Koen Leijnse (QAL) - Near-Term Quantum Algorithms for Sparse Matrix Reordering

Title: Near-Term Quantum Algorithms for Sparse Matrix Reordering
Abstract:
In power flow and network analysis, characteristics of networks are often computed by solving large linear systems. In practice, these networks and their resulting linear systems tend to be sparse. In order to improve computation speed of Gaussian elimination, these linear systems are reordered with the goal to keep the decomposed matrix factors as sparse as possible. Finding the optimal elimination order is NP-hard and in the power flow & networks community, heuristics are often applied to find ‘good’ orders. At Quantum Application Lab we are investigating the implementation of the matrix reordering problem on near-term quantum hardware, employing different types of available quantum hardware to solve the problem using methods such as Quantum Annealing, QAOA and Analog Quantum Computing.
Date of talk: 2024-01-26