Probabilistic safety assessment is a mainstream method to assess the safety and reliability of various safety-critical systems, such as nuclear plants, airplanes, railways, etc. However when these systems are very large, the time spent on analysis is no more compatible with the industrial use of these models to support decision making.
The objective of this use-case is to apply novel Quantum Computing methods (algorithms for approximation, divide & quantum, Markov chains) to enhance the use Probabilistic Safety Assesment methods in Risk informed decision making, and use relevant methods and approaches which are now beyond the capacity of classical computers but may be possible in the “big spaces” provided by quantum computing.
Our publications and public deliverables
- Hybrid divide-and-conquer approach for tree search algorithms (public version of deliverable D6.4)
- D6.6 Divide and quantum open source software (report)
D6.6 Divide and quantum open source software (github)
- D6.8 State-of-the-art of SAT and PSA solvers in the light of Quantum Computing
- Quantum Approach for Vertex Separator Problem in Directed Graphs (IFIP International Conference on Artificial Intelligence Applications and Innovations 2022)
- D6.18 QPSA Quantum Walks and Markov Algorithms (report)