embedded systems, resulting in imprecise localization and extended debugging cycles. Our method employs a QBN to model the dependencies between hardware components, software modules, and diagnostic events observed during runtime. By integrating quantum-inspired inference algorithms, we significantly accelerate the probabilistic reasoning process, enabling rapid and accurate defect localization even in highly complex embedded environments. This approach is readily implementable with existing hardware-in-the-loop simulation and runtime monitoring tools, promising a substantial decrease in debugging time and improved system reliability.