10180073Quantum-Calibrated Thermal Stress Monitoring for Steel BridgesClosedCollaborative R&DInnovate UK**Quantum-Enhanced Bridge Monitoring using AI and Quantum Sensing to Keep Infrastructure Safe** Leeds Works Ltd, a UK startup specialising in AI and machine learning, is developing an innovative monitoring system that combines **quantum sensing** with **data-driven analytics** to improve the safety and reliability of steel bridges. As temperatures rise and fall, steel structures naturally expand and contract. When bearings or joints seize, this movement stops, building up hidden stress that can lead to cracks, costly repairs, or even rail buckling. Current monitoring systems rely on conventional sensors that can drift or lose accuracy over time, making it difficult to spot these problems early. Our project aims to change that. We're creating a **quantum-enhanced, machine-learning system** that continuously tracks how a bridge reacts to temperature changes. The system blends traditional sensors with **quantum devices**, such as NV-diamond thermometers and optically pumped magnetometers, which provide drift-free reference measurements. These quantum sensors act as ultra-precise anchors that keep the entire network accurate over long periods. All sensor data is analysed through a cloud-based platform powered by machine learning. The system learns what "normal" behaviour looks like for each structure and automatically flags unusual patterns that could indicate early signs of stress or failure. The result is a smarter, self-improving monitoring platform that can support preventative maintenance and reduce the risk of unexpected failures. This project demonstrates how emerging **quantum technologies and AI** can work together to make the UK's transport infrastructure **safer, more resilient, and more efficient**.