UMD professors Groth and Modarres awarded EPRI-QLab seed grants on quantum computing in probabilistic risk assessment

Professors Katrina Groth and Mohammad Modarres, from the Center for Risk and Reliability at University of Maryland, have been awarded EPRI-QLab seed grants to lead two innovative projects. Leveraging a generous gift by the Electric Power Research Institute (EPRI) and QLab resources, these grants will fund research exploring the application of quantum computing technologies to probabilistic risk assessment.

Probabilistic Risk Assessment (PRA) is a class of rigorous methodologies utilized to estimate risks in industrial systems and facilities, providing crucial information for decision-making. PRA is widely used to ensure the safe and reliable deployment of critical energy technologies, including nuclear power, energy storage, and hydrogen vehicles. However, developing and analyzing these complex models is highly resource-intensive. When dealing with uncertainties, current PRA methods often rely on classical Monte Carlo methods that require over a hundred thousand samples, resulting in heavy computational burdens and high computation times that hinder real-time operational decisions.

Supported by the EPRI-QLab seed grants, Professors Groth and Modarres, along with Postdoctoral researcher Ruixue Li and Ph.D. student Saman Marandi, will tackle these computational bottlenecks through two complementary projects.

EQuIP-PRA: Exploring Quantum Improvement Potential for Probabilistic Risk Assessment

This project investigates how quantum computing can increase the accuracy and speed of solving various aspects of PRA models. The research will initially focus on demonstrating the feasibility of implementing PRA models on quantum platforms by translating small fault tree and Bayesian network structures into quantum representations. The team will then benchmark key performance metrics. If successful, this research will open new applications with significant implications for enhancing the deployment of modern energy systems.

Quantum Algorithms for Real-Time Importance Measure Calculation Under Uncertainty in Probabilistic Risk Assessment 

This project focuses heavily on nuclear plant safety assessment. A key feature of PRA is calculating safety importance measures to prioritize critical components for operation and safety improvements. This project will seek hybrid quantum-classical algorithms designed to achieve a quadratic speedup in PRA importance measure calculations. By incorporating occurrences of model and parametric uncertainties into quantum computation, the team aims to achieve simultaneous uncertainty propagation. The proposed algorithm will be applied to various nuclear plant operating configurations to find conditional importance rankings using current quantum computing devices.

By merging UMD's world-class expertise in reliability engineering with cutting-edge quantum capabilities, these projects aim to transform how we assess and manage risk in our most critical infrastructures.