Quantum computing promises significant improvements of computation capabilities in various fields, such as machine learning and complex optimization problems. Rapid technological advancements suggest that adiabatic and gate base quantum computing may see practical applications in the near future. In this work, we adopt quantum computing paradigms to develop solvers for two well-known combinatorial optimization problems in information fusion and resource management: 1) multitarget data association and weapon target assignment. These problems are NP-hard (non)linear integer programming optimization tasks, which become computationally expensive for large problem sizes. We derive the problem formulations adapted for the use in quantum algorithms and present solvers based on adiabatic quantum computing and the quantum approximative optimization algorithm. The feasibility of the models is demonstrated by numerical simulation and first experiments on quantum hardware.
Quantum Computing for Applications in Data Fusion
IEEE Transactions on Aerospace and Electronic Systems ; 59 , 2 ; 2002-2012
2023-04-01
1710314 byte
Article (Journal)
Electronic Resource
English
FUSION OF CLASSICAL COMPUTING, ARTIFICIAL INTELLIGENCE OR QUANTUM COMPUTING FOR VEHICLE OPERATION
European Patent Office | 2025
|Applications in distributed quantum computing networks
NTRS | 2001
|Quantum Computing for Radar Remote Sensing Applications
German Aerospace Center (DLR) | 2021
|