Motor disabilities from conditions like spinal cord injuries and strokes pose significant challenges to mobility and quality of life, driving the need for innovative rehabilitation strategies. Aquatic exoskeletons, combining hydrotherapy’s therapeutic benefits with assistive technology, offer a promising approach to enhance recovery through precise control. Objective: this study proposes a theoretical framework to improve the control of a single-degree-of-freedom aquatic robot for exoskeleton rehabilitation, using a hybrid Fuzzy-PI control strategy optimized by the Quantum Approximate Optimization Algorithm (QAOA). Method: the aquatic robot, modeled as a pendulum driven by a DC motor, integrates a Fuzzy controller to mitigate initial movement noise and a PI controller with gains optimized by QAOA. The system’s dynamics are simulated numerically, with QAOA employing quantum gates and COBYLA optimization to explore gain combinations, aiming for precise and stable control. Results: the theoretical simulations suggest that the hybrid Fuzzy-PI approach, with QAOA-optimized gains, enhances control precision and stability compared to baseline parameters, offering potential for smoother movements in aquatic rehabilitation. Conclusion: this theoretical analysis highlights the potential of future QAOA-optimized hybrid control for aquatic exoskeletons, which could reach personalized rehabilitation through adaptive control. However, as a simulation-based study, it lacks experimental validation on physical prototypes. Future work should focus on real-world testing across diverse motion ranges and patient profiles to confirm its efficacy, pending advancements in quantum hardware.