Quantum Computing

The increasing size and interconnectivity of complex engineering systems as well as the global trend towards industry 4.0 and the immense amount of information demands the exploration of innovative approaches to store, maintain and process information at an unprecedented scale. Quantum computing emerges as a promising avenue to achieve diagnostics and prognostics solutions that are exponentially faster that those existing today. The center aims to leverage quantum computing to improve the risk and reliability assessment of complex engineering systems and natural hazards, with the following main focus areas: (a) quantum machine learning, (b) quantum probabilistic inference, and (c) quantum optimization.

Hybrid Quantum-Classical Machine Learning approach design to perform a variety of tasks within the prognosis and health management field.

Encoding of Bayesian Networks in a Quantum Circuit. This approach allows us to use the capabilities of Quantum Computing in traditional probabilistic risk assessment.

Quantum-based Optimization using the Quantum Approximate Optimization Algorithm (QAOA) to solve tasks in sensor placement optimization for civil infrastructure.