Quantum Computing for Applications in Data Fusion

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.