AI-driven fault detection in superconducting circuits: enhancing reliability through physics-based models

Superconducting circuits, known for their ability to carry electrical currents with zero resistance, have emerged as critical components in quantum computing, magnetic resonance imaging (MRI), and other advanced technologies. However, maintaining their operational integrity requires sophisticated fault detection mechanisms due to the sensitivity of superconducting materials to minute environmental changes. This paper explores the application of AI-driven fault detection systems in superconducting circuits, integrating physics-based models to enhance reliability and precision. By leveraging machine learning algorithms and physics-informed neural networks, the proposed approach captures complex relationships between environmental variables, material properties, and circuit performance. This combination allows for early fault detection, minimizing disruptions and improving the lifespan of superconducting devices. The research demonstrates how AI can address challenges unique to superconducting circuits, contributing to more resilient systems in critical applications.