This paper explores the application of quantum computing, specifically quantum annealing using D-Wave systems, to address the Multiple Hypothesis Tracking (MHT) problem in tracking systems. This involves the critical task of assigning sensor detections to relevant targets amidst various challenges such as false alarms, noise, and target manoeuvres. The combinatorial nature of MHT presents a significant obstacle, with the number of hypotheses escalating exponentially over time, leading traditional solvers to struggle under substantial computational load. By benchmarking classical solvers against D-Wave’s hybrid quantum annealing approach, the study has demonstrated remarkable improvements in computational time, achieving up to 15-fold speed-ups during high false alarm density scenarios. This work highlights the potential of quantum computing over classical methods in efficiently tackling complex combinatorial optimization tasks, thereby offering a promising solution for demanding real-time tracking challenges. The research further suggests avenues for future exploration with different quantum computing technologies, underscoring the evolving landscape of technology in optimizing tracking system efficiency.