Quantum computing has emerged as a powerful tool to efficiently solve computational challenges, particularly in simulation and optimisation. However, hardware limitations prevent quantum computers from achieving the full theoretical potential. Among the quantum algorithms, quantum annealing is a prime candidate to solve optimisation problems. This makes it a natural candidate for search-based software testing in the Cyber-Physical Systems (CPS) domain, which demands effective test cases due to their safety-critical nature. This work explores the use of quantum annealing to enhance test case generation for CPS through a mutation-based approach. We encode test case mutation as a binary optimisation problem, and use quantum annealing to identify and target critical regions of the test cases for improvement. Our approach mechanises this process into an algorithm that uses D-Wave's quantum annealer to find the solution. As a main contribution, we offer insights into how quantum annealing can advance software testing methodologies by empirically evaluating the correlation between problem size, hardware limitations, and the effectiveness of the results. Moreover, we compare the proposed method against state-of-the-art classical optimisation algorithms, targeting efficiency (time to generate test cases) and effectiveness (fault detection rates). Results indicate that quantum annealing enables faster test case generation while achieving comparable fault detection performance to state-of-the-art alternatives.